AI1703_(week7).ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/thanhtruongphan/HomeworkAI/blob/main/AI1703_(week7).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1 biến "
      ],
      "metadata": {
        "id": "Od186RVwOoqz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Tiền Xử lý"
      ],
      "metadata": {
        "id": "enoovtNFHe0L"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Rb2lSyh7Gdqm"
      },
      "outputs": [],
      "source": [
        "import pandas as pd "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url_mt = '/content/drive/MyDrive/A_HOC TAP/Nam 4, Hoc ky 2/Artificial Intelligence/Code_AI/files/mtcars.csv'\n",
        "data_mt = pd.read_csv(url_mt)\n",
        "data_mt.head()"
      ],
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          "height": 206
        },
        "id": "rt7G8RtZGx8U",
        "outputId": "58d392ed-a414-47f6-bd7c-7b7806fd9fe3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Unnamed: 0   mpg   disp   hp     wt\n",
              "0          Mazda RX4  21.0  160.0  110  2.620\n",
              "1      Mazda RX4 Wag  21.0  160.0  110  2.875\n",
              "2         Datsun 710  22.8  108.0   93  2.320\n",
              "3     Hornet 4 Drive  21.4  258.0  110  3.215\n",
              "4  Hornet Sportabout  18.7  360.0  175  3.440"
            ],
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>mpg</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mazda RX4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>2.620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mazda RX4 Wag</td>\n",
              "      <td>21.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>2.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Datsun 710</td>\n",
              "      <td>22.8</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93</td>\n",
              "      <td>2.320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hornet 4 Drive</td>\n",
              "      <td>21.4</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hornet Sportabout</td>\n",
              "      <td>18.7</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.440</td>\n",
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "data_mt.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "d7a6e9ec-a596-4ced-ee90-e42dee6cae1a"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/UntitledFolder')"
      ],
      "metadata": {
        "id": "woAoW15BHMxP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_mt = data_mt.corr()\n",
        "print(correlation_mt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9J5W_TAHayx",
        "outputId": "191d2840-8698-4c54-d3d7-38f0d7e4a773"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           mpg      disp        hp        wt\n",
            "mpg   1.000000 -0.847551 -0.776168 -0.867659\n",
            "disp -0.847551  1.000000  0.790949  0.887980\n",
            "hp   -0.776168  0.790949  1.000000  0.658748\n",
            "wt   -0.867659  0.887980  0.658748  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "sns.heatmap(correlation_mt, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "MXWzEmJ3H4xV",
        "outputId": "1823deeb-395f-4540-9e13-9f72f71e91cb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qNubdsxrKKl0"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data_mt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "6fsWtxiSJZ_0",
        "outputId": "cf4d1a56-8360-473f-9fa6-8cb325bb77e1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f7ec8534730>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "no7hWj1AJg_M"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Linear Bậc 1"
      ],
      "metadata": {
        "id": "iqfAMN1cH2TW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1 import library \n",
        "from sklearn import linear_model"
      ],
      "metadata": {
        "id": "oPJWwlTzKLlL"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2 fit-model, find intercept & coef\n",
        "model = linear_model.LinearRegression()\n",
        "model.fit(X=pd.DataFrame(data_mt['wt']),y= data_mt['mpg'])\n",
        "print(\"He so intercept la : \", model.intercept_)\n",
        "print(\"He so coef la : \", model.coef_)"
      ],
      "metadata": {
        "id": "If92E7dgKSsQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3871e2c1-a767-4787-bed3-b1c24ba3156a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "He so intercept la :  37.28512616734204\n",
            "He so coef la :  [-5.34447157]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "mpg = -5.34wt + 37.285"
      ],
      "metadata": {
        "id": "9u2KSO-HLVOJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#3 kiem tra do chinh xac\n",
        "model.score(X=pd.DataFrame(data_mt['wt']),y= data_mt['mpg'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4-9cjAbLPjp",
        "outputId": "3e78ae6b-fd7c-4c28-d2ad-759175ad0c70"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7528327936582646"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4 ve do thi \n",
        "y_pred = model.predict(X=pd.DataFrame(data_mt['wt']))\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(data_mt['wt'],y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "J-wioYrTNJCw",
        "outputId": "ba8522f7-d5ee-46fe-9d80-a1baafafaa92"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec67b0340>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5xBVaveYNkZh"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 2"
      ],
      "metadata": {
        "id": "YLMK_SlBO9cF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#2 fit \n",
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],\n",
        "                data_mt['wt']**2]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNTbdGJXO9xH",
        "outputId": "31f5c25b-009c-4af6-b7cf-a0c7586db009"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  49.93081094945181\n",
            "Coef cua mo hinh la :  [-13.38033708   1.17108689]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3 score\n",
        "print('Do chinh xac cua mo hinh la : ',model.score(X,y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrOZeWe5PjnW",
        "outputId": "ec63802f-bf81-4861-c006-baf4c62626b4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Do chinh xac cua mo hinh la :  0.8190613581384094\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "mpg = -13.38wt + 1.1wt^2 + 49.93"
      ],
      "metadata": {
        "id": "rykv7KZhRUpP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zZH_2tzWPry7"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#4 plot \n",
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range = pd.DataFrame([range,range**2]).T\n",
        "y_pred = model.predict(X=X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "l32XKC70Qv9F",
        "outputId": "c57dd103-c949-4de2-963c-48e6e7a89dd9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ed83886d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F8AGJc-HSDRP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P6uYVrs3SXGj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###bậc 3"
      ],
      "metadata": {
        "id": "uBQkgfiQS6hx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCHFHmAMTDLF",
        "outputId": "6e6dc634-ad0e-4a0d-a0d8-16f596a39bf4"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  48.40369622795221\n",
            "Coef cua mo hinh la :  [-11.82597602   0.68937919   0.04593618]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NPw4W3gTLo0",
        "outputId": "79fa7fbf-1d98-4e6d-9c9a-3f2f60236d43"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8191284628478992"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "id": "_Um2IUwsXnpc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "outputId": "08efce6f-3bb8-443c-b328-750cf627b177"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3d01e80>]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 4"
      ],
      "metadata": {
        "id": "MQg9T5M9STkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEvJX7dJST-f",
        "outputId": "7d0e85cb-fb39-4355-ff38-ae2d2d1cde85"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  14.455833054796258\n",
            "Coef cua mo hinh la :  [ 36.61945631 -23.70175729   5.20042113  -0.38627878]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6h6E5o_SUEd",
        "outputId": "6a6cd8f8-b292-4225-e252-a360fba7353b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8219709873435187"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "3ksJuNLuSUHR",
        "outputId": "d82662d7-624e-4f57-a3d7-5f8aedc2de9c"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3ce46d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zjO0I_pRSUZp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 5"
      ],
      "metadata": {
        "id": "I4-pgOPiSsPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NAawCVnZSsmb",
        "outputId": "d82e92fb-6b7b-4b3f-cdc7-27a21117f2f9"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -87.71816314301019\n",
            "Coef cua mo hinh la :  [ 218.88967608 -147.77842682   45.60934841   -6.70138798    0.37968337]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpEBYfD6Sssu",
        "outputId": "3d5aa853-a50d-4fbb-d4b1-2b2e0bffb18e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8251088272653132"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "YuFIhYVoSs5M",
        "outputId": "dfe4a19e-df25-4d8b-a508-79485809b5fc"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec61510d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 7 "
      ],
      "metadata": {
        "id": "YBTzfod7TIeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFtUKQUmTIxK",
        "outputId": "ad8e8ac3-4215-47e5-c4e7-061da1a073d3"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -2387.286256135588\n",
            "Coef cua mo hinh la :  [ 5.71347281e+03 -5.57680321e+03  2.92547757e+03 -8.93984147e+02\n",
            "  1.59440329e+02 -1.53893379e+01  6.21028572e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vm-PRE2FTI5T",
        "outputId": "2fbb52e4-ccaa-459e-e4cc-fba6982785fd"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8380739929060077"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "azjSSYiBTJD8",
        "outputId": "79792792-17c6-4ba7-f8a0-459bd9fe285f"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3a47160>]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gV3ixhAeTJNi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 9"
      ],
      "metadata": {
        "id": "3iNWzEJ_TbuH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0fGJV2xTcAE",
        "outputId": "1f5ea601-59b5-4b54-fddb-351dbc231e80"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  38889.610890257565\n",
            "Coef cua mo hinh la :  [-1.25317627e+05  1.74784059e+05 -1.38423294e+05  6.86522656e+04\n",
            " -2.21343938e+04  4.64432999e+03 -6.12230173e+02  4.60606389e+01\n",
            " -1.50849985e+00]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8948K1HTcJk",
        "outputId": "b8680ead-103a-468a-9161-4a6f1e616452"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8646786785489688"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "Q6RxJvYFTcTh",
        "outputId": "009ee028-4510-4a77-8c70-8112e33a91a3"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3d42df0>]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 13 "
      ],
      "metadata": {
        "id": "K_eDkIq2VT7h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9,data_mt['wt']**10,\n",
        "                data_mt['wt']**11,data_mt['wt']**12,\n",
        "                data_mt['wt']**13]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sxvr3AARVUT5",
        "outputId": "649afd0f-8c3e-4b43-b55c-b8ac3ebad06c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -17924.755432355403\n",
            "Coef cua mo hinh la :  [ 3.71870708e+04 -1.11935404e+04 -2.73527312e+04  1.74923731e+04\n",
            "  1.94582373e+04 -3.37840508e+04  2.30525606e+04 -9.32336224e+03\n",
            "  2.44117997e+03 -4.19739539e+02  4.59106860e+01 -2.90313644e+00\n",
            "  8.08994091e-02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWaBrh0dVUd1",
        "outputId": "78f11437-4d67-48ed-802c-d634c54fff1b"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8784498973119335"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sWsEJYunVUzM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZXO0qG3QTcjs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 14 "
      ],
      "metadata": {
        "id": "S8TDrXKZVtA9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9,data_mt['wt']**10,\n",
        "                data_mt['wt']**11,data_mt['wt']**12,\n",
        "                data_mt['wt']**13,data_mt['wt']**14]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQSFWwStVtQ4",
        "outputId": "68da16d8-75f6-464f-eea3-24f0b0fa388e"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -4739.037068068236\n",
            "Coef cua mo hinh la :  [ 6.82294357e+03  1.71186897e+03 -4.96439227e+03 -2.63872627e+03\n",
            "  4.80831363e+03  1.77759415e+03 -6.21675245e+03  4.96758258e+03\n",
            " -2.18465110e+03  6.04127905e+02 -1.08023874e+02  1.21722270e+01\n",
            " -7.88002041e-01  2.23827146e-02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ap0JYZ0EVtaO",
        "outputId": "b547b764-64cd-4569-9717-636a5806715a"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8790911067088647"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 15 (độ chính xác cao nhất)"
      ],
      "metadata": {
        "id": "JHSKz-PjVZ5w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9,data_mt['wt']**10,\n",
        "                data_mt['wt']**11,data_mt['wt']**12,\n",
        "                data_mt['wt']**13,data_mt['wt']**14,\n",
        "                data_mt['wt']**15]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-_xH_tJT-aM",
        "outputId": "5a741335-51e9-43c2-de8f-0680ad580b41"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -1183.775900530815\n",
            "Coef cua mo hinh la :  [ 1.15482832e+03  8.26985531e+02 -3.77299068e+02 -9.90227356e+02\n",
            " -8.54738725e+01  1.01497570e+03  5.84542984e+01 -1.14654333e+03\n",
            "  1.05398762e+03 -4.97390219e+02  1.43996618e+02 -2.66141482e+01\n",
            "  3.07575388e+00 -2.03158530e-01  5.86600122e-03]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tGV8QsOUk3J",
        "outputId": "697ff600-ef4d-4fb3-e1ce-772903e2de11"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8793156609915943"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "_6hzDvG_hIev",
        "outputId": "033ff23d-5a5d-426f-c308-517a9a9a923e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec6151ee0>]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 16 "
      ],
      "metadata": {
        "id": "cgcrd8ozUgBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9,data_mt['wt']**10,\n",
        "                data_mt['wt']**11,data_mt['wt']**12,\n",
        "                data_mt['wt']**13,data_mt['wt']**14,\n",
        "                data_mt['wt']**15,data_mt['wt']**16]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQGRKQUBUgVD",
        "outputId": "b5a47810-2331-4445-abb5-444732bcdb42"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -244.76206092834474\n",
            "Coef cua mo hinh la :  [ 1.66697176e+02  1.84883359e+02  4.71881524e+01 -1.29217347e+02\n",
            " -1.50987656e+02  4.14330624e+01  1.81474022e+02 -3.02010903e+01\n",
            " -1.96632989e+02  2.06534321e+02 -1.04036722e+02  3.14494423e+01\n",
            " -6.00125063e+00  7.11063296e-01 -4.79218772e-02  1.40690300e-03]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QvSRy58Ugcc",
        "outputId": "34e99e1c-4e4f-45fc-fe2d-8b873d98c7b4"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8792804976423304"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15,range**16]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "w0NjROoHUgoB",
        "outputId": "0b739783-68fb-4bf4-a949-1be9796f8357"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3c4e280>]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T6EDujI7UhMP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 17 "
      ],
      "metadata": {
        "id": "G0BeZw5DU4Z4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9,data_mt['wt']**10,\n",
        "                data_mt['wt']**11,data_mt['wt']**12,\n",
        "                data_mt['wt']**13,data_mt['wt']**14,\n",
        "                data_mt['wt']**15,data_mt['wt']**16,\n",
        "                data_mt['wt']**17]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izAU2wiVU4r6",
        "outputId": "a535e960-e573-4be9-bdc8-c587f74f7ac4"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  -11.611911009252069\n",
            "Coef cua mo hinh la :  [ 1.54381067e+01  2.20725925e+01  1.47153635e+01 -3.86140972e+00\n",
            " -1.93787134e+01 -1.37852299e+01  1.18174525e+01  2.21809235e+01\n",
            " -9.75635640e+00 -2.45552294e+01  3.08826949e+01 -1.69836656e+01\n",
            "  5.45244855e+00 -1.08915227e+00  1.33841079e-01 -9.29349401e-03\n",
            "  2.79714508e-04]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FWBXFaUU4xp",
        "outputId": "06b2f431-784c-496b-c421-a370f64e8589"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8791597510790989"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15,range**16,range**17]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "u9slP2GHU42g",
        "outputId": "e0d871ac-7984-4a4c-8f78-c4954789c6f0"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3add4f0>]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P42gMO4mU5P6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Bậc 18 "
      ],
      "metadata": {
        "id": "0rRsKy5HT-YP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data_mt['wt'],data_mt['wt']**2,\n",
        "                data_mt['wt']**3,data_mt['wt']**4,\n",
        "                data_mt['wt']**5,data_mt['wt']**6,\n",
        "                data_mt['wt']**7,data_mt['wt']**8,\n",
        "                data_mt['wt']**9,data_mt['wt']**10,\n",
        "                data_mt['wt']**11,data_mt['wt']**12,\n",
        "                data_mt['wt']**13,data_mt['wt']**14,\n",
        "                data_mt['wt']**15,data_mt['wt']**16,\n",
        "                data_mt['wt']**17,data_mt['wt']**18]).T \n",
        "y=data_mt['mpg']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9jut9ymT-2m",
        "outputId": "3eb1cd96-1d09-4007-c946-de76debe18a7"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  34.726606567204\n",
            "Coef cua mo hinh la :  [-1.03103438e+00 -1.94821472e+00 -2.29932638e+00 -1.67153927e+00\n",
            " -8.82124409e-02  1.61855008e+00  2.01024013e+00  3.66820514e-01\n",
            " -1.63735045e+00 -9.94707976e-01  1.31124220e+00  1.08562195e-01\n",
            " -5.76894885e-01  3.07182871e-01 -8.07199462e-02  1.18705449e-02\n",
            " -9.37328843e-04  3.10852301e-05]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaE7kMxUT_Gq",
        "outputId": "7a0af1b3-4ba9-48eb-b87a-9d928cdcdb2b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8790910914795742"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(1.5,5.5,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15,range**16,range**17,range**18]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data_mt.plot(kind='scatter',x='wt',y='mpg',figsize=(9,9),color='black',xlim=(0,7))\n",
        "plt.plot(range,y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "_FOKrAWkT_SG",
        "outputId": "b51c11d2-1f89-412a-93a2-33633a791547"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7ec3c88940>]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1KbCdDQmT_d1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 biến "
      ],
      "metadata": {
        "id": "W9fs-RVpWRyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = linear_model.LinearRegression()\n",
        "model.fit(X=data_mt.loc[:,['wt','hp']],y=data_mt['mpg'])\n",
        "print('intercept la : ',model.intercept_)\n",
        "print('coef la : ',model.coef_)\n",
        "print('do chinh xac la : ',model.score(X=data_mt.loc[:,['wt','hp']],y=data_mt['mpg']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjE3S_-jWWgT",
        "outputId": "abd4741e-ecb9-4733-9407-95c0269ab1e5"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept la :  37.227270116447215\n",
            "coef la :  [-3.87783074 -0.03177295]\n",
            "do chinh xac la :  0.8267854518827914\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 biến "
      ],
      "metadata": {
        "id": "Zz5p01QMW2DF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "mpg = -3.87wt - 0.03hp + 37.22"
      ],
      "metadata": {
        "id": "B1POfwzKXQKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = linear_model.LinearRegression()\n",
        "model.fit(X=data_mt.loc[:,['wt','hp','disp']],y=data_mt['mpg'])\n",
        "print('intercept la : ',model.intercept_)\n",
        "print('coef la : ',model.coef_)\n",
        "print('do chinh xac la : ',model.score(X=data_mt.loc[:,['wt','hp','disp']],y=data_mt['mpg']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLQ3v3CMXEXk",
        "outputId": "a5ab3210-98a0-4863-cf8b-6aa37d090ca8"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept la :  37.10550526903184\n",
            "coef la :  [-3.80089058e+00 -3.11565508e-02 -9.37009081e-04]\n",
            "do chinh xac la :  0.8268361424946447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model = linear_model.LinearRegression()\n",
        "# model.fit(X=data.loc[:,['wt','hp','disp','mpg']],y=data['mpg'])\n",
        "# print('intercept la : ',model.intercept_)\n",
        "# print('coef la : ',model.coef_)\n",
        "# print('do chinh xac la : ',model.score(X=data.loc[:,['wt','hp','disp','mpg']],y=data['mpg']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-WfD3CLYDCG",
        "outputId": "bd8514d3-c68d-4768-a3f1-58b272249b88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept la :  2.1316282072803006e-14\n",
            "coef la :  [-8.34633719e-16 -4.85722573e-17 -6.93889390e-18  1.00000000e+00]\n",
            "do chinh xac la :  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q2GXH-B4YVAE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}