Bike_sharing_MLR_Ankit.ipynb

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      "name": "bike.ipynb",
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      "cell_type": "markdown",
      "source": [
        "# Importing the modules"
      ],
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "# importing all the modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns  \n",
        "import datetime    \n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "import statsmodels\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.metrics import mean_squared_error\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the data set\n",
        "bike_data=pd.read_csv('/content/day.csv')\n",
        "bike_data.head(3)"
      ],
      "metadata": {
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          "height": 143
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        "id": "EE3F-dhrYYkr",
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      "execution_count": 2,
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              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  01-01-2018       1   0     1        0        6           0   \n",
              "1        2  02-01-2018       1   0     1        0        0           0   \n",
              "2        3  03-01-2018       1   0     1        0        1           1   \n",
              "\n",
              "   weathersit       temp     atemp      hum  windspeed  casual  registered  \\\n",
              "0           2  14.110847  18.18125  80.5833  10.749882     331         654   \n",
              "1           2  14.902598  17.68695  69.6087  16.652113     131         670   \n",
              "2           1   8.050924   9.47025  43.7273  16.636703     120        1229   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
              "2  1349  "
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      "source": [
        "Checking the shape and null value in our data set"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Checking the shape and null values in particular columns\n",
        "print(bike_data.shape)\n",
        "bike_data[bike_data.isnull()].sum()"
      ],
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        "colab": {
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      "outputs": [
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          "text": [
            "(730, 16)\n"
          ]
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              "instant       0.0\n",
              "dteday          0\n",
              "season        0.0\n",
              "yr            0.0\n",
              "mnth          0.0\n",
              "holiday       0.0\n",
              "weekday       0.0\n",
              "workingday    0.0\n",
              "weathersit    0.0\n",
              "temp          0.0\n",
              "atemp         0.0\n",
              "hum           0.0\n",
              "windspeed     0.0\n",
              "casual        0.0\n",
              "registered    0.0\n",
              "cnt           0.0\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dropping the columns which is of no use"
      ],
      "metadata": {
        "id": "3SmC4wgWCb8a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dropping these columns  not useful in finding out target variable as count\n",
        "bike_data=bike_data.drop(['instant','dteday','casual','registered','atemp'],axis=1)"
      ],
      "metadata": {
        "id": "w0inh9mVPCD0"
      },
      "execution_count": 4,
      "outputs": []
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        "bike_data.head()"
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              "   season  yr  mnth  holiday  weekday  workingday  weathersit       temp  \\\n",
              "0       1   0     1        0        6           0           2  14.110847   \n",
              "1       1   0     1        0        0           0           2  14.902598   \n",
              "2       1   0     1        0        1           1           1   8.050924   \n",
              "3       1   0     1        0        2           1           1   8.200000   \n",
              "4       1   0     1        0        3           1           1   9.305237   \n",
              "\n",
              "       hum  windspeed   cnt  \n",
              "0  80.5833  10.749882   985  \n",
              "1  69.6087  16.652113   801  \n",
              "2  43.7273  16.636703  1349  \n",
              "3  59.0435  10.739832  1562  \n",
              "4  43.6957  12.522300  1600  "
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44485b7b-a867-4696-8033-2dc6aa7b28c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Replacing the data into string categories"
      ],
      "metadata": {
        "id": "dDPbgX43DEL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing the numerical values with respect to categories \n",
        "bike_data['season']=bike_data['season'].replace({1:'spring', 2:'summer', 3:'fall', 4:'winter'})\n",
        "\n",
        "bike_data['yr']=bike_data['yr'].replace({0: 2018, 1:2019})\n",
        "\n",
        "bike_data['mnth']=bike_data['mnth'].replace({1:'January', 2:'February', 3:'March', 4:'April',\n",
        "                                             5:'May', 6:'June', 7:'July', 8:'August',\n",
        "                                             9:'September', 10:'October', 11:'November', 12:'December'})\n",
        "\n",
        "bike_data['holiday']=bike_data['holiday'].replace({0:'Not_official_holiday', 1:'Official_holiday'})\n",
        "\n",
        "\n",
        "bike_data['weekday']=bike_data['weekday'].replace({0:'Sunday', 1:'Monday',2:'Tuesday', 3:'Wednesday',\n",
        "                                                   4:'Thrusday', 5:'Friday',6:'Saturday'})\n",
        "\n",
        "\n",
        "bike_data['workingday']=bike_data['workingday'].replace({0: 'Not_working', 1: 'Working'})\n",
        "\n",
        "bike_data['weathersit']=bike_data['weathersit'].replace({1: 'Clear', 2: 'Mist_Coludy',\n",
        "                                                         3: 'Light_snow', 4:'Heavy_rain'})\n"
      ],
      "metadata": {
        "id": "3DRzvJXTRu4O"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bike_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dhIVL0iVWcqi",
        "outputId": "b97b9bcc-a0d5-4ab9-97fa-8e47463b35dc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season    yr     mnth               holiday    weekday   workingday  \\\n",
              "0  spring  2018  January  Not_official_holiday   Saturday  Not_working   \n",
              "1  spring  2018  January  Not_official_holiday     Sunday  Not_working   \n",
              "2  spring  2018  January  Not_official_holiday     Monday      Working   \n",
              "3  spring  2018  January  Not_official_holiday    Tuesday      Working   \n",
              "4  spring  2018  January  Not_official_holiday  Wednesday      Working   \n",
              "\n",
              "    weathersit       temp      hum  windspeed   cnt  \n",
              "0  Mist_Coludy  14.110847  80.5833  10.749882   985  \n",
              "1  Mist_Coludy  14.902598  69.6087  16.652113   801  \n",
              "2        Clear   8.050924  43.7273  16.636703  1349  \n",
              "3        Clear   8.200000  59.0435  10.739832  1562  \n",
              "4        Clear   9.305237  43.6957  12.522300  1600  "
            ],
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              "      <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>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>cnt</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Not_working</td>\n",
              "      <td>Mist_Coludy</td>\n",
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              "      <th>1</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Sunday</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Working</td>\n",
              "      <td>Clear</td>\n",
              "      <td>8.050924</td>\n",
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              "      <td>16.636703</td>\n",
              "      <td>1349</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Tuesday</td>\n",
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              "      <td>Clear</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>59.0435</td>\n",
              "      <td>10.739832</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Wednesday</td>\n",
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              "      <td>Clear</td>\n",
              "      <td>9.305237</td>\n",
              "      <td>43.6957</td>\n",
              "      <td>12.522300</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking for outliers"
      ],
      "metadata": {
        "id": "M2JldjU2DsFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bike_data.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "ebQFqHLmhIgR",
        "outputId": "ef112e12-4893-49c1-8c4f-d6952718b229"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                yr        temp         hum   windspeed          cnt\n",
              "count   730.000000  730.000000  730.000000  730.000000   730.000000\n",
              "mean   2018.500000   20.319259   62.765175   12.763620  4508.006849\n",
              "std       0.500343    7.506729   14.237589    5.195841  1936.011647\n",
              "min    2018.000000    2.424346    0.000000    1.500244    22.000000\n",
              "25%    2018.000000   13.811885   52.000000    9.041650  3169.750000\n",
              "50%    2018.500000   20.465826   62.625000   12.125325  4548.500000\n",
              "75%    2019.000000   26.880615   72.989575   15.625589  5966.000000\n",
              "max    2019.000000   35.328347   97.250000   34.000021  8714.000000"
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2018.500000</td>\n",
              "      <td>20.319259</td>\n",
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              "      <td>1936.011647</td>\n",
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              "      <th>min</th>\n",
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              "      <td>22.000000</td>\n",
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              "      <th>25%</th>\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Univariate analysis "
      ],
      "metadata": {
        "id": "H_HGOFbOD9Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1,2,figsize=(10,5))\n",
        "sns.countplot(bike_data['season'],ax=axes[0],order = bike_data['season'].value_counts().index)\n",
        "sns.countplot(bike_data['yr'],ax=axes[1],order = bike_data['yr'].value_counts().index)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "PDgfqMTTh8QN",
        "outputId": "63a48d05-344f-4314-cbb8-a54f18f3f242"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trying to analyze the count for different season and year how it had affected the  bike sharing number"
      ],
      "metadata": {
        "id": "Dqs3yeAfVrqU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1,2,figsize=(10,5))\n",
        "sns.countplot(bike_data['holiday'],ax=axes[0],order = bike_data['holiday'].value_counts().index)\n",
        "sns.countplot(bike_data['workingday'],ax=axes[1],order = bike_data['workingday'].value_counts().index)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "y82Y6YN4jJ49",
        "outputId": "cd2a337d-ac34-4b42-8bcc-ad732636b6b3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trying to analyze the different holiday and working day  how it had affected the  bike sharing number"
      ],
      "metadata": {
        "id": "IhoEeducV7nb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1,2,figsize=(12,6))\n",
        "sns.countplot(y=bike_data['mnth'],ax=axes[0],order = bike_data['mnth'].value_counts().index)\n",
        "sns.countplot(y=bike_data['weekday'],ax=axes[1],order = bike_data['weekday'].value_counts().index)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "3bGTmX37ivpN",
        "outputId": "97a81d90-96f6-404d-f322-124c3790e3ec"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trying to analyze the count different month and week day how it had affected the  bike sharing number"
      ],
      "metadata": {
        "id": "tAWHamUdWBby"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(bike_data['weathersit'],order = bike_data['weathersit'].value_counts().index)\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "5cOlHzHRklYH",
        "outputId": "1635500d-9c08-429e-c539-72f29003b437"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[9,9])\n",
        "sns.heatmap(bike_data.corr(),  annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "jrXBqEpYkLjX",
        "outputId": "c52c90cb-98e9-4a3d-99f9-b9db98a54f01"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "got a correlation between all the numerical variables and analyzed them with respect to cnt variable"
      ],
      "metadata": {
        "id": "wrVq0ErTWK8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bike_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6P_VTOcSmBwh",
        "outputId": "9ba10d78-d98a-42d0-a128-7ddf11d050ac"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday',\n",
              "       'weathersit', 'temp', 'hum', 'windspeed', 'cnt'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sns.pairplot(data=bike_data,x_vars=['season', 'weekday'],y_vars='cnt')\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "KjGpbO-cl0em",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "bb2e6a49-1b35-446e-8fa4-05109c2b5428"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x180 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data=bike_data,x_vars=[ 'yr','weathersit', 'temp'],y_vars='cnt')\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "8hlZpK2fSgdj",
        "outputId": "81f018e5-edbf-4b7e-be70-fad9a30494b2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x180 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data=bike_data,x_vars=['hum', 'windspeed','mnth'],y_vars='cnt')\n",
        "plt.tight_layout()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "s7mHBxI_SiEB",
        "outputId": "ba1db520-90c2-4b23-a3cd-2f4f9615b97c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x180 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysed with various plot of cnt with other parameter"
      ],
      "metadata": {
        "id": "3_tylT2gWuOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bike_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVgB0rEOn_PQ",
        "outputId": "a40df7af-890c-4c82-c2fa-8ec67f571669"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 730 entries, 0 to 729\n",
            "Data columns (total 11 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   season      730 non-null    object \n",
            " 1   yr          730 non-null    int64  \n",
            " 2   mnth        730 non-null    object \n",
            " 3   holiday     730 non-null    object \n",
            " 4   weekday     730 non-null    object \n",
            " 5   workingday  730 non-null    object \n",
            " 6   weathersit  730 non-null    object \n",
            " 7   temp        730 non-null    float64\n",
            " 8   hum         730 non-null    float64\n",
            " 9   windspeed   730 non-null    float64\n",
            " 10  cnt         730 non-null    int64  \n",
            "dtypes: float64(3), int64(2), object(6)\n",
            "memory usage: 62.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Encoding"
      ],
      "metadata": {
        "id": "Fv-CYc5bW7Oc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applied dummy encording for the categorical variable "
      ],
      "metadata": {
        "id": "YkCccjMOW93h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "status=pd.get_dummies(bike_data[['season','mnth','weekday','weathersit','holiday','workingday']],drop_first=True)\n",
        "status\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "FB9apBlgyCSt",
        "outputId": "a95732e5-9954-41b1-cdf3-f62ce273b448"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     season_spring  season_summer  season_winter  mnth_August  mnth_December  \\\n",
              "0                1              0              0            0              0   \n",
              "1                1              0              0            0              0   \n",
              "2                1              0              0            0              0   \n",
              "3                1              0              0            0              0   \n",
              "4                1              0              0            0              0   \n",
              "..             ...            ...            ...          ...            ...   \n",
              "725              1              0              0            0              1   \n",
              "726              1              0              0            0              1   \n",
              "727              1              0              0            0              1   \n",
              "728              1              0              0            0              1   \n",
              "729              1              0              0            0              1   \n",
              "\n",
              "     mnth_February  mnth_January  mnth_July  mnth_June  mnth_March  ...  \\\n",
              "0                0             1          0          0           0  ...   \n",
              "1                0             1          0          0           0  ...   \n",
              "2                0             1          0          0           0  ...   \n",
              "3                0             1          0          0           0  ...   \n",
              "4                0             1          0          0           0  ...   \n",
              "..             ...           ...        ...        ...         ...  ...   \n",
              "725              0             0          0          0           0  ...   \n",
              "726              0             0          0          0           0  ...   \n",
              "727              0             0          0          0           0  ...   \n",
              "728              0             0          0          0           0  ...   \n",
              "729              0             0          0          0           0  ...   \n",
              "\n",
              "     weekday_Monday  weekday_Saturday  weekday_Sunday  weekday_Thrusday  \\\n",
              "0                 0                 1               0                 0   \n",
              "1                 0                 0               1                 0   \n",
              "2                 1                 0               0                 0   \n",
              "3                 0                 0               0                 0   \n",
              "4                 0                 0               0                 0   \n",
              "..              ...               ...             ...               ...   \n",
              "725               0                 0               0                 1   \n",
              "726               0                 0               0                 0   \n",
              "727               0                 1               0                 0   \n",
              "728               0                 0               1                 0   \n",
              "729               1                 0               0                 0   \n",
              "\n",
              "     weekday_Tuesday  weekday_Wednesday  weathersit_Light_snow  \\\n",
              "0                  0                  0                      0   \n",
              "1                  0                  0                      0   \n",
              "2                  0                  0                      0   \n",
              "3                  1                  0                      0   \n",
              "4                  0                  1                      0   \n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merging them with the main dataframe"
      ],
      "metadata": {
        "id": "XHUImqoeXHpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result=pd.concat([bike_data,status],axis=1)\n",
        "result.head()"
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      },
      "execution_count": 20,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season    yr     mnth               holiday    weekday   workingday  \\\n",
              "0  spring  2018  January  Not_official_holiday   Saturday  Not_working   \n",
              "1  spring  2018  January  Not_official_holiday     Sunday  Not_working   \n",
              "2  spring  2018  January  Not_official_holiday     Monday      Working   \n",
              "3  spring  2018  January  Not_official_holiday    Tuesday      Working   \n",
              "4  spring  2018  January  Not_official_holiday  Wednesday      Working   \n",
              "\n",
              "    weathersit       temp      hum  windspeed  ...  weekday_Monday  \\\n",
              "0  Mist_Coludy  14.110847  80.5833  10.749882  ...               0   \n",
              "1  Mist_Coludy  14.902598  69.6087  16.652113  ...               0   \n",
              "2        Clear   8.050924  43.7273  16.636703  ...               1   \n",
              "3        Clear   8.200000  59.0435  10.739832  ...               0   \n",
              "4        Clear   9.305237  43.6957  12.522300  ...               0   \n",
              "\n",
              "   weekday_Saturday  weekday_Sunday  weekday_Thrusday  weekday_Tuesday  \\\n",
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              "4                 0               0                 0                0   \n",
              "\n",
              "   weekday_Wednesday  weathersit_Light_snow  weathersit_Mist_Coludy  \\\n",
              "0                  0                      0                       1   \n",
              "1                  0                      0                       1   \n",
              "2                  0                      0                       0   \n",
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              "4                  1                      0                       0   \n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Not_working</td>\n",
              "      <td>Mist_Coludy</td>\n",
              "      <td>14.902598</td>\n",
              "      <td>69.6087</td>\n",
              "      <td>16.652113</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Working</td>\n",
              "      <td>Clear</td>\n",
              "      <td>8.050924</td>\n",
              "      <td>43.7273</td>\n",
              "      <td>16.636703</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Working</td>\n",
              "      <td>Clear</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>59.0435</td>\n",
              "      <td>10.739832</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spring</td>\n",
              "      <td>2018</td>\n",
              "      <td>January</td>\n",
              "      <td>Not_official_holiday</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>Working</td>\n",
              "      <td>Clear</td>\n",
              "      <td>9.305237</td>\n",
              "      <td>43.6957</td>\n",
              "      <td>12.522300</td>\n",
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              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 35 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing the previous categorical columns whose encoding is completed"
      ],
      "metadata": {
        "id": "Zy4EELNcXNn8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result=result.drop(['season','mnth','weekday','weathersit','holiday','workingday'],axis=1)\n",
        "result.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Db2p6nxt0z0L",
        "outputId": "4593cd67-f81e-451b-83d3-c558c7b37536"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     yr       temp      hum  windspeed   cnt  season_spring  season_summer  \\\n",
              "0  2018  14.110847  80.5833  10.749882   985              1              0   \n",
              "1  2018  14.902598  69.6087  16.652113   801              1              0   \n",
              "2  2018   8.050924  43.7273  16.636703  1349              1              0   \n",
              "3  2018   8.200000  59.0435  10.739832  1562              1              0   \n",
              "4  2018   9.305237  43.6957  12.522300  1600              1              0   \n",
              "\n",
              "   season_winter  mnth_August  mnth_December  ...  weekday_Monday  \\\n",
              "0              0            0              0  ...               0   \n",
              "1              0            0              0  ...               0   \n",
              "2              0            0              0  ...               1   \n",
              "3              0            0              0  ...               0   \n",
              "4              0            0              0  ...               0   \n",
              "\n",
              "   weekday_Saturday  weekday_Sunday  weekday_Thrusday  weekday_Tuesday  \\\n",
              "0                 1               0                 0                0   \n",
              "1                 0               1                 0                0   \n",
              "2                 0               0                 0                0   \n",
              "3                 0               0                 0                1   \n",
              "4                 0               0                 0                0   \n",
              "\n",
              "   weekday_Wednesday  weathersit_Light_snow  weathersit_Mist_Coludy  \\\n",
              "0                  0                      0                       1   \n",
              "1                  0                      0                       1   \n",
              "2                  0                      0                       0   \n",
              "3                  0                      0                       0   \n",
              "4                  1                      0                       0   \n",
              "\n",
              "   holiday_Official_holiday  workingday_Working  \n",
              "0                         0                   0  \n",
              "1                         0                   0  \n",
              "2                         0                   1  \n",
              "3                         0                   1  \n",
              "4                         0                   1  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
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          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Normalization"
      ],
      "metadata": {
        "id": "iQP5WqfWXWUX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applied mim max scalar normalization to make very value between 0 and 1"
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      "metadata": {
        "id": "Lw2rE7ejXYRd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "# X dataframe normalization\n",
        "\n",
        "num_vars=['yr','temp','hum','windspeed','cnt']\n",
        "result[num_vars]=scaler.fit_transform(result[num_vars])\n",
        "result.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_Ag5OwuF9kzZ",
        "outputId": "7ec8821b-fbb6-4721-e5c0-7a11bff65903"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    yr      temp       hum  windspeed       cnt  season_spring  season_summer  \\\n",
              "0  0.0  0.355170  0.828620   0.284606  0.110792              1              0   \n",
              "1  0.0  0.379232  0.715771   0.466215  0.089623              1              0   \n",
              "2  0.0  0.171000  0.449638   0.465740  0.152669              1              0   \n",
              "3  0.0  0.175530  0.607131   0.284297  0.177174              1              0   \n",
              "4  0.0  0.209120  0.449313   0.339143  0.181546              1              0   \n",
              "\n",
              "   season_winter  mnth_August  mnth_December  ...  weekday_Monday  \\\n",
              "0              0            0              0  ...               0   \n",
              "1              0            0              0  ...               0   \n",
              "2              0            0              0  ...               1   \n",
              "3              0            0              0  ...               0   \n",
              "4              0            0              0  ...               0   \n",
              "\n",
              "   weekday_Saturday  weekday_Sunday  weekday_Thrusday  weekday_Tuesday  \\\n",
              "0                 1               0                 0                0   \n",
              "1                 0               1                 0                0   \n",
              "2                 0               0                 0                0   \n",
              "3                 0               0                 0                1   \n",
              "4                 0               0                 0                0   \n",
              "\n",
              "   weekday_Wednesday  weathersit_Light_snow  weathersit_Mist_Coludy  \\\n",
              "0                  0                      0                       1   \n",
              "1                  0                      0                       1   \n",
              "2                  0                      0                       0   \n",
              "3                  0                      0                       0   \n",
              "4                  1                      0                       0   \n",
              "\n",
              "   holiday_Official_holiday  workingday_Working  \n",
              "0                         0                   0  \n",
              "1                         0                   0  \n",
              "2                         0                   1  \n",
              "3                         0                   1  \n",
              "4                         0                   1  \n",
              "\n",
              "[5 rows x 29 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check point to save the data in bike_data_enc variable\n",
        "bike_data_enc=result"
      ],
      "metadata": {
        "id": "LVeGo2n4o9AX"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Splitting the data"
      ],
      "metadata": {
        "id": "V8OyuFqfXvTW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting the x part of the data removing only the  cnt variable and putting in X data frame"
      ],
      "metadata": {
        "id": "OjRqgdRXXxoB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=bike_data_enc.drop(['cnt','holiday_Official_holiday','season_spring','mnth_July','mnth_March'],1)\n",
        "\n",
        "# trying to drop variable based on multiple model selection\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "kgyjIiov7T-7",
        "outputId": "846f7823-e2ed-437b-cc5f-cc1077e5f7db"
      },
      "execution_count": 24,
      "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"
          ]
        },
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            "text/plain": [
              "    yr      temp       hum  windspeed  season_summer  season_winter  \\\n",
              "0  0.0  0.355170  0.828620   0.284606              0              0   \n",
              "1  0.0  0.379232  0.715771   0.466215              0              0   \n",
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              "4  0.0  0.209120  0.449313   0.339143              0              0   \n",
              "\n",
              "   mnth_August  mnth_December  mnth_February  mnth_January  ...  \\\n",
              "0            0              0              0             1  ...   \n",
              "1            0              0              0             1  ...   \n",
              "2            0              0              0             1  ...   \n",
              "3            0              0              0             1  ...   \n",
              "4            0              0              0             1  ...   \n",
              "\n",
              "   mnth_September  weekday_Monday  weekday_Saturday  weekday_Sunday  \\\n",
              "0               0               0                 1               0   \n",
              "1               0               0                 0               1   \n",
              "2               0               1                 0               0   \n",
              "3               0               0                 0               0   \n",
              "4               0               0                 0               0   \n",
              "\n",
              "   weekday_Thrusday  weekday_Tuesday  weekday_Wednesday  \\\n",
              "0                 0                0                  0   \n",
              "1                 0                0                  0   \n",
              "2                 0                0                  0   \n",
              "3                 0                1                  0   \n",
              "4                 0                0                  1   \n",
              "\n",
              "   weathersit_Light_snow  weathersit_Mist_Coludy  workingday_Working  \n",
              "0                      0                       1                   0  \n",
              "1                      0                       1                   0  \n",
              "2                      0                       0                   1  \n",
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              "      <td>0.0</td>\n",
              "      <td>0.209120</td>\n",
              "      <td>0.449313</td>\n",
              "      <td>0.339143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 24 columns</p>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "          const element = document.querySelector('#df-096adb27-d01e-4b1d-87e3-75382827605a');\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting the y part of the data taking only the  cnt variable and putting in y data frame as cnt is out target variable"
      ],
      "metadata": {
        "id": "FMJSfh5DX8_f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=bike_data_enc['cnt']\n",
        "y=y.values.reshape(-1,1)\n",
        "print(y.shape)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4yQ9wos7SVO",
        "outputId": "87f779d9-7b8e-4831-e857-09b7758573a7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(730, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dividing the data in Test and train split with train as 70 and test as 30 ratio"
      ],
      "metadata": {
        "id": "XQFwmiN3YIM_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y, train_size=0.7,random_state=100)\n",
        "\n",
        "\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "\n",
        "\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8ddvS832oDu",
        "outputId": "0b9fab84-574a-4c15-d7d8-24996adc25f8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(510, 24)\n",
            "(220, 24)\n",
            "(510, 1)\n",
            "(220, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prediction"
      ],
      "metadata": {
        "id": "PYy4bnYDacHE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying the const for variable other than cnt for our multiple linear regression, also applying OLS model to fit the normalised data"
      ],
      "metadata": {
        "id": "hZwo3imdYTYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_sm=sm.add_constant(X_train)\n",
        "X_test_sm=sm.add_constant(X_test)\n",
        "\n",
        "lr=sm.OLS(y_train,X_train_sm)\n",
        "lr_model=lr.fit()\n",
        "lr_model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 998
        },
        "id": "BcvyPfcF-lQb",
        "outputId": "f44280c4-df6a-483b-ca16-f349b9a991c1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       0.852\n",
              "Model:                            OLS   Adj. R-squared:                  0.844\n",
              "Method:                 Least Squares   F-statistic:                     116.1\n",
              "Date:                Wed, 16 Mar 2022   Prob (F-statistic):          8.38e-184\n",
              "Time:                        16:26:07   Log-Likelihood:                 525.72\n",
              "No. Observations:                 510   AIC:                            -1001.\n",
              "Df Residuals:                     485   BIC:                            -895.6\n",
              "Df Model:                          24                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==========================================================================================\n",
              "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "const                      0.2234      0.043      5.151      0.000       0.138       0.309\n",
              "yr                         0.2322      0.008     28.983      0.000       0.216       0.248\n",
              "temp                       0.4538      0.033     13.576      0.000       0.388       0.519\n",
              "hum                       -0.1549      0.038     -4.061      0.000      -0.230      -0.080\n",
              "windspeed                 -0.1963      0.027     -7.395      0.000      -0.249      -0.144\n",
              "season_summer              0.0747      0.015      4.986      0.000       0.045       0.104\n",
              "season_winter              0.1328      0.022      5.946      0.000       0.089       0.177\n",
              "mnth_August                0.0536      0.017      3.154      0.002       0.020       0.087\n",
              "mnth_December             -0.0382      0.025     -1.509      0.132      -0.088       0.012\n",
              "mnth_February             -0.0397      0.022     -1.827      0.068      -0.082       0.003\n",
              "mnth_January              -0.0700      0.022     -3.174      0.002      -0.113      -0.027\n",
              "mnth_June                  0.0177      0.018      0.977      0.329      -0.018       0.053\n",
              "mnth_May                   0.0276      0.018      1.497      0.135      -0.009       0.064\n",
              "mnth_November             -0.0284      0.029     -0.967      0.334      -0.086       0.029\n",
              "mnth_October               0.0179      0.028      0.629      0.530      -0.038       0.074\n",
              "mnth_September             0.1145      0.018      6.286      0.000       0.079       0.150\n",
              "weekday_Monday            -0.0205      0.015     -1.340      0.181      -0.051       0.010\n",
              "weekday_Saturday           0.0852      0.030      2.830      0.005       0.026       0.144\n",
              "weekday_Sunday             0.0316      0.030      1.047      0.295      -0.028       0.091\n",
              "weekday_Thrusday          -0.0090      0.016     -0.577      0.564      -0.039       0.022\n",
              "weekday_Tuesday           -0.0181      0.016     -1.165      0.244      -0.049       0.012\n",
              "weekday_Wednesday         -0.0059      0.015     -0.398      0.691      -0.035       0.023\n",
              "weathersit_Light_snow     -0.2533      0.026     -9.656      0.000      -0.305      -0.202\n",
              "weathersit_Mist_Coludy    -0.0591      0.010     -5.686      0.000      -0.080      -0.039\n",
              "workingday_Working         0.0868      0.027      3.265      0.001       0.035       0.139\n",
              "==============================================================================\n",
              "Omnibus:                       81.924   Durbin-Watson:                   2.060\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              220.513\n",
              "Skew:                          -0.791   Prob(JB):                     1.31e-48\n",
              "Kurtosis:                       5.806   Cond. No.                         28.7\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.852</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.844</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   116.1</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Wed, 16 Mar 2022</td> <th>  Prob (F-statistic):</th> <td>8.38e-184</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>16:26:07</td>     <th>  Log-Likelihood:    </th> <td>  525.72</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   510</td>      <th>  AIC:               </th> <td>  -1001.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   485</td>      <th>  BIC:               </th> <td>  -895.6</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    24</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                  <td>    0.2234</td> <td>    0.043</td> <td>    5.151</td> <td> 0.000</td> <td>    0.138</td> <td>    0.309</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>yr</th>                     <td>    0.2322</td> <td>    0.008</td> <td>   28.983</td> <td> 0.000</td> <td>    0.216</td> <td>    0.248</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>temp</th>                   <td>    0.4538</td> <td>    0.033</td> <td>   13.576</td> <td> 0.000</td> <td>    0.388</td> <td>    0.519</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>hum</th>                    <td>   -0.1549</td> <td>    0.038</td> <td>   -4.061</td> <td> 0.000</td> <td>   -0.230</td> <td>   -0.080</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>windspeed</th>              <td>   -0.1963</td> <td>    0.027</td> <td>   -7.395</td> <td> 0.000</td> <td>   -0.249</td> <td>   -0.144</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>season_summer</th>          <td>    0.0747</td> <td>    0.015</td> <td>    4.986</td> <td> 0.000</td> <td>    0.045</td> <td>    0.104</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>season_winter</th>          <td>    0.1328</td> <td>    0.022</td> <td>    5.946</td> <td> 0.000</td> <td>    0.089</td> <td>    0.177</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_August</th>            <td>    0.0536</td> <td>    0.017</td> <td>    3.154</td> <td> 0.002</td> <td>    0.020</td> <td>    0.087</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_December</th>          <td>   -0.0382</td> <td>    0.025</td> <td>   -1.509</td> <td> 0.132</td> <td>   -0.088</td> <td>    0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_February</th>          <td>   -0.0397</td> <td>    0.022</td> <td>   -1.827</td> <td> 0.068</td> <td>   -0.082</td> <td>    0.003</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_January</th>           <td>   -0.0700</td> <td>    0.022</td> <td>   -3.174</td> <td> 0.002</td> <td>   -0.113</td> <td>   -0.027</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_June</th>              <td>    0.0177</td> <td>    0.018</td> <td>    0.977</td> <td> 0.329</td> <td>   -0.018</td> <td>    0.053</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_May</th>               <td>    0.0276</td> <td>    0.018</td> <td>    1.497</td> <td> 0.135</td> <td>   -0.009</td> <td>    0.064</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_November</th>          <td>   -0.0284</td> <td>    0.029</td> <td>   -0.967</td> <td> 0.334</td> <td>   -0.086</td> <td>    0.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_October</th>           <td>    0.0179</td> <td>    0.028</td> <td>    0.629</td> <td> 0.530</td> <td>   -0.038</td> <td>    0.074</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mnth_September</th>         <td>    0.1145</td> <td>    0.018</td> <td>    6.286</td> <td> 0.000</td> <td>    0.079</td> <td>    0.150</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weekday_Monday</th>         <td>   -0.0205</td> <td>    0.015</td> <td>   -1.340</td> <td> 0.181</td> <td>   -0.051</td> <td>    0.010</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weekday_Saturday</th>       <td>    0.0852</td> <td>    0.030</td> <td>    2.830</td> <td> 0.005</td> <td>    0.026</td> <td>    0.144</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weekday_Sunday</th>         <td>    0.0316</td> <td>    0.030</td> <td>    1.047</td> <td> 0.295</td> <td>   -0.028</td> <td>    0.091</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weekday_Thrusday</th>       <td>   -0.0090</td> <td>    0.016</td> <td>   -0.577</td> <td> 0.564</td> <td>   -0.039</td> <td>    0.022</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weekday_Tuesday</th>        <td>   -0.0181</td> <td>    0.016</td> <td>   -1.165</td> <td> 0.244</td> <td>   -0.049</td> <td>    0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weekday_Wednesday</th>      <td>   -0.0059</td> <td>    0.015</td> <td>   -0.398</td> <td> 0.691</td> <td>   -0.035</td> <td>    0.023</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weathersit_Light_snow</th>  <td>   -0.2533</td> <td>    0.026</td> <td>   -9.656</td> <td> 0.000</td> <td>   -0.305</td> <td>   -0.202</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>weathersit_Mist_Coludy</th> <td>   -0.0591</td> <td>    0.010</td> <td>   -5.686</td> <td> 0.000</td> <td>   -0.080</td> <td>   -0.039</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>workingday_Working</th>     <td>    0.0868</td> <td>    0.027</td> <td>    3.265</td> <td> 0.001</td> <td>    0.035</td> <td>    0.139</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>81.924</td> <th>  Durbin-Watson:     </th> <td>   2.060</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 220.513</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.791</td> <th>  Prob(JB):          </th> <td>1.31e-48</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 5.806</td> <th>  Cond. No.          </th> <td>    28.7</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predicting the R2 score for both Train and test"
      ],
      "metadata": {
        "id": "IB9FtL0TYqij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pred=lr_model.predict(X_train_sm)\n",
        "print('Train R2 score:',r2_score(y_train, y_train_pred))\n",
        "y_test_pred=lr_model.predict(X_test_sm)\n",
        "print('Test R2 score:',r2_score(y_test, y_test_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NecliAFNRhC1",
        "outputId": "6b1fb31f-c071-41d6-f5b1-4f151ba81ad0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train R2 score: 0.8517883502664847\n",
            "Test R2 score: 0.8209957293354585\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MSE_train=mean_squared_error(y_train, y_train_pred)\n",
        "MSE_test=mean_squared_error(y_test, y_test_pred)\n",
        "\n",
        "print('MSE Train :',MSE_train)\n",
        "print('MSE Test :',MSE_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgfC_fe3ZEC3",
        "outputId": "0acf226f-03aa-4ba2-a00c-c6530ffeafef"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE Train : 0.007450001761056115\n",
            "MSE Test : 0.008558960230584238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Checking for the RFE value and Evaluation"
      ],
      "metadata": {
        "id": "JzTSQERzRA_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lt=LinearRegression()\n",
        "lt.fit(X_train,y_train)\n",
        "rfe=RFE(lt)\n",
        "rfe=rfe.fit(X_train,y_train)\n",
        "list(zip(X_train.columns, rfe.support_, rfe.ranking_))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dunzMrFJOuCF",
        "outputId": "a6c52a89-a307-4531-a5db-956503bff828"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('yr', True, 1),\n",
              " ('temp', True, 1),\n",
              " ('hum', True, 1),\n",
              " ('windspeed', True, 1),\n",
              " ('season_summer', True, 1),\n",
              " ('season_winter', True, 1),\n",
              " ('mnth_August', False, 2),\n",
              " ('mnth_December', False, 4),\n",
              " ('mnth_February', False, 6),\n",
              " ('mnth_January', False, 3),\n",
              " ('mnth_June', False, 9),\n",
              " ('mnth_May', False, 7),\n",
              " ('mnth_November', False, 5),\n",
              " ('mnth_October', False, 8),\n",
              " ('mnth_September', True, 1),\n",
              " ('weekday_Monday', False, 10),\n",
              " ('weekday_Saturday', True, 1),\n",
              " ('weekday_Sunday', True, 1),\n",
              " ('weekday_Thrusday', False, 12),\n",
              " ('weekday_Tuesday', False, 11),\n",
              " ('weekday_Wednesday', False, 13),\n",
              " ('weathersit_Light_snow', True, 1),\n",
              " ('weathersit_Mist_Coludy', True, 1),\n",
              " ('workingday_Working', True, 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the feature which is useful for our Bike sharing part"
      ],
      "metadata": {
        "id": "kocvl3nZY4OK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "col=X_train.columns[rfe.support_]\n",
        "col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvEMK-f-QVfM",
        "outputId": "49e1bd06-a985-4880-9b94-9b0905e35afd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['yr', 'temp', 'hum', 'windspeed', 'season_summer', 'season_winter',\n",
              "       'mnth_September', 'weekday_Saturday', 'weekday_Sunday',\n",
              "       'weathersit_Light_snow', 'weathersit_Mist_Coludy',\n",
              "       'workingday_Working'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the feature which is not useful for our Bike sharing part"
      ],
      "metadata": {
        "id": "dNpMRcqOY_TD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "col=X_train.columns[~rfe.support_]\n",
        "col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0S7pc07OQgTW",
        "outputId": "dddc2ab1-451b-46b0-dea2-a96c950dfb07"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mnth_August', 'mnth_December', 'mnth_February', 'mnth_January',\n",
              "       'mnth_June', 'mnth_May', 'mnth_November', 'mnth_October',\n",
              "       'weekday_Monday', 'weekday_Thrusday', 'weekday_Tuesday',\n",
              "       'weekday_Wednesday'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking the VIF value"
      ],
      "metadata": {
        "id": "mZoo7wJAQ6ys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "vif=pd.DataFrame()\n",
        "vif['Features']=X_train.columns\n",
        "vif['VIF']=[variance_inflation_factor(X_train.values,i) for i in range(X_train.shape[1])]\n",
        "vif['VIF']=round(vif['VIF'],2)\n",
        "vif=vif.sort_values(by='VIF', ascending=False)\n",
        "vif"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "86sRvVOIMY1L",
        "outputId": "7597c42c-ee3d-4507-f4be-5ed147b194a1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Features    VIF\n",
              "2                      hum  34.70\n",
              "1                     temp  20.30\n",
              "23      workingday_Working  18.69\n",
              "5            season_winter   8.02\n",
              "3                windspeed   5.78\n",
              "16        weekday_Saturday   5.37\n",
              "17          weekday_Sunday   5.20\n",
              "12           mnth_November   4.53\n",
              "13            mnth_October   4.37\n",
              "4            season_summer   3.37\n",
              "7            mnth_December   3.25\n",
              "22  weathersit_Mist_Coludy   2.35\n",
              "9             mnth_January   2.31\n",
              "20       weekday_Wednesday   2.25\n",
              "15          weekday_Monday   2.13\n",
              "0                       yr   2.11\n",
              "19         weekday_Tuesday   2.02\n",
              "18        weekday_Thrusday   2.01\n",
              "11                mnth_May   1.82\n",
              "6              mnth_August   1.80\n",
              "8            mnth_February   1.76\n",
              "14          mnth_September   1.72\n",
              "10               mnth_June   1.61\n",
              "21   weathersit_Light_snow   1.27"
            ],
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              "\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>Features</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hum</td>\n",
              "      <td>34.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>temp</td>\n",
              "      <td>20.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>workingday_Working</td>\n",
              "      <td>18.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>season_winter</td>\n",
              "      <td>8.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>windspeed</td>\n",
              "      <td>5.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>weekday_Saturday</td>\n",
              "      <td>5.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>weekday_Sunday</td>\n",
              "      <td>5.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>mnth_November</td>\n",
              "      <td>4.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>mnth_October</td>\n",
              "      <td>4.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>season_summer</td>\n",
              "      <td>3.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>mnth_December</td>\n",
              "      <td>3.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>weathersit_Mist_Coludy</td>\n",
              "      <td>2.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>mnth_January</td>\n",
              "      <td>2.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>weekday_Wednesday</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>weekday_Monday</td>\n",
              "      <td>2.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yr</td>\n",
              "      <td>2.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>weekday_Tuesday</td>\n",
              "      <td>2.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>weekday_Thrusday</td>\n",
              "      <td>2.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>mnth_May</td>\n",
              "      <td>1.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>mnth_August</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>mnth_February</td>\n",
              "      <td>1.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>mnth_September</td>\n",
              "      <td>1.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>mnth_June</td>\n",
              "      <td>1.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>weathersit_Light_snow</td>\n",
              "      <td>1.27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "          const element = document.querySelector('#df-9f4bea2f-80fd-466c-b46f-f18197c063af');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg_sum=pd.DataFrame(X_train_sm.columns.values,columns=['Features'])\n",
        "reg_sum['Weights']=lr_model.params.values\n",
        "reg_sum.head(10)\n",
        "# taking top 10 feature"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "NmdOqRmgjbA3",
        "outputId": "8d9a0851-1993-4ebb-ae87-8c136d341470"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Features   Weights\n",
              "0          const  0.223442\n",
              "1             yr  0.232226\n",
              "2           temp  0.453779\n",
              "3            hum -0.154925\n",
              "4      windspeed -0.196343\n",
              "5  season_summer  0.074679\n",
              "6  season_winter  0.132812\n",
              "7    mnth_August  0.053579\n",
              "8  mnth_December -0.038232\n",
              "9  mnth_February -0.039744"
            ],
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              "\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yr</td>\n",
              "      <td>0.232226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>temp</td>\n",
              "      <td>0.453779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hum</td>\n",
              "      <td>-0.154925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>windspeed</td>\n",
              "      <td>-0.196343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>season_summer</td>\n",
              "      <td>0.074679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>season_winter</td>\n",
              "      <td>0.132812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>mnth_August</td>\n",
              "      <td>0.053579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>mnth_December</td>\n",
              "      <td>-0.038232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>mnth_February</td>\n",
              "      <td>-0.039744</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bb1be97-05c2-4e8c-b2db-0885ae2c7e40')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5bb1be97-05c2-4e8c-b2db-0885ae2c7e40 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5bb1be97-05c2-4e8c-b2db-0885ae2c7e40');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,y_test_pred,color='blue',alpha=0.7)\n",
        "plt.xlabel('y_test',fontsize=10)\n",
        "plt.ylabel('Predicted y test',fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "p_3xE-4tlso_",
        "outputId": "add137eb-0cef-47b9-d8d0-2e17c9ea9bf9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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FkLrzLF5s2duLFqU/X6bhwA88YAIbJGPbb9m1tZlojI+bYIyMmNVx7rksFVKMUDxIYIolvr1Q40g0cW/aZNFANTVeO9KaGm9dPiypQlJXrgxmYblxvvKK5aMcO+a9NzBgwrp2rdV1OnzYLJ0jR2w7lcWWTTjw008HSxT0W3YjI8BVVwGXXWYiogqsWDHZWU6KA4oHCUyxxLcXYhzJlsa2b7e7YbesAmS/Lp+sttLTT6e3sDZuBD76UeCxx8wKOn3anNp9fd5kL2Kfc/Wdpk3z6jz5zxcvlkD6mk+5EHK/Zbd1q/3GJ07YY+tWCkexEql4iMj1IvKyiOwVkS8lOeY3RGS3iOwSkX8o9BiJR7FUHy3EOJLdUbs+1s6hC+RmXT5RSY99+6ykxrZtwE9+AvzoR/b+E0/YRL9xI3Dbbfb9R0ZsHBUV9vA7tQcGbEIfHDTRAEz8BgcnLmslEkvAxrNhg71eu3biclex3FCQwhNZnoeIVALoBfCrAA4CeBbATaq623fMQgDfAfArqnpCRGar6tFU52WeR34plvj2fI8jWX7H3r22nPLmmyYYqslzETL9Dm7/Y4+ZL6S62q4hYmNoaLBe3m+8Ye87K0jVwlvr64F587w8lHSFBmfPtuOS5Wa4/tyJkuKA8kyYm4qUTJKgiLwHwDpV/WBs+88AQFX/ynfMXwPoVdX7gp6X4kFyQapEt64ui7bascP2L1sG3HVXeOFINOl++tPmcK6vB375S8v5GB83gaiosNczZ9rrEyc8B3NFhYlHRYWJzfXXewl57lojI8Brr3ljaG+3YzdsAG65xXwhZ85Y1FNbmzmqjxxJnOviT/orlhsKkh2llCQ4D8Drvu2DAK6KO2YRAIjIzwBUwsTmscIMj0xl3N02MDnBzlkH2RDfrtU93323hcI2NpooNDYCb79ty2TuPu/UKbN6ROz57Fk7VsQEYvr0iUt4zqeyaZMde/y4WSAHDpjwvfCC+T/Gx80aGRqypMW2NovKSpf0x4S5qUmxZ5hXAVgIYBWA+QCeFJHLVfVt/0EisgbAGgBo9TciJiRD/BNuPu6o/dnl+/eb/6GiwoSiv9+ExS1HuWUmv5N+aMgc35WV9jw87B2zbt3kcboJPpHFs26dWRnHj5tIuesdOAB84xv2G7BNK4knSvF4A8AFvu35sX1+DgL4haqOANgnIr0wMXnWf5CqbgawGbBlq7yNmEwpsrmjTreUs2CBhcq67HLAlqgAswCGhuzhtgGzPFRtWWlkxPa1tdnEPjBg+9etA269Nfm4Elk8o6MmXkuW2LLW4KCdq7HRG3MyK4xMXaKMtnoWwEIRWSAiNQA+CWBL3DHfh1kdEJFm2DLWq4UcJElMsZQpKUaCZMB3ddmdvard6Q8O2rLTtGler+6aGhMWtzzlrIyGBltumjfPlpXmzTMfx/e/n1o4APPT9PZaZ71t28zacILQ1GR5FddcY+ddutQ+k0mbVlL+RGZ5qOqoiHQBeBzmz7hfVXeJyHoAPaq6JfbeB0RkN4AxALep6vGoxkwM/9KHf3LkhGIk82f4i/t1dtokfOqUOalVraZVdbUJiSs8WFEBvPe99pnhYW+J65VXzFIIUxa+uxs4dMgTp8FBu/6cObbv1KnklgX9GiSetNFWIlKrqkPp9hULjLbKP8XcP7sYCFrG3f87btvmLVPV1poF4H7TlSuBL3/ZC8utrbXjzj8f+P3ft2TCHTtswq+rM6skkX9m1SrgF7/wwn7duOrqgL/4CzsPI6amLjkP1RWR51R1ebp9xQLFI//ks8dFOeBEwYXGDg6aRXHppZYx7YjvZbFzp+13Jc8PH7Zlqr177X3Aqzq7eLEJyYEDQGur5XCMjNjfYcYME517750oALNmebkiZ8+ac9wticUn+pGpR1jxSOrzEJE5IrICwHQRuUJElsceqwDUJfscKX+YVZyari6b+H/5S5ukKyvt+dChiX6P+LpOK1YAy5ebA9uVGnnpJbNIRke90N3GRquldfSo7T940BOXigoTqzfftFyUeERMmGbOtAgrt1RGSFhS+Tw+COBzsCioDQBihi76Adye32GRYiZVDgQxjh+f2J/inHMs/yJoU6POTou+cs2RxsftMTBgFsTgoL3X0GDhviKeJeiq0rokRsfSpVYNGDBBGxsz0VlelGsIpNhJKh6q+i0A3xKRj6vq/yvgmEiRk+8ciFLGLUW5goQu89stYZ05E+w8O3Z4ZcmdgIyN2fbIiFkLIma1+Kvoul4YbjU6vj/4zJleGHBNjfX7WL8+5z8DmQIEibaaLyKNMIvjXgDLAXxJVX+Y15GRomaqR98ky+NwkVbO4nDiMTxsy0WDg8GvoWo+D78QuRyQd70L+PVf90qZDAx4+SBOdN7xDusz8tZbdv2aGjvf+eebr2R01F4TkglB8jx+W1VPAfgAgCYAnwbw1byOipAC4/JWFi2yZkmLFyfPX0mVx+Hu8KuqvKQ+EZuoAZvog9DWZtFW8b6l5mbg0UfN8X7rrWYBLltm16uoMIGorTVREDE/y/i4icb4uC2nvfqqLWFdfbWNb+1aq9DLvB0ShiDi4XwdNwD4tqru8u0jpORxYtDbaxFj/f3m8E7WLClVJ0MXTNDYaCGwgN31O6tg7txg43Hl08fHTXhGRy2J77vfnewz6e4GHn8cuPFG4JJLLCR382bLCamu9sqwV1WZRXLmzMSxj4xYZnrU7YVJaRFEPLaJyA9h4vG4iMwAMJ7fYRFSOJwYpGuW5EjVAMn1GmlpMR+F81c0NJgQxEdcJRvP9Onmu6iq8h7Jlrzil9BWrrRtZ7m4SCzAs4T8uKitqNsLk9IiiHh8AcCXALxLVQcB1AD4fF5HRUgBcWIwODixGKG/WZIff6jy8eOW4Pfkk2atALaUdM455ptw1W6rq6296ty56SflffuA1183i8A5wOvr7Tzxn41fQuvtBW6/3Z5nzjTx6u/3wn0BzyJyuGZRfqJoL0xKiyDioQCWAPij2HY9gGl5GxEhBcaJQV2d1yFwdNS2E+WvOOviwAFg1y6vjEhTk03kL7xg+6ZPNwvETeJAsEm5vt6q67rlquFhE4Dq6smfjV9Cc9ZTXx9w8cVmRbmckYoKK0UyZ87ELoxVVV5DKAfzdkg6gojH3wJ4D4CbYtv9AO7J24gIKTAuqe/0aZu033rLkvpaWhLnr7hQ5ePHbQKur7c6U62t9vruu+25psbOd+qUWTG9vcEm5ZMnJzrbAbvO8PDkz8Yvobm6WIODJmbLlpmTvabGfCEPPmj+EH+Rw3XrTJiibi9MSosgobpXqepyEXkeAGLtYGvyPC5CCoqqF6nkfBWzZlk47KZNZlH4Q3I7O+0OftmyiWVaGhqsw5/LpXDLVqq2/2c/szv9+fOt/3iiKrj795vVcvasl68B2IS+cuXEY+vrgWeesSWuujpb4hoa8qK6mppMGOLrjsWHWb/znZ7fxPX6iP/OhPgJYnmMxPqNKwCISAvoMCdlxKZN5ot497uB978f+MAHgKtiPS0feCB5FFKyMi2zZlnV29paW05y2dzOmmhstGWo22+3ENlEuF7kfud2RYWNx12/u9ssprNn7b2hIbM4hobMagpjRXR2mrhs2GDHq4aLvGKJ/qlHEPH4BoBHAMwWkb8E8DSAv0r9EULySy4nq2TRU9u3Jw7JveMOm2wfe8wsiaeeMh+Dm6hvu80rTVJd7VkBLtHP9e2oqrIlrniWLrUlqqEhL+KqstKc8P4oqE2bzPq5/HI7nytXsnix5atk0nsjVRhyMoL0LyHlR9plK1V9UES2AXg/LL/jo6r6Yt5HRkgSct1PxHX16+vzuui1tNh78aIyNGTRVf7lpP5+c5JfdZU3hkceMWf60JAX3VRZaY/hYcu1GB01sdm4cWI59I99zAobvhpre+aS/xYtmuhwd61snbMe8KobZ1oaP12/8kQE6V9Cyo+0loeIPKCqL6nqPaq6SVVfFJEHCjE4QhKRyd1xKlautLLnp0/bJH36tG23tk5elnrpJa/qbWWlZ0nU1tpdvpss77rLqtZWV9v5AK8/uCslImKPP/1T4Ec/srLqW7cC99wD/MEfmFUxfbo5vJcuteeBAfuuq1fb0tgzz0ysbZVtlFQmFZNT5b2Q8iXIstWl/o2Y/2NFfoZDSHpyPVk9/TRw0UVeX436etueNcsmfn8U0sCAV6/KhdK6arfx1/dbJ3V1tu0KI6ra56qqvB7itbW27803ge99zyKjFi8GFi40ITp1yt47fNisrYsvNn/Hzp0Tl82yiZJyYchhIq9Yon9qkqqfx5+JSD+ApSJyKvboB3AUwD8XbISkrMiFryLXk9W+fRb95Pp3r1hh2wMDk3t3T59u1oJzgDvGxy1Kyn2vO+/0nPDXXgu8733AhRd6olFZaZP/6KgnRiImJq6ceqLe4eefbxZJY6NZIs7f8fLLuektnkm/8kwEh5Q+QToJ/pWq/lmBxpM17CRYvPh9Ff4+IGEnvFydxxGmrW5npznIE/23aWy0JbCBAeuo6JaaHOPjwL//u+131/rBDyZ29HP+ERfaG0+xdnFMVmWYlA456yToKCXhIMVNrnwVqe6OM7Fs/BnjPT3md9ixY3JOxcaNwO7diYXDWSQnTpjvYmjIGi8dP27vHz9u/onRUTv3gQPmq3DncuXWT52ypahlyxKPtViXiFyob0+PPVM4yp+0lkepQcujeMn3XXM2FsnGjcCXv2wTt4idw98HfONGy8twNa9cnaiGBi/B0C1nubyO/n6zJFpbTSwA609+5oxtj4x40VdjY54Tfdo04F//NfGYc211EeLIueVBSK7I911zNpbN975nE/fMmeYor6yc2Af87ru9iruuX0dlpQnA9On2DJgInD5t38tFY+3ZY5+77DILAW5ttaWr6mqzbt75Tlveamiw57lzPSGIt6SA8D4JP0zmI7kibZ6HiGwAcH+sjwchGROk93mma+fd3cATT3h38ZWVJgRtbcGisFzbV3/vC7cfsOUol+xXU+NV4R0etrpWhw971oTLCp8WKx8qAlx55eQyJoD9Bs3Nnm/E+Vrcd0qWz5JJHkeu82PI1CaI5fEigM0i8gsR+V0RmZnvQZHsKNa7y3SRPJlmKrvPjY/bstPYmFeJdufOyWG9yYhfwfVvz5plfgyHv6Vrd7cVG3SfqawEZswwiwQw0UhkcS1bljpKKdf5LLk+H5naBHGY36eq7wXwGQDtAHaIyD+IyPvyPTgSnmIqFZFIxFI5VjOd3Nznqqq8xDvAm+yDuPWWLTOrwfkyRkdt2zmub7vN9rlihWfP2vZtt9n7nZ22HNXQ4FXUdedqbk4sEnfdlVpMc53PwmQ+kksC+TxiiYEXxx7HAGwH8EUReSiPYyMZUCx3l5mIWKaTm/vc0JB9Z3/y3qWXehneqbjrLrMiKiq8QoPnn2/7Aat++5WvmEVx+rQ9f+UrE6viLl0KtLdbst/QkD23t1uuRzKRSCWmqZpOFUN+DJnaBClP8jcAXoK1of2Kqq5Q1a+p6q8BuCLfAyThKJa7y0xELNPJbcEC4OBBzxpwlobL6A4yOXZ22tLTqlWW0e36gPsn81tvteucPm3P8eXUu7rMCb5wIfDe99pzdbXntwkbypqu6VRYAWEyH8klQSyPHQDeqaq/o6rPxL13ZR7GRLKgWO4uMxGxTCe3ri4rIjg+Pjnr+5VXgk+O2eYqZJKdHeR8yZpO5TI/hpCwRJrnISLXA9gIoBLAfar61STHfRzAd2F91FNmBEz1PI9iyQMIk7XtJ1W0Var3GhrMSe4XkMpKWzrq78/serk4PhcUa1Y5KS9KJs8j5ke5B8CHYD3SbxKRJQmOmwHgVgC/KOwIS5NiubtMZUWkigZLdvefzodSXW0C0tTkhb66fIxkhPXLRBWMUCzWZCYUa+QfyZ4okwSvBLBXVV9V1WEADwH4SILj/hzA1wCcLeTgoiIX/9mKoVREMhEDMpuA0/lQ0kVLZXLObI/PFaXqqyimyD+Se1JV1T031SMH154H4HXf9sHYPv8YlgO4QFX/LdWJRGSNiPSISE9fX18OhhYN5fafLZGIZToBp/OhpIuWCnLOY8esKdS//ItV1V20aKKARxWMUCzWZFiKJZ0jFBcAABfRSURBVPKP5IdUGebbYH3LBUArgBOx1+cAOAAgr0aziFQA+DqAz6U7VlU3A9gMmM8jn+PKJ1OhI1smneoAW6KJ96H4l25ctFQYf4S/g+DJk+Yzcf01+vvt0dvrZWGnG0M+cWG9pUSmf2tSGiS1PFR1gaq+A8ATAH5NVZtVtQnAhwH8MAfXfgPABb7t+bF9jhkALgOwVUReA/BuAFtEJLBDJwqyWXYqljDbfJLp+n0QH4orffLpT9vz2rWp/wb+DoIuxPfsWXOyux7jfX3e3XKpLh9FRSn7akh6gvg83q2qj7oNVf0BgKtzcO1nASwUkQUiUgPgkwC2+K5zMiZY7araDuDnAG5MF20VJdkuO5Xrfza/oB49akluYSfgoD6U3l6rftvbm/5v4O8gODrqFTIcG7P3KyutfpUT8FJdPooKim15k7YwIoBDIvI/APzf2PbNAA5le2FVHRWRLgCPw0J171fVXSKyHkCPqm5JfYbiI9tlpyCFA0uN+GJ8AwNe17wjR8KFuyZaulm9euJv3tfnWQytrRP/Bu7ZLWtt327RWYDXza+y0nO6j41ZSfX45TGKRTCc2LJJVHkSpJPguQDuBHAtzAfyJID1qvpW/ocXnijzPHIRj19uHdkyzfcISvxv/tRTVldqeNhaygL2N9izx3PYOmF+7jkTiro6E4qBAa+nhls+bG+3MGBaGKTcCZvnkdbyiInErSJSr6oBqgRNXXLhUC23O9sgTtNsBDP+N6+rM2vNlU8H7G8wOGh9MvxWYUWF14ejpsaq4J45Y69nzLBzLVoUPFGRkKlEkNpWV4vIblhpdojIMhH527yPrAThGu9k0vlxsvUTxf/mLS1mTbS0TPwbOIsjntpaewwPm2B0dFgJkIMHzW8SJlGRkKlEEIf53wD4IIDjAKCq22FLWCQOOlQnk05Qs80FiP/NFy2yareLFk38GyxdOlnEABOawUGzMtraTEiSWYrMWyDEI4jDHKr6urgmCcZYfoZT+pTbslO2pHOaumWtY8eA/fttIp8+3VrAhrlG/G8eX/EWmBiMcPCgV8K9osJe79xpS1vr1ye+DvMWCPEIYnm8LiJXA1ARqRaRP0FsCYuQIHR2mmAsWGAT7aZN3lKPK6f+4os2gdfUmID09dkx6fJmgubVxFsox48DixcDV1xhOR1jY15nwGTiX66h1IRkQpBoq2ZY5dvrYBnmPwTwR4y2IkFJVekXAD78YUvOAyzSqaYGuPBCa/06MJC8QnA2FYQziYwrlorFhOSDfFTVXayqN6vqeao6W1U/BeCSzIdISo1sizWm8xW4iCc/dXWWh5Hqc9n4IDKxIujTIsQjiHj874D7SIkRRBRyEWGUquzKpk0W5dTYCJx7rlkbtbVWNsQdl+hz6c6bjkwj44qhYjEhxUCqqrrvEZG1AFpE5Iu+xzpYRjgpYYKKQi4ijFLd5e/bZ0tUY2MTW8gODiaOkPJbB9n4IGhFEJIdqSyPGgANsIisGb7HKQCfyP/QSK7xWxo332zLRelEIRfFGlPd5S9YYJbGJZd4+RYVFTbG9etTWwfZ5tXQiiAkc1JV1e1W1btghRHv8j2+rqp7CjjGkqZYOqnFWxonTwKvvWZRRw6/KLhxv/IK8MwzE48bGLBjV62yZaZZszwHdiJS3eU7AaipscinZcss32L9+vTWAa0HQqIjSLTVjwCsVtW3Y9uzADykqh8swPhCU0zRVsUUnRNfY2rbNi/zesUK2+dqTrkCjfX1Fj67a5e9f9llNskfPmzLSm+/bXWfRMximDsXuPfecN+tuxu44w5gxw7bXrbMGjhRAAgpLPmItmp2wgEAqnoCwOxMBjfVKKaM5Pjlp7Y2e+7vn7zk4x93S4uJxrRpwEsvmbjMmWOhtTU1Jh5VVfb6xIlw382JKwBcfXXyLHBCSPERRDzGRaTVbYhIG6y6LklDMTV3incuNzdbxdiZMycv+cSPu7kZuPJKc2w//LCJjOu656istH1hvlsxiSshJBxBypN8GcDTItINSxK8BsCavI6qTIiybWk8iXqFVFcDDz44eYko3bgXLLBSIqOjnoCMjZn1Eea7sdwHIaVLWstDVR8DsBzAPwF4CMAKVX083wMrB4qpym4Y53K6cXd1WU7G8LBFbI2O2utZs8J9t0ShtgcPmk8l6gADQkhqkjrMReRiVX1JRJYnel9Vn8vryDKkmBzmQG76PxSih0T8NVautDatya7Z3Q3ceadlgQPmr3ARUmGu6Q8oOHjQkgMvugiYP5/lPwgpJGEd5qnE415VvUVEfprgbVXVX8l0kPmk2MQjWwoRsZXpNXItjIcPW1vY1lbv/Vx2HSSEJCdn4lGqlJt45LuNq7tGb69VsnW9LVparCdGsmvkQ9Ry0caXEJIZOWtDKyIfS/VBVf1emIGRzCiEU3n7dpugXcjt0JAlEJ45k/wz/kgpwHvetClz8SimAANCSGpSRVv9Wux5NoCrAfwktv0+AP8OgOJRAHI9oSZaahoctPdc5FRVlddhLxn5ELVEEWFTvY0vIcVKqvIkn1fVzwOoBrBEVT+uqh8HcGlsHykAuYzYSlYM0TWJHB2d+Fxfn/xc+WiMxHIjhJQOQfI8LlBVf1PQIwBakx1Mcku6Nq5B6e62YognT1oJ9LY2S/4DrJJte/tEn8f8+ebzSEa+rAS28SWkNAgiHj8WkccB/GNs+zcBPJG/IZF4wkyoiZalAJvoT560/uBDQ9b29ZJLLF+jrs4SBhcuDC4EuRI1QkhpEijaSkR+HcC1sc0nVfWRvI4qC8ot2ioMySKgGhrMutizx4TD+TRqa00wXDFECgEhU5ecRVvF8RyAflV9QkTqRGSGqvZnNkSSL5JFQG3fboUH29uB3bttX2WlFUV0FgaXiwghYUhbnkREbgHwXQB/F9s1D8D38zkokhnJCjECZoU0NZkvY3AQOHbMKy+ydi1LgRBCwhGkqu4fAHgvrIMgYo2gclKSXUSuF5GXRWSviHwpwftfFJHdIrJDRH4cq+hLkpAsAmrpUrMwDhwAXn/dcjmmT7dkvFdftWWsTHqTE0KmLkHEY0hVh92GiFQhByXZRaQSwD0APgRgCYCbRGRJ3GHPA+hQ1aUw6+evs71uKRK0G2GysN716825ffy47a+vt/4cdXUmJAcOsBw6ISQcQcSjW0RuBzBdRH4VwMMA/iUH174SwF5VfTUmTg8B+Ij/AFX9qaq6VLWfA5ifg+uWFMlyMxIJSKo8ic5Oa+J07bXWOXBszCyOykovGZDl0AkhQQniMP/vAP4rgF8C+B0AjwK4LwfXngfgdd/2QQBXpTj+CwB+kIPrlhRhy4Ckcnz7s9Xr6izyCrDXAEuBEEKCk9LyiC0tvaiq96rqalX9ROx1QaspisinAHQAuDvJ+2tEpEdEevr6+go5tLyTy26E/mWttjZzmA8PWxXbKHuNEEJKj5TioapjAF72t6HNIW8AuMC3PT+2bwIich2sm+GNqjqUZJybVbVDVTtaWlryMNToyGUZEP+y1siILV8tX245HywFQggJQ5Blq1kAdonIMwBOu52qemOW134WwEIRWQATjU8C+C3/ASJyBSxE+HpVPZrl9UqSXJcBYT4HISQXBBGP/5mPC6vqqIh0AXgcQCWA+1V1l4isB9Cjqltgy1QNAB4Wq953IAeiVVKwDAghpBhJ1UlwGoDfBXARzFn+96o6WsCxZUS5lycpREtaQsjUI2x5klQ+j2/BnNS/hOVibMhybCRG0LyNRJ9zYbtVVcDWrcANNwCrVhVvcl+m35UQUtykEo8lqvopVf07AJ8AcE2BxlTWhMnbiMeF7fb1Adu2ASdOmOP7+eeLMzs8m+9KCCluUonHiHtRCstVpYI/b6OiIlxm9759lpuxZ49tV1RYtdyBARORYssOz+a7EkKKm1QO82Uicir2WmAZ5qdir1VVG5N/lCSiuxt44gmvRIhryJQqb8Pv4zh82CrhqlpmuKOy0u7qq4usv2Mh+q8TQqIhqXioamWy90h43BJOVZWVBvE3ZKqpSZy34e/Pcd55Zl0cOmRtY12cg6oXwus/R7KmUIV0tue6/zohpHgIUtuK5AC3hLNwoVkegFkMe/cmz9uIX/ZpbbUWshUVJkCACcf4uImSO0ciX8OaNcAtt+Tf/+B3kB89atZSLvqvE0KKC4pHgXBlRpqagCVLrIvf6Kg94jO73QT86KNAb69Vw3UsWWKWymWX2blGR01M1q3zzpHI1/DWW+Zgz6f/IV60ALOMRCYXaiSElDZBOwmSLPEv4TQ12ePUKZtQ44XDLVU1NlrF2927TTSamkw43vUuoKXFfByJlp8S+RqGh73Xx44B+/ebFVBRYdfMxYSerIhjS4uFFRNCygeKR4EIUmakuxu4+Wbg5Elbnpo1Czhzxu7e9+0zsTh9Ov3deyJfQ02NPR87Zr6WykrvsXZtbiwCOsgJmTpw2apApOq1AXgWx8mT1uVvaMiaNI2NmfXR12fLP0Em+URNoc4918Ro714vUmt83HwwuVq+ymURR0JIcUPxiFGITOjOTuDhh4GeHnv2i4Bb8pkxwwRDFTh71kSksdEm/viJOdV14oVq82bg3ns9P0ttrbcUlivrIFknQzrICSk/kta2KlUyqW3l9zP4l5QK6dzt6LAlnxMnzMcxOGgC4kJxXUjv7NkmPJmyevXkJS3ne8nmvA7W3iKkNAlb24o+D4Tv1pcPnJ/CRWM9+6wJR1WVCUdzs93NZ2sh5LrEezws+U7I1IDLVshtt75M8S/5zJoFnHOO+T6uuMKEA8iN/yCd74UQQoJAywPFkQkd37fj0kstm7ymxiyOXFoItA4IIdlCywPF4+j1O9S3bjUHNy0EQkgxQod5jEwcvXQOE0LKhbAOc4pHhhRDhBYhhOSKXHYSJClgrwpCyFSG4pEhxRChRQghUUHxyJAgpTjYv5sQUq5QPDIkXYQW+3cTQsoZikeGpEu2o0+EEFLOMEkwC1Il27E8OSGknKHlkSdYnpwQUs5QPPJEsWStE0JIPqB45AkWICSElDOR+jxE5HoAGwFUArhPVb8a934tgG8DWAHgOIDfVNXXCj3OTGEBQkJIuRKZ5SEilQDuAfAhAEsA3CQiS+IO+wKAE6p6EYC/AfC1wo6SEEJIIqJctroSwF5VfVVVhwE8BOAjccd8BMC3Yq+/C+D9IiIFHCMhhJAERCke8wC87ts+GNuX8BhVHQVwEkBTQUZHCCEkKWXhMBeRNSLSIyI9fX19UQ+HEELKnijF4w0AF/i258f2JTxGRKoAzIQ5ziegqptVtUNVO1paWvI0XEIIIY4oxeNZAAtFZIGI1AD4JIAtccdsAfDZ2OtPAPiJllsDEkIIKUEiC9VV1VER6QLwOCxU935V3SUi6wH0qOoWAH8P4AER2QvgLZjAEEIIiZhI8zxU9VEAj8btu8P3+iyA1YUeFyGEkNSUhcOcEEJIYaF4EEIICQ3FgxBCSGgoHoQQQkJD8SCEEBIaigchhJDQUDwIIYSEhuJBCCEkNBQPQgghoaF4EEIICQ3FgxBCSGgoHoQQQkJD8SCEEBIaigchhJDQUDwIIYSEhuJBCCEkNBQPQgghoaF4EEIICQ3FgxBCSGgoHoQQQkJD8SCEEBIaigchhJDQUDwIIYSEhuJBCCEkNBQPQgghoaF4xNHdDaxeDXR02HN3d9QjIoSQ4oPi4aO7G1i7Fjh6FDjvPHteu5YCQggh8VA8fGzaBNTXA42NQEWFPdfX235CCCEekYiHiJwrIj8SkT2x51kJjnmniPyHiOwSkR0i8pv5Hte+fUBDw8R9DQ22nxBCiEdUlseXAPxYVRcC+HFsO55BAJ9R1UsBXA/gf4nIOfkc1IIFwMDAxH0DA7afEEKIR1Ti8REA34q9/haAj8YfoKq9qron9voQgKMAWvI5qK4u4PRp4NQpYHzcnk+ftv2EEEI8ohKP81T1zdjrwwDOS3WwiFwJoAbAK/kcVGcnsGEDMHs2cOSIPW/YYPsJIYR4VOXrxCLyBIA5Cd76sn9DVVVENMV55gJ4AMBnVXU8yTFrAKwBgNbW1ozHDJhQUCwIISQ1eRMPVb0u2XsickRE5qrqmzFxOJrkuEYA/wbgy6r68xTX2gxgMwB0dHQkFSJCCCG5Iaplqy0APht7/VkA/xx/gIjUAHgEwLdV9bsFHBshhJA0RCUeXwXwqyKyB8B1sW2ISIeI3Bc75jcAXAvgcyLyQuzxzmiGSwghxI+oltcqT0dHh/b09EQ9DEIIKSlEZJuqdgQ+vtzEQ0T6AOyPehxFQDOAY1EPokjgb2Hwd/Dgb2H4f4c2VQ2cDlF24kEMEekJcxdRzvC3MPg7ePC3MLL5HVjbihBCSGgoHoQQQkJD8ShfNkc9gCKCv4XB38GDv4WR8e9AnwchhJDQ0PIghBASGooHIYSQ0FA8ShwRuV5EXhaRvSIyqS+KiHxRRHbHGmr9WETaohhnvkn3O/iO+7iIqIiUbZhmkN9CRH4j9u9il4j8Q6HHWAgC/N9oFZGfisjzsf8fN0QxznwjIveLyFER2ZnkfRGRb8R+px0isjzQiVWVjxJ9AKiElal/B6xk/XYAS+KOeR+Autjr3wPwT1GPO4rfIXbcDABPAvg5gI6oxx3hv4mFAJ4HMCu2PTvqcUf0O2wG8Hux10sAvBb1uPP0W1wLYDmAnUnevwHADwAIgHcD+EWQ89LyKG2uBLBXVV9V1WEAD8Eabf0nqvpTVR2Mbf4cwPwCj7EQpP0dYvw5gK8BOFvIwRWYIL/FLQDuUdUTAKCqCatalzhBfgcF0Bh7PRPAoQKOr2Co6pMA3kpxyEdgBWhVrXr5ObFq5ymheJQ28wC87ts+GNuXjC/A7jDKjbS/Q8wUv0BV/62QA4uAIP8mFgFYJCI/E5Gfi8j1BRtd4QjyO6wD8CkROQjgUQB/WJihFR1h5xEAeeznQYoLEfkUgA4AU67VlYhUAPg6gM9FPJRioQq2dLUKZok+KSKXq+rbkY6q8NwE4JuqukFE3gPgARG5TJM0nSMToeVR2rwB4ALf9vzYvgmIyHWwDo43qupQgcZWSNL9DjMAXAZgq4i8BlvX3VKmTvMg/yYOAtiiqiOqug9AL0xMyokgv8MXAHwHAFT1PwBMgxUKnGoEmkfioXiUNs8CWCgiC2LNsz4Ja7T1n4jIFQD+DiYc5bi2DaT5HVT1pKo2q2q7qrbDfD83qmo51u5P+28CwPdhVgdEpBm2jPVqIQdZAIL8DgcAvB8AROQSmHj0FXSUxcEWAJ+JRV29G8BJVX0z3Ye4bFXCqOqoiHQBeBwWXXK/qu4SkfUAelR1C4C7ATQAeFhEAOCAqt4Y2aDzQMDfYUoQ8Ld4HMAHRGQ3gDEAt6nq8ehGnXsC/g5rAdwrIn8Mc55/TmPhR+WEiPwj7GahOebfuRNANQCo6v+B+XtuALAXwCCAzwc6bxn+VoQQQvIMl60IIYSEhuJBCCEkNBQPQgghoaF4EEIICQ3Fg5A8ISLtIvJbWXz+9lyOh5BcQvEgJH+0A8hYPABQPEjRQvEgJCQisl5E/ptv+y9F5NYEh34VwDUi8oKI/LGIVIrI3SLybKz09e/EPj9XRJ6MHbdTRK4Rka8CmB7b92CBvhohgWGeByEhEZF2AN9T1eWxull7AFwZn2gnIqsA/Imqfji2vQZW/vwvRKQWwM8ArAbwMQDTVPUvRaQSVkK/X0QGVLWhYF+MkBAww5yQkKjqayJyPFb65TwAzwfM0P4AgKUi8onY9kxYTalnAdwvItUAvq+qL+Rl4ITkEIoHIZlxH6xK7xwA9wf8jAD4Q1V9fNIbItcC+C8AvikiX1fVb+dqoITkA/o8CMmMRwBcD+BdsPpJieiHVfR1PA7g92IWBkRkkYjUx1oDH1HVe2Gi5NqAjrhjCSk2aHkQkgGqOiwiPwXwtqqOJTlsB4AxEdkO4JsANsIisJ4Tq1LZB+CjsKJ1t4nICIABAJ+JfX4zgB0i8pyq3pyv70JIJtBhTkgGxBzlzwFYrap7oh4PIYWGy1aEhERElsDKV/+YwkGmKrQ8CMkSEbkcwANxu4dU9aooxkNIIaB4EEIICQ2XrQghhISG4kEIISQ0FA9CCCGhoXgQQggJDcWDEEJIaCgehBBCQvP/AXYX1c31GkCGAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_train,y_train_pred,color='blue',alpha=0.7)\n",
        "plt.xlabel('y_train',fontsize=10)\n",
        "plt.ylabel('Predicted y train',fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "kqB75OTYklPs",
        "outputId": "7b7d003a-638c-4b40-f0f5-0a797332575e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusion"
      ],
      "metadata": {
        "id": "_Z1OQWMHmSNa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Train* data R2: `85.17 `  *Test* data R2 : `82.02`"
      ],
      "metadata": {
        "id": "7tI5yxLhmVVV"
      }
    }
  ]
}