Multiple_Linear_Regression_Day_3.ipynb

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  "metadata": {
    "colab": {
      "name": "Multiple Linear Regression Day 3",
      "provenance": [],
      "mount_file_id": "1nKHDCD_RtuNoCmqaDVeWip_s9oGdGNUK",
      "authorship_tag": "ABX9TyMGOuss+2AE4SkSVSyEpBgg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zohaib304/MachineLearning/blob/main/Multiple_Linear_Regression_Day_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxLJqUkGoGp1"
      },
      "source": [
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from mpl_toolkits.mplot3d import Axes3D\r\n",
        "\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "Gfo8Lml7pUNh",
        "outputId": "077d8eed-87d8-47af-f73e-5819898b6570"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Dataset/housing.csv')\r\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...  TAX  PTRATIO       B  LSTAT  MEDV\n",
              "0  0.00632  18.0   2.31     0  0.538  ...  296     15.3  396.90   4.98  24.0\n",
              "1  0.02731   0.0   7.07     0  0.469  ...  242     17.8  396.90   9.14  21.6\n",
              "2  0.02729   0.0   7.07     0  0.469  ...  242     17.8  392.83   4.03  34.7\n",
              "3  0.03237   0.0   2.18     0  0.458  ...  222     18.7  394.63   2.94  33.4\n",
              "4  0.06905   0.0   2.18     0  0.458  ...  222     18.7  396.90   5.33  36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6IUlN1Fr63-"
      },
      "source": [
        "df = df.reset_index()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIcfsZJYrjT1"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0mRz2tEppZd"
      },
      "source": [
        "# Splitting the Dataset into the Independent Feature Matrix:\r\n",
        "X = df[['CRIM', 'ZN', 'INDUS',\t'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']]\r\n",
        "# Extracting the Dataset to Get the Dependent Vector\r\n",
        "y = df[['MEDV']]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4l77NWeWqnhD"
      },
      "source": [
        "X = pd.DataFrame(X)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7pTBUzqsBGO",
        "outputId": "1ce8129e-f04b-4eb6-8dcd-9121da2f4ee0"
      },
      "source": [
        "X.isna().sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "level_0    0\n",
              "index      0\n",
              "CRIM       0\n",
              "ZN         0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NOX        0\n",
              "RM         0\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "B          0\n",
              "LSTAT      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEsjUEQRwA44"
      },
      "source": [
        "#Multiple Linear Regression: Scikit-Learn\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3DY_UdRqIc7"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wYEAKhlq-T3"
      },
      "source": [
        "model = LinearRegression()"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66jrPZXVrAgL",
        "outputId": "6ca7f85d-f252-490c-8d30-fa2ba9c8878c"
      },
      "source": [
        "model.fit(x_train, y_train)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbBSSnsOt4PW"
      },
      "source": [
        "y_predict = model.predict(x_test)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cfSN2eSuqOt",
        "outputId": "5f43fbba-51f4-4656-d8ad-ad5f5a9ff5dd"
      },
      "source": [
        "my_apartment = [[0.00632,\t18.0,\t2.31,\t0, 0.538, 6.575, 65.2, 4.0900, 1, 296, 15.3, 396.90, 4.98]]\r\n",
        "yy = model.predict(my_apartment)\r\n",
        "print(yy)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[30.41900005]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKVKl2_wvmtm"
      },
      "source": [
        "# Visualizing Results with Matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaEdu1Skvj_0"
      },
      "source": [
        "The x-axis should represent actual rent prices and the y-axis should represent predicted rent prices.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "AVUeW-vivZWb",
        "outputId": "b31b60a6-3599-4a65-843d-dfce0be268da"
      },
      "source": [
        "plt.scatter(y_test,y_predict,alpha=0.4)\r\n",
        "plt.xlabel('Actual price')\r\n",
        "plt.ylabel('Prediction Price')\r\n",
        "plt.title('Graph')\r\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBuUjeCAwsu_"
      },
      "source": [
        "#Correlations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "896aZB2Bv1c1",
        "outputId": "08d211d0-6a16-4bc6-9eb7-86f150f04013"
      },
      "source": [
        "plt.scatter(df[['LSTAT']], df[['MEDV']], alpha=0.4)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f70788bbf28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rsMgvGPjx-7y"
      },
      "source": [
        "#Evaluating the Model's Accuracy\r\n",
        "\r\n",
        "When trying to evaluate the accuracy of our multiple linear regression model, one technique we can use is Residual Analysis.\r\n",
        "\r\n",
        "The difference between the actual value y, and the predicted value ŷ is the residual e."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hObSs-Bkxx5W",
        "outputId": "64653439-3676-47b4-fa71-f806eab1d605"
      },
      "source": [
        "print(\"Train score:\")\r\n",
        "print(model.score(x_train, y_train))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train score:\n",
            "0.7387829862692457\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KwPknPwXzIhJ",
        "outputId": "42ec9075-d033-495a-d316-9c6e377f0229"
      },
      "source": [
        "print(\"Test score:\")\r\n",
        "print(model.score(x_test, y_test))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test score:\n",
            "0.7200277678580298\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "hpMHs3vwztbo",
        "outputId": "e5c8be45-c1fa-4c42-87b2-6528ba65bd75"
      },
      "source": [
        "plt.scatter(y_test, y_predict)\r\n",
        "plt.plot(range(50), range(50))\r\n",
        "\r\n",
        "plt.xlabel(\"Prices\")\r\n",
        "plt.ylabel(\"Predicted prices\")\r\n",
        "plt.title(\"Actual Rent vs Predicted Rent\")\r\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYOPoEYtz1XN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}