code/analyze/hedonic regression/Hedonic Regression_skin_gender_sentiment.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Import Relevant Data"
      ],
      "metadata": {
        "id": "e_6In3wyUiLY"
      }
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    {
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        "id": "9AbWMvxOqIvG"
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      "source": [
        "import pandas as pd\n",
        "import ast\n",
        "import numpy as np\n",
        "import math\n",
        "import time\n",
        "from pandas.core.frame import DataFrame\n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp=pd.read_csv('https://raw.githubusercontent.com/SunYutongAmber/SignatureWork/main/Data/2017to2022_rarity_gas_usd_price.csv')\n",
        "df_cryp"
      ],
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        "outputId": "edd0108a-c445-4ac6-d4b9-0b8d3c3d9a56"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "1               1             1  2017-07-03  1218  3828       0.49     6548   \n",
              "2               2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "3               3             3  2019-03-20   920  1472       0.99     5719   \n",
              "4               4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
              "17821       17821         17821  2022-07-17  2292  2070      88.88     4656   \n",
              "17822       17822         17822  2022-07-17  5922   916      88.00     4749   \n",
              "17823       17823         17823  2022-07-18   107  1044      84.00     4569   \n",
              "17824       17824         17824  2022-07-21   244   801      86.68     4755   \n",
              "17825       17825         17825  2022-07-23    10  1618     110.00     9544   \n",
              "\n",
              "        type  gender skin_tone  ...  skin_tone_color  \\\n",
              "0      Human    Male    Albino  ...          #F2DC5D   \n",
              "1      Human    Male    Albino  ...          #F2DC5D   \n",
              "2      Human  Female    Medium  ...          #DB9065   \n",
              "3      Human  Female    Medium  ...          #DB9065   \n",
              "4      Human  Female      Dark  ...          #A4031F   \n",
              "...      ...     ...       ...  ...              ...   \n",
              "17821  Human  Female    Medium  ...          #DB9065   \n",
              "17822  Human    Male    Medium  ...          #DB9065   \n",
              "17823  Human    Male     Light  ...          #F2A359   \n",
              "17824  Human    Male      Dark  ...          #A4031F   \n",
              "17825  Human  Female     Light  ...          #F2A359   \n",
              "\n",
              "                                                 img_url  \\\n",
              "0      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "1      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "2      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "3      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "4      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "...                                                  ...   \n",
              "17821  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17822  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17823  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17824  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17825  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "\n",
              "      Rarity_Score_1_rarest Rarity_Score_2_Average  Rarity_Score_3_multiple  \\\n",
              "0                  5.158689               3.635788                 0.000000   \n",
              "1                  5.158689               3.635788                 0.000000   \n",
              "2                 48.311485              20.756003               891.396281   \n",
              "3                 48.311485              20.756003               891.396281   \n",
              "4                  8.104202               4.654820                69.188677   \n",
              "...                     ...                    ...                      ...   \n",
              "17821             21.880604              12.483109                67.515097   \n",
              "17822              6.622251               5.319922               144.347171   \n",
              "17823              4.402877               3.774773                52.620913   \n",
              "17824              5.721903               3.182949                 0.000000   \n",
              "17825             18.425587               9.659756               425.229311   \n",
              "\n",
              "          timestamp      date_y  gas_eth_price        date  eth_usd_price  \n",
              "0      1.498176e+09  1498176000   1.720330e+08  1498176000         326.85  \n",
              "1      1.499040e+09  1499040000   1.180929e+08  1499040000         276.41  \n",
              "2      1.498176e+09  1498176000   1.720330e+08  1498176000         326.85  \n",
              "3      1.553040e+09  1553040000   5.115253e+07  1553040000         140.22  \n",
              "4      1.498176e+09  1498176000   1.720330e+08  1498176000         326.85  \n",
              "...             ...         ...            ...         ...            ...  \n",
              "17821  1.658016e+09  1658016000   1.046061e+08  1658016000        1337.97  \n",
              "17822  1.658016e+09  1658016000   1.046061e+08  1658016000        1337.97  \n",
              "17823  1.658102e+09  1658102400   1.475703e+08  1658102400        1583.55  \n",
              "17824  1.658362e+09  1658361600   1.027865e+08  1658361600        1575.68  \n",
              "17825  1.658534e+09  1658534400   6.633055e+07  1658534400        1549.19  \n",
              "\n",
              "[17826 rows x 22 columns]"
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>date_x</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>eth_price</th>\n",
              "      <th>punk_id</th>\n",
              "      <th>type</th>\n",
              "      <th>gender</th>\n",
              "      <th>skin_tone</th>\n",
              "      <th>...</th>\n",
              "      <th>skin_tone_color</th>\n",
              "      <th>img_url</th>\n",
              "      <th>Rarity_Score_1_rarest</th>\n",
              "      <th>Rarity_Score_2_Average</th>\n",
              "      <th>Rarity_Score_3_multiple</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>date_y</th>\n",
              "      <th>gas_eth_price</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>1406</td>\n",
              "      <td>1218</td>\n",
              "      <td>0.03</td>\n",
              "      <td>6548</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Albino</td>\n",
              "      <td>...</td>\n",
              "      <td>#F2DC5D</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
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              "      <td>326.85</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-07-03</td>\n",
              "      <td>1218</td>\n",
              "      <td>3828</td>\n",
              "      <td>0.49</td>\n",
              "      <td>6548</td>\n",
              "      <td>Human</td>\n",
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              "      <td>#F2DC5D</td>\n",
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              "      <td>5.158689</td>\n",
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              "      <td>1.499040e+09</td>\n",
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              "      <td>1.180929e+08</td>\n",
              "      <td>1499040000</td>\n",
              "      <td>276.41</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>1406</td>\n",
              "      <td>1218</td>\n",
              "      <td>0.04</td>\n",
              "      <td>5719</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>#DB9065</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>48.311485</td>\n",
              "      <td>20.756003</td>\n",
              "      <td>891.396281</td>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>1498176000</td>\n",
              "      <td>1.720330e+08</td>\n",
              "      <td>1498176000</td>\n",
              "      <td>326.85</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2019-03-20</td>\n",
              "      <td>920</td>\n",
              "      <td>1472</td>\n",
              "      <td>0.99</td>\n",
              "      <td>5719</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>#DB9065</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>48.311485</td>\n",
              "      <td>20.756003</td>\n",
              "      <td>891.396281</td>\n",
              "      <td>1.553040e+09</td>\n",
              "      <td>1553040000</td>\n",
              "      <td>5.115253e+07</td>\n",
              "      <td>1553040000</td>\n",
              "      <td>140.22</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>1406</td>\n",
              "      <td>2518</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1841</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Dark</td>\n",
              "      <td>...</td>\n",
              "      <td>#A4031F</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>8.104202</td>\n",
              "      <td>4.654820</td>\n",
              "      <td>69.188677</td>\n",
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              "      <th>17821</th>\n",
              "      <td>17821</td>\n",
              "      <td>17821</td>\n",
              "      <td>2022-07-17</td>\n",
              "      <td>2292</td>\n",
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              "      <td>88.88</td>\n",
              "      <td>4656</td>\n",
              "      <td>Human</td>\n",
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              "      <td>#DB9065</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>21.880604</td>\n",
              "      <td>12.483109</td>\n",
              "      <td>67.515097</td>\n",
              "      <td>1.658016e+09</td>\n",
              "      <td>1658016000</td>\n",
              "      <td>1.046061e+08</td>\n",
              "      <td>1658016000</td>\n",
              "      <td>1337.97</td>\n",
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              "      <th>17822</th>\n",
              "      <td>17822</td>\n",
              "      <td>17822</td>\n",
              "      <td>2022-07-17</td>\n",
              "      <td>5922</td>\n",
              "      <td>916</td>\n",
              "      <td>88.00</td>\n",
              "      <td>4749</td>\n",
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              "      <td>#DB9065</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>6.622251</td>\n",
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              "      <th>17823</th>\n",
              "      <td>17823</td>\n",
              "      <td>17823</td>\n",
              "      <td>2022-07-18</td>\n",
              "      <td>107</td>\n",
              "      <td>1044</td>\n",
              "      <td>84.00</td>\n",
              "      <td>4569</td>\n",
              "      <td>Human</td>\n",
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              "      <td>Light</td>\n",
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              "      <td>#F2A359</td>\n",
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              "      <td>4.402877</td>\n",
              "      <td>3.774773</td>\n",
              "      <td>52.620913</td>\n",
              "      <td>1.658102e+09</td>\n",
              "      <td>1658102400</td>\n",
              "      <td>1.475703e+08</td>\n",
              "      <td>1658102400</td>\n",
              "      <td>1583.55</td>\n",
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              "      <th>17824</th>\n",
              "      <td>17824</td>\n",
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              "      <td>244</td>\n",
              "      <td>801</td>\n",
              "      <td>86.68</td>\n",
              "      <td>4755</td>\n",
              "      <td>Human</td>\n",
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              "      <td>#A4031F</td>\n",
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              "      <td>1.027865e+08</td>\n",
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              "      <th>17825</th>\n",
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              "      <td>10</td>\n",
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              "      <td>425.229311</td>\n",
              "      <td>1.658534e+09</td>\n",
              "      <td>1658534400</td>\n",
              "      <td>6.633055e+07</td>\n",
              "      <td>1658534400</td>\n",
              "      <td>1549.19</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cryptopunk Price(USD)"
      ],
      "metadata": {
        "id": "-1zrf7Y5v6ZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp['Price']=np.array(df_cryp['eth_price'])*np.array(df_cryp['eth_usd_price'])\n",
        "df_cryp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "uL9boAIeaIXp",
        "outputId": "8a8e3e6a-b6d0-4be1-cac2-7ef14f3bce28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "1               1             1  2017-07-03  1218  3828       0.49     6548   \n",
              "2               2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "3               3             3  2019-03-20   920  1472       0.99     5719   \n",
              "4               4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
              "17821       17821         17821  2022-07-17  2292  2070      88.88     4656   \n",
              "17822       17822         17822  2022-07-17  5922   916      88.00     4749   \n",
              "17823       17823         17823  2022-07-18   107  1044      84.00     4569   \n",
              "17824       17824         17824  2022-07-21   244   801      86.68     4755   \n",
              "17825       17825         17825  2022-07-23    10  1618     110.00     9544   \n",
              "\n",
              "        type  gender skin_tone  ...  \\\n",
              "0      Human    Male    Albino  ...   \n",
              "1      Human    Male    Albino  ...   \n",
              "2      Human  Female    Medium  ...   \n",
              "3      Human  Female    Medium  ...   \n",
              "4      Human  Female      Dark  ...   \n",
              "...      ...     ...       ...  ...   \n",
              "17821  Human  Female    Medium  ...   \n",
              "17822  Human    Male    Medium  ...   \n",
              "17823  Human    Male     Light  ...   \n",
              "17824  Human    Male      Dark  ...   \n",
              "17825  Human  Female     Light  ...   \n",
              "\n",
              "                                                 img_url  \\\n",
              "0      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "1      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "2      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "3      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "4      https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "...                                                  ...   \n",
              "17821  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17822  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17823  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17824  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "17825  https://www.larvalabs.com/cryptopunks/cryptopu...   \n",
              "\n",
              "      Rarity_Score_1_rarest Rarity_Score_2_Average Rarity_Score_3_multiple  \\\n",
              "0                  5.158689               3.635788                0.000000   \n",
              "1                  5.158689               3.635788                0.000000   \n",
              "2                 48.311485              20.756003              891.396281   \n",
              "3                 48.311485              20.756003              891.396281   \n",
              "4                  8.104202               4.654820               69.188677   \n",
              "...                     ...                    ...                     ...   \n",
              "17821             21.880604              12.483109               67.515097   \n",
              "17822              6.622251               5.319922              144.347171   \n",
              "17823              4.402877               3.774773               52.620913   \n",
              "17824              5.721903               3.182949                0.000000   \n",
              "17825             18.425587               9.659756              425.229311   \n",
              "\n",
              "          timestamp      date_y  gas_eth_price        date  eth_usd_price  \\\n",
              "0      1.498176e+09  1498176000   1.720330e+08  1498176000         326.85   \n",
              "1      1.499040e+09  1499040000   1.180929e+08  1499040000         276.41   \n",
              "2      1.498176e+09  1498176000   1.720330e+08  1498176000         326.85   \n",
              "3      1.553040e+09  1553040000   5.115253e+07  1553040000         140.22   \n",
              "4      1.498176e+09  1498176000   1.720330e+08  1498176000         326.85   \n",
              "...             ...         ...            ...         ...            ...   \n",
              "17821  1.658016e+09  1658016000   1.046061e+08  1658016000        1337.97   \n",
              "17822  1.658016e+09  1658016000   1.046061e+08  1658016000        1337.97   \n",
              "17823  1.658102e+09  1658102400   1.475703e+08  1658102400        1583.55   \n",
              "17824  1.658362e+09  1658361600   1.027865e+08  1658361600        1575.68   \n",
              "17825  1.658534e+09  1658534400   6.633055e+07  1658534400        1549.19   \n",
              "\n",
              "             Price  \n",
              "0           9.8055  \n",
              "1         135.4409  \n",
              "2          13.0740  \n",
              "3         138.8178  \n",
              "4          81.7125  \n",
              "...            ...  \n",
              "17821  118918.7736  \n",
              "17822  117741.3600  \n",
              "17823  133018.2000  \n",
              "17824  136579.9424  \n",
              "17825  170410.9000  \n",
              "\n",
              "[17826 rows x 23 columns]"
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>date_x</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>eth_price</th>\n",
              "      <th>punk_id</th>\n",
              "      <th>type</th>\n",
              "      <th>gender</th>\n",
              "      <th>skin_tone</th>\n",
              "      <th>...</th>\n",
              "      <th>img_url</th>\n",
              "      <th>Rarity_Score_1_rarest</th>\n",
              "      <th>Rarity_Score_2_Average</th>\n",
              "      <th>Rarity_Score_3_multiple</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>date_y</th>\n",
              "      <th>gas_eth_price</th>\n",
              "      <th>date</th>\n",
              "      <th>eth_usd_price</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>Male</td>\n",
              "      <td>Albino</td>\n",
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              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>5.158689</td>\n",
              "      <td>3.635788</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1498176000</td>\n",
              "      <td>326.85</td>\n",
              "      <td>9.8055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-07-03</td>\n",
              "      <td>1218</td>\n",
              "      <td>3828</td>\n",
              "      <td>0.49</td>\n",
              "      <td>6548</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Albino</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>5.158689</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>1.499040e+09</td>\n",
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              "      <td>1.180929e+08</td>\n",
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              "      <td>276.41</td>\n",
              "      <td>135.4409</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>1406</td>\n",
              "      <td>1218</td>\n",
              "      <td>0.04</td>\n",
              "      <td>5719</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>48.311485</td>\n",
              "      <td>20.756003</td>\n",
              "      <td>891.396281</td>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>1498176000</td>\n",
              "      <td>1.720330e+08</td>\n",
              "      <td>1498176000</td>\n",
              "      <td>326.85</td>\n",
              "      <td>13.0740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2019-03-20</td>\n",
              "      <td>920</td>\n",
              "      <td>1472</td>\n",
              "      <td>0.99</td>\n",
              "      <td>5719</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>48.311485</td>\n",
              "      <td>20.756003</td>\n",
              "      <td>891.396281</td>\n",
              "      <td>1.553040e+09</td>\n",
              "      <td>1553040000</td>\n",
              "      <td>5.115253e+07</td>\n",
              "      <td>1553040000</td>\n",
              "      <td>140.22</td>\n",
              "      <td>138.8178</td>\n",
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              "      <td>2017-06-23</td>\n",
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              "      <td>2518</td>\n",
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              "      <td>1841</td>\n",
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              "      <td>Female</td>\n",
              "      <td>Dark</td>\n",
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              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>8.104202</td>\n",
              "      <td>4.654820</td>\n",
              "      <td>69.188677</td>\n",
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              "      <td>1498176000</td>\n",
              "      <td>1.720330e+08</td>\n",
              "      <td>1498176000</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>17821</th>\n",
              "      <td>17821</td>\n",
              "      <td>17821</td>\n",
              "      <td>2022-07-17</td>\n",
              "      <td>2292</td>\n",
              "      <td>2070</td>\n",
              "      <td>88.88</td>\n",
              "      <td>4656</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>21.880604</td>\n",
              "      <td>12.483109</td>\n",
              "      <td>67.515097</td>\n",
              "      <td>1.658016e+09</td>\n",
              "      <td>1658016000</td>\n",
              "      <td>1.046061e+08</td>\n",
              "      <td>1658016000</td>\n",
              "      <td>1337.97</td>\n",
              "      <td>118918.7736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17822</th>\n",
              "      <td>17822</td>\n",
              "      <td>17822</td>\n",
              "      <td>2022-07-17</td>\n",
              "      <td>5922</td>\n",
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              "      <td>88.00</td>\n",
              "      <td>4749</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>6.622251</td>\n",
              "      <td>5.319922</td>\n",
              "      <td>144.347171</td>\n",
              "      <td>1.658016e+09</td>\n",
              "      <td>1658016000</td>\n",
              "      <td>1.046061e+08</td>\n",
              "      <td>1658016000</td>\n",
              "      <td>1337.97</td>\n",
              "      <td>117741.3600</td>\n",
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              "    <tr>\n",
              "      <th>17823</th>\n",
              "      <td>17823</td>\n",
              "      <td>17823</td>\n",
              "      <td>2022-07-18</td>\n",
              "      <td>107</td>\n",
              "      <td>1044</td>\n",
              "      <td>84.00</td>\n",
              "      <td>4569</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Light</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>4.402877</td>\n",
              "      <td>3.774773</td>\n",
              "      <td>52.620913</td>\n",
              "      <td>1.658102e+09</td>\n",
              "      <td>1658102400</td>\n",
              "      <td>1.475703e+08</td>\n",
              "      <td>1658102400</td>\n",
              "      <td>1583.55</td>\n",
              "      <td>133018.2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17824</th>\n",
              "      <td>17824</td>\n",
              "      <td>17824</td>\n",
              "      <td>2022-07-21</td>\n",
              "      <td>244</td>\n",
              "      <td>801</td>\n",
              "      <td>86.68</td>\n",
              "      <td>4755</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Dark</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>5.721903</td>\n",
              "      <td>3.182949</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.658362e+09</td>\n",
              "      <td>1658361600</td>\n",
              "      <td>1.027865e+08</td>\n",
              "      <td>1658361600</td>\n",
              "      <td>1575.68</td>\n",
              "      <td>136579.9424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17825</th>\n",
              "      <td>17825</td>\n",
              "      <td>17825</td>\n",
              "      <td>2022-07-23</td>\n",
              "      <td>10</td>\n",
              "      <td>1618</td>\n",
              "      <td>110.00</td>\n",
              "      <td>9544</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Light</td>\n",
              "      <td>...</td>\n",
              "      <td>https://www.larvalabs.com/cryptopunks/cryptopu...</td>\n",
              "      <td>18.425587</td>\n",
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              "      <td>425.229311</td>\n",
              "      <td>1.658534e+09</td>\n",
              "      <td>1658534400</td>\n",
              "      <td>6.633055e+07</td>\n",
              "      <td>1658534400</td>\n",
              "      <td>1549.19</td>\n",
              "      <td>170410.9000</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Volume of Active Market Wallet & Daily Change Rate"
      ],
      "metadata": {
        "id": "J4dKQuNozYBe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_group=df_cryp.groupby('date_x').count()\n",
        "active_wallet=np.array(df_group['Unnamed: 0'])*2\n",
        "df_wallet=DataFrame({\"active_wallet\": active_wallet,\n",
        "                     \"date\":df_group.index})\n",
        "df_wallet"
      ],
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        "id": "-ghy2fF6ze8V",
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      "execution_count": null,
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              "      active_wallet        date\n",
              "0                34  2017-06-23\n",
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        "df_wallet['active_wallet_shift']=df_wallet['active_wallet'].shift(1)\n",
        "df_wallet['active_wallet_shift'][0]=3\n",
        "df_wallet"
      ],
      "metadata": {
        "id": "n0oMzl-AUf5M",
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          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "3a660a78-2647-4833-8e95-97aa4205da07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-51ec93477ae8>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_wallet['active_wallet_shift'][0]=3\n"
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              "      active_wallet        date  active_wallet_shift\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wallet_change=(np.array(df_wallet['active_wallet'])-np.array(df_wallet['active_wallet_shift']))/np.array(df_wallet['active_wallet_shift'])\n",
        "df_wallet['wallet_change']=wallet_change\n",
        "df_wallet['wallet_change']"
      ],
      "metadata": {
        "id": "WpryJodXWaUf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18c34aaf-5bea-4f2d-8a22-282193d81540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       10.333333\n",
              "1        0.000000\n",
              "2       -0.294118\n",
              "3       -0.583333\n",
              "4        3.600000\n",
              "          ...    \n",
              "1364     0.000000\n",
              "1365     0.000000\n",
              "1366     2.000000\n",
              "1367    -0.500000\n",
              "1368    -0.333333\n",
              "Name: wallet_change, Length: 1369, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp=df_cryp.merge(df_wallet, left_on='date_x', right_on='date')"
      ],
      "metadata": {
        "id": "ya1hHDzy0TLC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd2b24f9-0362-4a1b-d109-cfe543bd235d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:9190: FutureWarning: Passing 'suffixes' which cause duplicate columns {'date_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
            "  return merge(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.to_csv('wallet_rarity_usd_gas_merged.csv')\n",
        "df_cryp"
      ],
      "metadata": {
        "id": "G972gTLWSQiX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "89417d16-de4f-49b7-8ab0-7669af5488a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "1               2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2               4             4  2017-06-23  1406  2518       0.25     1841   \n",
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              "4               9             9  2017-06-23  1406   888       0.20     4641   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
              "17821       17516         17516  2022-02-16  4228  6193      76.48     9469   \n",
              "17822       17663         17663  2022-05-20  4791  3709      54.54     2642   \n",
              "17823       17664         17664  2022-05-20   854  3564      50.80     6776   \n",
              "17824       17665         17665  2022-05-20  1064   677      51.51     2265   \n",
              "17825       17758         17758  2022-06-22  6280  6307     825.00     8531   \n",
              "\n",
              "         type  gender  skin_tone  ...     timestamp      date_y gas_eth_price  \\\n",
              "0       Human    Male     Albino  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "1       Human  Female     Medium  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "2       Human  Female       Dark  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "3       Human    Male     Medium  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "4       Human  Female      Light  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "...       ...     ...        ...  ...           ...         ...           ...   \n",
              "17821   Human    Male     Medium  ...  1.644970e+09  1644969600  3.597364e+08   \n",
              "17822   Human    Male       Dark  ...  1.653005e+09  1653004800  1.376851e+08   \n",
              "17823   Human  Female     Medium  ...  1.653005e+09  1653004800  1.376851e+08   \n",
              "17824   Human    Male      Light  ...  1.653005e+09  1653004800  1.376851e+08   \n",
              "17825  Zombie    Male  Non-human  ...  1.655856e+09  1655856000  1.666721e+08   \n",
              "\n",
              "           date_x  eth_usd_price        Price  active_wallet      date_y  \\\n",
              "0      1498176000         326.85       9.8055             34  2017-06-23   \n",
              "1      1498176000         326.85      13.0740             34  2017-06-23   \n",
              "2      1498176000         326.85      81.7125             34  2017-06-23   \n",
              "3      1498176000         326.85      98.0550             34  2017-06-23   \n",
              "4      1498176000         326.85      65.3700             34  2017-06-23   \n",
              "...           ...            ...          ...            ...         ...   \n",
              "17821  1644969600        3124.33  238948.7584              2  2022-02-16   \n",
              "17822  1653004800        1956.88  106728.2352              6  2022-05-20   \n",
              "17823  1653004800        1956.88   99409.5040              6  2022-05-20   \n",
              "17824  1653004800        1956.88  100798.8888              6  2022-05-20   \n",
              "17825  1655856000        1048.30  864847.5000              2  2022-06-22   \n",
              "\n",
              "       active_wallet_shift  wallet_change  \n",
              "0                      3.0      10.333333  \n",
              "1                      3.0      10.333333  \n",
              "2                      3.0      10.333333  \n",
              "3                      3.0      10.333333  \n",
              "4                      3.0      10.333333  \n",
              "...                    ...            ...  \n",
              "17821                  4.0      -0.500000  \n",
              "17822                 12.0      -0.500000  \n",
              "17823                 12.0      -0.500000  \n",
              "17824                 12.0      -0.500000  \n",
              "17825                 34.0      -0.941176  \n",
              "\n",
              "[17826 rows x 27 columns]"
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              "      <td>Human</td>\n",
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              "      <td>1.498176e+09</td>\n",
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              "      <td>1498176000</td>\n",
              "      <td>326.85</td>\n",
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              "      <td>34</td>\n",
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              "      <td>1498176000</td>\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-10658b8c-f9ad-44e4-bf21-933bf155942e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Volume of Cryptopunks Sales Daily Change Rate"
      ],
      "metadata": {
        "id": "btQ_hVPdYUX2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp"
      ],
      "metadata": {
        "id": "cMaWAx2Oe5JS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "fb36c3ca-979b-41ab-efdb-0ca062f93434"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "1               2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2               4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "3               7             7  2017-06-23  1406  1904       0.30     5916   \n",
              "4               9             9  2017-06-23  1406   888       0.20     4641   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
              "17821       17516         17516  2022-02-16  4228  6193      76.48     9469   \n",
              "17822       17663         17663  2022-05-20  4791  3709      54.54     2642   \n",
              "17823       17664         17664  2022-05-20   854  3564      50.80     6776   \n",
              "17824       17665         17665  2022-05-20  1064   677      51.51     2265   \n",
              "17825       17758         17758  2022-06-22  6280  6307     825.00     8531   \n",
              "\n",
              "         type  gender  skin_tone  ...     timestamp      date_y gas_eth_price  \\\n",
              "0       Human    Male     Albino  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "1       Human  Female     Medium  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "2       Human  Female       Dark  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "3       Human    Male     Medium  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "4       Human  Female      Light  ...  1.498176e+09  1498176000  1.720330e+08   \n",
              "...       ...     ...        ...  ...           ...         ...           ...   \n",
              "17821   Human    Male     Medium  ...  1.644970e+09  1644969600  3.597364e+08   \n",
              "17822   Human    Male       Dark  ...  1.653005e+09  1653004800  1.376851e+08   \n",
              "17823   Human  Female     Medium  ...  1.653005e+09  1653004800  1.376851e+08   \n",
              "17824   Human    Male      Light  ...  1.653005e+09  1653004800  1.376851e+08   \n",
              "17825  Zombie    Male  Non-human  ...  1.655856e+09  1655856000  1.666721e+08   \n",
              "\n",
              "           date_x  eth_usd_price        Price  active_wallet      date_y  \\\n",
              "0      1498176000         326.85       9.8055             34  2017-06-23   \n",
              "1      1498176000         326.85      13.0740             34  2017-06-23   \n",
              "2      1498176000         326.85      81.7125             34  2017-06-23   \n",
              "3      1498176000         326.85      98.0550             34  2017-06-23   \n",
              "4      1498176000         326.85      65.3700             34  2017-06-23   \n",
              "...           ...            ...          ...            ...         ...   \n",
              "17821  1644969600        3124.33  238948.7584              2  2022-02-16   \n",
              "17822  1653004800        1956.88  106728.2352              6  2022-05-20   \n",
              "17823  1653004800        1956.88   99409.5040              6  2022-05-20   \n",
              "17824  1653004800        1956.88  100798.8888              6  2022-05-20   \n",
              "17825  1655856000        1048.30  864847.5000              2  2022-06-22   \n",
              "\n",
              "       active_wallet_shift  wallet_change  \n",
              "0                      3.0      10.333333  \n",
              "1                      3.0      10.333333  \n",
              "2                      3.0      10.333333  \n",
              "3                      3.0      10.333333  \n",
              "4                      3.0      10.333333  \n",
              "...                    ...            ...  \n",
              "17821                  4.0      -0.500000  \n",
              "17822                 12.0      -0.500000  \n",
              "17823                 12.0      -0.500000  \n",
              "17824                 12.0      -0.500000  \n",
              "17825                 34.0      -0.941176  \n",
              "\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "sales=df_cryp.groupby('timestamp')['Price'].sum()\n",
        "sales"
      ],
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        "id": "PAd-UxP3apct",
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          "base_uri": "https://localhost:8080/"
        },
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      "execution_count": null,
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            "text/plain": [
              "timestamp\n",
              "1.498176e+09       681.155400\n",
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              "1.658880e+09    285864.723200\n",
              "Name: Price, Length: 1369, dtype: float64"
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          "metadata": {},
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    {
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        "df_sales=DataFrame({'daily_sales':sales,\n",
        "                    'date_z':df_group.index})\n",
        "df_sales"
      ],
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              "                daily_sales      date_z\n",
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        "df_sales['sales_shift']=df_sales['daily_sales'].shift(1)\n",
        "df_sales['sales_shift'][0]=0.42\n",
        "change_sales=(np.array(df_sales['daily_sales'])-np.array(df_sales['sales_shift']))/np.array(df_sales['sales_shift'])\n",
        "change_sales"
      ],
      "metadata": {
        "id": "3EEX74Zcb9g1",
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        },
        "outputId": "f30ba64c-b450-4423-f634-ed8b7baca52f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-c0bd02db3cfe>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_sales['sales_shift'][0]=0.42\n",
            "<ipython-input-12-c0bd02db3cfe>:3: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  change_sales=(np.array(df_sales['daily_sales'])-np.array(df_sales['sales_shift']))/np.array(df_sales['sales_shift'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "array([ 1.62079857e+03,  5.98805559e-01, -5.51925036e-01, ...,\n",
              "        9.23974816e-01, -3.66519486e-01, -2.35358180e-01])"
            ]
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    {
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        "df_sales"
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      "execution_count": null,
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            "text/plain": [
              "                daily_sales      date_z    sales_shift  daily_sales_change\n",
              "timestamp                                                                 \n",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp=df_cryp.merge(df_sales,left_on='timestamp',right_on='timestamp')"
      ],
      "metadata": {
        "id": "O-JNiSLPc5_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.to_csv('sales_wallet_rarity_gas_usd.csv')"
      ],
      "metadata": {
        "id": "PalJCEnpdYYL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
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      ],
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          "height": 679
        },
        "outputId": "cb21b155-7723-401b-ef93-62a532b0019e"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "1               2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2               4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "3               7             7  2017-06-23  1406  1904       0.30     5916   \n",
              "4               9             9  2017-06-23  1406   888       0.20     4641   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
              "17821       17516         17516  2022-02-16  4228  6193      76.48     9469   \n",
              "17822       17663         17663  2022-05-20  4791  3709      54.54     2642   \n",
              "17823       17664         17664  2022-05-20   854  3564      50.80     6776   \n",
              "17824       17665         17665  2022-05-20  1064   677      51.51     2265   \n",
              "17825       17758         17758  2022-06-22  6280  6307     825.00     8531   \n",
              "\n",
              "         type  gender  skin_tone  ...  eth_usd_price        Price  \\\n",
              "0       Human    Male     Albino  ...         326.85       9.8055   \n",
              "1       Human  Female     Medium  ...         326.85      13.0740   \n",
              "2       Human  Female       Dark  ...         326.85      81.7125   \n",
              "3       Human    Male     Medium  ...         326.85      98.0550   \n",
              "4       Human  Female      Light  ...         326.85      65.3700   \n",
              "...       ...     ...        ...  ...            ...          ...   \n",
              "17821   Human    Male     Medium  ...        3124.33  238948.7584   \n",
              "17822   Human    Male       Dark  ...        1956.88  106728.2352   \n",
              "17823   Human  Female     Medium  ...        1956.88   99409.5040   \n",
              "17824   Human    Male      Light  ...        1956.88  100798.8888   \n",
              "17825  Zombie    Male  Non-human  ...        1048.30  864847.5000   \n",
              "\n",
              "      active_wallet      date_y  active_wallet_shift  wallet_change  \\\n",
              "0                34  2017-06-23                  3.0      10.333333   \n",
              "1                34  2017-06-23                  3.0      10.333333   \n",
              "2                34  2017-06-23                  3.0      10.333333   \n",
              "3                34  2017-06-23                  3.0      10.333333   \n",
              "4                34  2017-06-23                  3.0      10.333333   \n",
              "...             ...         ...                  ...            ...   \n",
              "17821             2  2022-02-16                  4.0      -0.500000   \n",
              "17822             6  2022-05-20                 12.0      -0.500000   \n",
              "17823             6  2022-05-20                 12.0      -0.500000   \n",
              "17824             6  2022-05-20                 12.0      -0.500000   \n",
              "17825             2  2022-06-22                 34.0      -0.941176   \n",
              "\n",
              "       daily_sales      date_z   sales_shift  daily_sales_change  \n",
              "0         681.1554  2017-06-23  4.200000e-01         1620.798571  \n",
              "1         681.1554  2017-06-23  4.200000e-01         1620.798571  \n",
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              "4         681.1554  2017-06-23  4.200000e-01         1620.798571  \n",
              "...            ...         ...           ...                 ...  \n",
              "17821  238948.7584  2022-02-16  5.893749e+05           -0.594573  \n",
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              "17823  306936.6280  2022-05-20  9.036830e+05           -0.660349  \n",
              "17824  306936.6280  2022-05-20  9.036830e+05           -0.660349  \n",
              "17825  864847.5000  2022-06-22  1.436300e+06           -0.397864  \n",
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              "      <td>2022-05-20</td>\n",
              "      <td>9.036830e+05</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bed12a0a-de14-417d-8fd4-e71e7891f57c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      fill: #1967D2;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bed12a0a-de14-417d-8fd4-e71e7891f57c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Change Gender and Skin tone to dummy variable"
      ],
      "metadata": {
        "id": "fMx4dd6AbnC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gender_col=df_cryp['gender']\n",
        "skin_col=df_cryp['skin_tone']\n",
        "#cat_cols = df_cryp.select_dtypes(include=['object','category']).columns\n",
        "dummies = pd.get_dummies(gender_col, drop_first=True)\n",
        "df_cryp[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp.head()\n",
        "\n",
        "\n",
        "dummies = pd.get_dummies(skin_col, drop_first=True)\n",
        "df_cryp[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "91AtYtaabvEj",
        "outputId": "fff9cd04-9a20-4713-f940-874c1ae0856f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0           0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "1           2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2           4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "3           7             7  2017-06-23  1406  1904       0.30     5916   \n",
              "4           9             9  2017-06-23  1406   888       0.20     4641   \n",
              "\n",
              "    type  gender skin_tone  ...  wallet_change daily_sales      date_z  \\\n",
              "0  Human    Male    Albino  ...      10.333333    681.1554  2017-06-23   \n",
              "1  Human  Female    Medium  ...      10.333333    681.1554  2017-06-23   \n",
              "2  Human  Female      Dark  ...      10.333333    681.1554  2017-06-23   \n",
              "3  Human    Male    Medium  ...      10.333333    681.1554  2017-06-23   \n",
              "4  Human  Female     Light  ...      10.333333    681.1554  2017-06-23   \n",
              "\n",
              "  sales_shift  daily_sales_change  Male  Dark  Light  Medium  Non-human  \n",
              "0        0.42         1620.798571     1     0      0       0          0  \n",
              "1        0.42         1620.798571     0     0      0       1          0  \n",
              "2        0.42         1620.798571     0     1      0       0          0  \n",
              "3        0.42         1620.798571     1     0      0       1          0  \n",
              "4        0.42         1620.798571     0     0      1       0          0  \n",
              "\n",
              "[5 rows x 36 columns]"
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>date_x</th>\n",
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              "      <td>1841</td>\n",
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              "      <td>Dark</td>\n",
              "      <td>...</td>\n",
              "      <td>10.333333</td>\n",
              "      <td>681.1554</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>0.42</td>\n",
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              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>1406</td>\n",
              "      <td>1904</td>\n",
              "      <td>0.30</td>\n",
              "      <td>5916</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>10.333333</td>\n",
              "      <td>681.1554</td>\n",
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              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>1406</td>\n",
              "      <td>888</td>\n",
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              "      <td>4641</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Light</td>\n",
              "      <td>...</td>\n",
              "      <td>10.333333</td>\n",
              "      <td>681.1554</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1620.798571</td>\n",
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              "</table>\n",
              "<p>5 rows × 36 columns</p>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-59e54574-3d32-41bb-896d-6eb0d23dae23 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-59e54574-3d32-41bb-896d-6eb0d23dae23');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ETH_USD_change"
      ],
      "metadata": {
        "id": "dH5e4s_vXGP2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBF2pPZrc3ee",
        "outputId": "2517643c-2550-40e8-d8c4-d118d809b3d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'date_x', 'from', 'to', 'eth_price',\n",
              "       'punk_id', 'type', 'gender', 'skin_tone', 'attr_count', 'attributes',\n",
              "       'skin_tone_color', 'img_url', 'Rarity_Score_1_rarest',\n",
              "       'Rarity_Score_2_Average', 'Rarity_Score_3_multiple', 'timestamp',\n",
              "       'date_y', 'gas_eth_price', 'date_x', 'eth_usd_price', 'Price',\n",
              "       'active_wallet', 'date_y', 'active_wallet_shift', 'wallet_change',\n",
              "       'daily_sales', 'date_z', 'sales_shift', 'daily_sales_change', 'Male',\n",
              "       'Dark', 'Light', 'Medium', 'Non-human'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratio=df_cryp.groupby('timestamp')['eth_usd_price'].mean()\n",
        "ratio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82Fa2KZYXLVq",
        "outputId": "f4f560da-b16a-402c-a107-af579ff0e26b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp\n",
              "1.498176e+09     326.85\n",
              "1.498262e+09     304.54\n",
              "1.498349e+09     279.36\n",
              "1.498435e+09     253.68\n",
              "1.498522e+09     286.14\n",
              "                 ...   \n",
              "1.658448e+09    1535.71\n",
              "1.658534e+09    1549.19\n",
              "1.658707e+09    1438.01\n",
              "1.658794e+09    1449.61\n",
              "1.658880e+09    1636.88\n",
              "Name: eth_usd_price, Length: 1369, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ratio=DataFrame({'daily_ratio':ratio,\n",
        "                    'date_z':df_group.index})\n",
        "df_ratio"
      ],
      "metadata": {
        "id": "wkuwSz6bv9lP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "07c980cb-e2aa-4bd8-a113-d5863caa5b53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              daily_ratio      date_z\n",
              "timestamp                            \n",
              "1.498176e+09       326.85  2017-06-23\n",
              "1.498262e+09       304.54  2017-06-24\n",
              "1.498349e+09       279.36  2017-06-25\n",
              "1.498435e+09       253.68  2017-06-26\n",
              "1.498522e+09       286.14  2017-06-27\n",
              "...                   ...         ...\n",
              "1.658448e+09      1535.71  2022-07-22\n",
              "1.658534e+09      1549.19  2022-07-23\n",
              "1.658707e+09      1438.01  2022-07-25\n",
              "1.658794e+09      1449.61  2022-07-26\n",
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              "\n",
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              "  "
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "df_ratio['ratio_shift']=df_ratio['daily_ratio'].shift(1)\n",
        "df_ratio['ratio_shift'][0]=0.42\n",
        "change_ratio=(np.array(df_ratio['daily_ratio'])-np.array(df_ratio['ratio_shift']))/np.array(df_ratio['ratio_shift'])\n",
        "change_ratio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXYJh6QKdYsw",
        "outputId": "56241cee-a918-4e96-8a8a-d9d971f65aa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-501d9779c5a1>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_ratio['ratio_shift'][0]=0.42\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7.77214286e+02, -6.82576105e-02, -8.26820779e-02, ...,\n",
              "       -7.17665361e-02,  8.06670329e-03,  1.29186471e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ratio['daily_ratio_change']=change_ratio\n",
        "df_ratio"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "W9TlDRgcdYvS",
        "outputId": "9ea20db2-d2f3-42ed-fe1d-738f1feb9d39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              daily_ratio      date_z  ratio_shift  daily_ratio_change\n",
              "timestamp                                                             \n",
              "1.498176e+09       326.85  2017-06-23         0.42          777.214286\n",
              "1.498262e+09       304.54  2017-06-24       326.85           -0.068258\n",
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              "1.498435e+09       253.68  2017-06-26       279.36           -0.091924\n",
              "1.498522e+09       286.14  2017-06-27       253.68            0.127956\n",
              "...                   ...         ...          ...                 ...\n",
              "1.658448e+09      1535.71  2022-07-22      1575.68           -0.025367\n",
              "1.658534e+09      1549.19  2022-07-23      1535.71            0.008778\n",
              "1.658707e+09      1438.01  2022-07-25      1549.19           -0.071767\n",
              "1.658794e+09      1449.61  2022-07-26      1438.01            0.008067\n",
              "1.658880e+09      1636.88  2022-07-27      1449.61            0.129186\n",
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              "<p>1369 rows × 4 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-53c0b821-c0c2-4490-9f2b-7b4a8f60f40c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp=df_cryp.merge(df_ratio,left_on='timestamp',right_on='timestamp')"
      ],
      "metadata": {
        "id": "r1ZwWd6teS4t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp['daily_ratio_change']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpiKP3-OeeWh",
        "outputId": "fc461cc7-c68b-4adc-befd-64bbbaca906e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        777.214286\n",
              "1        777.214286\n",
              "2        777.214286\n",
              "3        777.214286\n",
              "4        777.214286\n",
              "            ...    \n",
              "17821     -0.019298\n",
              "17822     -0.030182\n",
              "17823     -0.030182\n",
              "17824     -0.030182\n",
              "17825     -0.067938\n",
              "Name: daily_ratio_change, Length: 17826, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ROI"
      ],
      "metadata": {
        "id": "ew6RwxB4HwLo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## roi1"
      ],
      "metadata": {
        "id": "VzIo8Xn55cXn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "number = df_cryp.groupby(['punk_id']).count()\n",
        "max(number['type'])"
      ],
      "metadata": {
        "id": "ccC9508SiAIV",
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              "15"
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          "metadata": {},
          "execution_count": 25
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    {
      "cell_type": "code",
      "source": [
        "roi = df_cryp.groupby(['punk_id']).agg({'Price':['min','max']})\n",
        "#roi.rename(columns={'data1':'data1_min'},inplace=True)\n",
        "roi=roi['Price']\n",
        "roi"
      ],
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        "id": "DqMX2aOIHy7q",
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          "height": 455
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        "outputId": "7b5ee670-3332-4b19-8d6a-c761ab441b38"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "roi['ROI'] = (roi['max']-roi['min'])/roi['min']\n",
        "roi"
      ],
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        "id": "96cd9kWinUKo",
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              "                   min          max           ROI\n",
              "punk_id                                          \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## roi2"
      ],
      "metadata": {
        "id": "s-W5d-B65Z8O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roi2 = df_cryp.groupby(['punk_id'])['timestamp','punk_id','Price']\n",
        "roi2.get_group(197)"
      ],
      "metadata": {
        "id": "3Qd2mbwwmBLo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "7ba9141e-778c-4d38-e31a-180ac97e674c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-5cb3efb4e904>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  roi2 = df_cryp.groupby(['punk_id'])['timestamp','punk_id','Price']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          timestamp  punk_id      Price\n",
              "13355  1.638490e+09      197  295352.40\n",
              "14663  1.642810e+09      197       0.00\n",
              "14664  1.642810e+09      197       0.00\n",
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              "      <td>197</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14664</th>\n",
              "      <td>1.642810e+09</td>\n",
              "      <td>197</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16989</th>\n",
              "      <td>1.640822e+09</td>\n",
              "      <td>197</td>\n",
              "      <td>241155.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16990</th>\n",
              "      <td>1.640822e+09</td>\n",
              "      <td>197</td>\n",
              "      <td>237445.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49308fe4-d532-4527-9dac-554a41621db6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-49308fe4-d532-4527-9dac-554a41621db6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-49308fe4-d532-4527-9dac-554a41621db6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.loc[1,'timestamp']"
      ],
      "metadata": {
        "id": "Wt31qpQd7J7_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d975c7c-1729-4551-d52f-19fba4d25f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1498176000.0"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list_roi=[]\n",
        "rq=[] #date\n",
        "for i in roi.index:\n",
        "  if len(list(roi2.get_group(i).index)) >1:\n",
        "    for j in range(len(list(roi2.get_group(i).index)[0:-1])):\n",
        "     \n",
        "      a=(roi2.get_group(i).iloc[j+1,2]-roi2.get_group(i).iloc[j,2])/roi2.get_group(i).iloc[j,2]\n",
        "      list_roi.append(a) \n",
        "      b=df_cryp.loc[j,'timestamp']\n",
        "      rq.append(b)\n",
        "  \n",
        "len(list_roi)"
      ],
      "metadata": {
        "id": "AZlT4IJsoe4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc793b35-2873-45da-fe2e-dc213b74a3f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-ae1cd30103ba>:7: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  a=(roi2.get_group(i).iloc[j+1,2]-roi2.get_group(i).iloc[j,2])/roi2.get_group(i).iloc[j,2]\n",
            "<ipython-input-30-ae1cd30103ba>:7: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  a=(roi2.get_group(i).iloc[j+1,2]-roi2.get_group(i).iloc[j,2])/roi2.get_group(i).iloc[j,2]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11551"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ROI=DataFrame({'Time':rq,\n",
        "               'ROI':list_roi})\n",
        "ROI"
      ],
      "metadata": {
        "id": "43ZHwdA18Alf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "8bf491f0-a36f-4bbb-cfa9-a672610b095c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Time       ROI\n",
              "0      1.498176e+09  0.901046\n",
              "1      1.498176e+09 -0.334105\n",
              "2      1.498176e+09 -0.900295\n",
              "3      1.498176e+09 -0.361620\n",
              "4      1.498176e+09  0.993912\n",
              "...             ...       ...\n",
              "11546  1.498176e+09 -0.162594\n",
              "11547  1.498176e+09 -0.804058\n",
              "11548  1.498176e+09 -0.999999\n",
              "11549  1.498176e+09  4.846569\n",
              "11550  1.498176e+09 -0.970561\n",
              "\n",
              "[11551 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ad64538-68dc-42f1-997b-c5baa7b1fe07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>ROI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>0.901046</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.334105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.900295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.361620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>0.993912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>11546</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.162594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11547</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.804058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11548</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.999999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11549</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>4.846569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11550</th>\n",
              "      <td>1.498176e+09</td>\n",
              "      <td>-0.970561</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11551 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ad64538-68dc-42f1-997b-c5baa7b1fe07')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ad64538-68dc-42f1-997b-c5baa7b1fe07 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ad64538-68dc-42f1-997b-c5baa7b1fe07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df_cryp=df_cryp.merge(ROI,left_on='timestamp',right_on='Time')"
      ],
      "metadata": {
        "id": "CQYOQuGQ8QwZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df_cryp=df_cryp[df_cryp['ROI']<100000]"
      ],
      "metadata": {
        "id": "DPRDW6Dd1Qol"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df_cryp.to_csv('roi_sales_wallet_rarity_gas_usd.csv')"
      ],
      "metadata": {
        "id": "1q_nttgvGYGz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df_cryp=pd.read_csv('https://raw.githubusercontent.com/HCI-Blockchain/ICWSM-2023/main/data/hedonic_data/roi_sales_wallet_rarity_gas_usd.csv')\n",
        "df_cryp.columns"
      ],
      "metadata": {
        "id": "HPrmy_uJigRU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b2764a5-7f30-4f3b-d6e9-ae5d669e0809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'date_x', 'from', 'to', 'eth_price',\n",
              "       'punk_id', 'type', 'gender', 'skin_tone', 'attr_count', 'attributes',\n",
              "       'skin_tone_color', 'img_url', 'Rarity_Score_1_rarest',\n",
              "       'Rarity_Score_2_Average', 'Rarity_Score_3_multiple', 'timestamp',\n",
              "       'date_y', 'gas_eth_price', 'date_x', 'eth_usd_price', 'Price',\n",
              "       'active_wallet', 'date_y', 'active_wallet_shift', 'wallet_change',\n",
              "       'daily_sales', 'date_z_x', 'sales_shift', 'daily_sales_change', 'Male',\n",
              "       'Dark', 'Light', 'Medium', 'Non-human', 'daily_ratio', 'date_z_y',\n",
              "       'ratio_shift', 'daily_ratio_change'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation"
      ],
      "metadata": {
        "id": "18YRSzq2iUte"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisations\n",
        "\n",
        "#print(train.head(3))\n",
        "# correlation heatmap   \n",
        " \n",
        "#plt.figure(figsize=(14,12))\n",
        "#plt.title('Pearson Correlation of Features',y=1.05,size=15)\n",
        "#sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0,\n",
        "            #square=True,linecolor='white',annot=True)\n",
        "#plt.show()"
      ],
      "metadata": {
        "id": "a1MoypOUQrrH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0e981aa7-e313-4d84-eff0-403e760bbecc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'将数据进行可视化'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Stationay Test"
      ],
      "metadata": {
        "id": "aumf2EmBh1mU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Augmented Dickey Fuller test (ADF Test)："
      ],
      "metadata": {
        "id": "kgUB9vPJNvqn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller, kpss\n",
        "df = df_cryp\n",
        "\n",
        "# ADF Test\n",
        "result = adfuller(df.Price.values, autolag='AIC')\n",
        "print(f'ADF Statistic: {result[0]}')\n",
        "print(f'p-value: {result[1]}')\n",
        "for key, value in result[4].items():\n",
        "    print('Critial Values:')\n",
        "    print(f'   {key}, {value}')"
      ],
      "metadata": {
        "id": "c92mYI5tiemV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbcec2b8-5ce7-46ad-e923-73dd152e39df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -14.120467340536699\n",
            "p-value: 2.4300005847698592e-26\n",
            "Critial Values:\n",
            "   1%, -3.4307175739704157\n",
            "Critial Values:\n",
            "   5%, -2.861702453666342\n",
            "Critial Values:\n",
            "   10%, -2.566856469840447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ADF Test\n",
        "#df = df_cryp\n",
        "#result = adfuller(df.ROI.values, autolag='AIC')\n",
        "#print(f'ADF Statistic: {result[0]}')\n",
        "#print(f'p-value: {result[1]}')\n",
        "#for key, value in result[4].items():\n",
        "#    print('Critial Values:')\n",
        "#    print(f'   {key}, {value}')"
      ],
      "metadata": {
        "id": "FadtscVPr6Zg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kwiatkowski-Phillips-Schmidt-Shin – KPSS test (trend stationary)："
      ],
      "metadata": {
        "id": "TJSxhZAsNw7X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# KPSS Test\n",
        "result = kpss(df.Price.values, regression='c')\n",
        "print('\\nKPSS Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "for key, value in result[3].items():\n",
        "    print('Critial Values:')\n",
        "    print(f'   {key}, {value}')"
      ],
      "metadata": {
        "id": "jjMQzbqgODo9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f38cbab7-3e4a-4cfc-f593-5eec4ff86d5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "KPSS Statistic: 0.398660\n",
            "p-value: 0.077733\n",
            "Critial Values:\n",
            "   10%, 0.347\n",
            "Critial Values:\n",
            "   5%, 0.463\n",
            "Critial Values:\n",
            "   2.5%, 0.574\n",
            "Critial Values:\n",
            "   1%, 0.739\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# KPSS Test\n",
        "#result = kpss(df.ROI.values, regression='c')\n",
        "#print('\\nKPSS Statistic: %f' % result[0])\n",
        "#print('p-value: %f' % result[1])\n",
        "#for key, value in result[3].items():\n",
        "#    print('Critial Values:')\n",
        "#    print(f'   {key}, {value}')"
      ],
      "metadata": {
        "id": "ie4ps0fzTf6q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation Test"
      ],
      "metadata": {
        "id": "AQKArAfIJ0UX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFQ01xGKN1bg",
        "outputId": "ff9bf2f6-15e2-4cfb-fd93-0696759c0988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'date_x', 'from', 'to', 'eth_price',\n",
              "       'punk_id', 'type', 'gender', 'skin_tone', 'attr_count', 'attributes',\n",
              "       'skin_tone_color', 'img_url', 'Rarity_Score_1_rarest',\n",
              "       'Rarity_Score_2_Average', 'Rarity_Score_3_multiple', 'timestamp',\n",
              "       'date_y', 'gas_eth_price', 'date_x', 'eth_usd_price', 'Price',\n",
              "       'active_wallet', 'date_y', 'active_wallet_shift', 'wallet_change',\n",
              "       'daily_sales', 'date_z_x', 'sales_shift', 'daily_sales_change', 'Male',\n",
              "       'Dark', 'Light', 'Medium', 'Non-human', 'daily_ratio', 'date_z_y',\n",
              "       'ratio_shift', 'daily_ratio_change'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=df_cryp[['Male','Dark','Light','Medium','Non-human','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','wallet_change','daily_sales_change','daily_ratio_change']]"
      ],
      "metadata": {
        "id": "8IqzNaGVLeFP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import modules\n",
        "import matplotlib.pyplot as mp\n",
        "import pandas as pd\n",
        "import seaborn as sb\n",
        "\n",
        "# import file with data\n",
        "# data = pd.read_csv(\"C:\\\\Users\\\\Vanshi\\\\Desktop\\\\bestsellers.csv\")\n",
        "\n",
        "# prints data that will be plotted\n",
        "# columns shown here are selected by corr() since\n",
        "# they are ideal for the plot\n",
        "print(data.corr())\n",
        "\n",
        "# plotting correlation heatmap\n",
        "dataplot = sb.heatmap(data.corr(), cmap=\"YlGnBu\", annot=True)\n",
        "\n",
        "# displaying heatmap\n",
        "mp.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "synxq1qYJ3d5",
        "outputId": "30f10596-ff24-4cfb-b787-8bf1609f1f23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                             Male      Dark     Light    Medium  Non-human  \\\n",
            "Male                     1.000000 -0.004993  0.021071 -0.018996   0.049190   \n",
            "Dark                    -0.004993  1.000000 -0.435148 -0.449528  -0.047860   \n",
            "Light                    0.021071 -0.435148  1.000000 -0.413998  -0.044077   \n",
            "Medium                  -0.018996 -0.449528 -0.413998  1.000000  -0.045534   \n",
            "Non-human                0.049190 -0.047860 -0.044077 -0.045534   1.000000   \n",
            "Rarity_Score_3_multiple -0.012300 -0.000259 -0.006362  0.013619  -0.005391   \n",
            "gas_eth_price           -0.001973 -0.002221  0.007681 -0.009233  -0.015711   \n",
            "eth_usd_price           -0.038736  0.019149 -0.001882 -0.024859  -0.031612   \n",
            "wallet_change            0.013319  0.034089 -0.017183 -0.010798  -0.004456   \n",
            "daily_sales_change      -0.006412 -0.002564 -0.000735 -0.001453   0.063472   \n",
            "daily_ratio_change       0.002548 -0.009532 -0.003465  0.003580  -0.002167   \n",
            "\n",
            "                         Rarity_Score_3_multiple  gas_eth_price  \\\n",
            "Male                                   -0.012300      -0.001973   \n",
            "Dark                                   -0.000259      -0.002221   \n",
            "Light                                  -0.006362       0.007681   \n",
            "Medium                                  0.013619      -0.009233   \n",
            "Non-human                              -0.005391      -0.015711   \n",
            "Rarity_Score_3_multiple                 1.000000      -0.003829   \n",
            "gas_eth_price                          -0.003829       1.000000   \n",
            "eth_usd_price                          -0.008692       0.237472   \n",
            "wallet_change                          -0.004179      -0.158428   \n",
            "daily_sales_change                     -0.000674      -0.004474   \n",
            "daily_ratio_change                     -0.000471      -0.022209   \n",
            "\n",
            "                         eth_usd_price  wallet_change  daily_sales_change  \\\n",
            "Male                         -0.038736       0.013319           -0.006412   \n",
            "Dark                          0.019149       0.034089           -0.002564   \n",
            "Light                        -0.001882      -0.017183           -0.000735   \n",
            "Medium                       -0.024859      -0.010798           -0.001453   \n",
            "Non-human                    -0.031612      -0.004456            0.063472   \n",
            "Rarity_Score_3_multiple      -0.008692      -0.004179           -0.000674   \n",
            "gas_eth_price                 0.237472      -0.158428           -0.004474   \n",
            "eth_usd_price                 1.000000      -0.163233           -0.013019   \n",
            "wallet_change                -0.163233       1.000000            0.035545   \n",
            "daily_sales_change           -0.013019       0.035545            1.000000   \n",
            "daily_ratio_change           -0.040125       0.046238           -0.000756   \n",
            "\n",
            "                         daily_ratio_change  \n",
            "Male                               0.002548  \n",
            "Dark                              -0.009532  \n",
            "Light                             -0.003465  \n",
            "Medium                             0.003580  \n",
            "Non-human                         -0.002167  \n",
            "Rarity_Score_3_multiple           -0.000471  \n",
            "gas_eth_price                     -0.022209  \n",
            "eth_usd_price                     -0.040125  \n",
            "wallet_change                      0.046238  \n",
            "daily_sales_change                -0.000756  \n",
            "daily_ratio_change                 1.000000  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hedonic Regression"
      ],
      "metadata": {
        "id": "oFZYk1-vH8xp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histogram"
      ],
      "metadata": {
        "id": "ljISwMrrbBIr"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BGrcUfXwbARP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2017-2021"
      ],
      "metadata": {
        "id": "Hd0fGIAmMjgG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.sort_values('timestamp',inplace=True)\n",
        "df_cryp=df_cryp.dropna()\n",
        "df_cryp"
      ],
      "metadata": {
        "id": "DrUj_JJ6MeEh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "c181bb29-2033-4bbf-ae1a-833375c58720"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "15             29            29  2017-06-23  1406   166       0.01     3134   \n",
              "14             28            28  2017-06-23   166  4722       0.10     5056   \n",
              "13             26            26  2017-06-23  1406  1218       0.06     6208   \n",
              "12             24            24  2017-06-23  1406  2518       0.20     5545   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
              "17754       17613         17613  2022-07-26  3002  1044      94.90     5600   \n",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Since the price of Cryptopunks' price is not stationary, we will transform the price into log price\n",
        "df_cryp['Log_price']=np.log(np.array(df_cryp['Price']))\n",
        "\n",
        "df_cryp=df_cryp[df_cryp['Log_price']>-100000]\n",
        "df_cryp=df_cryp[df_cryp['Log_price']<100000]\n",
        "df_cryp=df_cryp[df_cryp['daily_sales_change']>-100000]\n",
        "df_cryp=df_cryp[df_cryp['daily_sales_change']<100000]\n",
        "df_cryp=df_cryp[df_cryp['wallet_change']>-100000]\n",
        "df_cryp=df_cryp[df_cryp['wallet_change']<100000]\n",
        "\n",
        "df_cryp=df_cryp[df_cryp['eth_usd_price']>-100000]\n",
        "df_cryp=df_cryp[df_cryp['eth_usd_price']<1000000000]\n",
        "df_cryp=df_cryp[df_cryp['gas_eth_price']>-100000]\n",
        "df_cryp=df_cryp[df_cryp['gas_eth_price']<10000000000]\n",
        "\n",
        "df_cryp=df_cryp[df_cryp['Rarity_Score_3_multiple']>-100000]\n",
        "df_cryp=df_cryp[df_cryp['Rarity_Score_3_multiple']<10000000]\n",
        "\n",
        "df_cryp=df_cryp.dropna()\n",
        "len(df_cryp)"
      ],
      "metadata": {
        "id": "EI7RGVk1IAI8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5acba847-7fff-4761-e686-90d14460d63c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-a1d017ba1cc1>:2: RuntimeWarning: divide by zero encountered in log\n",
            "  df_cryp['Log_price']=np.log(np.array(df_cryp['Price']))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17204"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.sort_values('timestamp',inplace=True)\n",
        "df_cryp=df_cryp.dropna()\n",
        "df_cryp1=df_cryp.iloc[:15000,:]"
      ],
      "metadata": {
        "id": "QNNoXoi2Mq3P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#conver categorical variable into factor\n",
        "\n",
        "gender_col=df_cryp1['gender']\n",
        "skin_col=df_cryp1['skin_tone']\n",
        "#cat_cols = df_cryp.select_dtypes(include=['object','category']).columns\n",
        "dummies = pd.get_dummies(gender_col, drop_first=True)\n",
        "df_cryp1[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp1.head()\n",
        "\n",
        "\n",
        "dummies = pd.get_dummies(skin_col, drop_first=True)\n",
        "df_cryp1[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp1.head()"
      ],
      "metadata": {
        "id": "4pm5s5XVMrDl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "c795094d-18ef-4088-dc74-d33e7d7b37b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[k1] = value[k2]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0           0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "8          15            15  2017-06-23  1406  1218       0.03     5621   \n",
              "1           2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2           4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "3           7             7  2017-06-23  1406  1904       0.30     5916   \n",
              "\n",
              "    type  gender skin_tone  ...  Male Dark Light Medium  Non-human  \\\n",
              "0  Human    Male    Albino  ...     1    0     0      0          0   \n",
              "8  Human  Female    Medium  ...     0    0     0      1          0   \n",
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              "2  Human  Female      Dark  ...     0    1     0      0          0   \n",
              "3  Human    Male    Medium  ...     1    0     0      1          0   \n",
              "\n",
              "   daily_ratio    date_z_y  ratio_shift  daily_ratio_change  Log_price  \n",
              "0       326.85  2017-06-23         0.42          777.214286   2.282943  \n",
              "8       326.85  2017-06-23         0.42          777.214286   2.282943  \n",
              "1       326.85  2017-06-23         0.42          777.214286   2.570626  \n",
              "2       326.85  2017-06-23         0.42          777.214286   4.403207  \n",
              "3       326.85  2017-06-23         0.42          777.214286   4.585529  \n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 41 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57b064b4-2469-4ecc-b2e0-13f95620d30d')\"\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "tss = TimeSeriesSplit()\n",
        "print(tss)\n",
        "\n",
        "# change the train and test split parameters \n",
        "tss = TimeSeriesSplit(gap=0, max_train_size=None, n_splits=2, test_size=None)\n",
        "\n",
        "for train_idx, test_idx in tss.split(df_cryp1):\n",
        "    print(\"TRAIN:\", train_idx, \"TEST:\", test_idx)\n",
        "train_df = df_cryp1.filter(items=train_idx, axis=0)\n",
        "test_df =  df_cryp1.filter(items=test_idx, axis=0)   \n",
        " \n",
        "train_df.head()\n",
        "test_df.head()"
      ],
      "metadata": {
        "id": "hQH_qsJ_MeIJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "4959603f-3ba4-4ffd-8766-2b389e6b584e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)\n",
            "TRAIN: [   0    1    2 ... 4997 4998 4999] TEST: [5000 5001 5002 ... 9997 9998 9999]\n",
            "TRAIN: [   0    1    2 ... 9997 9998 9999] TEST: [10000 10001 10002 ... 14997 14998 14999]\n"
          ]
        },
        {
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          "data": {
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              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "10000        3322          3322  2019-12-30  5117  6023     0.4349     8288   \n",
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              "10004        4987          4987  2019-12-30  3854  6023     0.4200     7281   \n",
              "\n",
              "        type gender skin_tone  ...  Male Dark Light Medium  Non-human  \\\n",
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              "10003  Human   Male     Light  ...     1    0     1      0          0   \n",
              "10004  Human   Male      Dark  ...     1    1     0      0          0   \n",
              "\n",
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              "10000       131.41  2019-12-30       134.55           -0.023337   4.045683  \n",
              "10001       131.41  2019-12-30       134.55           -0.023337   3.934146  \n",
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              "      <td>0</td>\n",
              "      <td>131.41</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>134.55</td>\n",
              "      <td>-0.023337</td>\n",
              "      <td>3.884070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10003</th>\n",
              "      <td>4312</td>\n",
              "      <td>4312</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>1892</td>\n",
              "      <td>1745</td>\n",
              "      <td>1.5000</td>\n",
              "      <td>3543</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Light</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>131.41</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>134.55</td>\n",
              "      <td>-0.023337</td>\n",
              "      <td>5.283787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10004</th>\n",
              "      <td>4987</td>\n",
              "      <td>4987</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>3854</td>\n",
              "      <td>6023</td>\n",
              "      <td>0.4200</td>\n",
              "      <td>7281</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Dark</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>131.41</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>134.55</td>\n",
              "      <td>-0.023337</td>\n",
              "      <td>4.010822</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 41 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a54f97fa-c086-4270-9b25-10ae2d7ca746')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a54f97fa-c086-4270-9b25-10ae2d7ca746');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_R = ['Log_price','Dark','Light','Medium','Non-human','Male','skin_tone','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']\n",
        "df_R_train = train_df[cols_R]\n",
        "df_R_test = test_df[cols_R]\n",
        "df_R_test.head()"
      ],
      "metadata": {
        "id": "Abk1LfGOMeMU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "a4d1c24b-cb4b-435f-ed26-f25f3637ae9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Log_price  Dark  Light  Medium  Non-human  Male skin_tone  \\\n",
              "10000   4.045683     1      0       0          0     1      Dark   \n",
              "10001   3.934146     0      0       1          0     1    Medium   \n",
              "10002   3.884070     0      0       0          0     1    Albino   \n",
              "10003   5.283787     0      1       0          0     1     Light   \n",
              "10004   4.010822     1      0       0          0     1      Dark   \n",
              "\n",
              "       wallet_change  daily_sales_change  Rarity_Score_3_multiple  \\\n",
              "10000            8.5            3.556549                91.282717   \n",
              "10001            8.5            3.556549                 0.000000   \n",
              "10002            8.5            3.556549                49.506087   \n",
              "10003            8.5            3.556549               419.371852   \n",
              "10004            8.5            3.556549                20.507157   \n",
              "\n",
              "       gas_eth_price  eth_usd_price  daily_ratio_change  \n",
              "10000   5.035276e+07         131.41           -0.023337  \n",
              "10001   5.035276e+07         131.41           -0.023337  \n",
              "10002   5.035276e+07         131.41           -0.023337  \n",
              "10003   5.035276e+07         131.41           -0.023337  \n",
              "10004   5.035276e+07         131.41           -0.023337  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-92f22589-cf0a-43c4-9158-826236e055f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Log_price</th>\n",
              "      <th>Dark</th>\n",
              "      <th>Light</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Non-human</th>\n",
              "      <th>Male</th>\n",
              "      <th>skin_tone</th>\n",
              "      <th>wallet_change</th>\n",
              "      <th>daily_sales_change</th>\n",
              "      <th>Rarity_Score_3_multiple</th>\n",
              "      <th>gas_eth_price</th>\n",
              "      <th>eth_usd_price</th>\n",
              "      <th>daily_ratio_change</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10000</th>\n",
              "      <td>4.045683</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Dark</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.556549</td>\n",
              "      <td>91.282717</td>\n",
              "      <td>5.035276e+07</td>\n",
              "      <td>131.41</td>\n",
              "      <td>-0.023337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10001</th>\n",
              "      <td>3.934146</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Medium</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.556549</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.035276e+07</td>\n",
              "      <td>131.41</td>\n",
              "      <td>-0.023337</td>\n",
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              "    <tr>\n",
              "      <th>10002</th>\n",
              "      <td>3.884070</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Albino</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.556549</td>\n",
              "      <td>49.506087</td>\n",
              "      <td>5.035276e+07</td>\n",
              "      <td>131.41</td>\n",
              "      <td>-0.023337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10003</th>\n",
              "      <td>5.283787</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Light</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.556549</td>\n",
              "      <td>419.371852</td>\n",
              "      <td>5.035276e+07</td>\n",
              "      <td>131.41</td>\n",
              "      <td>-0.023337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10004</th>\n",
              "      <td>4.010822</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Dark</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.556549</td>\n",
              "      <td>20.507157</td>\n",
              "      <td>5.035276e+07</td>\n",
              "      <td>131.41</td>\n",
              "      <td>-0.023337</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-92f22589-cf0a-43c4-9158-826236e055f9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mul_lr():   \n",
        "    \n",
        "    X_train = pd.DataFrame(df_R_train,columns=['Dark','Light','Medium','Non-human','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_train = pd.DataFrame(df_R_train['Log_price'])\n",
        "    X_test = pd.DataFrame(df_R_test,columns=['Dark','Light','Medium','Non-human','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_test = pd.DataFrame(df_R_test['Log_price'])\n",
        "    #X_train,X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2,random_state=100)\n",
        "    print ('X_train.shape={}\\n y_train.shape ={}\\n X_test.shape={}\\n,  y_test.shape={}'.format(X_train.shape,y_train.shape, X_test.shape,y_test.shape))\n",
        "    linreg = LinearRegression()\n",
        "    model=linreg.fit(X_train, y_train)\n",
        "    print (model)\n",
        "    # the intercept after training \n",
        "    print (linreg.intercept_)\n",
        "    # the weights\n",
        "    print (linreg.coef_)\n",
        "mul_lr()"
      ],
      "metadata": {
        "id": "yTMxY76JYhVL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a67e4eb-bc04-4d51-a4ec-40c1ef7c1211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train.shape=(8967, 11)\n",
            " y_train.shape =(8967, 1)\n",
            " X_test.shape=(3773, 11)\n",
            ",  y_test.shape=(3773, 1)\n",
            "LinearRegression()\n",
            "[5.15979244]\n",
            "[[-1.58059903e-01 -9.81745161e-02 -1.24261686e-01  2.40245262e+00\n",
            "  -1.17759681e-01 -5.12791483e-02  2.06273668e-02  7.80242490e-05\n",
            "   1.54825741e-09  2.21536929e-03 -4.57218772e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.array(df_cryp1['Log_price']) \n",
        "X = np.array(df_cryp1[['Dark','Light','Medium','Non-human','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']]) \n",
        "X2 = sm.add_constant(X)\n",
        "est = sm.OLS(y, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ],
      "metadata": {
        "id": "FONwmWL0YhdR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b3b2c0a-d8aa-43aa-87fa-8a13cc4dcf5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.783\n",
            "Model:                            OLS   Adj. R-squared:                  0.783\n",
            "Method:                 Least Squares   F-statistic:                     4920.\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):               0.00\n",
            "Time:                        04:51:18   Log-Likelihood:                -27481.\n",
            "No. Observations:               15000   AIC:                         5.499e+04\n",
            "Df Residuals:                   14988   BIC:                         5.508e+04\n",
            "Df Model:                          11                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          4.9553      0.051     96.529      0.000       4.855       5.056\n",
            "x1            -0.1263      0.047     -2.699      0.007      -0.218      -0.035\n",
            "x2            -0.0516      0.047     -1.093      0.274      -0.144       0.041\n",
            "x3            -0.1185      0.047     -2.519      0.012      -0.211      -0.026\n",
            "x4             2.0012      0.178     11.250      0.000       1.653       2.350\n",
            "x5            -0.1311      0.026     -4.961      0.000      -0.183      -0.079\n",
            "x6            -0.0357      0.003    -11.246      0.000      -0.042      -0.029\n",
            "x7             0.0065      0.002      3.063      0.002       0.002       0.011\n",
            "x8          4.038e-05   7.38e-06      5.473      0.000    2.59e-05    5.48e-05\n",
            "x9          1.908e-09   4.57e-11     41.707      0.000    1.82e-09       2e-09\n",
            "x10            0.0022   1.05e-05    205.135      0.000       0.002       0.002\n",
            "x11           -0.0164      0.004     -3.676      0.000      -0.025      -0.008\n",
            "==============================================================================\n",
            "Omnibus:                    23341.446   Durbin-Watson:                   0.933\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         29998043.328\n",
            "Skew:                          -9.556   Prob(JB):                         0.00\n",
            "Kurtosis:                     221.247   Cond. No.                     6.84e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 6.84e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.formula.api as smf\n",
        "from statsmodels.iolib.summary2 import summary_col\n",
        "\n",
        "df_R_train['Non_human']=df_R_train['Non-human']\n",
        "\n",
        "#x = [1, 3, 5, 6, 8, 3, 4, 5, 1, 3, 5, 6, 8, 3, 4, 5, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#y = [0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#d = { \"x\": pd.Series(x), \"y\": pd.Series(y)}\n",
        "#df = pd.DataFrame(d)\n",
        "#df['xsqr'] = df['x']**2  \n",
        "mod = smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+Rarity_Score_3_multiple',data=df_R_train)\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "\n",
        "\n",
        "mod2= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price',data=df_R_train)\n",
        "res2 = mod2.fit()\n",
        "print(res2.summary())\n",
        "\n",
        "mod3= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price+daily_ratio_change',data=df_R_train)\n",
        "res3 = mod3.fit()\n",
        "print(res3.summary())\n",
        "\n",
        "dfoutput = summary_col([res,res2,res3],stars=True)\n",
        "print(dfoutput)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ci2xH3go5Ryl",
        "outputId": "0701ad81-28a8-4bee-9053-ed033c924a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.005\n",
            "Model:                            OLS   Adj. R-squared:                  0.004\n",
            "Method:                 Least Squares   F-statistic:                     7.753\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):           2.45e-08\n",
            "Time:                        04:51:18   Log-Likelihood:                -22311.\n",
            "No. Observations:                8967   AIC:                         4.464e+04\n",
            "Df Residuals:                    8960   BIC:                         4.469e+04\n",
            "Df Model:                           6                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                  10.3099      0.112     92.259      0.000      10.091      10.529\n",
            "Dark                       -0.0598      0.117     -0.512      0.609      -0.289       0.169\n",
            "Light                      -0.1074      0.119     -0.905      0.366      -0.340       0.125\n",
            "Medium                     -0.2342      0.118     -1.982      0.047      -0.466      -0.003\n",
            "Non_human                   1.0227      0.438      2.334      0.020       0.164       1.882\n",
            "Male                       -0.3804      0.065     -5.820      0.000      -0.508      -0.252\n",
            "Rarity_Score_3_multiple  1.264e-05   2.84e-05      0.445      0.656    -4.3e-05    6.83e-05\n",
            "==============================================================================\n",
            "Omnibus:                     4735.701   Durbin-Watson:                   0.262\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            95111.341\n",
            "Skew:                          -2.087   Prob(JB):                         0.00\n",
            "Kurtosis:                      18.399   Cond. No.                     1.58e+04\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.58e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.100\n",
            "Model:                            OLS   Adj. R-squared:                  0.099\n",
            "Method:                 Least Squares   F-statistic:                     110.0\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          2.18e-196\n",
            "Time:                        04:51:18   Log-Likelihood:                -21864.\n",
            "No. Observations:                8967   AIC:                         4.375e+04\n",
            "Df Residuals:                    8957   BIC:                         4.382e+04\n",
            "Df Model:                           9                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   9.7017      0.116     83.640      0.000       9.474       9.929\n",
            "Dark                       -0.0895      0.111     -0.805      0.421      -0.308       0.129\n",
            "Light                      -0.1621      0.113     -1.435      0.151      -0.384       0.059\n",
            "Medium                     -0.2851      0.112     -2.535      0.011      -0.506      -0.065\n",
            "Non_human                   1.1128      0.417      2.668      0.008       0.295       1.930\n",
            "Male                       -0.3645      0.062     -5.861      0.000      -0.486      -0.243\n",
            "wallet_change              -0.0819      0.005    -16.743      0.000      -0.091      -0.072\n",
            "daily_sales_change         -0.0033      0.000     -7.860      0.000      -0.004      -0.002\n",
            "Rarity_Score_3_multiple  1.617e-05    2.7e-05      0.598      0.550   -3.68e-05    6.92e-05\n",
            "gas_eth_price            2.037e-09   1.04e-10     19.584      0.000    1.83e-09    2.24e-09\n",
            "==============================================================================\n",
            "Omnibus:                     4680.128   Durbin-Watson:                   0.276\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           128868.236\n",
            "Skew:                          -1.951   Prob(JB):                         0.00\n",
            "Kurtosis:                      21.157   Cond. No.                     7.26e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 7.26e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.101\n",
            "Model:                            OLS   Adj. R-squared:                  0.100\n",
            "Method:                 Least Squares   F-statistic:                     100.4\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          4.01e-198\n",
            "Time:                        04:51:18   Log-Likelihood:                -21858.\n",
            "No. Observations:                8967   AIC:                         4.374e+04\n",
            "Df Residuals:                    8956   BIC:                         4.382e+04\n",
            "Df Model:                          10                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   9.7006      0.116     83.686      0.000       9.473       9.928\n",
            "Dark                       -0.0914      0.111     -0.822      0.411      -0.309       0.126\n",
            "Light                      -0.1699      0.113     -1.504      0.132      -0.391       0.051\n",
            "Medium                     -0.2889      0.112     -2.570      0.010      -0.509      -0.069\n",
            "Non_human                   1.0440      0.417      2.502      0.012       0.226       1.862\n",
            "Male                       -0.3637      0.062     -5.852      0.000      -0.486      -0.242\n",
            "wallet_change              -0.1282      0.014     -9.283      0.000      -0.155      -0.101\n",
            "daily_sales_change          0.0385      0.012      3.302      0.001       0.016       0.061\n",
            "Rarity_Score_3_multiple  1.486e-05    2.7e-05      0.550      0.582   -3.81e-05    6.78e-05\n",
            "gas_eth_price            2.026e-09   1.04e-10     19.482      0.000    1.82e-09    2.23e-09\n",
            "daily_ratio_change         -0.0866      0.024     -3.587      0.000      -0.134      -0.039\n",
            "==============================================================================\n",
            "Omnibus:                     4700.212   Durbin-Watson:                   0.277\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           130894.501\n",
            "Skew:                          -1.959   Prob(JB):                         0.00\n",
            "Kurtosis:                      21.303   Cond. No.                     7.27e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 7.27e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-52-2e1eb70d9902>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_R_train['Non_human']=df_R_train['Non-human']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==============================================================\n",
            "                        Log_price I Log_price II Log_price III\n",
            "--------------------------------------------------------------\n",
            "Dark                    -0.0598     -0.0895      -0.0914      \n",
            "                        (0.1168)    (0.1112)     (0.1112)     \n",
            "Intercept               10.3099***  9.7017***    9.7006***    \n",
            "                        (0.1117)    (0.1160)     (0.1159)     \n",
            "Light                   -0.1074     -0.1621      -0.1699      \n",
            "                        (0.1187)    (0.1130)     (0.1129)     \n",
            "Male                    -0.3804***  -0.3645***   -0.3637***   \n",
            "                        (0.0654)    (0.0622)     (0.0621)     \n",
            "Medium                  -0.2342**   -0.2851**    -0.2889**    \n",
            "                        (0.1182)    (0.1125)     (0.1124)     \n",
            "Non_human               1.0227**    1.1128***    1.0440**     \n",
            "                        (0.4382)    (0.4171)     (0.4173)     \n",
            "R-squared               0.0052      0.0995       0.1008       \n",
            "R-squared Adj.          0.0045      0.0986       0.0998       \n",
            "Rarity_Score_3_multiple 0.0000      0.0000       0.0000       \n",
            "                        (0.0000)    (0.0000)     (0.0000)     \n",
            "daily_ratio_change                               -0.0866***   \n",
            "                                                 (0.0241)     \n",
            "daily_sales_change                  -0.0033***   0.0385***    \n",
            "                                    (0.0004)     (0.0117)     \n",
            "gas_eth_price                       0.0000***    0.0000***    \n",
            "                                    (0.0000)     (0.0000)     \n",
            "wallet_change                       -0.0819***   -0.1282***   \n",
            "                                    (0.0049)     (0.0138)     \n",
            "==============================================================\n",
            "Standard errors in parentheses.\n",
            "* p<.1, ** p<.05, ***p<.01\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2021-2022"
      ],
      "metadata": {
        "id": "OoLrZrFG54vk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp=df_cryp.dropna()\n",
        "len(df_cryp)"
      ],
      "metadata": {
        "id": "ZEg-ZxzmIUOF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e245653-017d-49fc-a16b-b6cccba74dae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17204"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.sort_values('timestamp',inplace=True)\n",
        "df_cryp=df_cryp.dropna()\n",
        "df_cryp2=df_cryp.iloc[15000:,:]\n",
        "print(df_cryp2.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QONuxOk57bL",
        "outputId": "574dd95c-15f4-4c50-8fc5-4b2cf5033e79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Int64Index([ 7016,  7017,  7000,  7019,  6999,  6998,  6987,  7011,  7037,\n",
            "             7010,\n",
            "            ...\n",
            "            15984, 15982, 15981, 15980, 15985, 17754, 17753, 17752, 17783,\n",
            "            17784],\n",
            "           dtype='int64', length=2204)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender_col=df_cryp2['gender']\n",
        "skin_col=df_cryp2['skin_tone']"
      ],
      "metadata": {
        "id": "3et7RYbX-UC9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#conver categorical variable into factor\n",
        "\n",
        "#cat_cols = df_cryp.select_dtypes(include=['object','category']).columns\n",
        "dummies = pd.get_dummies(gender_col, drop_first=True)\n",
        "df_cryp2[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp2.head()\n",
        "\n",
        "\n",
        "dummies = pd.get_dummies(skin_col, drop_first=True)\n",
        "df_cryp2[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "zzZE1Ab_6iHg",
        "outputId": "fa0781ed-0f45-4093-a328-6968e3df7fd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[k1] = value[k2]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "7016       14869         14869  2021-12-24  2642   168      89.95     3021   \n",
              "7017       15023         15023  2021-12-24  3096  1894      59.95     9598   \n",
              "7000       10541         10541  2021-12-24  3099  5447      75.90     5543   \n",
              "7019       15520         15520  2021-12-24  2888  1036      75.00     9853   \n",
              "6999       10292         10292  2021-12-24  3206  2408      76.00     6196   \n",
              "\n",
              "       type  gender skin_tone  ...  Male Dark Light Medium  Non-human  \\\n",
              "7016  Human    Male    Medium  ...     1    0     0      1          0   \n",
              "7017  Human  Female      Dark  ...     0    1     0      0          0   \n",
              "7000  Human    Male    Medium  ...     1    0     0      1          0   \n",
              "7019  Human    Male     Light  ...     1    0     1      0          0   \n",
              "6999  Human    Male     Light  ...     1    0     1      0          0   \n",
              "\n",
              "      daily_ratio    date_z_y  ratio_shift  daily_ratio_change  Log_price  \n",
              "7016      4047.87  2021-12-24      4112.35            -0.01568  12.805200  \n",
              "7017      4047.87  2021-12-24      4112.35            -0.01568  12.399457  \n",
              "7000      4047.87  2021-12-24      4112.35            -0.01568  12.635363  \n",
              "7019      4047.87  2021-12-24      4112.35            -0.01568  12.623434  \n",
              "6999      4047.87  2021-12-24      4112.35            -0.01568  12.636679  \n",
              "\n",
              "[5 rows x 41 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ef2617a-e4de-4e7a-831a-929a5f8f8858\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>date_x</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>eth_price</th>\n",
              "      <th>punk_id</th>\n",
              "      <th>type</th>\n",
              "      <th>gender</th>\n",
              "      <th>skin_tone</th>\n",
              "      <th>...</th>\n",
              "      <th>Male</th>\n",
              "      <th>Dark</th>\n",
              "      <th>Light</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Non-human</th>\n",
              "      <th>daily_ratio</th>\n",
              "      <th>date_z_y</th>\n",
              "      <th>ratio_shift</th>\n",
              "      <th>daily_ratio_change</th>\n",
              "      <th>Log_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7016</th>\n",
              "      <td>14869</td>\n",
              "      <td>14869</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>2642</td>\n",
              "      <td>168</td>\n",
              "      <td>89.95</td>\n",
              "      <td>3021</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4047.87</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>4112.35</td>\n",
              "      <td>-0.01568</td>\n",
              "      <td>12.805200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7017</th>\n",
              "      <td>15023</td>\n",
              "      <td>15023</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>3096</td>\n",
              "      <td>1894</td>\n",
              "      <td>59.95</td>\n",
              "      <td>9598</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>Dark</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4047.87</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>4112.35</td>\n",
              "      <td>-0.01568</td>\n",
              "      <td>12.399457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7000</th>\n",
              "      <td>10541</td>\n",
              "      <td>10541</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>3099</td>\n",
              "      <td>5447</td>\n",
              "      <td>75.90</td>\n",
              "      <td>5543</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Medium</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4047.87</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>4112.35</td>\n",
              "      <td>-0.01568</td>\n",
              "      <td>12.635363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7019</th>\n",
              "      <td>15520</td>\n",
              "      <td>15520</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>2888</td>\n",
              "      <td>1036</td>\n",
              "      <td>75.00</td>\n",
              "      <td>9853</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Light</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4047.87</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>4112.35</td>\n",
              "      <td>-0.01568</td>\n",
              "      <td>12.623434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6999</th>\n",
              "      <td>10292</td>\n",
              "      <td>10292</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>3206</td>\n",
              "      <td>2408</td>\n",
              "      <td>76.00</td>\n",
              "      <td>6196</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Light</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4047.87</td>\n",
              "      <td>2021-12-24</td>\n",
              "      <td>4112.35</td>\n",
              "      <td>-0.01568</td>\n",
              "      <td>12.636679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 41 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ef2617a-e4de-4e7a-831a-929a5f8f8858')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ef2617a-e4de-4e7a-831a-929a5f8f8858 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8ef2617a-e4de-4e7a-831a-929a5f8f8858');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "tss = TimeSeriesSplit()\n",
        "print(tss)\n",
        "\n",
        "# change the train and test split parameters \n",
        "tss = TimeSeriesSplit(gap=0, max_train_size=None, n_splits=2, test_size=None)\n",
        "\n",
        "for train_idx, test_idx in tss.split(df_cryp2):\n",
        "    print(\"TRAIN:\", train_idx, \"TEST:\", test_idx)\n",
        "train_df = df_cryp2.filter(items=train_idx, axis=0)\n",
        "test_df =  df_cryp2.filter(items=test_idx, axis=0)   \n",
        " \n",
        "train_df.head()\n",
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jFMIV5NJ6iNN",
        "outputId": "6d1583fb-6a29-4e47-8ef5-b479737601bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
            "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
            "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
            "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
            "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
            " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
            " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
            " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
            " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
            " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
            " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
            " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
            " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
            " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
            " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
            " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
            " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
            " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
            " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
            " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
            " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
            " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
            " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
            " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
            " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
            " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
            " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
            " 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
            " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
            " 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593\n",
            " 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611\n",
            " 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629\n",
            " 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647\n",
            " 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665\n",
            " 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683\n",
            " 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701\n",
            " 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719\n",
            " 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735] TEST: [ 736  737  738  739  740  741  742  743  744  745  746  747  748  749\n",
            "  750  751  752  753  754  755  756  757  758  759  760  761  762  763\n",
            "  764  765  766  767  768  769  770  771  772  773  774  775  776  777\n",
            "  778  779  780  781  782  783  784  785  786  787  788  789  790  791\n",
            "  792  793  794  795  796  797  798  799  800  801  802  803  804  805\n",
            "  806  807  808  809  810  811  812  813  814  815  816  817  818  819\n",
            "  820  821  822  823  824  825  826  827  828  829  830  831  832  833\n",
            "  834  835  836  837  838  839  840  841  842  843  844  845  846  847\n",
            "  848  849  850  851  852  853  854  855  856  857  858  859  860  861\n",
            "  862  863  864  865  866  867  868  869  870  871  872  873  874  875\n",
            "  876  877  878  879  880  881  882  883  884  885  886  887  888  889\n",
            "  890  891  892  893  894  895  896  897  898  899  900  901  902  903\n",
            "  904  905  906  907  908  909  910  911  912  913  914  915  916  917\n",
            "  918  919  920  921  922  923  924  925  926  927  928  929  930  931\n",
            "  932  933  934  935  936  937  938  939  940  941  942  943  944  945\n",
            "  946  947  948  949  950  951  952  953  954  955  956  957  958  959\n",
            "  960  961  962  963  964  965  966  967  968  969  970  971  972  973\n",
            "  974  975  976  977  978  979  980  981  982  983  984  985  986  987\n",
            "  988  989  990  991  992  993  994  995  996  997  998  999 1000 1001\n",
            " 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015\n",
            " 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029\n",
            " 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043\n",
            " 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057\n",
            " 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071\n",
            " 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085\n",
            " 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099\n",
            " 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113\n",
            " 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127\n",
            " 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141\n",
            " 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155\n",
            " 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169\n",
            " 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183\n",
            " 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197\n",
            " 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211\n",
            " 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225\n",
            " 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239\n",
            " 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253\n",
            " 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267\n",
            " 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281\n",
            " 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295\n",
            " 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309\n",
            " 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323\n",
            " 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337\n",
            " 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351\n",
            " 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365\n",
            " 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379\n",
            " 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393\n",
            " 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407\n",
            " 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421\n",
            " 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435\n",
            " 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449\n",
            " 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463\n",
            " 1464 1465 1466 1467 1468 1469]\n",
            "TRAIN: [   0    1    2 ... 1467 1468 1469] TEST: [1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483\n",
            " 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497\n",
            " 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511\n",
            " 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525\n",
            " 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539\n",
            " 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553\n",
            " 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567\n",
            " 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581\n",
            " 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595\n",
            " 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609\n",
            " 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623\n",
            " 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637\n",
            " 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651\n",
            " 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665\n",
            " 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679\n",
            " 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693\n",
            " 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707\n",
            " 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721\n",
            " 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735\n",
            " 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749\n",
            " 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763\n",
            " 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777\n",
            " 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791\n",
            " 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805\n",
            " 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819\n",
            " 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833\n",
            " 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847\n",
            " 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861\n",
            " 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875\n",
            " 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889\n",
            " 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903\n",
            " 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917\n",
            " 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931\n",
            " 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945\n",
            " 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959\n",
            " 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973\n",
            " 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987\n",
            " 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001\n",
            " 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015\n",
            " 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029\n",
            " 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043\n",
            " 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057\n",
            " 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071\n",
            " 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085\n",
            " 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099\n",
            " 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113\n",
            " 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127\n",
            " 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141\n",
            " 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "cols_R = ['Log_price','Dark','Light','Medium','Non-human','Male','skin_tone','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']\n",
        "df_R_train = train_df[cols_R]\n",
        "df_R_test = test_df[cols_R]\n",
        "df_R_test.head()"
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        "id": "oDdwuhU96iUe",
        "outputId": "d99cd8e0-97e6-454b-9d00-84a033abe630"
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              "      Log_price  Dark  Light  Medium  Non-human  Male skin_tone  \\\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-71ec9074-5471-45aa-a594-2241af6fdbd8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-71ec9074-5471-45aa-a594-2241af6fdbd8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mul_lr():   \n",
        "    \n",
        "    X_train = pd.DataFrame(df_R_train,columns=['Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_train = pd.DataFrame(df_R_train['Log_price'])\n",
        "    X_test = pd.DataFrame(df_R_test,columns=['Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_test = pd.DataFrame(df_R_test['Log_price'])\n",
        "    #X_train,X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2,random_state=100)\n",
        "    print ('X_train.shape={}\\n y_train.shape ={}\\n X_test.shape={}\\n,  y_test.shape={}'.format(X_train.shape,y_train.shape, X_test.shape,y_test.shape))\n",
        "    linreg = LinearRegression()\n",
        "    model=linreg.fit(X_train, y_train)\n",
        "    print (model)\n",
        "    print (linreg.intercept_)\n",
        "    print (linreg.coef_)\n",
        "mul_lr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw-TWXfo66WD",
        "outputId": "f24e829c-8460-467e-afec-f56eb1148d9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train.shape=(3, 10)\n",
            " y_train.shape =(3, 1)\n",
            " X_test.shape=(36, 10)\n",
            ",  y_test.shape=(36, 1)\n",
            "LinearRegression()\n",
            "[16.14266561]\n",
            "[[-3.30681663e-18  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
            "   0.00000000e+00  0.00000000e+00  2.10390645e-04  0.00000000e+00\n",
            "  -1.29502090e-03  0.00000000e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.array(df_cryp2['Price']) \n",
        "X = np.array(df_cryp2[['Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']]) \n",
        "X2 = sm.add_constant(X)\n",
        "est = sm.OLS(y, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Yepq5Uw7LuV",
        "outputId": "46dfce03-7ad8-4aaa-a194-afaecdc0f144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.086\n",
            "Model:                            OLS   Adj. R-squared:                  0.082\n",
            "Method:                 Least Squares   F-statistic:                     20.62\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):           5.64e-37\n",
            "Time:                        04:51:18   Log-Likelihood:                -32137.\n",
            "No. Observations:                2204   AIC:                         6.430e+04\n",
            "Df Residuals:                    2193   BIC:                         6.436e+04\n",
            "Df Model:                          10                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       1.447e+05   4.98e+04      2.904      0.004     4.7e+04    2.42e+05\n",
            "x1         -2.181e+05   4.09e+04     -5.335      0.000   -2.98e+05   -1.38e+05\n",
            "x2         -2.124e+05   4.15e+04     -5.117      0.000   -2.94e+05   -1.31e+05\n",
            "x3         -2.094e+05   4.11e+04     -5.096      0.000    -2.9e+05   -1.29e+05\n",
            "x4          2.797e+04   2.35e+04      1.191      0.234   -1.81e+04     7.4e+04\n",
            "x5         -7.441e+04   7671.528     -9.699      0.000   -8.94e+04   -5.94e+04\n",
            "x6          7.522e+04   6982.754     10.773      0.000    6.15e+04    8.89e+04\n",
            "x7             1.4769      4.885      0.302      0.762      -8.104      11.057\n",
            "x8           1.96e-06   6.04e-05      0.032      0.974      -0.000       0.000\n",
            "x9            93.9638     14.188      6.623      0.000      66.141     121.786\n",
            "x10         1.948e+05   2.08e+05      0.937      0.349   -2.13e+05    6.03e+05\n",
            "==============================================================================\n",
            "Omnibus:                     6113.184   Durbin-Watson:                   2.092\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        176824227.512\n",
            "Skew:                          34.260   Prob(JB):                         0.00\n",
            "Kurtosis:                    1388.928   Cond. No.                     6.90e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 6.9e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_R_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "pcuMPE1kCKRk",
        "outputId": "3d1a7f4b-1635-4b3d-bd27-29aa3cef166b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Log_price  Dark  Light  Medium  Non-human  Male skin_tone  wallet_change  \\\n",
              "836  12.254117     0      1       0          0     1     Light      -0.571429   \n",
              "837  12.163145     0      1       0          0     1     Light      -0.571429   \n",
              "838  12.296677     0      1       0          0     1     Light      -0.571429   \n",
              "\n",
              "     daily_sales_change  Rarity_Score_3_multiple  gas_eth_price  \\\n",
              "836           -0.582692               338.468339   1.812064e+08   \n",
              "837           -0.582692                20.555967   1.812064e+08   \n",
              "838           -0.582692               122.148458   1.812064e+08   \n",
              "\n",
              "     eth_usd_price  daily_ratio_change  \n",
              "836        3041.21            0.006437  \n",
              "837        3041.21            0.006437  \n",
              "838        3041.21            0.006437  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-999c28a5-08bb-4d20-9d50-8f688423b06e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "      <th></th>\n",
              "      <th>Log_price</th>\n",
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              "      <th>Light</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Non-human</th>\n",
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              "      <th>eth_usd_price</th>\n",
              "      <th>daily_ratio_change</th>\n",
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              "      <td>Light</td>\n",
              "      <td>-0.571429</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Light</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>-0.582692</td>\n",
              "      <td>122.148458</td>\n",
              "      <td>1.812064e+08</td>\n",
              "      <td>3041.21</td>\n",
              "      <td>0.006437</td>\n",
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              "  </tbody>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-999c28a5-08bb-4d20-9d50-8f688423b06e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.formula.api as smf\n",
        "from statsmodels.iolib.summary2 import summary_col\n",
        "\n",
        "df_R_train['Non_human']=df_R_train['Non-human']\n",
        "\n",
        "#x = [1, 3, 5, 6, 8, 3, 4, 5, 1, 3, 5, 6, 8, 3, 4, 5, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#y = [0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#d = { \"x\": pd.Series(x), \"y\": pd.Series(y)}\n",
        "#df = pd.DataFrame(d)\n",
        "#df['xsqr'] = df['x']**2  \n",
        "mod = smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+Rarity_Score_3_multiple',data=df_R_train)\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "\n",
        "\n",
        "mod2= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price',data=df_R_train)\n",
        "res2 = mod2.fit()\n",
        "print(res2.summary())\n",
        "\n",
        "mod3= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price+daily_ratio_change',data=df_R_train)\n",
        "res3 = mod3.fit()\n",
        "print(res3.summary())\n",
        "\n",
        "dfoutput = summary_col([res,res2,res3],stars=True)\n",
        "print(dfoutput)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3VviarLBHyw",
        "outputId": "7203ee0b-c123-4d94-9dcf-ef6ac23c5996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.251\n",
            "Model:                            OLS   Adj. R-squared:                 -0.498\n",
            "Method:                 Least Squares   F-statistic:                    0.3348\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):              0.666\n",
            "Time:                        04:51:18   Log-Likelihood:                 4.8399\n",
            "No. Observations:                   3   AIC:                            -5.680\n",
            "Df Residuals:                       1   BIC:                            -7.483\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   4.0681      0.025    161.288      0.004       3.748       4.389\n",
            "Dark                    -1.535e-15   9.52e-18   -161.283      0.004   -1.66e-15   -1.41e-15\n",
            "Light                       4.0681      0.025    161.288      0.004       3.748       4.389\n",
            "Medium                           0          0        nan        nan           0           0\n",
            "Non_human                        0          0        nan        nan           0           0\n",
            "Male                        4.0681      0.025    161.288      0.004       3.748       4.389\n",
            "Rarity_Score_3_multiple     0.0002      0.000      0.579      0.666      -0.004       0.005\n",
            "==============================================================================\n",
            "Omnibus:                          nan   Durbin-Watson:                   1.887\n",
            "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.448\n",
            "Skew:                           0.577   Prob(JB):                        0.799\n",
            "Kurtosis:                       1.500   Cond. No.                     9.54e+16\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The input rank is higher than the number of observations.\n",
            "[3] The smallest eigenvalue is 1.43e-29. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.251\n",
            "Model:                            OLS   Adj. R-squared:                 -0.498\n",
            "Method:                 Least Squares   F-statistic:                    0.3348\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):              0.666\n",
            "Time:                        04:51:18   Log-Likelihood:                 4.8399\n",
            "No. Observations:                   3   AIC:                            -5.680\n",
            "Df Residuals:                       1   BIC:                            -7.483\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                1.961e-14   3.32e-14      0.590      0.661   -4.03e-13    4.42e-13\n",
            "Dark                    -1.243e-20   2.15e-20     -0.579      0.666   -2.85e-19    2.61e-19\n",
            "Light                    3.717e-16    2.3e-18    161.295      0.004    3.42e-16    4.01e-16\n",
            "Medium                           0          0        nan        nan           0           0\n",
            "Non_human                        0          0        nan        nan           0           0\n",
            "Male                     3.717e-16    2.3e-18    161.295      0.004    3.42e-16    4.01e-16\n",
            "wallet_change           -2.124e-16   1.32e-18   -161.292      0.004   -2.29e-16   -1.96e-16\n",
            "daily_sales_change      -2.166e-16   1.34e-18   -161.300      0.004   -2.34e-16      -2e-16\n",
            "Rarity_Score_3_multiple     0.0002      0.000      0.579      0.666      -0.004       0.005\n",
            "gas_eth_price            6.735e-08   4.18e-10    161.288      0.004     6.2e-08    7.27e-08\n",
            "==============================================================================\n",
            "Omnibus:                          nan   Durbin-Watson:                   1.887\n",
            "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.448\n",
            "Skew:                           0.577   Prob(JB):                        0.799\n",
            "Kurtosis:                       1.500   Cond. No.                     2.28e+16\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The input rank is higher than the number of observations.\n",
            "[3] The smallest eigenvalue is 1.9e-16. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.251\n",
            "Model:                            OLS   Adj. R-squared:                 -0.498\n",
            "Method:                 Least Squares   F-statistic:                    0.3348\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):              0.666\n",
            "Time:                        04:51:19   Log-Likelihood:                 4.8399\n",
            "No. Observations:                   3   AIC:                            -5.680\n",
            "Df Residuals:                       1   BIC:                            -7.483\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                1.961e-14   3.32e-14      0.590      0.661   -4.03e-13    4.42e-13\n",
            "Dark                    -1.243e-20   2.15e-20     -0.579      0.666   -2.85e-19    2.61e-19\n",
            "Light                    3.717e-16    2.3e-18    161.295      0.004    3.42e-16    4.01e-16\n",
            "Medium                           0          0        nan        nan           0           0\n",
            "Non_human                        0          0        nan        nan           0           0\n",
            "Male                     3.717e-16    2.3e-18    161.295      0.004    3.42e-16    4.01e-16\n",
            "wallet_change           -2.124e-16   1.32e-18   -161.292      0.004   -2.29e-16   -1.96e-16\n",
            "daily_sales_change      -2.166e-16   1.34e-18   -161.300      0.004   -2.34e-16      -2e-16\n",
            "Rarity_Score_3_multiple     0.0002      0.000      0.579      0.666      -0.004       0.005\n",
            "gas_eth_price            6.735e-08   4.18e-10    161.288      0.004     6.2e-08    7.27e-08\n",
            "daily_ratio_change       2.392e-18   1.48e-20    161.294      0.004     2.2e-18    2.58e-18\n",
            "==============================================================================\n",
            "Omnibus:                          nan   Durbin-Watson:                   1.887\n",
            "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.448\n",
            "Skew:                           0.577   Prob(JB):                        0.799\n",
            "Kurtosis:                       1.500   Cond. No.                     2.28e+16\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The input rank is higher than the number of observations.\n",
            "[3] The smallest eigenvalue is 1.9e-16. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n",
            "\n",
            "==============================================================\n",
            "                        Log_price I Log_price II Log_price III\n",
            "--------------------------------------------------------------\n",
            "Dark                    -0.0000***  -0.0000      -0.0000      \n",
            "                        (0.0000)    (0.0000)     (0.0000)     \n",
            "Intercept               4.0681***   0.0000       0.0000       \n",
            "                        (0.0252)    (0.0000)     (0.0000)     \n",
            "Light                   4.0681***   0.0000***    0.0000***    \n",
            "                        (0.0252)    (0.0000)     (0.0000)     \n",
            "Male                    4.0681***   0.0000***    0.0000***    \n",
            "                        (0.0252)    (0.0000)     (0.0000)     \n",
            "Medium                  0.0000      0.0000       0.0000       \n",
            "                        (0.0000)    (0.0000)     (0.0000)     \n",
            "Non_human               0.0000      0.0000       0.0000       \n",
            "                        (0.0000)    (0.0000)     (0.0000)     \n",
            "R-squared               0.2508      0.2508       0.2508       \n",
            "R-squared Adj.          -0.4984     -0.4984      -0.4984      \n",
            "Rarity_Score_3_multiple 0.0002      0.0002       0.0002       \n",
            "                        (0.0004)    (0.0004)     (0.0004)     \n",
            "daily_ratio_change                               0.0000***    \n",
            "                                                 (0.0000)     \n",
            "daily_sales_change                  -0.0000***   -0.0000***   \n",
            "                                    (0.0000)     (0.0000)     \n",
            "gas_eth_price                       0.0000***    0.0000***    \n",
            "                                    (0.0000)     (0.0000)     \n",
            "wallet_change                       -0.0000***   -0.0000***   \n",
            "                                    (0.0000)     (0.0000)     \n",
            "==============================================================\n",
            "Standard errors in parentheses.\n",
            "* p<.1, ** p<.05, ***p<.01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-62-2e1eb70d9902>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_R_train['Non_human']=df_R_train['Non-human']\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 3 samples were given.\n",
            "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 3 samples were given.\n",
            "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 3 samples were given.\n",
            "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2017-2022"
      ],
      "metadata": {
        "id": "pE4EPQwZMfrd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression \n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "N0_U1ydH8h9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.sort_values('timestamp',inplace=True)\n",
        "df_cryp=df_cryp.dropna()\n",
        "df_cryp"
      ],
      "metadata": {
        "id": "3epBaK3_IARE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "d22fc477-9a93-4dff-cccc-44b802c67672"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0               0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "4               9             9  2017-06-23  1406   888       0.20     4641   \n",
              "8              15            15  2017-06-23  1406  1218       0.03     5621   \n",
              "1               2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2               4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "...           ...           ...         ...   ...   ...        ...      ...   \n",
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              "17752       12450         12450  2022-07-26   980  1939      94.00     9099   \n",
              "17783       13214         13214  2022-07-27  3096  1028      69.69     6221   \n",
              "17784       17559         17559  2022-07-27   402  1955     104.95     2567   \n",
              "\n",
              "        type  gender skin_tone  ...  Male Dark Light Medium  Non-human  \\\n",
              "0      Human    Male    Albino  ...     1    0     0      0          0   \n",
              "4      Human  Female     Light  ...     0    0     1      0          0   \n",
              "8      Human  Female    Medium  ...     0    0     0      1          0   \n",
              "1      Human  Female    Medium  ...     0    0     0      1          0   \n",
              "2      Human  Female      Dark  ...     0    1     0      0          0   \n",
              "...      ...     ...       ...  ...   ...  ...   ...    ...        ...   \n",
              "17754  Human    Male    Albino  ...     1    0     0      0          0   \n",
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              "17752  Human    Male     Light  ...     1    0     1      0          0   \n",
              "17783  Human  Female      Dark  ...     0    1     0      0          0   \n",
              "17784  Human    Male     Light  ...     1    0     1      0          0   \n",
              "\n",
              "       daily_ratio    date_z_y  ratio_shift  daily_ratio_change  Log_price  \n",
              "0           326.85  2017-06-23         0.42          777.214286   2.282943  \n",
              "4           326.85  2017-06-23         0.42          777.214286   4.180063  \n",
              "8           326.85  2017-06-23         0.42          777.214286   2.282943  \n",
              "1           326.85  2017-06-23         0.42          777.214286   2.570626  \n",
              "2           326.85  2017-06-23         0.42          777.214286   4.403207  \n",
              "...            ...         ...          ...                 ...        ...  \n",
              "17754      1449.61  2022-07-26      1438.01            0.008067  11.831874  \n",
              "17753      1449.61  2022-07-26      1438.01            0.008067  11.513156  \n",
              "17752      1449.61  2022-07-26      1438.01            0.008067  11.822345  \n",
              "17783      1636.88  2022-07-27      1449.61            0.129186  11.644604  \n",
              "17784      1636.88  2022-07-27      1449.61            0.129186  12.054031  \n",
              "\n",
              "[17204 rows x 41 columns]"
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1449.61</td>\n",
              "      <td>2022-07-26</td>\n",
              "      <td>1438.01</td>\n",
              "      <td>0.008067</td>\n",
              "      <td>11.822345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17783</th>\n",
              "      <td>13214</td>\n",
              "      <td>13214</td>\n",
              "      <td>2022-07-27</td>\n",
              "      <td>3096</td>\n",
              "      <td>1028</td>\n",
              "      <td>69.69</td>\n",
              "      <td>6221</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1636.88</td>\n",
              "      <td>2022-07-27</td>\n",
              "      <td>1449.61</td>\n",
              "      <td>0.129186</td>\n",
              "      <td>11.644604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17784</th>\n",
              "      <td>17559</td>\n",
              "      <td>17559</td>\n",
              "      <td>2022-07-27</td>\n",
              "      <td>402</td>\n",
              "      <td>1955</td>\n",
              "      <td>104.95</td>\n",
              "      <td>2567</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>Light</td>\n",
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              "      <td>2022-07-27</td>\n",
              "      <td>1449.61</td>\n",
              "      <td>0.129186</td>\n",
              "      <td>12.054031</td>\n",
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              "  </tbody>\n",
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              "<p>17204 rows × 41 columns</p>\n",
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              "  "
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.columns"
      ],
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        "id": "WHO2ivErO_vA",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b91283c-07d3-4ea5-9dfd-b97f9280a3ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'date_x', 'from', 'to', 'eth_price',\n",
              "       'punk_id', 'type', 'gender', 'skin_tone', 'attr_count', 'attributes',\n",
              "       'skin_tone_color', 'img_url', 'Rarity_Score_1_rarest',\n",
              "       'Rarity_Score_2_Average', 'Rarity_Score_3_multiple', 'timestamp',\n",
              "       'date_y', 'gas_eth_price', 'date_x', 'eth_usd_price', 'Price',\n",
              "       'active_wallet', 'date_y', 'active_wallet_shift', 'wallet_change',\n",
              "       'daily_sales', 'date_z_x', 'sales_shift', 'daily_sales_change', 'Male',\n",
              "       'Dark', 'Light', 'Medium', 'Non-human', 'daily_ratio', 'date_z_y',\n",
              "       'ratio_shift', 'daily_ratio_change', 'Log_price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender_col=df_cryp['gender']\n",
        "skin_col=df_cryp['skin_tone']"
      ],
      "metadata": {
        "id": "CM1e0YGaZ1FB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#conver categorical variable into factor\n",
        "\n",
        "#cat_cols = df_cryp.select_dtypes(include=['object','category']).columns\n",
        "dummies = pd.get_dummies(gender_col, drop_first=True)\n",
        "df_cryp[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp.head()\n",
        "\n",
        "\n",
        "dummies = pd.get_dummies(skin_col, drop_first=True)\n",
        "df_cryp[dummies.columns] = dummies\n",
        "#df_cryp.drop(cat_cols, axis=1, inplace=True)\n",
        "df_cryp.head()"
      ],
      "metadata": {
        "id": "fN6NVsy19mCr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "7347f32d-2be5-40c7-bfc9-0ec9199d1a3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "0           0             0  2017-06-23  1406  1218       0.03     6548   \n",
              "4           9             9  2017-06-23  1406   888       0.20     4641   \n",
              "8          15            15  2017-06-23  1406  1218       0.03     5621   \n",
              "1           2             2  2017-06-23  1406  1218       0.04     5719   \n",
              "2           4             4  2017-06-23  1406  2518       0.25     1841   \n",
              "\n",
              "    type  gender skin_tone  ...  Male Dark Light Medium  Non-human  \\\n",
              "0  Human    Male    Albino  ...     1    0     0      0          0   \n",
              "4  Human  Female     Light  ...     0    0     1      0          0   \n",
              "8  Human  Female    Medium  ...     0    0     0      1          0   \n",
              "1  Human  Female    Medium  ...     0    0     0      1          0   \n",
              "2  Human  Female      Dark  ...     0    1     0      0          0   \n",
              "\n",
              "   daily_ratio    date_z_y  ratio_shift  daily_ratio_change  Log_price  \n",
              "0       326.85  2017-06-23         0.42          777.214286   2.282943  \n",
              "4       326.85  2017-06-23         0.42          777.214286   4.180063  \n",
              "8       326.85  2017-06-23         0.42          777.214286   2.282943  \n",
              "1       326.85  2017-06-23         0.42          777.214286   2.570626  \n",
              "2       326.85  2017-06-23         0.42          777.214286   4.403207  \n",
              "\n",
              "[5 rows x 41 columns]"
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              "  "
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          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "tss = TimeSeriesSplit()\n",
        "print(tss)\n",
        "\n",
        "# change the train and test split parameters \n",
        "tss = TimeSeriesSplit(gap=0, max_train_size=None, n_splits=2, test_size=None)\n",
        "\n",
        "for train_idx, test_idx in tss.split(df_cryp):\n",
        "    print(\"TRAIN:\", train_idx, \"TEST:\", test_idx)\n",
        "train_df = df_cryp.filter(items=train_idx, axis=0)\n",
        "test_df =  df_cryp.filter(items=test_idx, axis=0)   \n",
        " \n",
        "train_df.head()\n",
        "test_df.head()"
      ],
      "metadata": {
        "id": "cqHAvPIR6rRQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "498df6ee-3c27-4286-defd-80d4f782cbc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)\n",
            "TRAIN: [   0    1    2 ... 5733 5734 5735] TEST: [ 5736  5737  5738 ... 11467 11468 11469]\n",
            "TRAIN: [    0     1     2 ... 11467 11468 11469] TEST: [11470 11471 11472 ... 17201 17202 17203]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "11470        1249          1249  2021-08-16   161  1360      80.00     7468   \n",
              "11471        3687          3687  2021-08-16  2878  5634      56.89     3327   \n",
              "11472        4353          4353  2021-08-16  4284  3096      41.50     4414   \n",
              "11473        6216          6216  2021-08-16  2530  2521      45.00     6678   \n",
              "11474        6304          6304  2021-08-16  5950  5172      48.50     2863   \n",
              "\n",
              "        type  gender skin_tone  ...  Male Dark Light Medium  Non-human  \\\n",
              "11470  Human    Male     Light  ...     1    0     1      0          0   \n",
              "11471  Human    Male    Albino  ...     1    0     0      0          0   \n",
              "11472  Human    Male      Dark  ...     1    1     0      0          0   \n",
              "11473  Human  Female     Light  ...     0    0     1      0          0   \n",
              "11474  Human    Male      Dark  ...     1    1     0      0          0   \n",
              "\n",
              "       daily_ratio    date_z_y  ratio_shift  daily_ratio_change  Log_price  \n",
              "11470      3147.63  2021-08-16      3311.12           -0.049376  12.436432  \n",
              "11471      3147.63  2021-08-16      3311.12           -0.049376  12.095525  \n",
              "11472      3147.63  2021-08-16      3311.12           -0.049376  11.780098  \n",
              "11473      3147.63  2021-08-16      3311.12           -0.049376  11.861068  \n",
              "11474      3147.63  2021-08-16      3311.12           -0.049376  11.935969  \n",
              "\n",
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              "        buttonEl.style.display =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_R = ['Log_price','Dark','Light','Medium','Non-human','Male','skin_tone','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']\n",
        "cols_R"
      ],
      "metadata": {
        "id": "_PQPcKTS7T52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f49d789b-7a52-415b-a816-d218d110a2dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Log_price',\n",
              " 'Dark',\n",
              " 'Light',\n",
              " 'Medium',\n",
              " 'Non-human',\n",
              " 'Male',\n",
              " 'skin_tone',\n",
              " 'wallet_change',\n",
              " 'daily_sales_change',\n",
              " 'Rarity_Score_3_multiple',\n",
              " 'gas_eth_price',\n",
              " 'eth_usd_price',\n",
              " 'daily_ratio_change']"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_R_train = train_df[cols_R]\n",
        "df_R_test = test_df[cols_R]\n",
        "df_R_test.head()\n",
        "df_R_train.head()"
      ],
      "metadata": {
        "id": "Mxzv46978OmR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "14ea0f71-1f33-4c4e-c869-3dd7362743a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Log_price  Dark  Light  Medium  Non-human  Male skin_tone  wallet_change  \\\n",
              "0   2.282943     0      0       0          0     1    Albino      10.333333   \n",
              "1   2.570626     0      0       1          0     0    Medium      10.333333   \n",
              "2   4.403207     1      0       0          0     0      Dark      10.333333   \n",
              "3   4.585529     0      0       1          0     1    Medium      10.333333   \n",
              "4   4.180063     0      1       0          0     0     Light      10.333333   \n",
              "\n",
              "   daily_sales_change  Rarity_Score_3_multiple  gas_eth_price  eth_usd_price  \\\n",
              "0         1620.798571                 0.000000   1.720330e+08         326.85   \n",
              "1         1620.798571               891.396281   1.720330e+08         326.85   \n",
              "2         1620.798571                69.188677   1.720330e+08         326.85   \n",
              "3         1620.798571                28.734107   1.720330e+08         326.85   \n",
              "4         1620.798571               484.728383   1.720330e+08         326.85   \n",
              "\n",
              "   daily_ratio_change  \n",
              "0          777.214286  \n",
              "1          777.214286  \n",
              "2          777.214286  \n",
              "3          777.214286  \n",
              "4          777.214286  "
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              "      <td>484.728383</td>\n",
              "      <td>1.720330e+08</td>\n",
              "      <td>326.85</td>\n",
              "      <td>777.214286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e065fac3-54e0-4eb9-8bf2-b5540a7e09fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e065fac3-54e0-4eb9-8bf2-b5540a7e09fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e065fac3-54e0-4eb9-8bf2-b5540a7e09fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mul_lr():   \n",
        "    \n",
        "    X_train = pd.DataFrame(df_R_train,columns=['Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_train = pd.DataFrame(df_R_train['Log_price'])\n",
        "    X_test = pd.DataFrame(df_R_test,columns=['Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_test = pd.DataFrame(df_R_test['Log_price'])\n",
        "    #X_train,X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2,random_state=100)\n",
        "    print ('X_train.shape={}\\n y_train.shape ={}\\n X_test.shape={}\\n,  y_test.shape={}'.format(X_train.shape,y_train.shape, X_test.shape,y_test.shape))\n",
        "    linreg = LinearRegression()\n",
        "    model=linreg.fit(X_train, y_train)\n",
        "    print (model)\n",
        "    print (linreg.intercept_)\n",
        "    print (linreg.coef_)\n",
        "mul_lr()"
      ],
      "metadata": {
        "id": "TM0LsoFE9Cja",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd2032f1-7b50-4578-e672-14d0c0821c8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train.shape=(11159, 10)\n",
            " y_train.shape =(11159, 1)\n",
            " X_test.shape=(5465, 10)\n",
            ",  y_test.shape=(5465, 1)\n",
            "LinearRegression()\n",
            "[5.84898022]\n",
            "[[-2.88690412e-01 -2.25753375e-01 -2.21919895e-01 -1.36373863e-01\n",
            "  -3.70930182e-02 -5.87515751e-03  6.43544184e-05  1.05560855e-09\n",
            "   2.12116221e-03  8.75410832e-03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(df_R_train,columns=['Dark','Light','Medium','Non-human','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "y_train = pd.DataFrame(df_R_train['Log_price'])\n",
        "X_test = pd.DataFrame(df_R_test,columns=['Dark','Light','Medium','Non-human','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "y_test = pd.DataFrame(df_R_test['Log_price'])\n",
        "\n",
        "mdl = linear_model.LinearRegression().fit(X_train,y_train)\n",
        "y_pred = pd.DataFrame(np.array(mdl.predict(X_test)).astype(float),columns=['y_pred'])\n",
        "\n",
        "#prediction graph\n",
        "fig, ax = plt.subplots()\n",
        "y_test.hist(ax=ax, label='true', alpha=.75,bins=100)\n",
        "y_pred.hist(ax=ax, label='pred', alpha=.75)\n",
        "ax.legend()\n",
        "fig.show()\n",
        "\n",
        "#evaluate regression model performance Root Mean Squared Error, RMSE\n",
        "y_pred=y_pred.values.tolist()\n",
        "sum_mean=0\n",
        "for i in range(len(y_pred)):\n",
        "  sum_mean+=(y_pred[i]-y_test.values[i])**2\n",
        "sum_erro=np.sqrt(sum_mean/len(y_pred))  \n",
        "# calculate RMSE by hand\n",
        "print (\"RMSE by hand:\",sum_erro)\n",
        "#ROC curve\n",
        "plt.figure()\n",
        "plt.plot(range(len(y_pred)),y_pred,'b',label=\"predict\",alpha=1)\n",
        "plt.plot(range(len(y_pred)),y_test,'r',label=\"test\",alpha=1)\n",
        "plt.legend(loc=\"upper right\") \n",
        "#plt.xlabel(\"the number of sales\")\n",
        "plt.ylabel('value of cryptopunks')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wixQS04uOhFh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "cd68ed42-1bf2-4cf4-cafe-153cb9c9360c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE by hand: [1.90928499]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.array(df_cryp['Price']) \n",
        "X = np.array(df_cryp[['Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']]) \n",
        "X2 = sm.add_constant(X)\n",
        "est = sm.OLS(y, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ],
      "metadata": {
        "id": "Y6-tkNCuOmx7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35d27e6b-8473-49b9-cd6a-ffcbfffbf66e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.171\n",
            "Model:                            OLS   Adj. R-squared:                  0.171\n",
            "Method:                 Least Squares   F-statistic:                     355.5\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):               0.00\n",
            "Time:                        04:54:25   Log-Likelihood:            -2.3864e+05\n",
            "No. Observations:               17204   AIC:                         4.773e+05\n",
            "Df Residuals:                   17193   BIC:                         4.774e+05\n",
            "Df Model:                          10                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       1.427e+04   8016.121      1.781      0.075   -1437.762       3e+04\n",
            "x1         -7.688e+04   7223.046    -10.644      0.000    -9.1e+04   -6.27e+04\n",
            "x2         -6.878e+04   7298.160     -9.424      0.000   -8.31e+04   -5.45e+04\n",
            "x3         -7.151e+04   7264.404     -9.844      0.000   -8.57e+04   -5.73e+04\n",
            "x4          1.136e+04   4159.596      2.731      0.006    3206.216    1.95e+04\n",
            "x5          -897.4998    528.430     -1.698      0.089   -1933.277     138.277\n",
            "x6          1308.1161    358.727      3.647      0.000     604.974    2011.258\n",
            "x7             2.2988      1.107      2.077      0.038       0.129       4.468\n",
            "x8          -6.73e-05   7.41e-06     -9.086      0.000   -8.18e-05   -5.28e-05\n",
            "x9            98.2185      1.699     57.809      0.000      94.888     101.549\n",
            "x10        -2701.6570    746.934     -3.617      0.000   -4165.725   -1237.589\n",
            "==============================================================================\n",
            "Omnibus:                    54923.242   Durbin-Watson:                   1.823\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):      11990820307.659\n",
            "Skew:                          51.816   Prob(JB):                         0.00\n",
            "Kurtosis:                    4091.612   Cond. No.                     3.13e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.13e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.formula.api as smf\n",
        "from statsmodels.iolib.summary2 import summary_col\n",
        "\n",
        "df_R_train['Non_human']=df_R_train['Non-human']\n",
        "\n",
        "#x = [1, 3, 5, 6, 8, 3, 4, 5, 1, 3, 5, 6, 8, 3, 4, 5, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#y = [0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#d = { \"x\": pd.Series(x), \"y\": pd.Series(y)}\n",
        "#df = pd.DataFrame(d)\n",
        "#df['xsqr'] = df['x']**2  \n",
        "mod = smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+Rarity_Score_3_multiple',data=df_R_train)\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "\n",
        "\n",
        "mod2= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price',data=df_R_train)\n",
        "res2 = mod2.fit()\n",
        "print(res2.summary())\n",
        "\n",
        "mod3= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price+daily_ratio_change',data=df_R_train)\n",
        "res3 = mod3.fit()\n",
        "print(res3.summary())\n",
        "\n",
        "dfoutput = summary_col([res,res2,res3],stars=True)\n",
        "print(dfoutput)"
      ],
      "metadata": {
        "id": "JoRNqQ7dncw5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb0fd05b-350c-4d43-d92e-85d52fa9a2c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-78-2e1eb70d9902>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_R_train['Non_human']=df_R_train['Non-human']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.006\n",
            "Model:                            OLS   Adj. R-squared:                  0.005\n",
            "Method:                 Least Squares   F-statistic:                     10.29\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):           2.18e-11\n",
            "Time:                        04:54:28   Log-Likelihood:                -27898.\n",
            "No. Observations:               11159   AIC:                         5.581e+04\n",
            "Df Residuals:                   11152   BIC:                         5.586e+04\n",
            "Df Model:                           6                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                  10.4770      0.102    102.986      0.000      10.278      10.676\n",
            "Dark                       -0.2303      0.106     -2.172      0.030      -0.438      -0.022\n",
            "Light                      -0.2303      0.108     -2.137      0.033      -0.442      -0.019\n",
            "Medium                     -0.3467      0.107     -3.237      0.001      -0.557      -0.137\n",
            "Non_human                   0.8064      0.424      1.902      0.057      -0.025       1.638\n",
            "Male                       -0.3879      0.059     -6.555      0.000      -0.504      -0.272\n",
            "Rarity_Score_3_multiple   3.81e-05    2.2e-05      1.729      0.084   -5.08e-06    8.13e-05\n",
            "==============================================================================\n",
            "Omnibus:                     5132.455   Durbin-Watson:                   0.238\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            70307.047\n",
            "Skew:                          -1.849   Prob(JB):                         0.00\n",
            "Kurtosis:                      14.728   Cond. No.                     1.96e+04\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.96e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.121\n",
            "Model:                            OLS   Adj. R-squared:                  0.120\n",
            "Method:                 Least Squares   F-statistic:                     170.6\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          3.26e-304\n",
            "Time:                        04:54:28   Log-Likelihood:                -27209.\n",
            "No. Observations:               11159   AIC:                         5.444e+04\n",
            "Df Residuals:                   11149   BIC:                         5.451e+04\n",
            "Df Model:                           9                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   9.9827      0.104     96.361      0.000       9.780      10.186\n",
            "Dark                       -0.2354      0.100     -2.360      0.018      -0.431      -0.040\n",
            "Light                      -0.2568      0.101     -2.534      0.011      -0.455      -0.058\n",
            "Medium                     -0.3677      0.101     -3.650      0.000      -0.565      -0.170\n",
            "Non_human                   0.8582      0.399      2.152      0.031       0.077       1.640\n",
            "Male                       -0.3532      0.056     -6.345      0.000      -0.462      -0.244\n",
            "wallet_change              -0.1049      0.004    -25.102      0.000      -0.113      -0.097\n",
            "daily_sales_change         -0.0033      0.000     -8.006      0.000      -0.004      -0.003\n",
            "Rarity_Score_3_multiple  4.209e-05   2.07e-05      2.032      0.042    1.48e-06    8.27e-05\n",
            "gas_eth_price             1.96e-09   9.69e-11     20.229      0.000    1.77e-09    2.15e-09\n",
            "==============================================================================\n",
            "Omnibus:                     5247.335   Durbin-Watson:                   0.256\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           106849.131\n",
            "Skew:                          -1.775   Prob(JB):                         0.00\n",
            "Kurtosis:                      17.738   Cond. No.                     7.31e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 7.31e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.121\n",
            "Model:                            OLS   Adj. R-squared:                  0.120\n",
            "Method:                 Least Squares   F-statistic:                     153.7\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          2.09e-303\n",
            "Time:                        04:54:28   Log-Likelihood:                -27208.\n",
            "No. Observations:               11159   AIC:                         5.444e+04\n",
            "Df Residuals:                   11148   BIC:                         5.452e+04\n",
            "Df Model:                          10                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   9.9853      0.104     96.368      0.000       9.782      10.188\n",
            "Dark                       -0.2350      0.100     -2.356      0.019      -0.431      -0.039\n",
            "Light                      -0.2557      0.101     -2.523      0.012      -0.454      -0.057\n",
            "Medium                     -0.3672      0.101     -3.645      0.000      -0.565      -0.170\n",
            "Non_human                   0.8825      0.399      2.210      0.027       0.100       1.665\n",
            "Male                       -0.3538      0.056     -6.356      0.000      -0.463      -0.245\n",
            "wallet_change              -0.0932      0.010     -8.980      0.000      -0.114      -0.073\n",
            "daily_sales_change         -0.0144      0.009     -1.588      0.112      -0.032       0.003\n",
            "Rarity_Score_3_multiple  4.236e-05   2.07e-05      2.045      0.041    1.76e-06     8.3e-05\n",
            "gas_eth_price            1.961e-09   9.69e-11     20.243      0.000    1.77e-09    2.15e-09\n",
            "daily_ratio_change          0.0230      0.019      1.221      0.222      -0.014       0.060\n",
            "==============================================================================\n",
            "Omnibus:                     5243.531   Durbin-Watson:                   0.256\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           106646.140\n",
            "Skew:                          -1.774   Prob(JB):                         0.00\n",
            "Kurtosis:                      17.724   Cond. No.                     7.32e+09\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 7.32e+09. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "\n",
            "==============================================================\n",
            "                        Log_price I Log_price II Log_price III\n",
            "--------------------------------------------------------------\n",
            "Dark                    -0.2303**   -0.2354**    -0.2350**    \n",
            "                        (0.1061)    (0.0998)     (0.0998)     \n",
            "Intercept               10.4770***  9.9827***    9.9853***    \n",
            "                        (0.1017)    (0.1036)     (0.1036)     \n",
            "Light                   -0.2303**   -0.2568**    -0.2557**    \n",
            "                        (0.1078)    (0.1013)     (0.1013)     \n",
            "Male                    -0.3879***  -0.3532***   -0.3538***   \n",
            "                        (0.0592)    (0.0557)     (0.0557)     \n",
            "Medium                  -0.3467***  -0.3677***   -0.3672***   \n",
            "                        (0.1071)    (0.1007)     (0.1007)     \n",
            "Non_human               0.8064*     0.8582**     0.8825**     \n",
            "                        (0.4240)    (0.3988)     (0.3992)     \n",
            "R-squared               0.0055      0.1210       0.1212       \n",
            "R-squared Adj.          0.0050      0.1203       0.1204       \n",
            "Rarity_Score_3_multiple 0.0000*     0.0000**     0.0000**     \n",
            "                        (0.0000)    (0.0000)     (0.0000)     \n",
            "daily_ratio_change                               0.0230       \n",
            "                                                 (0.0189)     \n",
            "daily_sales_change                  -0.0033***   -0.0144      \n",
            "                                    (0.0004)     (0.0091)     \n",
            "gas_eth_price                       0.0000***    0.0000***    \n",
            "                                    (0.0000)     (0.0000)     \n",
            "wallet_change                       -0.1049***   -0.0932***   \n",
            "                                    (0.0042)     (0.0104)     \n",
            "==============================================================\n",
            "Standard errors in parentheses.\n",
            "* p<.1, ** p<.05, ***p<.01\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sentiment Regression"
      ],
      "metadata": {
        "id": "RZpRybsxcuTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sentiment=pd.read_csv(\"https://raw.githubusercontent.com/HCI-Blockchain/ICWSM-2023/main/data/analyzed_price%20regression/CryptoPunk_Sentiment%20Score%20Polarity_NAdroped.csv\")\n",
        "df_sentiment=df_sentiment[['date','polarity']]\n",
        "df_sentiment"
      ],
      "metadata": {
        "id": "sgny3iLAct-L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3726c78b-e1e1-4b2b-ac59-49b22bfb6627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  polarity\n",
              "0    2017-08-10  1.000000\n",
              "1    2017-08-14 -1.000000\n",
              "2    2017-08-31  1.000000\n",
              "3    2017-09-01 -1.000000\n",
              "4    2017-09-04 -1.000000\n",
              "..          ...       ...\n",
              "261  2021-07-18  0.000000\n",
              "262  2021-07-19  0.714286\n",
              "263  2021-07-20  0.600000\n",
              "264  2021-07-21  0.200000\n",
              "265  2021-07-22  0.600000\n",
              "\n",
              "[266 rows x 2 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp['date_z_y']"
      ],
      "metadata": {
        "id": "MD2RaIZjn8JA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d66b7e31-0140-488e-891a-2feaaf44bbc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2017-06-23\n",
              "4        2017-06-23\n",
              "8        2017-06-23\n",
              "1        2017-06-23\n",
              "2        2017-06-23\n",
              "            ...    \n",
              "17754    2022-07-26\n",
              "17753    2022-07-26\n",
              "17752    2022-07-26\n",
              "17783    2022-07-27\n",
              "17784    2022-07-27\n",
              "Name: date_z_y, Length: 17204, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp = df_cryp.merge(df_sentiment, left_on='date_z_y',\n",
        "                      right_on='date')\n",
        "df_cryp=df_cryp.dropna()"
      ],
      "metadata": {
        "id": "BCNYbSLgRkVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "tss = TimeSeriesSplit()\n",
        "print(tss)\n",
        "\n",
        "# change the train and test split parameters \n",
        "tss = TimeSeriesSplit(gap=0, max_train_size=None, n_splits=2, test_size=None)\n",
        "\n",
        "for train_idx, test_idx in tss.split(df_cryp):\n",
        "    print(\"TRAIN:\", train_idx, \"TEST:\", test_idx)\n",
        "train_df = df_cryp.filter(items=train_idx, axis=0)\n",
        "test_df =  df_cryp.filter(items=test_idx, axis=0)   \n",
        "\n",
        "train_df.head()\n",
        "test_df.head()"
      ],
      "metadata": {
        "id": "UnOrQJO4Iyms",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "c7adbe1d-70c9-49b7-940a-f5ea6daca3ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)\n",
            "TRAIN: [   0    1    2 ... 1926 1927 1928] TEST: [1929 1930 1931 ... 3854 3855 3856]\n",
            "TRAIN: [   0    1    2 ... 3854 3855 3856] TEST: [3857 3858 3859 ... 5782 5783 5784]\n"
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          "data": {
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              "      Unnamed: 0  Unnamed: 0.1      date_x  from    to  eth_price  punk_id  \\\n",
              "3857       13908         13908  2021-04-12   179  5795      19.99     7556   \n",
              "3858       13896         13896  2021-04-12  1727  1246      23.95     2878   \n",
              "3859       13866         13866  2021-04-12  1406   989      25.40     6113   \n",
              "3860       13889         13889  2021-04-12  3392  5721      27.99     6896   \n",
              "3861       13913         13913  2021-04-12  3392  1067      34.99     9481   \n",
              "\n",
              "       type  gender skin_tone  ...  Light Medium Non-human daily_ratio  \\\n",
              "3857  Human  Female    Medium  ...      0      1         0     2137.79   \n",
              "3858  Human    Male      Dark  ...      0      0         0     2137.79   \n",
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              "3860  Human  Female    Albino  ...      0      0         0     2137.79   \n",
              "3861  Human  Female      Dark  ...      0      0         0     2137.79   \n",
              "\n",
              "        date_z_y  ratio_shift  daily_ratio_change  Log_price        date  \\\n",
              "3857  2021-04-12      2150.54           -0.005929  10.662760  2021-04-12   \n",
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              "3861  2021-04-12      2150.54           -0.005929  11.222590  2021-04-12   \n",
              "\n",
              "      polarity  \n",
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              "3858      0.75  \n",
              "3859      0.75  \n",
              "3860      0.75  \n",
              "3861      0.75  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_R = ['Log_price','polarity','Dark','Light','Non-human','Medium','Male','skin_tone','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']\n",
        "cols_R"
      ],
      "metadata": {
        "id": "HY4kaXkQIzQm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b08cb23-b7c8-492e-c49d-070910f5a4e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Log_price',\n",
              " 'polarity',\n",
              " 'Dark',\n",
              " 'Light',\n",
              " 'Non-human',\n",
              " 'Medium',\n",
              " 'Male',\n",
              " 'skin_tone',\n",
              " 'wallet_change',\n",
              " 'daily_sales_change',\n",
              " 'Rarity_Score_3_multiple',\n",
              " 'gas_eth_price',\n",
              " 'eth_usd_price',\n",
              " 'daily_ratio_change']"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_R_train = train_df[cols_R]\n",
        "df_R_test = test_df[cols_R]\n",
        "df_R_test.head()"
      ],
      "metadata": {
        "id": "Da_cmx4kJr5w",
        "colab": {
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          "height": 288
        },
        "outputId": "45bfb48a-d791-4889-c4f6-d07d1d86d85a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Log_price  polarity  Dark  Light  Non-human  Medium  Male skin_tone  \\\n",
              "3857  10.662760      0.75     0      0          0       1     0    Medium   \n",
              "3858  10.843496      0.75     1      0          0       0     1      Dark   \n",
              "3859  10.902277      0.75     1      0          0       0     0      Dark   \n",
              "3860  10.999375      0.75     0      0          0       0     0    Albino   \n",
              "3861  11.222590      0.75     1      0          0       0     0      Dark   \n",
              "\n",
              "      wallet_change  daily_sales_change  Rarity_Score_3_multiple  \\\n",
              "3857      -0.018182           -0.074194                14.853675   \n",
              "3858      -0.018182           -0.074194               200.561128   \n",
              "3859      -0.018182           -0.074194               117.815767   \n",
              "3860      -0.018182           -0.074194                 0.000000   \n",
              "3861      -0.018182           -0.074194               418.559172   \n",
              "\n",
              "      gas_eth_price  eth_usd_price  daily_ratio_change  \n",
              "3857   4.589860e+08        2137.79           -0.005929  \n",
              "3858   4.589860e+08        2137.79           -0.005929  \n",
              "3859   4.589860e+08        2137.79           -0.005929  \n",
              "3860   4.589860e+08        2137.79           -0.005929  \n",
              "3861   4.589860e+08        2137.79           -0.005929  "
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              "\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1b140a4c-4752-4ee3-a8bc-c155bf84170e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1b140a4c-4752-4ee3-a8bc-c155bf84170e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(df_R_train,columns=['polarity','Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "y_train = pd.DataFrame(df_R_train['Log_price'])\n",
        "X_test = pd.DataFrame(df_R_test,columns=['polarit','Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "y_test = pd.DataFrame(df_R_test['Log_price'])"
      ],
      "metadata": {
        "id": "SCuWQKpQJr8G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mul_lr():   \n",
        "    X_train = pd.DataFrame(df_R_train,columns=['polarity','Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_train = pd.DataFrame(df_R_train['Log_price'])\n",
        "    X_test = pd.DataFrame(df_R_test,columns=['polarity','Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change'])\n",
        "    y_test = pd.DataFrame(df_R_test['Log_price'])\n",
        "    #X_train,X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2,random_state=100)\n",
        "    print ('X_train.shape={}\\n y_train.shape ={}\\n X_test.shape={}\\n,  y_test.shape={}'.format(X_train.shape,y_train.shape, X_test.shape,y_test.shape))\n",
        "    linreg = LinearRegression()\n",
        "    model=linreg.fit(X_train, y_train)\n",
        "    print (model)\n",
        "    print (linreg.intercept_)\n",
        "    print (linreg.coef_)\n",
        "mul_lr()"
      ],
      "metadata": {
        "id": "y6f0SkmKJr-I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e182b46-5f2e-408a-add8-895acea3d98a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train.shape=(3857, 11)\n",
            " y_train.shape =(3857, 1)\n",
            " X_test.shape=(1928, 11)\n",
            ",  y_test.shape=(1928, 1)\n",
            "LinearRegression()\n",
            "[6.18077468]\n",
            "[[-9.37809270e-02 -2.88971065e-01 -1.79541058e-01 -2.12735021e-01\n",
            "  -1.62287971e-01 -4.04063308e-02  2.04907982e-02  2.21970589e-05\n",
            "   5.78779302e-10  2.34452209e-03 -2.53818327e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cryp.describe()"
      ],
      "metadata": {
        "id": "NWn3rpa1LTt2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "710b9491-1e16-49a5-cde0-469a4c7a2b20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0  Unnamed: 0.1         from           to     eth_price  \\\n",
              "count   5785.000000   5785.000000  5785.000000  5785.000000  5.785000e+03   \n",
              "mean    9566.361452   9566.361452  3131.734659  3225.413483  2.552626e+01   \n",
              "std     4142.418774   4142.418774  1797.595830  1842.664804  6.252273e+01   \n",
              "min       34.000000     34.000000     3.000000     8.000000  4.000000e-17   \n",
              "25%     6527.000000   6527.000000  1566.000000  1564.000000  1.500000e+01   \n",
              "50%    10595.000000  10595.000000  3096.000000  3190.000000  2.140000e+01   \n",
              "75%    12939.000000  12939.000000  4738.000000  4844.000000  2.699000e+01   \n",
              "max    15487.000000  15487.000000  6434.000000  6434.000000  4.200000e+03   \n",
              "\n",
              "           punk_id   attr_count  Rarity_Score_1_rarest  \\\n",
              "count  5785.000000  5785.000000            5785.000000   \n",
              "mean   5335.916508     2.792913              10.979948   \n",
              "std    2698.139442     0.737729              11.264446   \n",
              "min      14.000000     0.000000               0.000000   \n",
              "25%    3056.000000     2.000000               4.928729   \n",
              "50%    5348.000000     3.000000               7.390420   \n",
              "75%    7662.000000     3.000000              12.299445   \n",
              "max    9998.000000     6.000000             100.000000   \n",
              "\n",
              "       Rarity_Score_2_Average  Rarity_Score_3_multiple  ...         Male  \\\n",
              "count             5785.000000              5785.000000  ...  5785.000000   \n",
              "mean                 6.362930               204.310132  ...     0.672947   \n",
              "std                  5.006478              2398.788085  ...     0.469177   \n",
              "min                  0.000000                 0.000000  ...     0.000000   \n",
              "25%                  3.647085                 0.000000  ...     0.000000   \n",
              "50%                  4.889871                32.992221  ...     1.000000   \n",
              "75%                  7.374069               108.528893  ...     1.000000   \n",
              "max                 77.811760            163669.688714  ...     1.000000   \n",
              "\n",
              "              Dark        Light       Medium    Non-human  daily_ratio  \\\n",
              "count  5785.000000  5785.000000  5785.000000  5785.000000  5785.000000   \n",
              "mean      0.300605     0.299049     0.304235     0.004322  1966.284501   \n",
              "std       0.458561     0.457881     0.460122     0.065602   686.942393   \n",
              "min       0.000000     0.000000     0.000000     0.000000    93.610000   \n",
              "25%       0.000000     0.000000     0.000000     0.000000  1778.070000   \n",
              "50%       0.000000     0.000000     0.000000     0.000000  1899.960000   \n",
              "75%       1.000000     1.000000     1.000000     0.000000  2150.540000   \n",
              "max       1.000000     1.000000     1.000000     1.000000  4181.280000   \n",
              "\n",
              "       ratio_shift  daily_ratio_change    Log_price     polarity  \n",
              "count  5785.000000         5785.000000  5785.000000  5785.000000  \n",
              "mean   1949.255196            0.008352    10.390525     0.492641  \n",
              "std     661.691966            0.057439     1.474345     0.518923  \n",
              "min      90.430000           -0.276749   -30.256703    -1.000000  \n",
              "25%    1779.400000           -0.021635    10.292424     0.333333  \n",
              "50%    1915.120000            0.009208    10.642912     0.600000  \n",
              "75%    2140.450000            0.038315    11.000349     1.000000  \n",
              "max    4181.280000            0.279043    15.853303     1.000000  \n",
              "\n",
              "[8 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-df16fd72-8340-4de4-9f31-7f6162e49ece\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>eth_price</th>\n",
              "      <th>punk_id</th>\n",
              "      <th>attr_count</th>\n",
              "      <th>Rarity_Score_1_rarest</th>\n",
              "      <th>Rarity_Score_2_Average</th>\n",
              "      <th>Rarity_Score_3_multiple</th>\n",
              "      <th>...</th>\n",
              "      <th>Male</th>\n",
              "      <th>Dark</th>\n",
              "      <th>Light</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Non-human</th>\n",
              "      <th>daily_ratio</th>\n",
              "      <th>ratio_shift</th>\n",
              "      <th>daily_ratio_change</th>\n",
              "      <th>Log_price</th>\n",
              "      <th>polarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5.785000e+03</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "      <td>5785.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9566.361452</td>\n",
              "      <td>9566.361452</td>\n",
              "      <td>3131.734659</td>\n",
              "      <td>3225.413483</td>\n",
              "      <td>2.552626e+01</td>\n",
              "      <td>5335.916508</td>\n",
              "      <td>2.792913</td>\n",
              "      <td>10.979948</td>\n",
              "      <td>6.362930</td>\n",
              "      <td>204.310132</td>\n",
              "      <td>...</td>\n",
              "      <td>0.672947</td>\n",
              "      <td>0.300605</td>\n",
              "      <td>0.299049</td>\n",
              "      <td>0.304235</td>\n",
              "      <td>0.004322</td>\n",
              "      <td>1966.284501</td>\n",
              "      <td>1949.255196</td>\n",
              "      <td>0.008352</td>\n",
              "      <td>10.390525</td>\n",
              "      <td>0.492641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4142.418774</td>\n",
              "      <td>4142.418774</td>\n",
              "      <td>1797.595830</td>\n",
              "      <td>1842.664804</td>\n",
              "      <td>6.252273e+01</td>\n",
              "      <td>2698.139442</td>\n",
              "      <td>0.737729</td>\n",
              "      <td>11.264446</td>\n",
              "      <td>5.006478</td>\n",
              "      <td>2398.788085</td>\n",
              "      <td>...</td>\n",
              "      <td>0.469177</td>\n",
              "      <td>0.458561</td>\n",
              "      <td>0.457881</td>\n",
              "      <td>0.460122</td>\n",
              "      <td>0.065602</td>\n",
              "      <td>686.942393</td>\n",
              "      <td>661.691966</td>\n",
              "      <td>0.057439</td>\n",
              "      <td>1.474345</td>\n",
              "      <td>0.518923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>4.000000e-17</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>93.610000</td>\n",
              "      <td>90.430000</td>\n",
              "      <td>-0.276749</td>\n",
              "      <td>-30.256703</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6527.000000</td>\n",
              "      <td>6527.000000</td>\n",
              "      <td>1566.000000</td>\n",
              "      <td>1564.000000</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>3056.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.928729</td>\n",
              "      <td>3.647085</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1778.070000</td>\n",
              "      <td>1779.400000</td>\n",
              "      <td>-0.021635</td>\n",
              "      <td>10.292424</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10595.000000</td>\n",
              "      <td>10595.000000</td>\n",
              "      <td>3096.000000</td>\n",
              "      <td>3190.000000</td>\n",
              "      <td>2.140000e+01</td>\n",
              "      <td>5348.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.390420</td>\n",
              "      <td>4.889871</td>\n",
              "      <td>32.992221</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1899.960000</td>\n",
              "      <td>1915.120000</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>10.642912</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12939.000000</td>\n",
              "      <td>12939.000000</td>\n",
              "      <td>4738.000000</td>\n",
              "      <td>4844.000000</td>\n",
              "      <td>2.699000e+01</td>\n",
              "      <td>7662.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12.299445</td>\n",
              "      <td>7.374069</td>\n",
              "      <td>108.528893</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2150.540000</td>\n",
              "      <td>2140.450000</td>\n",
              "      <td>0.038315</td>\n",
              "      <td>11.000349</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15487.000000</td>\n",
              "      <td>15487.000000</td>\n",
              "      <td>6434.000000</td>\n",
              "      <td>6434.000000</td>\n",
              "      <td>4.200000e+03</td>\n",
              "      <td>9998.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>77.811760</td>\n",
              "      <td>163669.688714</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4181.280000</td>\n",
              "      <td>4181.280000</td>\n",
              "      <td>0.279043</td>\n",
              "      <td>15.853303</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df16fd72-8340-4de4-9f31-7f6162e49ece')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.array(df_cryp['Log_price']) \n",
        "X = np.array(df_cryp[['polarity','Dark','Light','Medium','Male','wallet_change','daily_sales_change','Rarity_Score_3_multiple','gas_eth_price','eth_usd_price','daily_ratio_change']]) \n",
        "X2 = sm.add_constant(X)\n",
        "est = sm.OLS(y, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ],
      "metadata": {
        "id": "gJmGc1BLJsCK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cca8c547-ff5f-413e-e75d-c0aef0682fbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.410\n",
            "Model:                            OLS   Adj. R-squared:                  0.409\n",
            "Method:                 Least Squares   F-statistic:                     364.6\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):               0.00\n",
            "Time:                        04:54:54   Log-Likelihood:                -8928.2\n",
            "No. Observations:                5785   AIC:                         1.788e+04\n",
            "Df Residuals:                    5773   BIC:                         1.796e+04\n",
            "Df Model:                          11                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          7.7909      0.075    103.567      0.000       7.643       7.938\n",
            "x1            -0.0477      0.030     -1.606      0.108      -0.106       0.011\n",
            "x2            -0.3331      0.055     -6.023      0.000      -0.441      -0.225\n",
            "x3            -0.2195      0.055     -3.970      0.000      -0.328      -0.111\n",
            "x4            -0.2856      0.055     -5.175      0.000      -0.394      -0.177\n",
            "x5            -0.1468      0.032     -4.614      0.000      -0.209      -0.084\n",
            "x6            -0.0670      0.013     -5.285      0.000      -0.092      -0.042\n",
            "x7             0.0340      0.008      4.504      0.000       0.019       0.049\n",
            "x8          2.679e-05   6.22e-06      4.307      0.000    1.46e-05     3.9e-05\n",
            "x9          6.003e-10    5.3e-11     11.318      0.000    4.96e-10    7.04e-10\n",
            "x10            0.0014   2.23e-05     60.812      0.000       0.001       0.001\n",
            "x11           -2.1804      0.283     -7.695      0.000      -2.736      -1.625\n",
            "==============================================================================\n",
            "Omnibus:                    11464.524   Durbin-Watson:                   1.157\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         66366921.890\n",
            "Skew:                         -15.496   Prob(JB):                         0.00\n",
            "Kurtosis:                     526.807   Cond. No.                     1.18e+10\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.18e+10. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.formula.api as smf\n",
        "from statsmodels.iolib.summary2 import summary_col\n",
        "\n",
        "df_R_train['Non_human']=df_R_train['Non-human']\n",
        "\n",
        "#x = [1, 3, 5, 6, 8, 3, 4, 5, 1, 3, 5, 6, 8, 3, 4, 5, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#y = [0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7, 0, 1, 0, 1, 1, 4, 5, 7]\n",
        "#d = { \"x\": pd.Series(x), \"y\": pd.Series(y)}\n",
        "#df = pd.DataFrame(d)\n",
        "#df['xsqr'] = df['x']**2  \n",
        "mod = smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+Rarity_Score_3_multiple',data=df_R_train)\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "\n",
        "\n",
        "mod2= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price',data=df_R_train)\n",
        "res2 = mod2.fit()\n",
        "print(res2.summary())\n",
        "\n",
        "mod3= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price+daily_ratio_change',data=df_R_train)\n",
        "res3 = mod3.fit()\n",
        "print(res3.summary())\n",
        "\n",
        "mod4= smf.ols('Log_price ~ Dark+Light+Medium+Non_human+Male+wallet_change+daily_sales_change+Rarity_Score_3_multiple+gas_eth_price+daily_ratio_change+polarity',data=df_R_train)\n",
        "res4 = mod4.fit()\n",
        "print(res4.summary())\n",
        "\n",
        "dfoutput = summary_col([res,res2,res3,res4],stars=True)\n",
        "print(dfoutput)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLG1oQ1uBRVq",
        "outputId": "884fee62-91fd-4fcc-f66c-fff282e8566b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-90-b8f68979fc06>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_R_train['Non_human']=df_R_train['Non-human']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.019\n",
            "Model:                            OLS   Adj. R-squared:                  0.018\n",
            "Method:                 Least Squares   F-statistic:                     12.61\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):           3.91e-14\n",
            "Time:                        04:57:23   Log-Likelihood:                -7435.3\n",
            "No. Observations:                3857   AIC:                         1.488e+04\n",
            "Df Residuals:                    3850   BIC:                         1.493e+04\n",
            "Df Model:                           6                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                  10.3915      0.096    108.750      0.000      10.204      10.579\n",
            "Dark                       -0.2563      0.101     -2.526      0.012      -0.455      -0.057\n",
            "Light                      -0.1052      0.101     -1.039      0.299      -0.304       0.093\n",
            "Medium                     -0.2042      0.101     -2.014      0.044      -0.403      -0.005\n",
            "Non_human                   2.2438      0.383      5.854      0.000       1.492       2.995\n",
            "Male                       -0.2628      0.057     -4.594      0.000      -0.375      -0.151\n",
            "Rarity_Score_3_multiple   2.33e-05   9.47e-06      2.460      0.014    4.73e-06    4.19e-05\n",
            "==============================================================================\n",
            "Omnibus:                     5692.497   Durbin-Watson:                   0.722\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4997868.206\n",
            "Skew:                          -8.533   Prob(JB):                         0.00\n",
            "Kurtosis:                     178.521   Cond. No.                     4.09e+04\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 4.09e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.193\n",
            "Model:                            OLS   Adj. R-squared:                  0.191\n",
            "Method:                 Least Squares   F-statistic:                     102.3\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          5.20e-172\n",
            "Time:                        04:57:24   Log-Likelihood:                -7059.1\n",
            "No. Observations:                3857   AIC:                         1.414e+04\n",
            "Df Residuals:                    3847   BIC:                         1.420e+04\n",
            "Df Model:                           9                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   8.4509      0.111     76.005      0.000       8.233       8.669\n",
            "Dark                       -0.2548      0.092     -2.766      0.006      -0.435      -0.074\n",
            "Light                      -0.0688      0.092     -0.749      0.454      -0.249       0.111\n",
            "Medium                     -0.1649      0.092     -1.792      0.073      -0.345       0.016\n",
            "Non_human                   2.3714      0.348      6.806      0.000       1.688       3.055\n",
            "Male                       -0.2425      0.052     -4.670      0.000      -0.344      -0.141\n",
            "wallet_change              -0.0700      0.017     -4.107      0.000      -0.103      -0.037\n",
            "daily_sales_change          0.0527      0.010      5.151      0.000       0.033       0.073\n",
            "Rarity_Score_3_multiple  2.582e-05    8.6e-06      3.005      0.003    8.97e-06    4.27e-05\n",
            "gas_eth_price            2.885e-09   1.02e-10     28.416      0.000    2.69e-09    3.08e-09\n",
            "==============================================================================\n",
            "Omnibus:                     6187.636   Durbin-Watson:                   0.880\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          9592334.469\n",
            "Skew:                          -9.937   Prob(JB):                         0.00\n",
            "Kurtosis:                     246.501   Cond. No.                     1.01e+10\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.01e+10. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.202\n",
            "Model:                            OLS   Adj. R-squared:                  0.200\n",
            "Method:                 Least Squares   F-statistic:                     97.16\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          7.41e-180\n",
            "Time:                        04:57:24   Log-Likelihood:                -7038.4\n",
            "No. Observations:                3857   AIC:                         1.410e+04\n",
            "Df Residuals:                    3846   BIC:                         1.417e+04\n",
            "Df Model:                          10                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   8.2135      0.117     70.440      0.000       7.985       8.442\n",
            "Dark                       -0.2606      0.092     -2.844      0.004      -0.440      -0.081\n",
            "Light                      -0.0629      0.091     -0.688      0.492      -0.242       0.116\n",
            "Medium                     -0.1629      0.092     -1.779      0.075      -0.342       0.017\n",
            "Non_human                   2.3130      0.347      6.671      0.000       1.633       2.993\n",
            "Male                       -0.2398      0.052     -4.644      0.000      -0.341      -0.139\n",
            "wallet_change              -0.0664      0.017     -3.915      0.000      -0.100      -0.033\n",
            "daily_sales_change          0.0482      0.010      4.729      0.000       0.028       0.068\n",
            "Rarity_Score_3_multiple   2.48e-05   8.55e-06      2.900      0.004    8.03e-06    4.16e-05\n",
            "gas_eth_price            3.233e-09   1.15e-10     28.217      0.000    3.01e-09    3.46e-09\n",
            "daily_ratio_change          3.8218      0.594      6.433      0.000       2.657       4.987\n",
            "==============================================================================\n",
            "Omnibus:                     6224.444   Durbin-Watson:                   0.890\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          9987468.903\n",
            "Skew:                         -10.052   Prob(JB):                         0.00\n",
            "Kurtosis:                     251.480   Cond. No.                     1.71e+10\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.71e+10. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:              Log_price   R-squared:                       0.208\n",
            "Model:                            OLS   Adj. R-squared:                  0.205\n",
            "Method:                 Least Squares   F-statistic:                     91.62\n",
            "Date:                Sun, 05 Mar 2023   Prob (F-statistic):          4.25e-185\n",
            "Time:                        04:57:24   Log-Likelihood:                -7023.9\n",
            "No. Observations:                3857   AIC:                         1.407e+04\n",
            "Df Residuals:                    3845   BIC:                         1.415e+04\n",
            "Df Model:                          11                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept                   8.0478      0.120     66.972      0.000       7.812       8.283\n",
            "Dark                       -0.2732      0.091     -2.992      0.003      -0.452      -0.094\n",
            "Light                      -0.0755      0.091     -0.828      0.408      -0.254       0.103\n",
            "Medium                     -0.1714      0.091     -1.878      0.060      -0.350       0.008\n",
            "Non_human                   2.3048      0.345      6.671      0.000       1.627       2.982\n",
            "Male                       -0.2400      0.051     -4.663      0.000      -0.341      -0.139\n",
            "wallet_change              -0.0862      0.017     -4.984      0.000      -0.120      -0.052\n",
            "daily_sales_change          0.0544      0.010      5.323      0.000       0.034       0.074\n",
            "Rarity_Score_3_multiple  2.389e-05   8.52e-06      2.804      0.005    7.19e-06    4.06e-05\n",
            "gas_eth_price            3.334e-09   1.16e-10     28.820      0.000    3.11e-09    3.56e-09\n",
            "daily_ratio_change          4.1519      0.595      6.977      0.000       2.985       5.319\n",
            "polarity                    0.2313      0.043      5.398      0.000       0.147       0.315\n",
            "==============================================================================\n",
            "Omnibus:                     6230.424   Durbin-Watson:                   0.895\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         10083459.278\n",
            "Skew:                         -10.069   Prob(JB):                         0.00\n",
            "Kurtosis:                     252.676   Cond. No.                     1.72e+10\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.72e+10. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "\n",
            "=============================================================================\n",
            "                        Log_price I Log_price II Log_price III Log_price IIII\n",
            "-----------------------------------------------------------------------------\n",
            "Dark                    -0.2563**   -0.2548***   -0.2606***    -0.2732***    \n",
            "                        (0.1014)    (0.0921)     (0.0916)      (0.0913)      \n",
            "Intercept               10.3915***  8.4509***    8.2135***     8.0478***     \n",
            "                        (0.0956)    (0.1112)     (0.1166)      (0.1202)      \n",
            "Light                   -0.1052     -0.0688      -0.0629       -0.0755       \n",
            "                        (0.1013)    (0.0919)     (0.0915)      (0.0912)      \n",
            "Male                    -0.2628***  -0.2425***   -0.2398***    -0.2400***    \n",
            "                        (0.0572)    (0.0519)     (0.0516)      (0.0515)      \n",
            "Medium                  -0.2042**   -0.1649*     -0.1629*      -0.1714*      \n",
            "                        (0.1014)    (0.0920)     (0.0916)      (0.0912)      \n",
            "Non_human               2.2438***   2.3714***    2.3130***     2.3048***     \n",
            "                        (0.3833)    (0.3484)     (0.3467)      (0.3455)      \n",
            "R-squared               0.0193      0.1931       0.2017        0.2077        \n",
            "R-squared Adj.          0.0177      0.1912       0.1996        0.2054        \n",
            "Rarity_Score_3_multiple 0.0000**    0.0000***    0.0000***     0.0000***     \n",
            "                        (0.0000)    (0.0000)     (0.0000)      (0.0000)      \n",
            "daily_ratio_change                               3.8218***     4.1519***     \n",
            "                                                 (0.5941)      (0.5951)      \n",
            "daily_sales_change                  0.0527***    0.0482***     0.0544***     \n",
            "                                    (0.0102)     (0.0102)      (0.0102)      \n",
            "gas_eth_price                       0.0000***    0.0000***     0.0000***     \n",
            "                                    (0.0000)     (0.0000)      (0.0000)      \n",
            "polarity                                                       0.2313***     \n",
            "                                                               (0.0428)      \n",
            "wallet_change                       -0.0700***   -0.0664***    -0.0862***    \n",
            "                                    (0.0170)     (0.0170)      (0.0173)      \n",
            "=============================================================================\n",
            "Standard errors in parentheses.\n",
            "* p<.1, ** p<.05, ***p<.01\n"
          ]
        }
      ]
    }
  ]
}