multiple_linear_regression.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copie de Copie de multiple_linear_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CazISR8X_HUG"
      },
      "source": [
        "# Multiple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outils de prétraitement des données"
      ],
      "metadata": {
        "id": "2ZMZqzXiHt7c"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOyqYHTk_Q57"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_YHJjnD_Tja"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgC61-ah_WIz"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrxyEKGn_ez7"
      },
      "source": [
        "dataset = pd.read_csv('/content/Mincer.csv')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop(['inlf','hours','kidslt6','kidsge6','repwage','hushrs','husage','huseduc','huswage','faminc','mtr','unem','city','nwifeinc','lwage','lwage','expersq'],1,inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FO0F4fnIwabT",
        "outputId": "94580b2c-35b0-4589-fd5a-ccfb24decc83"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset['wage'])"
      ],
      "metadata": {
        "id": "daLU2cMEx9iM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de8940cd-e46c-4870-dc63-ef8e70a74034"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      3.3540\n",
            "1      1.3889\n",
            "2      4.5455\n",
            "3      1.0965\n",
            "4      4.5918\n",
            "        ...  \n",
            "748       NaN\n",
            "749       NaN\n",
            "750       NaN\n",
            "751       NaN\n",
            "752       NaN\n",
            "Name: wage, Length: 753, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "id": "n3bqumohtDY_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4d7d66c-329b-458d-cf96-e23d4a43b5e4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(753, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(dataset))\n",
        "#dataset=dataset.dropna().shape\n",
        "dataset=pd.DataFrame(dataset)"
      ],
      "metadata": {
        "id": "Wy2Miwl69end",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ede73d6a-00c1-4bac-99e7-713d412701b5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age2=[]\n",
        "for i in dataset['age']:\n",
        "  age2.append(i*i)\n",
        "print(age2)\n",
        "dataset['age2']=age2"
      ],
      "metadata": {
        "id": "nW3C_iZJswqb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd91825-9e02-437c-e111-f34b83af54f8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exper2=[]\n",
        "for i in dataset['fatheduc']:\n",
        "  exper2.append(i*i)\n",
        "print(exper2)\n",
        "dataset['exper2']=exper2"
      ],
      "metadata": {
        "id": "FcnJcj66A6KN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "500a511e-6128-4118-a2e7-1c6d79b8d6e3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "last_column = dataset.pop('wage') \n",
        "dataset.insert(7, 'wage', last_column)"
      ],
      "metadata": {
        "id": "DQhTLePnipYU"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=dataset.dropna()\n",
        "dataset.columns "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQVCvTgGcK5N",
        "outputId": "cb79db6a-7152-40e1-f321-645e90d5cd05"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'educ', 'motheduc', 'fatheduc', 'exper', 'age2', 'exper2',\n",
              "       'wage'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(dataset[['age', 'exper', 'age2', 'exper2','motheduc', 'fatheduc','wage']].corr(), cmap='Blues', annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "hsPCKtboHt-P",
        "outputId": "4d7c07d2-b25c-4850-a3c7-e10f6b59d9f9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "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": [
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "print(y)"
      ],
      "metadata": {
        "id": "8k7OwqVnjGPh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68b07c91-6190-4d1e-c7fe-6b1df62d0e17"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 3.35400009  1.38890004  4.5454998   1.09650004  4.59180021  4.74209976\n",
            "  8.33329964  7.84310007  2.12619996  4.6875      4.0630002   4.59180021\n",
            "  2.08330011  2.26679993  3.6796999   1.34720004  3.21429992  5.17500019\n",
            "  2.          7.55289984  3.50519991  3.57139993  3.25        3.25\n",
            "  2.15450001  3.78789997  4.          4.7269001   7.25589991  5.86709976\n",
            "  1.53849995  2.45900011  5.85109997  3.57139993  3.80679989  2.46379995\n",
            "  2.37529993  4.53509998  5.61829996 14.63099957  2.67860007  3.91939998\n",
            "  2.57290006  4.5374999   2.          3.47219992  2.01609993  4.57159996\n",
            "  2.27270007  2.63750005  2.28990006  1.09889996  1.17649996  1.60000002\n",
            "  1.87619996  4.04370022  9.63539982  8.04090023  4.59899998  2.14289999\n",
            "  4.4000001   3.53539991  2.71740007  6.25       11.93299961  3.59310007\n",
            "  6.94439983  2.91669989  3.07690001  3.75        5.72590017  3.67569995\n",
            "  5.16480017  8.22369957  4.33650017  4.98190022  0.35710001  2.96740007\n",
            "  1.          2.55539989  0.86019999  1.          2.92610002  3.5460999\n",
            "  1.62639999  8.33329964  3.09520006  2.70000005  5.25209999  1.41540003\n",
            "  4.7986002   1.66670001  1.12170005  0.5         0.71429998  2.7960999\n",
            "  4.85830021  1.74349999  2.46309996  2.42129993  1.5345      2.88179994\n",
            "  2.40689993  5.23260021  3.75        1.38890004  4.          3.23130012\n",
            "  3.40140009  1.33329999  9.30230045  4.5         4.62349987  3.95569992\n",
            "  5.81400013  0.5         4.08160019  6.          3.66669989  3.86129999\n",
            "  2.76290011  2.93099999  4.38840008  5.41669989  9.8611002   0.16159999\n",
            "  0.38260001  3.63639998  2.37470007  4.66669989  1.85189998  5.19999981\n",
            "  3.29859996  8.5333004   2.06349993  2.56410003  2.1875      6.25\n",
            "  3.33330011  4.44439983  6.6335001   8.42240047  4.39559984  2.44569993\n",
            "  1.22449994  1.625       3.          4.7269001   1.1293      7.40240002\n",
            "  4.45949984  2.47250009  1.88240004  4.          8.18999958  7.09679985\n",
            "  1.66670001  3.44499993  4.2347002   2.77780008  1.88919997  5.03520012\n",
            "  1.25        2.85710001  4.11670017  1.77779996 13.5539999   4.56269979\n",
            "  2.12770009  2.98909998  2.56539989  5.61219978  2.80539989  1.60699999\n",
            "  2.25        2.03250003  5.53200006  1.58449996  3.78789997  3.\n",
            "  8.65380001  4.21049976  4.6875      4.09840012 25.          2.63310003\n",
            "  6.          5.41260004  0.66420001  1.25        2.27539992  3.46149993\n",
            "  4.16669989  4.46869993  1.75        3.66939998  6.58260012  2.5999999\n",
            "  4.86509991  5.78700018  4.54080009  9.50570011 10.63799953  1.11109996\n",
            "  4.05410004  2.68709993  2.9762001   3.17280006  3.54999995 17.90699959\n",
            "  3.41739988  3.33330011  3.88599992  2.31110001  1.71080005  2.11430001\n",
            "  9.93309975  3.02769995  1.86049998  0.12819999  6.63269997  5.63910007\n",
            "  1.59899998  2.66669989  7.92080021  5.33979988  4.          6.04489994\n",
            "  6.25        2.9762001   4.23390007  3.49160004  4.96449995  2.76920009\n",
            "  3.65849996  5.39349985  0.65640002  4.76879978  8.55510044 10.45899963\n",
            "  2.63750005  6.84929991  5.08720016  0.96149999  4.30660009  7.06669998\n",
            "  2.52530003  7.89470005  4.14890003  8.17459965  9.59710026  2.0164001\n",
            "  7.62179995  3.19749999  1.60000002  4.09840012  1.48150003  3.63639998\n",
            "  1.          2.60419989  1.75        4.80000019  5.53000021  4.09840012\n",
            "  1.25        1.66670001  3.7901001   2.36360002 10.20400047  6.81820011\n",
            "  7.21460009  2.44840002  1.19869995  1.64100003  1.78209996  2.94120002\n",
            "  4.9689002   1.86049998  8.03569984  3.97160006  3.04159999  2.90700006\n",
            "  3.0611999   4.89659977  4.01609993  5.55560017  1.2227      2.68390012\n",
            "  2.67379999  9.32940006  3.13809991  0.54259998  8.6111002   3.66869998\n",
            "  2.31850004  2.88199997  3.16659999  3.64580011  6.25       10.2489996\n",
            "  3.20959997  7.65220022  1.97239995  4.69799995  2.12339997  2.33330011\n",
            "  2.38960004  1.25639999  1.09379995  3.75        3.30719995  5.13520002\n",
            "  6.63269997  4.56449986 11.84799957  3.75        4.36509991  3.93330002\n",
            "  3.33330011  3.31590009  3.56159997  1.60000002  2.22550011  4.78719997\n",
            "  5.80649996  2.3585      2.          1.89999998  5.12300014  5.49450016\n",
            "  6.32180023  7.14289999  2.375       2.54290009  2.17980003  2.5999999\n",
            "  3.72939992  4.375       4.44329977  4.28770018  1.66670001  3.25580001\n",
            "  5.41130018  2.20499992  4.0625      0.64819998  5.38259983  0.1709\n",
            " 23.46699905  9.57849979  3.6947999   2.22219992  1.74899995  1.15939999\n",
            "  6.99009991  3.9683001  21.42900085  0.47620001  2.15050006  1.85780001\n",
            "  4.3295002   8.92860031  2.77780008  2.6565001   2.5        18.2670002\n",
            "  0.81900001  2.         15.38500023  6.47749996  8.33329964  4.55030012\n",
            "  2.49580002  4.47970009  2.23239994  2.0710001   1.676       3.40910006\n",
            "  2.5         3.96090007  6.22749996  3.92860007  2.9000001   4.08160019\n",
            "  2.8499999   7.0152998   2.9388001   1.92309999  6.875       3.90019989\n",
            "  2.          4.97629976  1.20190001 22.5         6.8677001   3.56130004\n",
            "  1.97920001  5.37130022  1.74419999  5.0948      2.5         3.82500005\n",
            "  1.          3.0717001   1.71630001  4.02089977  5.45849991 25.\n",
            "  2.38969994  3.21339989  3.37700009  1.77779996  3.16330004  2.7026999\n",
            "  1.69270003  0.2137      6.76690006  1.74070001  2.5         4.48589993\n",
            "  2.57200003  3.46000004  4.78259993  2.3118      5.30609989  5.86749983\n",
            "  3.40910006  4.08160019]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kb_v_ae-A-20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c676003-f6e9-4a4e-fdd1-f7573aadd94f"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "print(X_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  49   11    7 ...   14 2401   49]\n",
            " [  34   12    7 ...   14 1156   49]\n",
            " [  37   12   10 ...    4 1369   49]\n",
            " ...\n",
            " [  45   12    7 ...    9 2025  100]\n",
            " [  45   12   10 ...   22 2025  100]\n",
            " [  31   12   10 ...   10  961  100]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6rCRtQ-YuFC",
        "outputId": "ef151348-d171-4c96-d8a2-6d6acb72d9a6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 4.09840012  3.37700009  2.9762001   4.74209976  4.08160019  0.66420001\n",
            "  1.74419999  7.84310007  4.54080009  3.25        3.07690001  4.56449986\n",
            "  3.50519991  2.56410003  1.19869995  3.6796999   4.3295002   0.47620001\n",
            "  6.63269997  8.03569984  4.39559984  4.98190022 11.93299961  4.04370022\n",
            "  1.          2.38960004  2.          4.97629976  3.67569995  2.5\n",
            "  2.68390012  3.0611999  25.          4.69799995  5.30609989  3.44499993\n",
            "  9.63539982  2.12619996  3.49160004  4.11670017  3.29859996  2.76920009\n",
            " 10.45899963  4.89659977  4.          2.90700006  6.25        2.88199997\n",
            "  0.81900001  2.0710001   3.92860007  1.41540003  2.52530003  2.3118\n",
            "  4.09840012  6.63269997  1.12170005  3.19749999  5.39349985  3.31590009\n",
            "  2.20499992  3.02769995  6.94439983  1.25        7.0152998   6.6335001\n",
            "  2.88179994  2.          6.84929991  2.22219992  5.17500019  3.\n",
            "  3.54999995  9.59710026  1.53849995  5.81400013  5.61219978  5.23260021\n",
            "  2.9762001   2.9388001   3.          4.08160019  2.12770009  3.9683001\n",
            "  2.76290011  2.68709993  4.62349987  7.06669998  2.1875      6.25\n",
            "  0.38260001  2.6565001   2.22550011  2.          8.5333004   3.57139993\n",
            "  4.02089977  1.25        3.80679989  6.76690006  1.74349999  8.33329964\n",
            "  3.53539991  4.56269979  1.60699999  4.80000019  3.75        4.48589993\n",
            "  2.5         0.65640002  3.40140009  5.12300014  2.01609993  0.64819998\n",
            "  2.31110001  1.60000002  8.22369957  6.25        2.38969994  4.96449995\n",
            "  8.55510044  5.86709976  4.21049976  5.13520002  1.89999998  1.09889996\n",
            "  1.15939999  3.96090007  2.5         4.7269001   4.5454998   7.89470005\n",
            "  1.66670001  2.63750005  3.0717001   3.66869998  3.33330011  3.88599992\n",
            "  3.75        8.17459965  3.64580011  7.65220022  2.11430001  7.14289999\n",
            "  1.25        1.71080005  2.46379995  4.23390007  4.05410004  3.65849996\n",
            "  4.01609993  3.41739988  2.91669989  2.15450001  1.92309999  9.93309975\n",
            "  2.          3.66669989  2.66669989  4.66669989  0.5         4.5\n",
            " 13.5539999   3.20959997  6.8677001   3.16659999  2.67860007  2.\n",
            "  1.25639999  2.55539989 10.20400047  4.9689002   2.26679993  1.74899995\n",
            "  4.78259993  6.25        5.03520012  3.5460999   5.86749983  1.\n",
            "  9.30230045  7.40240002  1.88240004  3.21429992  2.23239994  1.33329999\n",
            "  1.97239995  4.44439983  4.76879978  1.75        1.09379995  4.0625\n",
            "  4.14890003  2.375       7.55289984  6.81820011  4.          6.32180023\n",
            "  1.09650004 10.2489996   0.16159999  3.04159999  1.2227      2.96740007\n",
            " 25.          9.57849979  5.45849991  2.94120002  2.31850004  2.49580002\n",
            "  4.6875      4.08160019  0.86019999  1.          4.59180021  2.77780008\n",
            "  3.09520006  3.7901001   2.37529993  3.21339989  4.59899998  3.91939998\n",
            "  7.21460009  2.28990006 17.90699959  3.25580001  1.66670001 21.42900085\n",
            "  5.41669989  1.59899998  2.71740007  2.3585      1.85189998  5.41260004\n",
            "  3.25        4.5374999   3.35400009  9.50570011 18.2670002   2.46309996\n",
            "  2.12339997  0.2137      3.78789997  1.48150003  0.71429998  1.77779996\n",
            "  2.7960999   2.5         2.98909998  3.75        2.33330011  2.27270007\n",
            "  3.17280006  3.97160006  6.04489994  1.1293      2.44569993  2.17980003\n",
            "  8.65380001  5.19999981  2.8499999   3.40910006  5.53000021  5.80649996\n",
            "  1.11109996  1.62639999  2.93099999  4.55030012 22.5         1.66670001\n",
            "  2.92610002  2.36360002  3.86129999  2.15050006  0.54259998  8.04090023\n",
            "  1.60000002  3.63639998  2.9000001   2.57290006  1.38890004  3.56130004\n",
            "  1.64100003  6.22749996  5.61829996  1.60000002  4.47970009  2.37470007\n",
            "  3.13809991  7.25589991  4.09840012  8.33329964  2.85710001  2.47250009\n",
            "  4.30660009 10.63799953  2.45900011  5.85109997  3.63639998  2.63310003\n",
            "  5.0948      2.67379999  3.90019989  4.7269001   5.55560017  5.37130022\n",
            "  1.58449996  2.42129993  4.44329977  2.5999999   2.5999999   0.96149999\n",
            "  0.5         1.66670001  5.16480017  2.54290009  3.78789997  1.77779996\n",
            "  4.375       3.16330004  2.25        1.69270003 14.63099957  4.46869993\n",
            " 11.84799957  1.97920001  5.25209999  5.72590017  2.70000005  8.6111002\n",
            "  5.08720016  1.86049998  3.33330011  4.6875      1.85780001  3.66939998\n",
            "  2.0164001   4.78719997  4.16669989  6.          4.57159996  2.80539989]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#supprimmer les NA"
      ],
      "metadata": {
        "id": "epNX2ChLbSb9"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-McZVsQBINc"
      },
      "source": [
        "## Training the Multiple Linear Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywPjx0L1BMiD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46e653ea-05db-40c7-f083-1ad857f0eacd"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg = LinearRegression().fit(X_train, y_train)\n",
        "coefs = reg.fit(X_train, y_train).coef_\n",
        "intecept = reg .fit(X_train, y_train).intercept_\n",
        "print(intecept , coefs)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-4.343301882704624 [ 0.19574978  0.46285543 -0.06225286 -0.22366609  0.0384735  -0.00259535\n",
            "  0.01358347]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNkXL1YQBiBT"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQKmwvtdBkyb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fb7cbd4-7702-44bc-eb8d-0fcfa545a559"
      },
      "source": [
        "y_pred = reg.predict(X_test)\n",
        "np.set_printoptions(precision=8)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 4.15871052  1.78209996]\n",
            " [ 3.97026199  2.77780008]\n",
            " [ 3.76998392  1.87619996]\n",
            " [ 2.870819    3.30719995]\n",
            " [ 4.12568597  4.0630002 ]\n",
            " [ 7.03216806 15.38500023]\n",
            " [ 6.78987429  5.53200006]\n",
            " [ 6.33891473  5.49450016]\n",
            " [ 2.82910047  2.60419989]\n",
            " [ 3.29728669  2.27539992]\n",
            " [ 3.54549769  3.57139993]\n",
            " [ 4.05650347  4.36509991]\n",
            " [ 3.17320083  4.38840008]\n",
            " [ 3.77452901  3.46000004]\n",
            " [ 4.03980891  5.63910007]\n",
            " [ 5.92876762  7.62179995]\n",
            " [ 6.3716284   6.99009991]\n",
            " [ 2.4845371   0.12819999]\n",
            " [ 3.4926395   3.33330011]\n",
            " [ 1.51693813  3.82500005]\n",
            " [ 5.68747425  5.38259983]\n",
            " [ 3.33542209  3.46149993]\n",
            " [ 4.27273055  3.6947999 ]\n",
            " [ 3.60416235  1.38890004]\n",
            " [ 3.70395421  4.53509998]\n",
            " [ 3.04235906  2.06349993]\n",
            " [ 2.02962813  3.59310007]\n",
            " [ 4.76139831  4.2347002 ]\n",
            " [ 3.54906805  3.75      ]\n",
            " [ 3.65030233  2.08330011]\n",
            " [ 3.81400727  1.75      ]\n",
            " [ 5.6820819   6.        ]\n",
            " [ 1.86729422  1.74070001]\n",
            " [ 3.88085082  3.56159997]\n",
            " [ 4.15372443  4.86509991]\n",
            " [ 5.88733997  8.18999958]\n",
            " [ 4.70295716  4.85830021]\n",
            " [ 3.64744437  3.93330002]\n",
            " [ 4.3857735   0.1709    ]\n",
            " [ 4.17399282  1.86049998]\n",
            " [ 3.79076938  9.32940006]\n",
            " [ 5.97145095  4.        ]\n",
            " [ 4.90878219  8.92860031]\n",
            " [ 5.6038056   7.92080021]\n",
            " [ 2.89194242  1.34720004]\n",
            " [ 3.88946278  4.28770018]\n",
            " [ 5.12480733  4.4000001 ]\n",
            " [ 3.40117342  2.56539989]\n",
            " [ 4.15036438  6.58260012]\n",
            " [ 6.83937353  3.95569992]\n",
            " [ 3.73556877  2.57200003]\n",
            " [ 5.55042769  3.40910006]\n",
            " [ 5.40368217  8.33329964]\n",
            " [ 4.97915298  4.7986002 ]\n",
            " [ 6.08180627  4.45949984]\n",
            " [ 3.76342308  3.72939992]\n",
            " [ 3.71024195  2.40689993]\n",
            " [ 3.55841742  0.35710001]\n",
            " [ 3.91525378  1.676     ]\n",
            " [ 3.47810976  1.625     ]\n",
            " [ 5.60509096  5.78700018]\n",
            " [ 3.48140429  2.14289999]\n",
            " [ 2.67749768 23.46699905]\n",
            " [ 4.01198824  1.71630001]\n",
            " [ 4.92322871  2.7026999 ]\n",
            " [ 0.62059436  2.03250003]\n",
            " [ 5.20163875  1.        ]\n",
            " [ 2.47432379  2.44840002]\n",
            " [ 6.48778325  8.42240047]\n",
            " [ 4.66002252  6.875     ]\n",
            " [ 6.3362327   7.09679985]\n",
            " [ 3.87302435  1.88919997]\n",
            " [ 4.34253285  3.23130012]\n",
            " [ 3.59743143  1.20190001]\n",
            " [ 4.43117591  5.41130018]\n",
            " [ 5.96428697  9.8611002 ]\n",
            " [ 3.33137154  2.63750005]\n",
            " [ 3.83066016  1.17649996]\n",
            " [ 3.97114216  4.33650017]\n",
            " [ 3.83161796  4.        ]\n",
            " [ 3.37301285  3.47219992]\n",
            " [ 2.24316131  1.22449994]\n",
            " [ 4.76409635  4.59180021]\n",
            " [ 5.89950333  5.33979988]\n",
            " [ 5.56200543  6.47749996]\n",
            " [ 3.33050463  1.5345    ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing r2_score module\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "# predicting the accuracy score\n",
        "score=r2_score(y_test,y_pred)\n",
        "print('accuracy  socre is ',score)"
      ],
      "metadata": {
        "id": "XeTYKwMpWY2M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "738fd6fa-ecf3-4b18-a907-c83e332f01fd"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy  socre is  0.155256039798373\n"
          ]
        }
      ]
    }
  ]
}