Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Linear_Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPVM42W/8vQcO3hqBYyJPRE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SuperDataWorld/Python/blob/main/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y8HYCkhXvsoq"
      },
      "source": [
        "## Standard Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLcUO6J7umhS"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCPEnPDpyM14"
      },
      "source": [
        "## Build Test Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rHCoapSd4REN",
        "outputId": "b306983c-b246-4c37-ee5c-0993943abfa9"
      },
      "source": [
        "# a = 3 b = 2 c = 1\n",
        "\n",
        "a = [1,2,3,4,5,1,0]  \n",
        "b = [2,1,5,3,7,2,2]\n",
        "c = [6,3,4,5,7,1,1]\n",
        "tot = [13,11,23,223,236,28,25]\n",
        "\n",
        "df = pd.DataFrame({'a':a,'b':b,'c':c,'tot':tot})\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>tot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  tot\n",
              "0  1  2  6   13\n",
              "1  2  1  3   11\n",
              "2  3  5  4   23\n",
              "3  4  3  5  223\n",
              "4  5  7  7  236"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2qL4cLdygus"
      },
      "source": [
        "# Create Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ui_DU7V6yF4x",
        "outputId": "546cc593-9c2f-4339-c89b-15700d09cfba"
      },
      "source": [
        "# Assign the Training Data\n",
        "X = df[['a','b','c']]\n",
        "y = df.tot\n",
        "\n",
        "# Select a linear model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EwIrPgwyut8"
      },
      "source": [
        "# Test Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WbE5XUgyn96",
        "outputId": "02fcc6c4-badd-48c2-a316-9e76df0229d8"
      },
      "source": [
        "model.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([49.68356457, -2.07684098, -0.12059765])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZJrRnicyoHM",
        "outputId": "cf2b6fd1-d550-4e73-bb3b-a6cff042da72"
      },
      "source": [
        "# Make a prediction \n",
        "X_test = [[1,10,22],[2,4,8],[3,3,3],[20,2,10]]  \n",
        "y_test = [45,22,18,74]\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "print('pred')\n",
        "print(y_pred)\n",
        "print('test')\n",
        "print(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pred\n",
            "[-4.50907150e-01  6.33820704e+01  1.15745464e+02  9.61598719e+02]\n",
            "test\n",
            "[45, 22, 18, 74]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzwKnU7xy4oI"
      },
      "source": [
        "# More Complex Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhzm-wa7v2gy"
      },
      "source": [
        "## Get Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmD0nWFJvj6E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "827d224c-d970-4124-daaf-4a48027f6ebf"
      },
      "source": [
        "! wget https://raw.githubusercontent.com/SuperDataWorld/Python/main/Data/bikerental.csv "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-01 08:38:13--  https://raw.githubusercontent.com/SuperDataWorld/Python/main/Data/bikerental.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 56837 (56K) [text/plain]\n",
            "Saving to: ‘bikerental.csv.1’\n",
            "\n",
            "\rbikerental.csv.1      0%[                    ]       0  --.-KB/s               \rbikerental.csv.1    100%[===================>]  55.50K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2021-08-01 08:38:14 (5.24 MB/s) - ‘bikerental.csv.1’ saved [56837/56837]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "iaQxndU_uWXz",
        "outputId": "f5eaa818-dc3f-4b0c-bd2a-130b119939b2"
      },
      "source": [
        "df = pd.read_csv('bikerental.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant      dteday  season  yr  ...  windspeed  casual  registered   cnt\n",
              "0        1  2011-01-01       1   0  ...   0.160446     331         654   985\n",
              "1        2  2011-01-02       1   0  ...   0.248539     131         670   801\n",
              "2        3  2011-01-03       1   0  ...   0.248309     120        1229  1349\n",
              "3        4  2011-01-04       1   0  ...   0.160296     108        1454  1562\n",
              "4        5  2011-01-05       1   0  ...   0.186900      82        1518  1600\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbgciFDA26TB"
      },
      "source": [
        "# Deal with Categorical Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "gyBFZK9q1jGS",
        "outputId": "7b805110-ac98-470e-b590-e83eb26d0475"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant      dteday  season  yr  ...  windspeed  casual  registered   cnt\n",
              "0        1  2011-01-01       1   0  ...   0.160446     331         654   985\n",
              "1        2  2011-01-02       1   0  ...   0.248539     131         670   801\n",
              "2        3  2011-01-03       1   0  ...   0.248309     120        1229  1349\n",
              "3        4  2011-01-04       1   0  ...   0.160296     108        1454  1562\n",
              "4        5  2011-01-05       1   0  ...   0.186900      82        1518  1600\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeFK_ybc6DSm",
        "outputId": "166477d3-4efa-473f-c247-50919f49f0cf"
      },
      "source": [
        "df.yr.value_counts()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    366\n",
              "0    365\n",
              "Name: yr, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlZ6anAo6aJk",
        "outputId": "c35a86a3-3bd7-41a1-887b-3591320d277a"
      },
      "source": [
        "df.mnth.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12    62\n",
              "10    62\n",
              "8     62\n",
              "7     62\n",
              "5     62\n",
              "3     62\n",
              "1     62\n",
              "11    60\n",
              "9     60\n",
              "6     60\n",
              "4     60\n",
              "2     57\n",
              "Name: mnth, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JO3zuZjF6ucb",
        "outputId": "e9b54df8-6d8e-4e69-aff5-b99c3eb50bd2"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
              "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
              "       'casual', 'registered', 'cnt'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWwAZCeg6aG3"
      },
      "source": [
        "df.drop(['instant','dteday','registered', 'casual'], axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WPhJfhU4Pqq"
      },
      "source": [
        "## Create Data for ML and Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ajx7i4xH7Q5x"
      },
      "source": [
        "y = df['cnt']\n",
        "X = df.drop(['cnt'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxTJGnhT4f-o"
      },
      "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 = .2, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u99KJyAm44_y"
      },
      "source": [
        "## Train and Fit Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Vg2m3fYB_ImZ",
        "outputId": "76a7d7e1-0402-4562-9789-f5c42bfa4e84"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.254167</td>\n",
              "      <td>0.226642</td>\n",
              "      <td>0.652917</td>\n",
              "      <td>0.350133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.253333</td>\n",
              "      <td>0.255046</td>\n",
              "      <td>0.590000</td>\n",
              "      <td>0.155471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>728</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.253333</td>\n",
              "      <td>0.242400</td>\n",
              "      <td>0.752917</td>\n",
              "      <td>0.124383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.255833</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>0.350754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.215833</td>\n",
              "      <td>0.223487</td>\n",
              "      <td>0.577500</td>\n",
              "      <td>0.154846</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>731 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     season  yr  mnth  holiday  ...      temp     atemp       hum  windspeed\n",
              "0         1   0     1        0  ...  0.344167  0.363625  0.805833   0.160446\n",
              "1         1   0     1        0  ...  0.363478  0.353739  0.696087   0.248539\n",
              "2         1   0     1        0  ...  0.196364  0.189405  0.437273   0.248309\n",
              "3         1   0     1        0  ...  0.200000  0.212122  0.590435   0.160296\n",
              "4         1   0     1        0  ...  0.226957  0.229270  0.436957   0.186900\n",
              "..      ...  ..   ...      ...  ...       ...       ...       ...        ...\n",
              "726       1   1    12        0  ...  0.254167  0.226642  0.652917   0.350133\n",
              "727       1   1    12        0  ...  0.253333  0.255046  0.590000   0.155471\n",
              "728       1   1    12        0  ...  0.253333  0.242400  0.752917   0.124383\n",
              "729       1   1    12        0  ...  0.255833  0.231700  0.483333   0.350754\n",
              "730       1   1    12        0  ...  0.215833  0.223487  0.577500   0.154846\n",
              "\n",
              "[731 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwFVSE07455Y",
        "outputId": "205a2957-fd97-490a-a58a-b7661b5cbe98"
      },
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rc6nZrl_45qf"
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "O_oTORw77plL",
        "outputId": "5fcfefbb-d208-49e9-b461-8ee032f62ca1"
      },
      "source": [
        "y_test.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f76206b3610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I2E/rv2O+G/iLEWdaPIojMw8Bt9H6ZTfq7fc08HRm3lfGv0ar0EedC1q/6O7PzINlfJSZ3gM8mZk/yMwfA7fS2s9Guk8tanqBN+Fy/TuAxb9ib6N1Dnpx+q+Wv4RfALxYXubdDVwcESeV394Xl2k9iYgAbgQeyczPNSjXqRFxYhleT+u8/CO0ivzyFXIt5r0c+EY5mroDuKL89f4M4Czgm71kysxrMvO0zJygta98IzM/PMpMEbEhIk5YHKb1vO9jxNsvM58Fvh8RZ5dJFwEPjzpXcSU/PX2yuO5RZXoKuCAi3lx+Fhefp5HtU6/T70n0Yd9o/aX5u7TOr356yOu6hdZ5rh/TOkK5mtb5q13AY8C/AieXxwatD7T4HrAXmGpbzkeBx8vt1/rM9E5aLxm/DTxQbh9oQK5fAL5Vcu0Dfr9MP7PsmI/Tegl8fJn+pjL+eLn/zLZlfbrkfRR4/4C25TQ/fRfKyDKVdT9Ybg8t7sOj3n5leecC82Ub/gOtd2yMer/aQOuIdWPbtFFn+izwnbKff5HWO0kasZ97Kb0kVarpp1AkSSuwwCWpUha4JFXKApekSlngklQpC1ySKmWBS1Kl/g8BWd5zmeA7QAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "V8VpOySX8ABY",
        "outputId": "38b52c7e-c508-4459-d685-b567a0b0449b"
      },
      "source": [
        "pd.Series(y_pred).hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f761fdbac50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cXwQcLi/L7gEujYhTy0/XS8vY0CIigBuBRzPzsy3NeEZEnFKWT6JzjP5ROkV+5SIZu9mvBL5W9oruBK4qv3XfAJwNfL2JjJl5bWaemZkTdF5afy0zP9SmjBGxJiJO7i7TeYz20aLHOjOfA74TEeeUoUuAR9qUsbiaHx8+6eZoS76ngYsi4k3l+7t7H7bmufiaJg+oj+JC50F+Fvghnb2La+gcX9oFPA78K3Ba2Tbo/JOJbwNzwGTPPB8BniiXX2sw3zvpvNz7JvBguVzWsoy/AHyjZNwH/H4ZfyudJ9QTdF7KnljG31jWnyjXv7Vnrk+V7I8B7x/RYz7Fj9+F0pqMJctD5fIw8Kky3prHusx9PjBbHu9/oPMujdZkpHNI4nvA2p6x1uQrc38G+Fb5fvk8nXeStOa52L14Kr0kVarWQyiS9FPPApekSlngklQpC1ySKmWBS1KlLHBJqpQFLkmV+j8IX8Jaw145OAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJlkllSN56Rz"
      },
      "source": [
        "## Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h31x8pfh54av"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgZuktM753-q",
        "outputId": "99874096-1532-416b-a5da-41184bc004de"
      },
      "source": [
        "print('Coefficients:', model.coef_)\n",
        "print('Intercept:', model.intercept_)\n",
        "print('Root Mean squared error (RMSE):', np.sqrt(mean_squared_error(y_pred, y_test)))\n",
        "print('Coefficient of determination', r2_score(y_pred, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: [  524.72253637  2023.99754659   -38.44465798  -391.55076626\n",
            "    72.93700256   160.80489213  -632.8562835   2097.24783578\n",
            "  3488.04217936  -865.43941913 -2080.54039519]\n",
            "Intercept: 1248.3209284778145\n",
            "Root Mean squared error (RMSE): 831.2851545662686\n",
            "Coefficient of determination 0.7870269233159993\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BZntzs9-bN-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a14871d7-8609-41b2-8319-2e7bf218ecbd"
      },
      "source": [
        "plt.scatter(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f761fcc1e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "xvVHqHubB1ah",
        "outputId": "bce229f8-6ee1-4e34-f890-a58ec411c919"
      },
      "source": [
        "df2 = pd.DataFrame([pd.Series(y_test.values), pd.Series(y_pred), pd.Series(X_test.mnth.values)]) \n",
        "df2 = df2.transpose()\n",
        "df2.columns = ['Actual', 'Prediction', 'Month']\n",
        "df2 = df2.groupby(by = 'Month').sum().reset_index()\n",
        "df2['% diff'] = round((df2['Prediction'] / df2['Actual']) - 1, 2)\n",
        "df2"
      ],
      "execution_count": null,
      "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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Actual</th>\n",
              "      <th>Prediction</th>\n",
              "      <th>% diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15644.0</td>\n",
              "      <td>17997.086510</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>22356.0</td>\n",
              "      <td>23538.991753</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>71033.0</td>\n",
              "      <td>70308.066534</td>\n",
              "      <td>-0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>56060.0</td>\n",
              "      <td>52218.580100</td>\n",
              "      <td>-0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>32156.0</td>\n",
              "      <td>29602.308545</td>\n",
              "      <td>-0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.0</td>\n",
              "      <td>57643.0</td>\n",
              "      <td>56835.440641</td>\n",
              "      <td>-0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7.0</td>\n",
              "      <td>76722.0</td>\n",
              "      <td>80711.075405</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8.0</td>\n",
              "      <td>93638.0</td>\n",
              "      <td>92453.738911</td>\n",
              "      <td>-0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9.0</td>\n",
              "      <td>55594.0</td>\n",
              "      <td>51184.134672</td>\n",
              "      <td>-0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10.0</td>\n",
              "      <td>34500.0</td>\n",
              "      <td>35826.915226</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11.0</td>\n",
              "      <td>53126.0</td>\n",
              "      <td>59011.723582</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12.0</td>\n",
              "      <td>60596.0</td>\n",
              "      <td>62892.055433</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Month   Actual    Prediction  % diff\n",
              "0     1.0  15644.0  17997.086510    0.15\n",
              "1     2.0  22356.0  23538.991753    0.05\n",
              "2     3.0  71033.0  70308.066534   -0.01\n",
              "3     4.0  56060.0  52218.580100   -0.07\n",
              "4     5.0  32156.0  29602.308545   -0.08\n",
              "5     6.0  57643.0  56835.440641   -0.01\n",
              "6     7.0  76722.0  80711.075405    0.05\n",
              "7     8.0  93638.0  92453.738911   -0.01\n",
              "8     9.0  55594.0  51184.134672   -0.08\n",
              "9    10.0  34500.0  35826.915226    0.04\n",
              "10   11.0  53126.0  59011.723582    0.11\n",
              "11   12.0  60596.0  62892.055433    0.04"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7olLcK18ELeE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}