1Predicting_stars_quasars_and_galaxies.ipynb

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        "<a href=\"https://colab.research.google.com/github/AeleSahithi/Predicting_stars_quasars_and_galaxies/blob/main/1Predicting_stars_quasars_and_galaxies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# <center> Predicting Stars, Galaxies & Quasars with ML Model\n",
        "\n",
        "\n",
        "\n",
        "<center> <img src = \"https://chandra.harvard.edu/photo/2019/gsn069/gsn069_525.gif\" width = 55%>"
      ],
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      "source": [
        "# About the Problem & Dataset - <a name =\"h1\"></a>\n",
        "\n",
        "### So what exactly are stars, galaxies, and quasars?\n",
        "\n",
        "*     <p align=\"justify\">A GALAXY is a gravitationally bound system of stars, stellar remnants, interstellar gas, dust, and dark matter. Galaxies are categorised according to their visual morphology as elliptical, spiral, or irregular. Many galaxies are thought to have supermassive black holes at their active centers.</p>\n",
        "\n",
        "<br> <center> <img src = \"https://drive.google.com/uc?id=16taukAE02GkdbR2u1nLVKdVkVB_h7Jc1\" width = 50%> <br>\n",
        "\n",
        "*     <p align=\"justify\">A STAR is a type of astronomical object consisting of a luminous spheroid of plasma held together by its own gravity. The nearest star to Earth is the Sun.\n",
        "\n",
        "<br> <center> <img src = \"https://drive.google.com/uc?id=1mJGJ6MXuc2LUIP_cbzQBjHAH84a_XJPD\" width = 50%>\n",
        "\n",
        "\n",
        "*     <p align=\"justify\">A QUASAR, also known as a quasi-stellar object, is an extremely luminous active galactic nucleus (AGN). The power radiated by quasars is enormous. A typical quasar is 27 trillion times brighter than our sun! If you were to place a quasar at the distance of Pluto, it would vaporise all of Earth’s oceans to steam in a fifth of a second.\n",
        "\n",
        "<br>  <center> <img src = \"https://drive.google.com/uc?id=1leFEIYqPxz_expXFg_2v8bgkFQipk5F-\" width = 50%>\n",
        "\n",
        "The dataset used for this model is the [Sloan Digital Sky Survey DR14](https://www.kaggle.com/datasets/lucidlenn/sloan-digital-sky-survey)\n",
        "\n",
        "<p align=\"justify\">The data consists of 10,000 observations of space taken by the SDSS. Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy or quasar. 30% of it is used in testing the model performance and 70% in training of the model."
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      "cell_type": "markdown",
      "source": [
        "## Workflow of this notebook\n",
        "**1)** [Introducing Dataset](#h1)      \n",
        "**2)** [Importing necessary libraries and modules for this notebook](#h2)    \n",
        "**3)** [Exploring the Dataset](#h3)      \n",
        "**4)** [Preparing data for our model](#h4)           \n",
        "**5)** [Scaling the data for our model and checking the distribution plots](#h5)   \n",
        "**6)** [Building the ML models and checking results](#h6)        \n"
      ],
      "metadata": {
        "id": "4qPGtm4ORVPY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Note: Upload the notebook to a kaggle session at the dataset link and run the cells"
      ],
      "metadata": {
        "id": "c20MUd2-_hUC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing necessary libraries and modules for the dataset <a name = 'h2'></a>\n",
        "\n",
        "### First of all we need to import all the packages we need. Numpy and Pandas for data manipulation and all the modules from sklearn for the machine learning feature\n"
      ],
      "metadata": {
        "id": "RC3nC7sk_hUF"
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      "source": [
        "#Importing Necessary Libraries\n",
        "\n",
        "'''Operating System Functionality'''\n",
        "import os\n",
        "\n",
        "'''Data Handling & Linear Algebra'''\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "'''Visualisation'''\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "'''Manipulating Data and Model Building'''\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "'''Data Analysis'''\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "'''Measuring Classification Performance'''\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "'''Classification & Regression'''\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "'''Ignore warnings'''\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "%matplotlib inline"
      ],
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        "id": "i9UKbLaw_hUG",
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      "source": [
        "**Documents of the above used libraries and modules :-**\n",
        "- [os](https://docs.python.org/3/library/os.html)\n",
        "- [numpy](https://numpy.org/doc/1.23/user/absolute_beginners.html)\n",
        "- [pandas](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)\n",
        "- [matplotlib.pyplot](https://matplotlib.org/stable/tutorials/introductory/pyplot.html)\n",
        "- [seaborn](https://seaborn.pydata.org/tutorial/introduction.html)\n",
        "- [tensorflow](https://www.tensorflow.org/guide)\n",
        "- [keras](https://keras.io/guides/)\n",
        "- [All sklearn libraries](https://scikit-learn.org/stable/user_guide.html)\n"
      ],
      "metadata": {
        "id": "Ou0ShGjwT1wS"
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    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the dataset\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))"
      ],
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        "id": "OlcpMYvo_hUD",
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    {
      "cell_type": "code",
      "source": [
        "#Displaying the first 5 rows of the dataset\n",
        "data.head()"
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          "height": 206
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        "outputId": "6a5ef6bd-c49e-4f67-ba80-0b90be44095c"
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      "execution_count": null,
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef9025fa-f2b9-46d0-861a-63fd824e76e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"ra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.78343941253631,\n        \"min\": 8.235100497,\n        \"max\": 260.8843818,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          134.6492674,\n          157.0952241,\n          159.6062762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.212206579284373,\n        \"min\": -5.382632499,\n        \"max\": 68.54226541,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          54.38634445,\n          62.59739268,\n          -0.017859983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"u\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8286560094113752,\n        \"min\": 12.98897,\n        \"max\": 19.5999,\n        \"num_unique_values\": 9730,\n        \"samples\": [\n          16.99432,\n          18.27211,\n          19.51903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9454571752688143,\n        \"min\": 12.79955,\n        \"max\": 19.91897,\n        \"num_unique_values\": 9817,\n        \"samples\": [\n          17.88955,\n          18.89235,\n          15.92992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.067764349402935,\n        \"min\": 12.4316,\n        \"max\": 24.80204,\n        \"num_unique_values\": 9852,\n        \"samples\": [\n          17.62294,\n          15.54049,\n          18.64672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"i\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1418048192775498,\n        \"min\": 11.94721,\n        \"max\": 28.17963,\n        \"num_unique_values\": 9890,\n        \"samples\": [\n          17.2252,\n          18.65555,\n          16.67307\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2031880475759795,\n        \"min\": 11.61041,\n        \"max\": 22.83306,\n        \"num_unique_values\": 9896,\n        \"samples\": [\n          17.83397,\n          16.71616,\n          17.0249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"run\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 273,\n        \"min\": 308,\n        \"max\": 1412,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          1345,\n          1332,\n          752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rerun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 301,\n        \"max\": 301,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"camcol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"field\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162,\n        \"min\": 11,\n        \"max\": 768,\n        \"num_unique_values\": 703,\n        \"samples\": [\n          621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"STAR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"redshift\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3887740358114659,\n        \"min\": -0.004136078,\n        \"max\": 5.353854,\n        \"num_unique_values\": 9637,\n        \"samples\": [\n          0.000189883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"plate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1788,\n        \"min\": 266,\n        \"max\": 8410,\n        \"num_unique_values\": 487,\n        \"samples\": [\n          3848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mjd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1511,\n        \"min\": 51578,\n        \"max\": 57481,\n        \"num_unique_values\": 355,\n        \"samples\": [\n          54862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiberid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          658\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Analysis<a name ='h3'></a>"
      ],
      "metadata": {
        "id": "UJjEItwR_hUG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading data into a dataframe\n",
        "data = pd.read_csv(\"/content/Skyserver_SQL2_27_2018 6_51_39 PM.csv\")"
      ],
      "metadata": {
        "id": "uOxvi3G4_hUH",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.480369Z",
          "iopub.execute_input": "2022-10-27T16:28:19.480793Z",
          "iopub.status.idle": "2022-10-27T16:28:19.578307Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.480761Z",
          "shell.execute_reply": "2022-10-27T16:28:19.577248Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the dimensions of the dataset\n",
        "data.shape"
      ],
      "metadata": {
        "id": "lBb1y9rU_hUI",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.616623Z",
          "iopub.execute_input": "2022-10-27T16:28:19.616993Z",
          "iopub.status.idle": "2022-10-27T16:28:19.625096Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.616962Z",
          "shell.execute_reply": "2022-10-27T16:28:19.623769Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a9695d5-3759-4b93-a684-f730fc1dab26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### The object id columns are of little use in the analysis hence we can delete them from the dataset."
      ],
      "metadata": {
        "id": "Vk0aeBoC_hUJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop the object id columns, they are of no use in the analysis\n",
        "data.drop(['objid','specobjid'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "uRyJIAo0_hUJ",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.626703Z",
          "iopub.execute_input": "2022-10-27T16:28:19.627117Z",
          "iopub.status.idle": "2022-10-27T16:28:19.645626Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.627084Z",
          "shell.execute_reply": "2022-10-27T16:28:19.644151Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Data after dropping columns\n",
        "data.head(10)"
      ],
      "metadata": {
        "id": "uLBlBBwm_hUK",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:30:51.878238Z",
          "iopub.execute_input": "2022-10-27T16:30:51.879447Z",
          "iopub.status.idle": "2022-10-27T16:30:51.904413Z",
          "shell.execute_reply.started": "2022-10-27T16:30:51.879395Z",
          "shell.execute_reply": "2022-10-27T16:30:51.903031Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "1c0d20fb-5c0f-43f1-801d-3626a01b6eba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ra       dec         u         g         r         i         z  \\\n",
              "0  183.531326  0.089693  19.47406  17.04240  15.94699  15.50342  15.22531   \n",
              "1  183.598370  0.135285  18.66280  17.21449  16.67637  16.48922  16.39150   \n",
              "2  183.680207  0.126185  19.38298  18.19169  17.47428  17.08732  16.80125   \n",
              "3  183.870529  0.049911  17.76536  16.60272  16.16116  15.98233  15.90438   \n",
              "4  183.883288  0.102557  17.55025  16.26342  16.43869  16.55492  16.61326   \n",
              "5  183.847174  0.173694  19.43133  18.46779  18.16451  18.01475  18.04155   \n",
              "6  183.864379  0.019201  19.38322  17.88995  17.10537  16.66393  16.36955   \n",
              "7  183.900081  0.187473  18.97993  17.84496  17.38022  17.20673  17.07071   \n",
              "8  183.924588  0.097246  17.90616  16.97172  16.67541  16.53776  16.47596   \n",
              "9  183.973498  0.081626  18.67249  17.71375  17.49362  17.28284  17.22644   \n",
              "\n",
              "   run  rerun  camcol  field   class  redshift  plate    mjd  fiberid  \n",
              "0  752    301       4    267    STAR -0.000009   3306  54922      491  \n",
              "1  752    301       4    267    STAR -0.000055    323  51615      541  \n",
              "2  752    301       4    268  GALAXY  0.123111    287  52023      513  \n",
              "3  752    301       4    269    STAR -0.000111   3306  54922      510  \n",
              "4  752    301       4    269    STAR  0.000590   3306  54922      512  \n",
              "5  752    301       4    269    STAR  0.000315    324  51666      594  \n",
              "6  752    301       4    269  GALAXY  0.100242    287  52023      559  \n",
              "7  752    301       4    269    STAR  0.000315   3306  54922      515  \n",
              "8  752    301       4    270    STAR  0.000089    323  51615      595  \n",
              "9  752    301       4    270  GALAXY  0.040508    288  52000      400  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-663c51e2-26f2-40ac-a847-787e09f623ef\" 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>ra</th>\n",
              "      <th>dec</th>\n",
              "      <th>u</th>\n",
              "      <th>g</th>\n",
              "      <th>r</th>\n",
              "      <th>i</th>\n",
              "      <th>z</th>\n",
              "      <th>run</th>\n",
              "      <th>rerun</th>\n",
              "      <th>camcol</th>\n",
              "      <th>field</th>\n",
              "      <th>class</th>\n",
              "      <th>redshift</th>\n",
              "      <th>plate</th>\n",
              "      <th>mjd</th>\n",
              "      <th>fiberid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>183.531326</td>\n",
              "      <td>0.089693</td>\n",
              "      <td>19.47406</td>\n",
              "      <td>17.04240</td>\n",
              "      <td>15.94699</td>\n",
              "      <td>15.50342</td>\n",
              "      <td>15.22531</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>267</td>\n",
              "      <td>STAR</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>183.598370</td>\n",
              "      <td>0.135285</td>\n",
              "      <td>18.66280</td>\n",
              "      <td>17.21449</td>\n",
              "      <td>16.67637</td>\n",
              "      <td>16.48922</td>\n",
              "      <td>16.39150</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>267</td>\n",
              "      <td>STAR</td>\n",
              "      <td>-0.000055</td>\n",
              "      <td>323</td>\n",
              "      <td>51615</td>\n",
              "      <td>541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>183.680207</td>\n",
              "      <td>0.126185</td>\n",
              "      <td>19.38298</td>\n",
              "      <td>18.19169</td>\n",
              "      <td>17.47428</td>\n",
              "      <td>17.08732</td>\n",
              "      <td>16.80125</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>268</td>\n",
              "      <td>GALAXY</td>\n",
              "      <td>0.123111</td>\n",
              "      <td>287</td>\n",
              "      <td>52023</td>\n",
              "      <td>513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>183.870529</td>\n",
              "      <td>0.049911</td>\n",
              "      <td>17.76536</td>\n",
              "      <td>16.60272</td>\n",
              "      <td>16.16116</td>\n",
              "      <td>15.98233</td>\n",
              "      <td>15.90438</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>STAR</td>\n",
              "      <td>-0.000111</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>183.883288</td>\n",
              "      <td>0.102557</td>\n",
              "      <td>17.55025</td>\n",
              "      <td>16.26342</td>\n",
              "      <td>16.43869</td>\n",
              "      <td>16.55492</td>\n",
              "      <td>16.61326</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>STAR</td>\n",
              "      <td>0.000590</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>183.847174</td>\n",
              "      <td>0.173694</td>\n",
              "      <td>19.43133</td>\n",
              "      <td>18.46779</td>\n",
              "      <td>18.16451</td>\n",
              "      <td>18.01475</td>\n",
              "      <td>18.04155</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>STAR</td>\n",
              "      <td>0.000315</td>\n",
              "      <td>324</td>\n",
              "      <td>51666</td>\n",
              "      <td>594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>183.864379</td>\n",
              "      <td>0.019201</td>\n",
              "      <td>19.38322</td>\n",
              "      <td>17.88995</td>\n",
              "      <td>17.10537</td>\n",
              "      <td>16.66393</td>\n",
              "      <td>16.36955</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>GALAXY</td>\n",
              "      <td>0.100242</td>\n",
              "      <td>287</td>\n",
              "      <td>52023</td>\n",
              "      <td>559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>183.900081</td>\n",
              "      <td>0.187473</td>\n",
              "      <td>18.97993</td>\n",
              "      <td>17.84496</td>\n",
              "      <td>17.38022</td>\n",
              "      <td>17.20673</td>\n",
              "      <td>17.07071</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>STAR</td>\n",
              "      <td>0.000315</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>183.924588</td>\n",
              "      <td>0.097246</td>\n",
              "      <td>17.90616</td>\n",
              "      <td>16.97172</td>\n",
              "      <td>16.67541</td>\n",
              "      <td>16.53776</td>\n",
              "      <td>16.47596</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>270</td>\n",
              "      <td>STAR</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>323</td>\n",
              "      <td>51615</td>\n",
              "      <td>595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>183.973498</td>\n",
              "      <td>0.081626</td>\n",
              "      <td>18.67249</td>\n",
              "      <td>17.71375</td>\n",
              "      <td>17.49362</td>\n",
              "      <td>17.28284</td>\n",
              "      <td>17.22644</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>270</td>\n",
              "      <td>GALAXY</td>\n",
              "      <td>0.040508</td>\n",
              "      <td>288</td>\n",
              "      <td>52000</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-663c51e2-26f2-40ac-a847-787e09f623ef')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-663c51e2-26f2-40ac-a847-787e09f623ef 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-663c51e2-26f2-40ac-a847-787e09f623ef');\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",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "source": [
        "# @title g\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "data['g'].plot(kind='line', figsize=(8, 4), title='g')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "Xcnyjh3gc5Ew",
        "outputId": "61b4f90f-59dd-4f05-db12-3a8b33d30ce3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "nBupjC6p_hUL",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.678379Z",
          "iopub.execute_input": "2022-10-27T16:28:19.679196Z",
          "iopub.status.idle": "2022-10-27T16:28:19.689179Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.679146Z",
          "shell.execute_reply": "2022-10-27T16:28:19.687889Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48d24470-cf74-41a8-b69b-9c6e3b46cdff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "id": "Yc3IoRoq_hUL",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.690670Z",
          "iopub.execute_input": "2022-10-27T16:28:19.691039Z",
          "iopub.status.idle": "2022-10-27T16:28:19.766055Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.691008Z",
          "shell.execute_reply": "2022-10-27T16:28:19.765111Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "6beacbeb-8ad2-4e38-dba2-73ebd88091cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 ra           dec             u             g             r  \\\n",
              "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
              "mean     175.529987     14.836148     18.619355     17.371931     16.840963   \n",
              "std       47.783439     25.212207      0.828656      0.945457      1.067764   \n",
              "min        8.235100     -5.382632     12.988970     12.799550     12.431600   \n",
              "25%      157.370946     -0.539035     18.178035     16.815100     16.173333   \n",
              "50%      180.394514      0.404166     18.853095     17.495135     16.858770   \n",
              "75%      201.547279     35.649397     19.259232     18.010145     17.512675   \n",
              "max      260.884382     68.542265     19.599900     19.918970     24.802040   \n",
              "\n",
              "                  i             z           run    rerun        camcol  \\\n",
              "count  10000.000000  10000.000000  10000.000000  10000.0  10000.000000   \n",
              "mean      16.583579     16.422833    981.034800    301.0      3.648700   \n",
              "std        1.141805      1.203188    273.305024      0.0      1.666183   \n",
              "min       11.947210     11.610410    308.000000    301.0      1.000000   \n",
              "25%       15.853705     15.618285    752.000000    301.0      2.000000   \n",
              "50%       16.554985     16.389945    756.000000    301.0      4.000000   \n",
              "75%       17.258550     17.141447   1331.000000    301.0      5.000000   \n",
              "max       28.179630     22.833060   1412.000000    301.0      6.000000   \n",
              "\n",
              "              field      redshift         plate           mjd       fiberid  \n",
              "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000  \n",
              "mean     302.380100      0.143726   1460.986400  52943.533300    353.069400  \n",
              "std      162.577763      0.388774   1788.778371   1511.150651    206.298149  \n",
              "min       11.000000     -0.004136    266.000000  51578.000000      1.000000  \n",
              "25%      184.000000      0.000081    301.000000  51900.000000    186.750000  \n",
              "50%      299.000000      0.042591    441.000000  51997.000000    351.000000  \n",
              "75%      414.000000      0.092579   2559.000000  54468.000000    510.000000  \n",
              "max      768.000000      5.353854   8410.000000  57481.000000   1000.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2169708-46fc-4e59-9958-75cb40cf5c97\" 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>ra</th>\n",
              "      <th>dec</th>\n",
              "      <th>u</th>\n",
              "      <th>g</th>\n",
              "      <th>r</th>\n",
              "      <th>i</th>\n",
              "      <th>z</th>\n",
              "      <th>run</th>\n",
              "      <th>rerun</th>\n",
              "      <th>camcol</th>\n",
              "      <th>field</th>\n",
              "      <th>redshift</th>\n",
              "      <th>plate</th>\n",
              "      <th>mjd</th>\n",
              "      <th>fiberid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>175.529987</td>\n",
              "      <td>14.836148</td>\n",
              "      <td>18.619355</td>\n",
              "      <td>17.371931</td>\n",
              "      <td>16.840963</td>\n",
              "      <td>16.583579</td>\n",
              "      <td>16.422833</td>\n",
              "      <td>981.034800</td>\n",
              "      <td>301.0</td>\n",
              "      <td>3.648700</td>\n",
              "      <td>302.380100</td>\n",
              "      <td>0.143726</td>\n",
              "      <td>1460.986400</td>\n",
              "      <td>52943.533300</td>\n",
              "      <td>353.069400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47.783439</td>\n",
              "      <td>25.212207</td>\n",
              "      <td>0.828656</td>\n",
              "      <td>0.945457</td>\n",
              "      <td>1.067764</td>\n",
              "      <td>1.141805</td>\n",
              "      <td>1.203188</td>\n",
              "      <td>273.305024</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.666183</td>\n",
              "      <td>162.577763</td>\n",
              "      <td>0.388774</td>\n",
              "      <td>1788.778371</td>\n",
              "      <td>1511.150651</td>\n",
              "      <td>206.298149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.235100</td>\n",
              "      <td>-5.382632</td>\n",
              "      <td>12.988970</td>\n",
              "      <td>12.799550</td>\n",
              "      <td>12.431600</td>\n",
              "      <td>11.947210</td>\n",
              "      <td>11.610410</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>301.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>-0.004136</td>\n",
              "      <td>266.000000</td>\n",
              "      <td>51578.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>157.370946</td>\n",
              "      <td>-0.539035</td>\n",
              "      <td>18.178035</td>\n",
              "      <td>16.815100</td>\n",
              "      <td>16.173333</td>\n",
              "      <td>15.853705</td>\n",
              "      <td>15.618285</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>301.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>51900.000000</td>\n",
              "      <td>186.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>180.394514</td>\n",
              "      <td>0.404166</td>\n",
              "      <td>18.853095</td>\n",
              "      <td>17.495135</td>\n",
              "      <td>16.858770</td>\n",
              "      <td>16.554985</td>\n",
              "      <td>16.389945</td>\n",
              "      <td>756.000000</td>\n",
              "      <td>301.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>0.042591</td>\n",
              "      <td>441.000000</td>\n",
              "      <td>51997.000000</td>\n",
              "      <td>351.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>201.547279</td>\n",
              "      <td>35.649397</td>\n",
              "      <td>19.259232</td>\n",
              "      <td>18.010145</td>\n",
              "      <td>17.512675</td>\n",
              "      <td>17.258550</td>\n",
              "      <td>17.141447</td>\n",
              "      <td>1331.000000</td>\n",
              "      <td>301.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>0.092579</td>\n",
              "      <td>2559.000000</td>\n",
              "      <td>54468.000000</td>\n",
              "      <td>510.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>260.884382</td>\n",
              "      <td>68.542265</td>\n",
              "      <td>19.599900</td>\n",
              "      <td>19.918970</td>\n",
              "      <td>24.802040</td>\n",
              "      <td>28.179630</td>\n",
              "      <td>22.833060</td>\n",
              "      <td>1412.000000</td>\n",
              "      <td>301.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>5.353854</td>\n",
              "      <td>8410.000000</td>\n",
              "      <td>57481.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "    (() => {\n",
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              "\n",
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1.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          353.0694,\n          351.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking for null values to determine completeness of the dataset\n",
        "data.info()"
      ],
      "metadata": {
        "id": "iPNVRKoZ_hUL",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.769755Z",
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          "shell.execute_reply.started": "2022-10-27T16:28:19.770126Z",
          "shell.execute_reply": "2022-10-27T16:28:19.793021Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bf67f1a-ac98-4752-c6ba-c4d7105452ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 16 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   ra        10000 non-null  float64\n",
            " 1   dec       10000 non-null  float64\n",
            " 2   u         10000 non-null  float64\n",
            " 3   g         10000 non-null  float64\n",
            " 4   r         10000 non-null  float64\n",
            " 5   i         10000 non-null  float64\n",
            " 6   z         10000 non-null  float64\n",
            " 7   run       10000 non-null  int64  \n",
            " 8   rerun     10000 non-null  int64  \n",
            " 9   camcol    10000 non-null  int64  \n",
            " 10  field     10000 non-null  int64  \n",
            " 11  class     10000 non-null  object \n",
            " 12  redshift  10000 non-null  float64\n",
            " 13  plate     10000 non-null  int64  \n",
            " 14  mjd       10000 non-null  int64  \n",
            " 15  fiberid   10000 non-null  int64  \n",
            "dtypes: float64(8), int64(7), object(1)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No missing data so the dataset is complete with no voids and missing cells"
      ],
      "metadata": {
        "id": "MmNhOWgF_hUM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The Target from data is Data classification to Star Galaxy or Quasar, so the class column has 3 Categories and in this case we need to convert them into numeric data."
      ],
      "metadata": {
        "id": "VZnJSrnu_hUM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sklearn provides a very efficient tool for encoding the levels of categorical features into numeric values. ``` LabelEncoder().fit ``` encodes labels with a value between 0 and n_classes-1 where n is the number of distinct labels. If a label repeats it assigns the same value to as assigned earlier. You can read more about it [here](https://stackoverflow.com/questions/66056695/what-does-labelencoder-fit-do)!\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-6E-vHB6XSWc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder().fit(data['class'])\n",
        "data['class'] = le.transform(data['class'])"
      ],
      "metadata": {
        "id": "CPZDuCZf_hUM",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.796988Z",
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          "shell.execute_reply": "2022-10-27T16:28:19.805405Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shw6TW6_d6eF",
        "outputId": "bccad73d-d196-434f-fcc7-f8f01b42cdae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 16 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   ra        10000 non-null  float64\n",
            " 1   dec       10000 non-null  float64\n",
            " 2   u         10000 non-null  float64\n",
            " 3   g         10000 non-null  float64\n",
            " 4   r         10000 non-null  float64\n",
            " 5   i         10000 non-null  float64\n",
            " 6   z         10000 non-null  float64\n",
            " 7   run       10000 non-null  int64  \n",
            " 8   rerun     10000 non-null  int64  \n",
            " 9   camcol    10000 non-null  int64  \n",
            " 10  field     10000 non-null  int64  \n",
            " 11  class     10000 non-null  int64  \n",
            " 12  redshift  10000 non-null  float64\n",
            " 13  plate     10000 non-null  int64  \n",
            " 14  mjd       10000 non-null  int64  \n",
            " 15  fiberid   10000 non-null  int64  \n",
            "dtypes: float64(8), int64(8)\n",
            "memory usage: 1.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The Final Dataset <a name = 'h4'></a>"
      ],
      "metadata": {
        "id": "pxpyc0xa_hUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Printing the dataset after all the changes to check the dataset\n",
        "data.head(10)"
      ],
      "metadata": {
        "id": "-7wAHUPV_hUM",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:30:45.554797Z",
          "iopub.execute_input": "2022-10-27T16:30:45.555310Z",
          "iopub.status.idle": "2022-10-27T16:30:45.582276Z",
          "shell.execute_reply.started": "2022-10-27T16:30:45.555277Z",
          "shell.execute_reply": "2022-10-27T16:30:45.580815Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "aef26cdd-6ee2-4d7a-dfa6-d5b5e71db131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ra       dec         u         g         r         i         z  \\\n",
              "0  183.531326  0.089693  19.47406  17.04240  15.94699  15.50342  15.22531   \n",
              "1  183.598370  0.135285  18.66280  17.21449  16.67637  16.48922  16.39150   \n",
              "2  183.680207  0.126185  19.38298  18.19169  17.47428  17.08732  16.80125   \n",
              "3  183.870529  0.049911  17.76536  16.60272  16.16116  15.98233  15.90438   \n",
              "4  183.883288  0.102557  17.55025  16.26342  16.43869  16.55492  16.61326   \n",
              "5  183.847174  0.173694  19.43133  18.46779  18.16451  18.01475  18.04155   \n",
              "6  183.864379  0.019201  19.38322  17.88995  17.10537  16.66393  16.36955   \n",
              "7  183.900081  0.187473  18.97993  17.84496  17.38022  17.20673  17.07071   \n",
              "8  183.924588  0.097246  17.90616  16.97172  16.67541  16.53776  16.47596   \n",
              "9  183.973498  0.081626  18.67249  17.71375  17.49362  17.28284  17.22644   \n",
              "\n",
              "   run  rerun  camcol  field  class  redshift  plate    mjd  fiberid  \n",
              "0  752    301       4    267      2 -0.000009   3306  54922      491  \n",
              "1  752    301       4    267      2 -0.000055    323  51615      541  \n",
              "2  752    301       4    268      0  0.123111    287  52023      513  \n",
              "3  752    301       4    269      2 -0.000111   3306  54922      510  \n",
              "4  752    301       4    269      2  0.000590   3306  54922      512  \n",
              "5  752    301       4    269      2  0.000315    324  51666      594  \n",
              "6  752    301       4    269      0  0.100242    287  52023      559  \n",
              "7  752    301       4    269      2  0.000315   3306  54922      515  \n",
              "8  752    301       4    270      2  0.000089    323  51615      595  \n",
              "9  752    301       4    270      0  0.040508    288  52000      400  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ra</th>\n",
              "      <th>dec</th>\n",
              "      <th>u</th>\n",
              "      <th>g</th>\n",
              "      <th>r</th>\n",
              "      <th>i</th>\n",
              "      <th>z</th>\n",
              "      <th>run</th>\n",
              "      <th>rerun</th>\n",
              "      <th>camcol</th>\n",
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              "      <th>redshift</th>\n",
              "      <th>plate</th>\n",
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              "      <th>fiberid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>183.531326</td>\n",
              "      <td>0.089693</td>\n",
              "      <td>19.47406</td>\n",
              "      <td>17.04240</td>\n",
              "      <td>15.94699</td>\n",
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              "      <td>-0.000009</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>491</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>183.598370</td>\n",
              "      <td>0.135285</td>\n",
              "      <td>18.66280</td>\n",
              "      <td>17.21449</td>\n",
              "      <td>16.67637</td>\n",
              "      <td>16.48922</td>\n",
              "      <td>16.39150</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>267</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.000055</td>\n",
              "      <td>323</td>\n",
              "      <td>51615</td>\n",
              "      <td>541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>183.680207</td>\n",
              "      <td>0.126185</td>\n",
              "      <td>19.38298</td>\n",
              "      <td>18.19169</td>\n",
              "      <td>17.47428</td>\n",
              "      <td>17.08732</td>\n",
              "      <td>16.80125</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>268</td>\n",
              "      <td>0</td>\n",
              "      <td>0.123111</td>\n",
              "      <td>287</td>\n",
              "      <td>52023</td>\n",
              "      <td>513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>183.870529</td>\n",
              "      <td>0.049911</td>\n",
              "      <td>17.76536</td>\n",
              "      <td>16.60272</td>\n",
              "      <td>16.16116</td>\n",
              "      <td>15.98233</td>\n",
              "      <td>15.90438</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.000111</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>183.883288</td>\n",
              "      <td>0.102557</td>\n",
              "      <td>17.55025</td>\n",
              "      <td>16.26342</td>\n",
              "      <td>16.43869</td>\n",
              "      <td>16.55492</td>\n",
              "      <td>16.61326</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000590</td>\n",
              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>183.847174</td>\n",
              "      <td>0.173694</td>\n",
              "      <td>19.43133</td>\n",
              "      <td>18.46779</td>\n",
              "      <td>18.16451</td>\n",
              "      <td>18.01475</td>\n",
              "      <td>18.04155</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
              "      <td>269</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000315</td>\n",
              "      <td>324</td>\n",
              "      <td>51666</td>\n",
              "      <td>594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>183.864379</td>\n",
              "      <td>0.019201</td>\n",
              "      <td>19.38322</td>\n",
              "      <td>17.88995</td>\n",
              "      <td>17.10537</td>\n",
              "      <td>16.66393</td>\n",
              "      <td>16.36955</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
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              "      <td>52023</td>\n",
              "      <td>559</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>183.900081</td>\n",
              "      <td>0.187473</td>\n",
              "      <td>18.97993</td>\n",
              "      <td>17.84496</td>\n",
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              "      <td>3306</td>\n",
              "      <td>54922</td>\n",
              "      <td>515</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>183.924588</td>\n",
              "      <td>0.097246</td>\n",
              "      <td>17.90616</td>\n",
              "      <td>16.97172</td>\n",
              "      <td>16.67541</td>\n",
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              "      <td>595</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>183.973498</td>\n",
              "      <td>0.081626</td>\n",
              "      <td>18.67249</td>\n",
              "      <td>17.71375</td>\n",
              "      <td>17.49362</td>\n",
              "      <td>17.28284</td>\n",
              "      <td>17.22644</td>\n",
              "      <td>752</td>\n",
              "      <td>301</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.040508</td>\n",
              "      <td>288</td>\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4153aea6-bfc0-4593-9987-44d1ca1a9bba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"ra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.78343941253631,\n        \"min\": 8.235100497,\n        \"max\": 260.8843818,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          134.6492674,\n          157.0952241,\n          159.6062762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.212206579284373,\n        \"min\": -5.382632499,\n        \"max\": 68.54226541,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          54.38634445,\n          62.59739268,\n          -0.017859983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"u\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8286560094113752,\n        \"min\": 12.98897,\n        \"max\": 19.5999,\n        \"num_unique_values\": 9730,\n        \"samples\": [\n          16.99432,\n          18.27211,\n          19.51903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9454571752688143,\n        \"min\": 12.79955,\n        \"max\": 19.91897,\n        \"num_unique_values\": 9817,\n        \"samples\": [\n          17.88955,\n          18.89235,\n          15.92992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.067764349402935,\n        \"min\": 12.4316,\n        \"max\": 24.80204,\n        \"num_unique_values\": 9852,\n        \"samples\": [\n          17.62294,\n          15.54049,\n          18.64672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"i\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1418048192775498,\n        \"min\": 11.94721,\n        \"max\": 28.17963,\n        \"num_unique_values\": 9890,\n        \"samples\": [\n          17.2252,\n          18.65555,\n          16.67307\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2031880475759795,\n        \"min\": 11.61041,\n        \"max\": 22.83306,\n        \"num_unique_values\": 9896,\n        \"samples\": [\n          17.83397,\n          16.71616,\n          17.0249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"run\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 273,\n        \"min\": 308,\n        \"max\": 1412,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          1345,\n          1332,\n          752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rerun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 301,\n        \"max\": 301,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"camcol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"field\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162,\n        \"min\": 11,\n        \"max\": 768,\n        \"num_unique_values\": 703,\n        \"samples\": [\n          621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"redshift\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3887740358114659,\n        \"min\": -0.004136078,\n        \"max\": 5.353854,\n        \"num_unique_values\": 9637,\n        \"samples\": [\n          0.000189883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"plate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1788,\n        \"min\": 266,\n        \"max\": 8410,\n        \"num_unique_values\": 487,\n        \"samples\": [\n          3848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mjd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1511,\n        \"min\": 51578,\n        \"max\": 57481,\n        \"num_unique_values\": 355,\n        \"samples\": [\n          54862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fiberid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          658\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After lable encoding, Galaxies have been replaced by number 0, Quasars by number 1 and Stars by number 2"
      ],
      "metadata": {
        "id": "YgSLRw1W_hUN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the information about the data\n",
        "data.info()"
      ],
      "metadata": {
        "id": "ky_g5KxB_hUN",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.840811Z",
          "iopub.execute_input": "2022-10-27T16:28:19.841326Z",
          "iopub.status.idle": "2022-10-27T16:28:19.857396Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.841278Z",
          "shell.execute_reply": "2022-10-27T16:28:19.856124Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9e5c7bd-e1d7-4574-eb5d-3136bba6fd09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 16 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   ra        10000 non-null  float64\n",
            " 1   dec       10000 non-null  float64\n",
            " 2   u         10000 non-null  float64\n",
            " 3   g         10000 non-null  float64\n",
            " 4   r         10000 non-null  float64\n",
            " 5   i         10000 non-null  float64\n",
            " 6   z         10000 non-null  float64\n",
            " 7   run       10000 non-null  int64  \n",
            " 8   rerun     10000 non-null  int64  \n",
            " 9   camcol    10000 non-null  int64  \n",
            " 10  field     10000 non-null  int64  \n",
            " 11  class     10000 non-null  int64  \n",
            " 12  redshift  10000 non-null  float64\n",
            " 13  plate     10000 non-null  int64  \n",
            " 14  mjd       10000 non-null  int64  \n",
            " 15  fiberid   10000 non-null  int64  \n",
            "dtypes: float64(8), int64(8)\n",
            "memory usage: 1.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can understand that the dataset is complete with no null values. And since the dataset has only numerical values now, there's no need of further encoding and type conversions."
      ],
      "metadata": {
        "id": "B1WSq5q8KEIr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perform train and test split"
      ],
      "metadata": {
        "id": "5koiMgrm_hUN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('class', axis=1)\n",
        "y = data['class']"
      ],
      "metadata": {
        "id": "qUrXcdCT_hUN",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.859083Z",
          "iopub.execute_input": "2022-10-27T16:28:19.859559Z",
          "iopub.status.idle": "2022-10-27T16:28:19.867694Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.859500Z",
          "shell.execute_reply": "2022-10-27T16:28:19.866383Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Scaling <a name = 'h5'></a>"
      ],
      "metadata": {
        "id": "BMZgRJ1t_hUN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling means converting floating-point feature values from their natural range (for example, 100 to 900) into a standard range—usually 0 and 1 (or sometimes -1 to +1). ``` StandardScaler ``` follows Standard Normal Distribution (SND). Therefore, it makes mean = 0 and scales the data to unit variance. You can read more about other types of scalers [here](https://www.geeksforgeeks.org/standardscaler-minmaxscaler-and-robustscaler-techniques-ml/) and scaling in general [here](https://developers.google.com/machine-learning/data-prep/transform/normalization)."
      ],
      "metadata": {
        "id": "5MpPGkh-XzYy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler(copy=True , with_mean= True , with_std = True)\n"
      ],
      "metadata": {
        "id": "b40vrEGx_hUO",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.868986Z",
          "iopub.execute_input": "2022-10-27T16:28:19.869438Z",
          "iopub.status.idle": "2022-10-27T16:28:19.887766Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.869395Z",
          "shell.execute_reply": "2022-10-27T16:28:19.886479Z"
        },
        "trusted": true
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.fit_transform(X_train) #scaled x_train\n",
        "X_test = scaler.transform(X_test) #scaled y_train"
      ],
      "metadata": {
        "id": "n_KWYRKiRGqr"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Show data\n",
        "X[:20]"
      ],
      "metadata": {
        "id": "XV7_bA0P_hUO",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.889427Z",
          "iopub.execute_input": "2022-10-27T16:28:19.889770Z",
          "iopub.status.idle": "2022-10-27T16:28:19.901064Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.889739Z",
          "shell.execute_reply": "2022-10-27T16:28:19.900083Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8077acfc-a6f4-406c-d6d1-a9e6913f935c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.16745842, -0.58492272,  1.03148637, -0.34855938, -0.83728027,\n",
              "        -0.94605772, -0.99534154, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.21763043, -0.36973112,  1.03148936,  1.30931064,  0.66863177],\n",
              "       [ 0.16886159, -0.58311429,  0.05243046, -0.16653251, -0.15415531,\n",
              "        -0.08264457, -0.02604308, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.21763043, -0.36984929, -0.63621258, -0.87919741,  0.91101156],\n",
              "       [ 0.17057433, -0.58347525,  0.92156796,  0.86709322,  0.59315368,\n",
              "         0.44120145,  0.31452753, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.21147922, -0.05302706, -0.65633905, -0.60919097,  0.77527888],\n",
              "       [ 0.17455754, -0.58650069, -1.03063038, -0.81362749, -0.63669227,\n",
              "        -0.52660429, -0.43092107, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.20532801, -0.36999261,  1.03148936,  1.30931064,  0.76073609],\n",
              "       [ 0.17482457, -0.58441247, -1.29023238, -1.17251944, -0.37676237,\n",
              "        -0.02510121,  0.15827647, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.20532801, -0.36818949,  1.03148936,  1.30931064,  0.77043128],\n",
              "       [ 0.17406874, -0.58159078,  0.97991837,  1.15913585,  1.2396114 ,\n",
              "         1.25349122,  1.34542376, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.20532801, -0.36889881, -0.63565351, -0.8454466 ,  1.16793412],\n",
              "       [ 0.17442882, -0.58771882,  0.9218576 ,  0.54793007,  0.24763882,\n",
              "         0.07037522, -0.0442872 , -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.20532801, -0.11185311, -0.65633905, -0.60919097,  0.99826828],\n",
              "       [ 0.17517603, -0.58104423,  0.43515363,  0.50034225,  0.50505868,\n",
              "         0.54578672,  0.53849374, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.20532801, -0.36889818,  1.03148936,  1.30931064,  0.78497407],\n",
              "       [ 0.17568894, -0.58462313, -0.8607082 , -0.4233206 , -0.15505443,\n",
              "        -0.0401308 ,  0.04415727, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.1991768 , -0.36947888, -0.63621258, -0.87919741,  1.17278172],\n",
              "       [ 0.17671255, -0.58524271,  0.06412468,  0.36155588,  0.61126719,\n",
              "         0.61244768,  0.66793136, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.1991768 , -0.26550833, -0.65577999, -0.62441192,  0.22750057],\n",
              "       [ 0.17683178, -0.58308602,  0.81867332,  0.45518723,  0.32002733,\n",
              "         0.29758829,  0.31289013, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.1991768 , -0.36979707,  1.03148936,  1.30931064,  0.74134571],\n",
              "       [ 0.17905309, -0.58403354,  0.25791803,  0.15434418,  0.09531295,\n",
              "         0.1143867 ,  0.15158558, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.19302559, -0.3681061 ,  1.03148936,  1.30931064,  0.94009713],\n",
              "       [ 0.179404  , -0.58088408,  1.13821874,  0.86650088,  0.76483872,\n",
              "         0.78139043,  0.80556427, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.19302559, -0.36956736,  1.03148936,  1.30931064,  0.92555434],\n",
              "       [ 0.18062649, -0.58547992,  1.15921765,  0.37678737,  0.13715012,\n",
              "         0.08511578,  0.06767929, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.19302559, -0.36968672,  1.03148936,  1.30931064,  0.93524953],\n",
              "       [ 0.18123476, -0.58453441,  0.76913279,  0.18696495, -0.19217122,\n",
              "        -0.38043364, -0.54561378, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.19302559, -0.18427654, -0.65577999, -0.62441192,  0.17417702],\n",
              "       [ 0.18460582, -0.58026059,  0.14357054,  1.30915544,  1.45732014,\n",
              "         1.51360998,  1.2914644 , -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.18687438,  0.3297998 , -0.65633905, -0.60919097,  1.13400095],\n",
              "       [ 0.18190916, -0.58661793,  0.53127794,  0.76494705,  1.01778171,\n",
              "         1.1465937 ,  1.2341887 , -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.18687438, -0.36916795, -0.63565351, -0.8454466 , -1.54187183],\n",
              "       [ 0.18240867, -0.58061649,  0.72660397,  2.04202274,  2.15158106,\n",
              "         2.21584041,  2.33699748, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.18687438,  2.66073327, -0.65633905, -0.60919097,  1.11461057],\n",
              "       [ 0.18540578, -0.58577178,  0.51243926,  0.14716211,  0.03033282,\n",
              "         0.02413909,  0.05003362, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.18072317, -0.36909046, -0.63621258, -0.87919741,  1.33275238],\n",
              "       [ 0.18523939, -0.58156585, -0.96878016, -0.531168  , -0.28296358,\n",
              "        -0.33967158, -0.27939936, -0.83806089,  0.        ,  0.21085172,\n",
              "        -0.18072317, -0.18251527, -0.65633905, -0.60919097,  1.35214276]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the 30% test and 70% train split here\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=128)"
      ],
      "metadata": {
        "id": "62nGQzWj_hUO",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.902402Z",
          "iopub.execute_input": "2022-10-27T16:28:19.902762Z",
          "iopub.status.idle": "2022-10-27T16:28:19.913715Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.902731Z",
          "shell.execute_reply": "2022-10-27T16:28:19.912767Z"
        },
        "trusted": true
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Density Distribution Plots"
      ],
      "metadata": {
        "id": "bjCfuU7R_hUO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the number of labels for each class of the dataset where 0 = Galaxy, 1 = Quasar, 2 = Star.\n",
        "sns.countplot(x=data['class'])"
      ],
      "metadata": {
        "id": "raHk3kFI_hUO",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:19.914994Z",
          "iopub.execute_input": "2022-10-27T16:28:19.915523Z",
          "iopub.status.idle": "2022-10-27T16:28:20.173987Z",
          "shell.execute_reply.started": "2022-10-27T16:28:19.915491Z",
          "shell.execute_reply": "2022-10-27T16:28:20.172636Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "18f0a763-8c1c-404c-862c-fb896c81c215"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='class', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Some information about the filters used while gathering the data:\n",
        "\"U\" stands for ultraviolet.\n",
        "\"G\" stands for green.\n",
        "\"R\" stands for red.\n",
        "\"I\" stands for infrared."
      ],
      "metadata": {
        "id": "0fXyxPXY_hUP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Using pairplots to establish and understand interdependancy of train features\n",
        "sns.pairplot(data[['u','g','r','i','class']])"
      ],
      "metadata": {
        "id": "27g_H5kH_hUP",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:20.175315Z",
          "iopub.execute_input": "2022-10-27T16:28:20.175714Z",
          "iopub.status.idle": "2022-10-27T16:28:26.735266Z",
          "shell.execute_reply.started": "2022-10-27T16:28:20.175682Z",
          "shell.execute_reply": "2022-10-27T16:28:26.734160Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4a841847-5780-43df-b597-aa43cb3a401a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7a9513630040>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x1250 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Machine Learning models (Classification models) <a name = 'h6'></a>"
      ],
      "metadata": {
        "id": "eD_MM_BW_hUP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decision tree classifier -\n",
        "\n",
        "<p align=\"justify\">The decision of making strategic splits heavily affects a tree’s accuracy. The decision criteria are different for classification and regression trees.</p>\n",
        "\n",
        "<p align=\"justify\">Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In other words, we can say that the purity of the node increases with respect to the target variable. The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes.</a>\n",
        "\n",
        "<br> <img src=\"https://miro.medium.com/max/688/1*bcLAJfWN2GpVQNTVOCrrvw.png\" width = 50%>\n",
        "\n",
        "<br> The algorithm selection is also based on the type of target variables. Let us look at some algorithms used in Decision Trees:\n",
        "\n",
        "1. ID3 → (extension of D3)\n",
        "\n",
        "2. C4.5 → (successor of ID3)\n",
        "\n",
        "3. CART → (Classification And Regression Tree)\n",
        "\n",
        "4. CHAID → (Chi-square automatic interaction detection Performs multi-level splits when computing classification trees)\n",
        "\n",
        "5. MARS → (multivariate adaptive regression splines)\n",
        "\n",
        "<p align=\"justify\">To understand these, let's have a look at the most primitive of these - ID3.\n",
        "\n",
        "[Link for the example.](https://towardsdatascience.com/decision-trees-for-classification-id3-algorithm-explained-89df76e72df1)\n",
        "\n",
        "You can read further about the methods [here](https://www.kdnuggets.com/2020/01/decision-tree-algorithm-explained.html).  "
      ],
      "metadata": {
        "id": "5iZDfFuIacDx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's consider this example -\n",
        "\n",
        "![Screenshot 2022-12-23 at 12.21.52 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)"
      ],
      "metadata": {
        "id": "CB7wyB2PLibQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The initial node is called the <b>root</b> node (colored in blue), the final nodes are called the <b>leaf</b> nodes (colored in green) and the rest of the nodes are called <b>intermediate</b> or <b>internal</b> nodes.\n",
        "The root and intermediate nodes represent the decisions while the leaf nodes represent the outcomes.\n",
        "\n",
        "ID3 stands for Iterative Dichotomiser 3 and is named such because the algorithm iteratively (repeatedly) dichotomizes(divides) features into two or more groups at each step.\n",
        "Invented by Ross Quinlan, ID3 uses a top-down greedy approach to build a decision tree. In simple words, the top-down approach means that we start building the tree from the top and the greedy approach means that at each iteration we select the best feature at the present moment to create a node.\n",
        "<b>Most generally ID3 is only used for classification problems with nominal features only.</b>\n",
        "\n",
        "ID3 uses Information Gain or just Gain to find the best feature. Information Gain calculates the reduction in the entropy and measures how well a given feature separates or classifies the target classes. The feature with the highest Information Gain is selected as the best one.\n",
        "\n",
        "In simple words, Entropy is the measure of disorder and the Entropy of a dataset is the measure of disorder in the target feature of the dataset.\n",
        "In the case of binary classification (where the target column has only two types of classes) entropy is 0 if all values in the target column are homogenous(similar) and will be 1 if the target column has equal number values for both the classes.\n",
        "\n",
        "ID3 Steps -\n",
        "\n",
        "1. Calculate the Information Gain of each feature.\n",
        "2. Considering that all rows don’t belong to the same class, split the dataset S into subsets using the feature for which the Information Gain is maximum.\n",
        "3. Make a decision tree node using the feature with the maximum Information gain.\n",
        "4. If all rows belong to the same class, make the current node as a leaf node with the class as its label.\n",
        "5. Repeat for the remaining features until we run out of all features, or the decision tree has all leaf nodes.\n",
        "\n",
        "We denote our dataset as S, entropy is calculated as -\n",
        "![Screenshot 2022-12-23 at 12.29.43 AM.png](data:image/png;base64,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)\n",
        "Where n is the total number of classes in the target column;\n",
        "pᵢ is the probability of class ‘i’ or the ratio of “number of rows with class i in the target column” to the “total number of rows” in the dataset.\n",
        "\n",
        "Information Gain for a feature column A is calculated as -\n",
        "\n",
        "\n",
        "![Screenshot 2022-12-23 at 12.29.52 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)\n",
        "\n",
        "where Sᵥ is the set of rows in S for which the feature column A has value v, |Sᵥ| is the number of rows in Sᵥ and likewise |S| is the number of rows in S."
      ],
      "metadata": {
        "id": "qGjYvpBgMQ8v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree Classifier\n",
        "dtClassifer = DecisionTreeClassifier(max_leaf_nodes=15,max_depth=3)"
      ],
      "metadata": {
        "id": "kSrNZ-zE_hUP",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:26.736376Z",
          "iopub.execute_input": "2022-10-27T16:28:26.737133Z",
          "iopub.status.idle": "2022-10-27T16:28:26.740798Z",
          "shell.execute_reply.started": "2022-10-27T16:28:26.737098Z",
          "shell.execute_reply": "2022-10-27T16:28:26.740043Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear regression classifier -\n",
        "\n",
        "<p align=\"justify\">Machine learning generally involves predicting a quantitative outcome or a qualitative class. The former is commonly referred to as a regression problem. In the scenario of linear regression, the input is a continuous variable, and the prediction is a numerical value. When predicting a qualitative outcome (class), the task is considered a classification problem. Examples of classification problems include predicting what products a user will buy or if a target user will click on an online advertisement.\n",
        "\n",
        "<p align=\"justify\">Not all algorithms fit cleanly into this simple dichotomy, though, and logistic regression is a notable example. Logistic regression is part of the regression family as it involves predicting outcomes based on quantitative relationships between variables. However, unlike linear regression, it accepts both continuous and discrete variables as input and its output is qualitative. In addition, it predicts a discrete class such as “Yes/No” or “Customer/Non-customer”.\n",
        "\n",
        "<br> <img src = \"https://www.kdnuggets.com/wp-content/uploads/jessica_logistic_regression_work_1.jpg\" width = 30%>\n",
        "\n",
        "<br> <p align=\"justify\">In practice, the logistic regression algorithm analyzes relationships between variables. It assigns probabilities to discrete outcomes using the Sigmoid function, which converts numerical results into an expression of probability between 0 and 1.0. Probability is either 0 or 1, depending on whether the event happens or not. For binary predictions, you can divide the population into two groups with a cut-off of 0.5. Everything above 0.5 is considered to belong to group A, and everything below is considered to belong to group B.\n",
        "\n",
        "You can read further about them [here](https://www.kdnuggets.com/2022/07/logistic-regression-work.html#:~:text=is%20Logistic%20Regression%3F-,Logistic%20regression%20is%20a%20Machine%20Learning%20classification%20algorithm%20that%20is,the%20logistic%20of%20the%20result.)"
      ],
      "metadata": {
        "id": "yQzltN-VcIla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Linear Classifier - Logistic Regression\n",
        "LRClassifer = LogisticRegression()"
      ],
      "metadata": {
        "id": "CCetNq5uaeiY",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:28:26.741962Z",
          "iopub.execute_input": "2022-10-27T16:28:26.742436Z",
          "iopub.status.idle": "2022-10-27T16:28:26.754580Z",
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        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN classifier -\n",
        "\n",
        "The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other.\n",
        "\n",
        "“Birds of a feather flock together.”\n",
        "\n",
        "<br> <img src=\"https://miro.medium.com/max/640/1*wW8O-0xVQUFhBGexx2B6hg.png\" width = 40%>\n",
        "\n",
        "<br> <p align=\"justify\">Image showing how similar data points typically exist close to each other\n",
        "Notice in the image above that most of the time, similar data points are close to each other. The KNN algorithm hinges on this assumption being true enough for the algorithm to be useful. KNN captures the idea of similarity (sometimes called distance, proximity, or closeness) with some mathematics we might have learned in our childhood— calculating the distance between points on a graph.\n",
        "\n",
        "We have explained KNN in much more detail in our previous project on [Hunting for Exoplanets](https://www.youtube.com/watch?v=y1k2jc3YTeg)."
      ],
      "metadata": {
        "id": "GrPCwmT2awVl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Nearest Neighbor Classifier\n",
        "NeNeClassifier = KNeighborsClassifier(n_neighbors=3)"
      ],
      "metadata": {
        "id": "855TL3cJaf34",
        "execution": {
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        "trusted": true
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitting the models to the dataset\n",
        "dtClassifer.fit(X_train, y_train)\n",
        "\n",
        "LRClassifer.fit(X_train, y_train)\n",
        "\n",
        "NeNeClassifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "if8_Pa1c_hUQ",
        "execution": {
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          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "db9436aa-9322-4f95-d3df-a8a983f023a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the prediction set of the models\n",
        "y_preds = dtClassifer.predict(X_test)\n",
        "y_predsLR = LRClassifer.predict(X_test)\n",
        "y_predsNeNe = NeNeClassifier.predict(X_test)"
      ],
      "metadata": {
        "id": "JW8VDuNA_hUQ",
        "execution": {
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        "trusted": true
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the last 10 predictions on the test split as output by all the models\n",
        "\n",
        "print(y_preds[:10],'\\n',y_test[:10])\n",
        "\n",
        "print(\"\\n*******************************************************\")\n",
        "print(y_predsLR[:10],'\\n',y_test[:10])\n",
        "\n",
        "print(\"\\n*******************************************************\")\n",
        "print(y_predsNeNe[:10],'\\n',y_test[:10])"
      ],
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        "id": "b0_Zjb21_hUQ",
        "execution": {
          "iopub.status.busy": "2022-10-27T16:30:16.049882Z",
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      },
      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 0 2 2 0 0 2 1 0 0] \n",
            " 7054    2\n",
            "4692    0\n",
            "907     2\n",
            "4498    2\n",
            "9957    0\n",
            "3341    0\n",
            "7152    2\n",
            "1152    1\n",
            "7105    0\n",
            "2066    0\n",
            "Name: class, dtype: int64\n",
            "\n",
            "*******************************************************\n",
            "[2 0 2 2 0 0 2 1 0 0] \n",
            " 7054    2\n",
            "4692    0\n",
            "907     2\n",
            "4498    2\n",
            "9957    0\n",
            "3341    0\n",
            "7152    2\n",
            "1152    1\n",
            "7105    0\n",
            "2066    0\n",
            "Name: class, dtype: int64\n",
            "\n",
            "*******************************************************\n",
            "[2 0 2 2 0 0 2 1 0 0] \n",
            " 7054    2\n",
            "4692    0\n",
            "907     2\n",
            "4498    2\n",
            "9957    0\n",
            "3341    0\n",
            "7152    2\n",
            "1152    1\n",
            "7105    0\n",
            "2066    0\n",
            "Name: class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification Report"
      ],
      "metadata": {
        "id": "ADlkpZ2ZlJcc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "target_names = ['0', '1', '2']\n",
        "\n",
        "print('\\033[1m  Decision Tree -\\n  \\033[0m',classification_report(y_preds,y_test,target_names=target_names) + '\\n')\n",
        "print('\\033[1m  Linear Regression -\\n  \\033[0m',classification_report(y_predsLR,y_test,target_names=target_names) + '\\n')\n",
        "print(\"\\033[1m  KNN Classifier -\\n  \\033[0m\",classification_report(y_predsNeNe,y_test,target_names=target_names) + '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEq1-dwLbYws",
        "outputId": "d1e483ba-013a-462f-baeb-f71b9d653a0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m  Decision Tree -\n",
            "  \u001b[0m               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      1504\n",
            "           1       0.94      0.98      0.96       255\n",
            "           2       1.00      1.00      1.00      1241\n",
            "\n",
            "    accuracy                           0.99      3000\n",
            "   macro avg       0.98      0.99      0.98      3000\n",
            "weighted avg       0.99      0.99      0.99      3000\n",
            "\n",
            "\n",
            "\u001b[1m  Linear Regression -\n",
            "  \u001b[0m               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98      1469\n",
            "           1       0.94      0.97      0.96       258\n",
            "           2       0.99      0.96      0.98      1273\n",
            "\n",
            "    accuracy                           0.98      3000\n",
            "   macro avg       0.97      0.97      0.97      3000\n",
            "weighted avg       0.98      0.98      0.98      3000\n",
            "\n",
            "\n",
            "\u001b[1m  KNN Classifier -\n",
            "  \u001b[0m               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.89      0.91      1575\n",
            "           1       0.88      0.97      0.92       240\n",
            "           2       0.88      0.92      0.90      1185\n",
            "\n",
            "    accuracy                           0.91      3000\n",
            "   macro avg       0.90      0.93      0.91      3000\n",
            "weighted avg       0.91      0.91      0.91      3000\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Don't Forget!\n",
        "\n",
        "* Make sure you have handled the data leakage as Rohan had explained in Module 2, Video 3.\n",
        "* In case if you are solving the data leakage part now, re-run the notebook and take a look at the Classification Report.\n",
        "* Try to change some hyperparameters of ML Models to see if the model is improving the performance.\n"
      ],
      "metadata": {
        "id": "g-MaNgk_oGUO"
      }
    }
  ]
}