Flight_Price_Prediction_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Flight Price Prediction .ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bvm6eBMMu6KW",
        "colab_type": "code",
        "outputId": "f6fe9c23-3a8d-47f6-f8fe-6229fe1929c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ls\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data_Train.xlsx  \u001b[0m\u001b[01;34msample_data\u001b[0m/  Test_set.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79Jf3hrbd-92",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "df_train=pd.read_excel(\"Data_Train.xlsx\")\n",
        "df_test=pd.read_excel(\"Test_set.xlsx\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tVP0d_HJUaw",
        "colab_type": "code",
        "outputId": "c34ea83f-40d4-4a41-8397-d4c9295c8cbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "print(df_train.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       Airline Date_of_Journey    Source  ... Total_Stops Additional_Info  Price\n",
            "0       IndiGo      24/03/2019  Banglore  ...    non-stop         No info   3897\n",
            "1    Air India       1/05/2019   Kolkata  ...     2 stops         No info   7662\n",
            "2  Jet Airways       9/06/2019     Delhi  ...     2 stops         No info  13882\n",
            "3       IndiGo      12/05/2019   Kolkata  ...      1 stop         No info   6218\n",
            "4       IndiGo      01/03/2019  Banglore  ...      1 stop         No info  13302\n",
            "\n",
            "[5 rows x 11 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WTVIS6Bhke1",
        "colab_type": "code",
        "outputId": "2f07768e-f699-40da-f784-0dd111e0d5a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "print(df_test.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             Airline Date_of_Journey  ... Total_Stops              Additional_Info\n",
            "0        Jet Airways       6/06/2019  ...      1 stop                      No info\n",
            "1             IndiGo      12/05/2019  ...      1 stop                      No info\n",
            "2        Jet Airways      21/05/2019  ...      1 stop  In-flight meal not included\n",
            "3  Multiple carriers      21/05/2019  ...      1 stop                      No info\n",
            "4           Air Asia      24/06/2019  ...    non-stop                      No info\n",
            "\n",
            "[5 rows x 10 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7iA0axWisis",
        "colab_type": "code",
        "outputId": "bebcd241-22ff-4ff7-c6b8-ebf7ae8afa43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "#This will return how many rows have NaN values in each column\n",
        "df_train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              1\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        1\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PUN1wPljEDY",
        "colab_type": "code",
        "outputId": "3bb5efe5-2940-4945-f93c-a839d7340cba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "#Dropping these Nan Values\n",
        "df_train.dropna(inplace=True)\n",
        "df_train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              0\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        0\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_du7VSIkqGl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dropping duplicate rows if there are any\n",
        "no_of_duplicates=df_train[df_train.duplicated()]\n",
        "no_of_duplicates.shape\n",
        "df_train.drop_duplicates(keep='first',inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WP5YGYIHnB7m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHPT_9KYni4M",
        "colab_type": "text"
      },
      "source": [
        "# **Data Cleaning**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBo6SB-1nsjA",
        "colab_type": "code",
        "outputId": "6eb405b0-3e0e-4d36-be85-ad1fb63bedc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "print(df_train[\"Additional_Info\"].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No info                         8182\n",
            "In-flight meal not included     1926\n",
            "No check-in baggage included     318\n",
            "1 Long layover                    19\n",
            "Change airports                    7\n",
            "Business class                     4\n",
            "No Info                            3\n",
            "Red-eye flight                     1\n",
            "2 Long layover                     1\n",
            "1 Short layover                    1\n",
            "Name: Additional_Info, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2TxVxGLqJrn",
        "colab_type": "code",
        "outputId": "d75d15bd-87ba-44fa-a220-59d5544c23a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "# replace repeating value in train set\n",
        "df_train[\"Additional_Info\"] = df_train[\"Additional_Info\"].replace({'No Info': 'No info'})\n",
        "print(df_train[\"Additional_Info\"].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No info                         8185\n",
            "In-flight meal not included     1926\n",
            "No check-in baggage included     318\n",
            "1 Long layover                    19\n",
            "Change airports                    7\n",
            "Business class                     4\n",
            "Red-eye flight                     1\n",
            "2 Long layover                     1\n",
            "1 Short layover                    1\n",
            "Name: Additional_Info, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EL1O-d7oqrqd",
        "colab_type": "code",
        "outputId": "935e96b3-07c6-47ef-f794-1cff25783582",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "print(df_train[\"Destination\"].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cochin       4345\n",
            "Banglore     2860\n",
            "Delhi        1265\n",
            "New Delhi     914\n",
            "Hyderabad     697\n",
            "Kolkata       381\n",
            "Name: Destination, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhTW-Jy-r1HH",
        "colab_type": "code",
        "outputId": "8792a19a-7c54-4f43-8bb9-c85f910a5b30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "#delhi new delhi are same so we replace\n",
        "df_train=df_train.replace({\"Delhi\":\"New Delhi\"})\n",
        "df_test=df_test.replace({\"Delhi\":\"New Delhi\"})\n",
        "print(df_train[\"Destination\"].value_counts())\n",
        "print(df_test[\"Destination\"].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cochin       4345\n",
            "Banglore     2860\n",
            "New Delhi    2179\n",
            "Hyderabad     697\n",
            "Kolkata       381\n",
            "Name: Destination, dtype: int64\n",
            "Cochin       1145\n",
            "Banglore      710\n",
            "New Delhi     555\n",
            "Hyderabad     186\n",
            "Kolkata        75\n",
            "Name: Destination, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qh008YbKsFYJ",
        "colab_type": "code",
        "outputId": "69f72614-e89b-40ec-ac48-475f15f1e68b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "#dont rerun this cell again and again else it will give error\n",
        "df_train.Total_Stops.replace(['1 stop', 'non-stop', '2 stops', '3 stops', '4 stops'], [1, 0, 2, 3, 4], inplace=True)\n",
        "df_test.Total_Stops.replace(['1 stop', 'non-stop', '2 stops', '3 stops', '4 stops'], [1, 0, 2, 3, 4], inplace=True)\n",
        "df_train[\"Total_Stops\"] = df_train[\"Total_Stops\"].astype(int)\n",
        "df_test[\"Total_Stops\"] = df_test[\"Total_Stops\"].astype(int)\n",
        "df_train[\"Total_Stops\"].dtype\n",
        "print(df_train.Total_Stops)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0        0\n",
            "1        2\n",
            "2        2\n",
            "3        1\n",
            "4        1\n",
            "        ..\n",
            "10678    0\n",
            "10679    0\n",
            "10680    0\n",
            "10681    0\n",
            "10682    2\n",
            "Name: Total_Stops, Length: 10462, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtkGnYxT0Fvv",
        "colab_type": "code",
        "outputId": "fb29f836-6283-4bdd-80c9-01548722f35c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>24/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>0</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>1/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>9/06/2019</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>12/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>18:05</td>\n",
              "      <td>23:30</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>01/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>16:50</td>\n",
              "      <td>21:35</td>\n",
              "      <td>4h 45m</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey     Source  ... Total_Stops Additional_Info  Price\n",
              "0       IndiGo      24/03/2019   Banglore  ...           0         No info   3897\n",
              "1    Air India       1/05/2019    Kolkata  ...           2         No info   7662\n",
              "2  Jet Airways       9/06/2019  New Delhi  ...           2         No info  13882\n",
              "3       IndiGo      12/05/2019    Kolkata  ...           1         No info   6218\n",
              "4       IndiGo      01/03/2019   Banglore  ...           1         No info  13302\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCDcWZYPUvep",
        "colab_type": "code",
        "outputId": "ad1d7cce-6b55-4ded-e898-1ffa99641d83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(df_train.shape)\n",
        "print(df_test.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10462, 11)\n",
            "(2671, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZ_DV-gA0fhv",
        "colab_type": "code",
        "outputId": "d664ebdc-c310-421a-c5cf-43bde4abbff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "#combining the test and train data frames for a while \n",
        "#so we dont have to data preprocessing again and again for both test and train\n",
        "df_combine=df_train.append(df_test)\n",
        "print(df_combine)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                Airline Date_of_Journey  ... Additional_Info    Price\n",
            "0                IndiGo      24/03/2019  ...         No info   3897.0\n",
            "1             Air India       1/05/2019  ...         No info   7662.0\n",
            "2           Jet Airways       9/06/2019  ...         No info  13882.0\n",
            "3                IndiGo      12/05/2019  ...         No info   6218.0\n",
            "4                IndiGo      01/03/2019  ...         No info  13302.0\n",
            "...                 ...             ...  ...             ...      ...\n",
            "2666          Air India       6/06/2019  ...         No info      NaN\n",
            "2667             IndiGo      27/03/2019  ...         No info      NaN\n",
            "2668        Jet Airways       6/03/2019  ...         No info      NaN\n",
            "2669          Air India       6/03/2019  ...         No info      NaN\n",
            "2670  Multiple carriers      15/06/2019  ...         No info      NaN\n",
            "\n",
            "[13133 rows x 11 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUICVQI52ATM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Essentially all it does is use integer division (//) to test whether the dayofweek attribute of the DatetimeIndex is less than 5. Normally this would return just a True or False, but tacking on the astype(float) at the end returns a 1 or 0 rather than a boolea\n",
        "df_combine[\"Weekend\"] = ((pd.to_datetime(df_combine[\"Date_of_Journey\"], format = '%d/%m/%Y').dt.dayofweek) // 5 == 1).astype(int)\n",
        "df_combine[\"Day\"] = pd.to_datetime(df_combine[\"Date_of_Journey\"], format = '%d/%m/%Y').dt.day_name()\n",
        "df_combine[\"Date\"] = pd.to_datetime(df_combine[\"Date_of_Journey\"], format = '%d/%m/%Y').dt.day\n",
        "df_combine[\"Month\"] = pd.to_datetime(df_combine[\"Date_of_Journey\"], format = '%d/%m/%Y').dt.month"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6m6Hz8N4Xz8",
        "colab_type": "code",
        "outputId": "23005edc-6b34-482c-e054-d14cc1211a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "#we can drop Date_of_Journey as it is redundant now\n",
        "df_combine.drop(labels = 'Date_of_Journey', axis = 1, inplace = True)\n",
        "print(df_combine.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       Airline     Source Destination  ...        Day Date Month\n",
            "0       IndiGo   Banglore   New Delhi  ...     Sunday   24     3\n",
            "1    Air India    Kolkata    Banglore  ...  Wednesday    1     5\n",
            "2  Jet Airways  New Delhi      Cochin  ...     Sunday    9     6\n",
            "3       IndiGo    Kolkata    Banglore  ...     Sunday   12     5\n",
            "4       IndiGo   Banglore   New Delhi  ...     Friday    1     3\n",
            "\n",
            "[5 rows x 14 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLceppbz4nSV",
        "colab_type": "code",
        "outputId": "1eea89d1-3869-477e-b391-70fcf123abe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "source": [
        "df_combine.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Day</th>\n",
              "      <th>Date</th>\n",
              "      <th>Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>0</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>18:05</td>\n",
              "      <td>23:30</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>16:50</td>\n",
              "      <td>21:35</td>\n",
              "      <td>4h 45m</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline     Source Destination  ...        Day Date Month\n",
              "0       IndiGo   Banglore   New Delhi  ...     Sunday   24     3\n",
              "1    Air India    Kolkata    Banglore  ...  Wednesday    1     5\n",
              "2  Jet Airways  New Delhi      Cochin  ...     Sunday    9     6\n",
              "3       IndiGo    Kolkata    Banglore  ...     Sunday   12     5\n",
              "4       IndiGo   Banglore   New Delhi  ...     Friday    1     3\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RmfFkAtAfFK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#BREAKING ROUTE\n",
        "\n",
        "df_combine['Route_1'] = df_combine['Route'] .str.split('→ ').str[0]\n",
        "df_combine['Route_2'] = df_combine['Route'] .str.split('→ ').str[1]\n",
        "df_combine['Route_3'] = df_combine['Route'] .str.split('→ ').str[2]\n",
        "df_combine['Route_4'] = df_combine['Route'] .str.split('→ ').str[3]\n",
        "df_combine['Route_5'] = df_combine['Route'] .str.split('→ ').str[4]\n",
        "\n",
        "df_combine['Route_1'].fillna(\"None\",inplace = True)\n",
        "df_combine['Route_2'].fillna(\"None\",inplace = True)\n",
        "df_combine['Route_3'].fillna(\"None\",inplace = True)\n",
        "df_combine['Route_4'].fillna(\"None\",inplace = True)\n",
        "df_combine['Route_5'].fillna(\"None\",inplace = True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEWe2NN6DQCv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dropping route\n",
        "df_combine.drop(labels = 'Route', axis = 1, inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIS6P5VED3cw",
        "colab_type": "code",
        "outputId": "70ab34a0-d03c-45d9-b1e6-1b6b316e9290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "\n",
        "df_combine['Dep_Hour'] = pd.to_datetime(df_combine.Dep_Time).dt.hour\n",
        "df_combine['Dep_Minutes'] = pd.to_datetime(df_combine.Dep_Time).dt.minute\n",
        "df_combine.drop(labels = 'Dep_Time', axis = 1, inplace = True)\n",
        "df_combine['Arr_Hour'] = pd.to_datetime(df_combine.Arrival_Time).dt.hour\n",
        "df_combine['Arr_Minutes'] = pd.to_datetime(df_combine.Arrival_Time).dt.minute\n",
        "df_combine.drop(labels = 'Arrival_Time', axis = 1, inplace = True)\n",
        "#we can drop duration also because arrival and departure give us same info\n",
        "#df_combine.drop(labels = 'Duration', axis = 1, inplace = True)\n",
        "df_combine.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Day</th>\n",
              "      <th>Date</th>\n",
              "      <th>Month</th>\n",
              "      <th>Route_1</th>\n",
              "      <th>Route_2</th>\n",
              "      <th>Route_3</th>\n",
              "      <th>Route_4</th>\n",
              "      <th>Route_5</th>\n",
              "      <th>Dep_Hour</th>\n",
              "      <th>Dep_Minutes</th>\n",
              "      <th>Arr_Hour</th>\n",
              "      <th>Arr_Minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>0</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>BLR</td>\n",
              "      <td>DEL</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>CCU</td>\n",
              "      <td>IXR</td>\n",
              "      <td>BBI</td>\n",
              "      <td>BLR</td>\n",
              "      <td>None</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>19h</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>DEL</td>\n",
              "      <td>LKO</td>\n",
              "      <td>BOM</td>\n",
              "      <td>COK</td>\n",
              "      <td>None</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>CCU</td>\n",
              "      <td>NAG</td>\n",
              "      <td>BLR</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>4h 45m</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>BLR</td>\n",
              "      <td>NAG</td>\n",
              "      <td>DEL</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline     Source Destination  ... Dep_Minutes  Arr_Hour Arr_Minutes\n",
              "0       IndiGo   Banglore   New Delhi  ...          20         1          10\n",
              "1    Air India    Kolkata    Banglore  ...          50        13          15\n",
              "2  Jet Airways  New Delhi      Cochin  ...          25         4          25\n",
              "3       IndiGo    Kolkata    Banglore  ...           5        23          30\n",
              "4       IndiGo   Banglore   New Delhi  ...          50        21          35\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqqTE7Dp5Y7H",
        "colab_type": "code",
        "outputId": "6411465a-b9be-43a3-ec71-416dce5ff04f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "# cleaning duration column in datasets\n",
        "duration = list(df_combine[\"Duration\"])\n",
        "\n",
        "for i in range(len(duration)) :\n",
        "    if len(duration[i].split()) != 2:\n",
        "        if 'h' in duration[i] :\n",
        "            duration[i] = duration[i].strip() + ' 0m'\n",
        "        elif 'm' in duration[i] :\n",
        "            duration[i] = '0h {}'.format(duration[i].strip())\n",
        "\n",
        "dur_hours = []\n",
        "dur_minutes = []  \n",
        "dur_seconds = []\n",
        "for i in range(len(duration)) :\n",
        "    dur_minutes.append(int(duration[i].split()[0][:-1])*60 + int(duration[i].split()[1][:-1]))\n",
        "    dur_seconds.append(int(duration[i].split()[0][:-1])*60*60 + int(duration[i].split()[1][:-1])*60)\n",
        "df_combine[\"Duration_minutes\"] = dur_minutes\n",
        "df_combine.drop([\"Duration\"], axis=1, inplace = True)\n",
        "df_combine.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Day</th>\n",
              "      <th>Date</th>\n",
              "      <th>Month</th>\n",
              "      <th>Route_1</th>\n",
              "      <th>Route_2</th>\n",
              "      <th>Route_3</th>\n",
              "      <th>Route_4</th>\n",
              "      <th>Route_5</th>\n",
              "      <th>Dep_Hour</th>\n",
              "      <th>Dep_Minutes</th>\n",
              "      <th>Arr_Hour</th>\n",
              "      <th>Arr_Minutes</th>\n",
              "      <th>Duration_minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>BLR</td>\n",
              "      <td>DEL</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>CCU</td>\n",
              "      <td>IXR</td>\n",
              "      <td>BBI</td>\n",
              "      <td>BLR</td>\n",
              "      <td>None</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>2</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>DEL</td>\n",
              "      <td>LKO</td>\n",
              "      <td>BOM</td>\n",
              "      <td>COK</td>\n",
              "      <td>None</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>1140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>CCU</td>\n",
              "      <td>NAG</td>\n",
              "      <td>BLR</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>1</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>BLR</td>\n",
              "      <td>NAG</td>\n",
              "      <td>DEL</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>285</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline     Source Destination  ...  Arr_Hour Arr_Minutes  Duration_minutes\n",
              "0       IndiGo   Banglore   New Delhi  ...         1          10               170\n",
              "1    Air India    Kolkata    Banglore  ...        13          15               445\n",
              "2  Jet Airways  New Delhi      Cochin  ...         4          25              1140\n",
              "3       IndiGo    Kolkata    Banglore  ...        23          30               325\n",
              "4       IndiGo   Banglore   New Delhi  ...        21          35               285\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q527kAP1KP7z",
        "colab_type": "code",
        "outputId": "fcf8a338-a74d-4b09-bbdd-5481b06ba759",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        }
      },
      "source": [
        "df_combine.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline              object\n",
              "Source               object\n",
              "Destination          object\n",
              "Total_Stops           int64\n",
              "Additional_Info      object\n",
              "Price               float64\n",
              "Weekend               int64\n",
              "Day                  object\n",
              "Date                  int64\n",
              "Month                 int64\n",
              "Route_1              object\n",
              "Route_2              object\n",
              "Route_3              object\n",
              "Route_4              object\n",
              "Route_5              object\n",
              "Dep_Hour              int64\n",
              "Dep_Minutes           int64\n",
              "Arr_Hour              int64\n",
              "Arr_Minutes           int64\n",
              "Duration_minutes      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfrUDoPmLeZr",
        "colab_type": "code",
        "outputId": "f60cd25c-8b44-4890-b2df-0ec453ab60b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(df_combine[\"Route_2\"].value_counts())\n",
        "print(df_combine[\"Route_3\"].value_counts())\n",
        "print(df_combine[\"Route_4\"].value_counts())\n",
        "print(df_combine[\"Route_5\"].value_counts())\n",
        "#later on we can change this label encoder to one hot encoder to remove the problem of priority of label encoder\n",
        "#normalizing the data by changing the categorial data into integer variable\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "df_combine[\"Additional_Info\"] = le.fit_transform(df_combine[\"Additional_Info\"])\n",
        "df_combine[\"Airline\"] = le.fit_transform(df_combine[\"Airline\"])\n",
        "df_combine[\"Destination\"] = le.fit_transform(df_combine[\"Destination\"])\n",
        "df_combine[\"Source\"] = le.fit_transform(df_combine[\"Source\"])\n",
        "df_combine['Route_1']= le.fit_transform(df_combine[\"Route_1\"])\n",
        "df_combine['Route_2']= le.fit_transform(df_combine[\"Route_2\"])\n",
        "df_combine['Route_3']= le.fit_transform(df_combine[\"Route_3\"])\n",
        "df_combine['Route_4']= le.fit_transform(df_combine[\"Route_4\"])\n",
        "df_combine['Route_5']= le.fit_transform(df_combine[\"Route_5\"])\n",
        "df_combine['Day'] = le.fit_transform(df_combine[\"Day\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BOM     4885\n",
            "DEL     1925\n",
            "BLR      896\n",
            "DEL      815\n",
            "HYD      785\n",
            "HYD      703\n",
            "CCU      456\n",
            "MAA      322\n",
            "BLR      294\n",
            "JAI      284\n",
            "COK      262\n",
            "AMD      256\n",
            "BBI      139\n",
            "IDR      115\n",
            "GAU      103\n",
            "CCU       97\n",
            "PNQ       77\n",
            "GOI       76\n",
            "LKO       72\n",
            "NAG       71\n",
            "IXR       66\n",
            "BHO       64\n",
            "ATQ       51\n",
            "JDH       49\n",
            "BDQ       45\n",
            "IXB       25\n",
            "TRV       24\n",
            "UDR       23\n",
            "RPR       21\n",
            "VGA       19\n",
            "COK       19\n",
            "IXU       15\n",
            "IXC       14\n",
            "VNS       13\n",
            "KNU       12\n",
            "PAT       11\n",
            "GWL       10\n",
            "DED        6\n",
            "STV        5\n",
            "HBX        3\n",
            "JLR        1\n",
            "IXZ        1\n",
            "VTZ        1\n",
            "IXA        1\n",
            "NDC        1\n",
            "Name: Route_2, dtype: int64\n",
            "None    4324\n",
            "COK     4015\n",
            "BLR     2280\n",
            "BOM     1156\n",
            "DEL      703\n",
            "DEL      149\n",
            "MAA      102\n",
            "COK       88\n",
            "HYD       58\n",
            "AMD       52\n",
            "NAG       30\n",
            "GOI       20\n",
            "IDR       19\n",
            "VGA       18\n",
            "BBI       18\n",
            "HYD       16\n",
            "GAU       15\n",
            "BHO       14\n",
            "PNQ       11\n",
            "HBX        8\n",
            "JDH        7\n",
            "IMF        5\n",
            "ISK        5\n",
            "UDR        4\n",
            "VTZ        4\n",
            "IXR        3\n",
            "TRV        3\n",
            "IXC        3\n",
            "CCU        1\n",
            "TIR        1\n",
            "JAI        1\n",
            "Name: Route_3, dtype: int64\n",
            "None    11380\n",
            "COK      1184\n",
            "BLR       379\n",
            "DEL        96\n",
            "HYD        38\n",
            "BOM        31\n",
            "DEL         9\n",
            "TRV         5\n",
            "HYD         4\n",
            "GWL         3\n",
            "NAG         1\n",
            "AMD         1\n",
            "BBI         1\n",
            "BHO         1\n",
            "Name: Route_4, dtype: int64\n",
            "None    13077\n",
            "COK        29\n",
            "BLR        15\n",
            "DEL         8\n",
            "VGA         2\n",
            "HYD         2\n",
            "Name: Route_5, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqWZw0wNNn9h",
        "colab_type": "code",
        "outputId": "561a18f8-52a4-4e18-8726-fab8524db4fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "df_combine.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Day</th>\n",
              "      <th>Date</th>\n",
              "      <th>Month</th>\n",
              "      <th>Route_1</th>\n",
              "      <th>Route_2</th>\n",
              "      <th>Route_3</th>\n",
              "      <th>Route_4</th>\n",
              "      <th>Route_5</th>\n",
              "      <th>Dep_Hour</th>\n",
              "      <th>Dep_Minutes</th>\n",
              "      <th>Arr_Hour</th>\n",
              "      <th>Arr_Minutes</th>\n",
              "      <th>Duration_minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3897.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>24</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7662.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>13882.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>1140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>6218.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>13302.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>285</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline  Source  Destination  ...  Arr_Hour  Arr_Minutes  Duration_minutes\n",
              "0        3       0            4  ...         1           10               170\n",
              "1        1       2            0  ...        13           15               445\n",
              "2        4       4            1  ...         4           25              1140\n",
              "3        3       2            0  ...        23           30               325\n",
              "4        3       0            4  ...        21           35               285\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUMmeKMaZBxq",
        "colab_type": "text"
      },
      "source": [
        "PREPARING FOR MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_PNPHXOUQ7d",
        "colab_type": "code",
        "outputId": "4eca681b-13cd-4d39-a15e-aa1729e01bbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "df_train = df_combine[0:10462]\n",
        "df_test = df_combine[10462:]\n",
        "df_test = df_test.drop(['Price'], axis =1)\n",
        "x_train=df_train.drop([\"Price\"], axis =1)\n",
        "y_train=np.log1p(df_train.Price)\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10462, 19)\n",
            "(10462,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhNj8vLxzR-6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "# We have created a function to print accuracy metrics which can be used\n",
        "# to get accuracy metrics of all models in upcoming steps\n",
        "def print_accuracy_report(y_test, y_pred,X_test, model):\n",
        " print('R Squared(Accuracy)', metrics.r2_score(y_test, y_pred))   \n",
        " print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        " print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
        " print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        " print('Root Mean Squared Log Error',np.sqrt(mean_squared_log_error( y_test, y_pred )))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3jCVbFC0UW2",
        "colab_type": "code",
        "outputId": "95bea0db-2b84-401c-d125-9e2a7dcfe7a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "# we have created a function to generate linear regression model\n",
        "# which can then be called again after feature selection or other steps\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "def LinearRegressionModel(X,y):\n",
        " X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        " regressor = LinearRegression()  \n",
        " regressor.fit(X_train, y_train)\n",
        " y_pred = regressor.predict(X_test)\n",
        " print(print_accuracy_report(y_test, y_pred, X_test, regressor))\n",
        " return regressor\n",
        "linearModel = LinearRegressionModel(x_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.6061118288899254\n",
            "Mean Absolute Error: 0.2584467151754098\n",
            "Mean Squared Error: 0.10379663304204617\n",
            "Root Mean Squared Error: 0.32217484855594514\n",
            "Root Mean Squared Log Error 0.03236649045818157\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQAgjsIA2I79",
        "colab_type": "code",
        "outputId": "7f72f5e6-d693-4125-82a1-5635c9aae44d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#similarly define a function for random forest regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "def RandomForestRegressorModel(X,y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        "    rf = RandomForestRegressor(random_state=42)\n",
        "    rf.fit(X_train, y_train)\n",
        "    y_pred = rf.predict(X_test)\n",
        "    print(print_accuracy_report(y_test, y_pred, X_test, rf))\n",
        "    return rf\n",
        "randomForestModel = RandomForestRegressorModel(x_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.9349290726063062\n",
            "Mean Absolute Error: 0.07181771339273088\n",
            "Mean Squared Error: 0.017147362291571273\n",
            "Root Mean Squared Error: 0.13094793733225152\n",
            "Root Mean Squared Log Error 0.013063213290358757\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2cHwdP02Y0D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get feature importances from a model\n",
        "import matplotlib.pyplot as plt\n",
        "def plotFeatureImportances(model):\n",
        "    #first print all features importances in descending order\n",
        "    feature_importances = pd.DataFrame(model.feature_importances_,\n",
        "                                   index = x_train.columns,\n",
        "                                    columns=['importance']).sort_values('importance',ascending=False)\n",
        "    print(feature_importances)\n",
        "    # Next plot feature importances to get idea about where the curve breaks\n",
        "    # in the graph i.e. select top appropriate features\n",
        "    features = x_train.columns.tolist()\n",
        "    importances = model.feature_importances_\n",
        "    indices = np.argsort(importances)\n",
        "    plt.title('Feature Importances')\n",
        "    plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "    plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "    plt.xlabel('Relative Importance')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXv89EiU3Mmk",
        "colab_type": "code",
        "outputId": "197a839d-9709-47e3-8483-208d82332dd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "plotFeatureImportances(randomForestModel)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  importance\n",
            "Duration_minutes    0.600584\n",
            "Airline             0.096954\n",
            "Date                0.046357\n",
            "Additional_Info     0.045838\n",
            "Month               0.044986\n",
            "Total_Stops         0.034775\n",
            "Route_2             0.026620\n",
            "Day                 0.015808\n",
            "Dep_Minutes         0.015470\n",
            "Dep_Hour            0.015409\n",
            "Arr_Hour            0.015090\n",
            "Arr_Minutes         0.013700\n",
            "Route_3             0.007179\n",
            "Destination         0.005524\n",
            "Route_1             0.005311\n",
            "Weekend             0.005261\n",
            "Source              0.003106\n",
            "Route_4             0.001990\n",
            "Route_5             0.000039\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwkQTIvH675Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Since we need to send our final predictions made on test dataset in excel format\n",
        "# the below function can be used to generate excel file for a given model which can then be submitted\n",
        "# to the hackathon\n",
        "def generateFinalPredictionFile(model):\n",
        "    y_test_pred = model.predict(X_to_predict.values)\n",
        "    y_test_pred_exp = np.exp(y_test_pred)\n",
        "    print(y_test_pred_exp)\n",
        "    pd.DataFrame(y_test_pred_exp, columns = ['Price']).to_excel(\"Final_Pred.xlsx\", index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUIPxwtn7NxJ",
        "colab_type": "code",
        "outputId": "05af720a-8063-439e-ae7b-0f5331bc3a5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        }
      },
      "source": [
        "# select features using extratreesclassifier\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "def ExtraTreesRegressorModel(X,y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        "    rf = ExtraTreesRegressor()\n",
        "    rf.fit(X_train, y_train)\n",
        "    y_pred = rf.predict(X_test)\n",
        "    print(print_accuracy_report(y_test, y_pred, X_test, rf))\n",
        "    return rf\n",
        "extraTreeRegressorModel = ExtraTreesRegressorModel(x_train,y_train)\n",
        "plotFeatureImportances(extraTreeRegressorModel)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.9382912901007459\n",
            "Mean Absolute Error: 0.06688055788850797\n",
            "Mean Squared Error: 0.01626135737678956\n",
            "Root Mean Squared Error: 0.12752002735566503\n",
            "Root Mean Squared Log Error 0.01271406896535446\n",
            "None\n",
            "                  importance\n",
            "Route_3             0.215119\n",
            "Total_Stops         0.210996\n",
            "Duration_minutes    0.125292\n",
            "Airline             0.102721\n",
            "Route_2             0.056365\n",
            "Date                0.052031\n",
            "Month               0.048954\n",
            "Additional_Info     0.034241\n",
            "Destination         0.018900\n",
            "Arr_Hour            0.018873\n",
            "Route_4             0.018160\n",
            "Dep_Minutes         0.016878\n",
            "Dep_Hour            0.016819\n",
            "Day                 0.015898\n",
            "Source              0.013427\n",
            "Route_1             0.012924\n",
            "Arr_Minutes         0.012548\n",
            "Weekend             0.009677\n",
            "Route_5             0.000177\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8mt1caX7yYZ",
        "colab_type": "code",
        "outputId": "37c21bf1-973f-4725-af9a-bcb982a14976",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "# select features using XGboost\n",
        "from xgboost import XGBRegressor\n",
        "def XGBRegressorModel(X,y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        "    rf = XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
        "                             learning_rate=0.05, max_depth=3, \n",
        "                             min_child_weight=1.7817, n_estimators=2200,\n",
        "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
        "                             subsample=0.5213, silent=1,\n",
        "                             random_state =42, nthread = -1)\n",
        "    rf.fit(X_train, y_train)\n",
        "    y_pred = rf.predict(X_test)\n",
        "    print(print_accuracy_report(y_test, y_pred, X_test, rf))\n",
        "    return rf\n",
        "xgbRegressorModel = XGBRegressorModel(x_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.921421447668181\n",
            "Mean Absolute Error: 0.10132437318966415\n",
            "Mean Squared Error: 0.02070686494183075\n",
            "Root Mean Squared Error: 0.14389880104375696\n",
            "Root Mean Squared Log Error 0.014420614248796482\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24J8-98A9UvV",
        "colab_type": "code",
        "outputId": "a9cfe4fe-8421-4f47-ca2a-cafd32bbb92a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        }
      },
      "source": [
        "plotFeatureImportances(xgbRegressorModel)\n",
        "print(\"74478279728974892742789273984729874987489274\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  importance\n",
            "Total_Stops         0.448369\n",
            "Route_3             0.105457\n",
            "Duration_minutes    0.100771\n",
            "Airline             0.057461\n",
            "Additional_Info     0.043891\n",
            "Destination         0.041995\n",
            "Route_4             0.035657\n",
            "Month               0.031079\n",
            "Date                0.021589\n",
            "Day                 0.021127\n",
            "Route_2             0.019191\n",
            "Route_1             0.012867\n",
            "Weekend             0.012385\n",
            "Source              0.010475\n",
            "Dep_Hour            0.008027\n",
            "Route_5             0.007827\n",
            "Arr_Hour            0.007557\n",
            "Dep_Minutes         0.007184\n",
            "Arr_Minutes         0.007091\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "74478279728974892742789273984729874987489274\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfgOdxA59vti",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #bad model..explain????\n",
        "# from sklearn.svm import SVR\n",
        "# def SVRModel(X,y):\n",
        "#     X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42, test_size=0.3)\n",
        "#     regressor=SVR(kernel='linear',degree=1)\n",
        "#     regressor.fit(X_train, y_train)\n",
        "#     y_pred = regressor.predict(X_test)\n",
        "#     print(print_accuracy_report(y_test, y_pred, X_test, regressor))\n",
        "#     return regressor\n",
        "# svRegressorModel = SVRModel(x_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxUQScp8Fa84",
        "colab_type": "text"
      },
      "source": [
        "Graph of various features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUppm8HIFwuq",
        "colab_type": "code",
        "outputId": "8e19c1d5-2682-40d7-d619-e9820fead348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        }
      },
      "source": [
        "#descriptive statistics summary\n",
        "import seaborn as sns\n",
        "print(df_train['Price'].describe())\n",
        "#skewness and kurtosis\n",
        "print(\"Skewness: %f\" % df_train['Price'].skew())\n",
        "print(\"Kurtosis: %f\" % df_train['Price'].kurt())\n",
        "sns.distplot(df_train['Price'])\n",
        "#the displot is showing density probability plot in regards with price\n",
        "#so area under the curve adds up to one."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    10462.000000\n",
            "mean      9026.790289\n",
            "std       4624.849541\n",
            "min       1759.000000\n",
            "25%       5224.000000\n",
            "50%       8266.000000\n",
            "75%      12344.750000\n",
            "max      79512.000000\n",
            "Name: Price, dtype: float64\n",
            "Skewness: 1.857490\n",
            "Kurtosis: 13.527896\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f765c2db8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vKac7pE6eyj",
        "colab_type": "code",
        "outputId": "abaec8b1-d3ce-4ace-fb83-07a89023efdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "#Due to high positive screwness we will do log tansformation \n",
        "from scipy.stats import norm, skew \n",
        "sns.distplot(np.log1p(df_train[\"Price\"]) , fit=norm);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9vDLmfg6xSi",
        "colab_type": "code",
        "outputId": "196877e5-795f-48c2-83dc-f4c8ee334bb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "#scatter plot Duration_minutes/Price\n",
        "var = 'Duration_minutes'\n",
        "data = pd.concat([y_train, x_train[var]], axis=1)\n",
        "data.plot.scatter(x=var, y='Price');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atdD09aD7HzJ",
        "colab_type": "code",
        "outputId": "7eeacc1f-cd42-4abd-b65d-e9a669927826",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "carrier_count = x_train[\"Airline\"].value_counts()\n",
        "carrier_count.plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f767157e208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOoVHirK-EeY",
        "colab_type": "code",
        "outputId": "550334e7-a0d8-407a-d435-e734ea487890",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "#Variation of price against total number of stops:\n",
        "sns.set(style=\"darkgrid\")\n",
        "figure(figsize=(10,5)) \n",
        "sns.barplot(x=\"Total_Stops\", y=\"Price\", data=df_train, palette=\"Blues_d\")\n",
        "plt.title('Total stops')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Total stops')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTel8NAK-OBR",
        "colab_type": "code",
        "outputId": "a9c2c9a2-64b1-4a99-ecf8-a4cdbebec433",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "#Variation of price with different airline\n",
        "sns.set(style=\"darkgrid\")\n",
        "figure(figsize=(10,5)) \n",
        "sns.barplot(x=\"Airline\", y=\"Price\", data=df_train, palette=\"Blues_d\")\n",
        "plt.title('Price variation of different airlines')\n",
        "plt.xticks(fontsize=10, rotation=80)\n",
        "plt.yticks(fontsize=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([    0., 10000., 20000., 30000., 40000., 50000., 60000., 70000.,\n",
              "        80000.]), <a list of 9 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4fCt7LI-ij7",
        "colab_type": "code",
        "outputId": "8adcbfbc-3df1-4f2a-9ec9-b4acf6fa21bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "df_train['Additional_Info'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7671693cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBBsad9W-7WL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vqSUP-3_VjT",
        "colab_type": "code",
        "outputId": "05e589b7-df57-4972-a429-9119cdb16e88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "from scipy.stats import norm, skew \n",
        "sns.distplot(x_train[\"Dep_Minutes\"] , fit=norm);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntVrYYI4_tQI",
        "colab_type": "code",
        "outputId": "875cebc9-ee84-48a4-f89f-54c10a1b891c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "sns.distplot(np.log1p(x_train[\"Dep_Minutes\"]) , fit=norm);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xgykm1ItOg5Hzdn6h1iwzL3V3d/P//t//46c//Wnvm8Lt8PW1zY+Cd6q6vh13N6c+27m4OOLv4zIIFZmfv7+7tUvoU3V9Oxev1PXZbnx8wOd+D7d7fMoQ+d0P5PiqG9rZfKiEiaOCmDctqvf75jzW/m7LwUHbZzsXFx0uTlrOlTRw9xTb/RRiaX2GfXBwMFVVVRiNRjQaDUajkerqaoKDg3vb1NTUUFpayuOPPw5Ac3MziqLQ2trKSy+91O9i6upaMZmG4Q0QGg0trZ19Nmtv76LGaByEgszL39+dmpoWa5fRp/auntv6PdzJ8d3uPgfTQI/vtffzUEwKX5o58obHmfNY+7ut7u6+27m7OZEU5cuRPD0P3tWEVjN8BiEO5E26z7D39fUlMTGRrKwslixZQlZWFomJifj4+PS2CQkJ4dixY71fv/rqq7S3t/ODH/xggKULIWzZ2cJaTl+q4f5ZURZZXtBSkmP8OJZfRUFZI6Mjffp+wDDUr7e4F154gfXr15ORkcH69etZs2YNAKtWrSIvL8+iBQohbENXt5F3PrxEsK8LGRNHWLucAYkf4YWDVs3Zy7XWLsVq+tVnHx0dzYYNGz73/TfffPOm7b/1rW/dWVVCCJuTfaSE2qZO/uehlCHXFaJz0DAqwpszhbU8NDfWJu8JsLThszSOEHagx3R99atbcXTQYs7ZhXtMcLWqmW1HS0lLDCA8yJ22rs/XYOuX25Jj/DhbVEdFbZvN3hdgSRL2Qgwh/Vnm0NxLHHYauvnT5vNo1Coig9y/cP/Jcf5m26clJMf4wY4CzhTW2mXYD63PYkIIs/nkvoPq+vZb3ndw5HwVVQ0djI/3x3kIr5Pr7e5IRKA7Zwv7Hn47HA3d35wQ4o588inB3c3pC4cvdnT1kHX4KgHezsSGWX+e+juVHOPLlkMlNLcb8HDRWbucQSVn9kKIL3SqoAZDt5HJowPNdlFTpVb1eRezpfr/x8X6oQB5RfZ3di9n9kKIm6qobeNKRTNz08LxcjPfMoNd3UbOXrr1xGSW6v+PCHTH001HblEd05KC+37AMCJn9kKIz+kxmjiWX4W7iwNz08KtXY7ZqFQqkkb6cr64HqPJviZBkrAXQnxOXlEdLe3dTB4diIM5x3HagLHRvrR39djdYuTSjWMhHV09ZB+5Sm1TBx0GIyaTwrgYXzRD7GYUYX/qmzs5V1xPVIgHwb6u1i7H7EZFeqNWqci7UkdcuJe1yxk0kjwWUN/cyU/Xn2bbsatcrWyhsdXA+eJ6th4tpanVYO3yhPhCRpOJg7l6HB00pCbY9rj52+Xi5EBMmKfdXaSVM3szu1bdyq/+dQZDt5GnHkhmzEhfFI2Gd7blcyivkuwjJdwzOQJvd/Nd8BLCXM5crqOx1UD6+FCcdMM3HpKifHhv3xUaWrrs5rUoZ/Zm1GM08X9bzgPww0cmMGakb+//hQW4kTktAq1GzcFcPUZbv7dc2J2qhnbOF9cTG+ZJWMDwvsM0Ker6a/NcP9Y3GC4k7M1o29GrlNe08ZX5CTe9HdvVyYEpY4JoaOki184+Qgrb1mkwcuCsHjdnB1ITAqxdjsWFB7jh5aYjT8JeDJS+ro0th0tISwhgXIzfF7YLD3AjOsSDc1fqqG3sGMQKhbg5RVE4lKens8vIrHEhw270zc2oVCqSonw5X1JPj9E+hmAO/9/qIFAUhb/tKECn1bBibmyf7dMSA3DSaTlxsQZFke4cYV05BTWU17SRmuCPr2ffSwEOF2OjfenoMlJU3mTtUgaFhL0ZXCpr5GJpI0tnjMSzH3ca6hw0JEX5UNPYQVWDnN0L6ymvaeXoOT0RQe7Ej7CfYYgAoyJ90KhV5NpJV87wvdw+iLYeLcXdxYGZySH9fkxMmCe5RXXkFdURNEQXGRd3zmQy0dXZTvk1A92drTQ1NdLS0kJXVxcGgwGD4frfpo/v9uw2mqiobUelVuPgoEOnc8RB54SDzhFnF1dc3TypD9Ti6O+LVnvrl7e+to39Z/T4eDoxdUyQ3S3o4eyoJTbMk7yiepbNtnY1lidhf4dKq1rIu1LHfTOj0Dlo+v04rUbN6JE+nCqooaaxA/8htJ6n6L/Ozk4qy4tpqKumsb6axvqa3r+bG2tpb2tFUczbZ/zax397eHjg5eWDv78/wcEhhISEEhwcQnBwKO5efryxsRCtVsXCaSPBzqYO+ERSlC8b9hZR39yJj8fw7sKSsL9D246V4qTTkD4+dMCPjQv34tyVenKL6pgzIcwC1YnB0tPTQ0nJFYqL//OnpOQKFRXlN1yXcdA54uUTgJdPAKERMbi4euDi6sGY2FD8fX3w9PTC3d0dJycndLrrZ+46nQ6N5vqJRGtnN8fzK1EUE90GA92GTroNXRgMXXS0t9LW2oS/m0JbSxONjY00NtZTXV3NoUMHaGiov6FmtYMzQSER7GyKxds/lMDgCAKCR+DkbD+fNJOir4f9ueL6AX0yH4ok7O9AdWMHxy9UkZE2AhcnhwE/3kGrJiHCi7OFdTS3yZ21Q4WiKFRUlJOff44LF/K5cOE8ly8XYDBc/x1qNBrCw0cQFxfPXXPmY9B44e0biJdPAC6u7jftLklLDMS1HwuDqFQq1Go1oEbjrL1pMH/Rttrb2yktu8YbGw5Sdu0aMX49XCsr4cThXXR1/ufakY9fEGGRcYRHxhMWGceokcO3Lz/UzxVvd0fyiuok7MUX25dTjgoVd9/BrICxYV7kFtVxqayROanDZ3bB4cRkMnH58iVyck6Sk3Oac+fO0tR0fQSHo6Mj8fEJ3HvvMhISEhk5Mprw8BE4OFx/82/r6nsZwcGi0ujYcrqNFodInvtuJokjfThxoQo3V0eulZVRVXGVyoqr6MuKKL6UR+7J/QCse0VHUFgU4SMTiIpNYkR0Io6Ow6PbUaVSMTbal2P5VfQYTUNuIfWBkLC/TT1GE4fy9CTH+N7R7dYuTlrCA9woKm+mu8cE9nHntk1TFIXi4iJOnDjOmTOnyMv7T7iHhoYzbdpMRo0aw6hRo4mMjOrzQqgtaO3o5jf/OsPVyla+vnAUqQkBvYuGq1Sq3q6l+DFpwPWfQXNjHWUlBXQ1lXH69BmO7s3i0EcbUas1hEXGMjI2iai4sYRFxuHgMHRXfUqK8mXfmQoKrzWREOFt7XIsxvafpTbqzOVamtu7zfLRLy7ci9KqVs4W1jJrmH+UtFVdXR1cKcjl8oXT/P7yWaqrr5+Nh4aGMXfuXBITx5KcPJ7AwEArVzpwNY0d/O7fuVQ3dPDN+8aQEtv3BGcqlQpPbz88vf1IjvNnwqUaDF2dlBZfpPhSHlcu57F/53vs27EBB50jUXFj0d81Cxe/eDy9v/imQluUGOHdOwRTwl58zr6zFXi7O/bOsXEngn1dcHN24FCuXsJ+EDU11HIh9ygX805wtSgfo7EHnaMTE1In8uUvP8bEiZMJCgrG39+dmpoWa5d7W/JL6nl94zkUBZ5+IPmOwkzn6ERMwjhiEsYB0NnRRklhPoUXc7h0/hSvv/pLAAJDIogbNYH4MWmERcZ9fI3Bdjk7aokL9yLvSh0P3BVj7XIsRsL+NtQ0dpBfXM+iaZGo1Xc+NlmlUhEX7snpS7VU1LYR4jf85hC3FbXV5eSfPcqFs0cpLy0EwD8wjMmzFhI7ajwjohKZkhTWr4ultsxkUth27Crv779CiK8rT96fRKC3eUfZODm7kpCURkJSGsqXFHyc2tiydReXzp/i0O5NHNj1Pu4e3iQmT2ZU8hQiokf1jiqyNUlRvvxrT+GwHoI5tJ/RVnIgtwJUMGOs+c7Co0M9OVNYx8FcPQ+kD9+zi8GmKAqFhZfZv38Pe/ft5mpJMQChEbHcvWglicmT8AsY+LBZW1bT2MHfd16isLyJtIQAvnpPAs4WfvNSqVSMiBjJ9DlLmT5nKZ0dbVw6f4r8s0fIOfoRxw9sw8XVg8SxEzEuvAfFJRytduAj2CwlKfp62OdeqWP2uOH1fPiEhP0AmRSFQ3mVjB7pY9Z5RJwdtYwe6cPh85XcPzsKjY1/9LV1166V8dFHO/nwwx2UlpagVqsZkzSOBffPIXHspCHXr9wfPUYT54vreffDyzho1Ty+aBSTRgVa5c5YJ2dXxqbOZGzqTAxdnVy+kEP+2SOcO32IU0d24eTsyqjkyYxNnUlkzGjUauue8Yf4uuDrcX0IpoS9AKDgagMNLV0st8DZ96RRgeQV1XG+uJ6x0cMvjCytrq6W3bt3sWvXDi5cuL6uwLhx41m27EFmzboLB2d3mxkGaU5Gk8KViiZyC+to6+xhfJw/K+bG2kx3hM7RidHjpjB63BS6uw2YWorJyt7OuZxDnD76Ee6ePiRNmMHYCTMIDouyypuTSqUiKdqPI+cr6e4xDcuZP/sV9sXFxTz77LM0Njbi5eXF2rVriYyMvKHNa6+9xtatW1Gr1Tg4OPDUU08xY8YMS9RsVYfOVeLsqLnlNMa3a/RIH9ycHTiYVylh30+dnZ3s3fsRO3du4/Tpk5hMJmJj4/nGN75NevrdN4ye+WSo4XDRZTBSVNFEfkkD7Z09+Ho4MX1sMJnTRtrsNQcHBx3Jk6fj6BOPwfDfXDp3ktyT+zi2L5vDuzfhFxjK2NRZBLovAQZ3OGdSlA97c8q5fK2RUZE+g7rvwdCvZ8Tzzz/PihUrWLJkCZs2bWL16tW8/fbbN7QZO3Ysjz32GM7Ozly8eJFHHnmEgwcP4uRkG2cX5tBp6OFUQQ2TRgUMaB6c/tJq1EweHcjenHJaO7pxc7adPk1boigKBQUXyM7ezK5dO2hrayMkJJSVKx9lzpx5REaOtHaJFmPoNlJa1UJJZQulVa2YTAoB3s5MGR1EiJ/LkJrMTKdzZMz4aYwZP432tmbOnzlC7sn97M7+O7uz/05kzGhSJqUzetxUdI6Wz5HECG+0GhW5RXX2GfZ1dXXk5+ezbt06ADIzM3nppZeor6/Hx+c/P5BPn8XHx8ejKAqNjY0EBQVZoGzrOH2phq5uI1PHBFtsH9PGBLPr5DWOX6gifbzMl/NpLS3N7Ny5nezsTRQWXsbR0ZHZs9NZuHAJyckpQyroBqK1o5uzhbWcvlTDueJ6untM6BzUxIZ5Ehfuibf70D+hcnH1IG1aBmnTMmisr6aq+CTbsrfwwTuvkv3vNxmTMo2UyXMYMTLBYr9nJ52WhBHenCmsZXl6zLB7PvUZ9nq9nsDAwN4hUxqNhoCAAPR6/Q1h/2kbN25kxIgRwyroAQ6fq8TP04nYME+L7WNEoBth/m4cytNL2HN9qoKcnFNkZ29m//49GAwG4uMTePrp/2HOnAzc3d2tXaJF1DV1knO5htOXarhU1oRJUfB2d2TKmCAcHdQEeruYZdivLfLyCWDW5K8QN/4erl65QM7Rjzh3+nr/vm9ACCkT0xk3cTYeXnd+j8tnpcT587cdBVTUtt10adGhzOwde8ePH+d3v/sdf/7znwf8WF9f2/3h1jR0cOFqAw/eHU9AgMeAHltd3467W99nXy4ujgT4uJAxJZK3Np+jw6gwImhg+7IWf3/zhm5VVRUffPAB77//PmVlZXh4eLBs2TLuv/9+EhMTb2ubygB+D/6fWWPgdo9vIPt0dNSy63gpB85co/Da9ekZwgPd+dKcWCaPCSImzIuahg5OF1QPuP6+artVjQ4O2n4dgznbOTho8XB3Jil5PEnJ4+nq7ODMiQMcP/Qhu7LW81H234kfPZ72e5fiG5aEto/pGvr7M5k7OZK/7SigoLyZcaMs9wneGvoM++DgYKqqqjAajWg0GoxGI9XV1QQHf/4HkZOTw/e//33+8Ic/EBUVNeBi6uqu90HaouwjJSgKJI/0HvjdlBoNLa2dfTZrb++ixmgkKcILjVpF1v4ilg2BO/rMdYdpT08Phw8fIDt7M8eOHcFkMpGSMoGvfnUVM2fOxvHjftvb3Vd7V8+Afg+fuJPj688+m9sMnMyv5FRBDT1GE1EhHiybHU1KnP8NC9vU1rb2a3ufrb+v2tzdnG65ze7u/v3czNnu821UjEqZyaiUmdTV6Mk5tpszx/fws5d+jIurO2NTZzFhylwCQyI+ty13N6d+/0wAokM8OHCmnPRxtn83+0BOQvoMe19fXzbYSwAAAB2nSURBVBITE8nKymLJkiVkZWWRmJj4uS6c3NxcnnrqKV555RVGjx498KptmKIoHD5XSWyYJwFmvgvxZjxcdSRF+XL4fCX3zRr+Y+5LS6+Snb2J7du30tBQj5+fPw8//GUWLFhMaOjw7cpq7+zhbGEtheVNaDVqZowN5q6UUMICbPcTri3w9Q9mbubDpC94EKX1Ku+99z4nDm7n6L4sQkfEMH7KXJLGz7jtefnHx/mzYW8RdU2dw2pN3n5147zwwgs8++yz/OEPf8DDw4O1a9cCsGrVKr797W+TlJTEmjVr6OzsZPXq1b2P+/nPf058fLxlKh9EJZUt6Ova+cr8wTuWaUlBnCms5XxxA2Ojzd83aW0dHR3s3fsR2dmbyM09i0ajYerU6SxcuJiJE6cMiZkkb5eiKFy+1sSpghqMRhPx4V6snJ9AsCxPOSBqtYbk1EloPKJoa23m7Il9nD66iy3/fIPt7/+Z0SlTGT95LmOSxw9ouykfh/3pyzXcPYymHe/XKyo6OpoNGzZ87vtvvvlm77/fe+8981VlYw7nVaLVqElLCBi0fSbH+OHm7MDhc/phE/aKonDx4gWyszexa9dO2tvbCAsL57//+0kyMhbg6zv87y1o7+zmYG4llfXtBPpcHzLp4arDw3XoThFsC1zdPJh61yKmzM6kvPQyp498RN6pA5w5vhf/wFAyMxezOHNRv55jQT4uhPi5knPJDsPenvUYTRy7UEVKrN9trUZ1u7QaNZNGBbLvTAVtnd24DuK+za25ual3yGRRUeHHQybnsHDh4mE9ZPKzymvaOJirx2gyMXl0ILFhnnZz7INFpVIRFhFHWEQc8+99lPNnDnPm+G7WvfU6b//lj0yePI2FCxczefLUW356TIn1Y9vRUprbDXi4DI83Ygn7PuQV1dHa0c20pMEfRjotKYiPTl3j+IVq7koZWvN1mEwmTp8+SXb2Zg4c2Ns7ZPJ73/sBc+Zk4OZmP/3SiqKQW1TH2cI6vNx0zBoXjqebrFJjaTpHJ1ImpTNzzgLcaGT3h1vZsSObQ4f24+Pjy/z5C1m4cDHh4SM+99iJiYFkH7nKyYvVw2YItIR9Hw7k6vFw1TF65ODfURcR6E6ovyuH8vRDJuyrq6vYti2L7OzNVFbqcXNzJzNzCQsXLiE2Ns7a5Q26TkMP+85UUFrVSlSIB5NHBw7rpe9sVVh4BN/4xrdYteobHDlyiOzszfzzn+/w97+/TXJyCgsXLmb27Dm9d/yH+bsS6ufK0fzhc3OjhP0tNLR0kVtUx/xJI6wyIkalUjFtTDD/2lOIvq6NYF/bnOfeYDCwb98esrM3c/z49SGT48ensmrVN24YMmlvqhraeeXfuVTWtZOa4E9ihLd021iZVqtlxoxZzJgxi9raGrZv30p29mZ+8pM1/O53v2TOnHksXLiEhIREJo0K5P39V6ht7MDPa+ivuSthfwuHz+kxKQozxlrv5oopowP5994iDuVV8qXZ0Var42auXCli69br89PU138yZPIrLFy4mJCQofFJxFLOFtbyxy35qFQwJzVMFqSxQX5+/jzyyFd4+OEvc/ZsDtnZm9ixYyubN39AVFQMs9LvwWjw4tiFKhZOibR2uXdMwv4LmBSFA2f1JIzwItCKQ+I83RwZE+XD4XN67psZZfVb5FtaWti9eyfZ2Vu4eDEfrVZLeno6c+bMJy1t8rAeMtkfJkUh63AJmw4UEx7gxmOZo7hS0WTtssQtqFQqxo0bz7hx4/nOd77PRx/tIDt7M+v+9CoqtZY/FyYRoH2MCRPSbH6JxVux71fmLRSUNlLd2MGSGdafQXF6UjB/KKojv6SeMWZY83agPpmfZuvWLezbtweDoYuoqBiefPIp5s2bT2zsiCG7Rqs5tXf28FZ2PjmXa5k8OpCvzE+g5+O55sXQ4ObmxpIl97Nkyf0UFl7ijXV/5+TRPXzve98iKCiYe+7JZMGCRQQGDr15vyTsv8CBsxW4OGqZEOdv7VJIjvHD1UnLvrMVgxr2lZV6tm3LYtu2rI8vtrqxYMEiFizIJD4+UfqfP+XM5Vr+trOAplYDD82JZW5qGCqVip5hNoe+PYmJiWP1D3/IU69MZaRLJfVXj7Nu3Zv85S9/IjV1IgsXLmb69FnodENjaKaE/U00tRk4WVDNrORQi8xbP1AOWjUzk0PYcbzM4rdwt7e3c+DAXnbs2MqpUycAmDAhjccff4IZM2YN6YutXQYj12paaWzpormtG0VRUKtVOOk0eLo54uWmw2uAQyKvVraQdbiEU5dqCPV35Zv3JhEVMjQmrxN983DVMT4hmIJSZ37184eprbk+2mzr1i288MKP8PT0ZN68e1i4cAlRUbZ1Te2zJOxvYm9OOT1GhfQJtnOR8a7xoWw/XsqenHKzX6jt6enh5Mnj7Ny5jYMH99HZ2UlQUDCPPrqK+fMXEhQ0tGf/u1rZwu7T1+9X6Oq+PhmWs6MGtUqF0aTQ1W1E+Xj+Pa1GxdHzVcSGexId4klkkDs+n5qNVVEUqj+eAfVkQTX5JQ04O2q4d8ZI7pkcIcMqh6HZKaGcKqjh5MUapowJ4bHHHucrX/kaJ08eJzt7Ex988G82bPgHiYmjWbhwMenpd9vkfSQS9p/R3WNiT045Y6N9bWqoo5+nMymx/uw/W8HiaZF3/Injk6kLdu7cxu7dH9LQUI+7uwcZGQuYN+8exowZO+S7aVo7uvn33iIOnK3AwUHN+Dh/3F0c8PFwwvFTPz+jSaG5zUBDSxe1TR10dBnZebwMo6kUALVahbuLA0ajQkdXD8aPZ2b19XDkS7OjmT0uFBcneSkNV4kR3gR6O7PnTDlTxlzvq9doNEyaNIVJk6bQ2NjAzp3byM7ezC9/+VNeeeVXTJ48jblz5zFlyjSb+TQsz9DPOH6hiuY2A3NTbe9GijkTwjh9qYZjF6qYMfb2pl8tK7vK7t272LlzG2Vlpeh0OqZOnc7dd9/D5MlTcXAYutMyfFpuUS1/yrpAe2cPd6eFs3jaSBQVN11wXKNW4e3uiLe7I1EhHqQlBuKgVlFa1UpZTStdRoWK6ha0GjXOjhr8PJ17A2CovyGKvqlVKmaNC+Vfewq5Vt36uVlJvby8eeCBFSxb9hAXLpznww+3s2fPLvbv34OLiyszZsxi7twMJkxIs+poNQn7T1EUhQ9PlhHs68JoG1yDMmGEF6H+ruw6eY3pScH9Dpqysqvs2fMRe/d+RGHhZQBSUibw0EMrmTUrfVit9vTZoY//81BK74tzIAuO6xw0xIR5EhPmabb5+sXQNX1sMO/vv8KeM+WsnHfz2W9VKhWjRo1h1KgxfPOb3+XMmdPs2rWD/fv3sGPHVjw9vbjrrjnMmTOPpKTkQR/GKWH/KQWljZRWtfLljHibPGNTqVTMSwtn3daL5BbVkRzzxTP43Szgx4wZy5NPPsXs2ekEBAQOVtmDprvHxJtZ+Zy8WM2U0YF8eX7CDd01QtwuN2cHJiUGcChPz5LpI/ucHE2r1ZKaOpHU1Ik8/fQPOHbsMLt27WTbtiw2bnwPf/8AMjIW8LWv/Vfvkq+WJmH/MUVR2HiwGE83HVPH2O4Y2imjg9hyqITNh4oZG+3b+6akKApXrxazf//emwb8rFnpBAYOv4D/RKehh9+/n0d+SQMP3BVDxsRwm3zDFkPXPZMjOHyukg9PlHH/rP4PktDpdMyYMZsZM2bT3t7OwYP72bNnF3v2fMRDD60ctE/WEvYfy7/awKWyRh6+O84mhlt+Ea1GTebUSP6y7SJnLlejai/n4MH9HDq0n/Lya4D9BPwn2jq7+fU/z3K1soWvLUxkWtLQHj0kbFOInyupCQF8dOoaGRNH4OY88OtbLi4uzJs3n3nz5lugwluTsOfjs/oDV/B2d2Rmsm0HRXt7G911+TTmv88zH71Ed1cbDg4OjB+fxoMPPszUqTPw9x+8RVasra2zm1/94wzXalr55r1jSLGBm+DE8LVoaiQnLlaz62QZS2cMfJ1ta5KwB/Ku1FNU3syXM+Jx0NrWWb2iKBQWXub48SMcP36UvLyz9PT04OzihpNfPCsXz+eBJXfj4mI7w0QHy6eD/ol7kxh3i2sYQphDWIAb4+P8+fDkNealhQ/qgkZ3yu7DvsdoYsPeQvw8nZhuxdktP62xsYETJ45x/PhRTpw4Sn19PQAxMbEsW/YQU6ZMI3HUGF7862kuNJnQOtjGON7B1NpxPejLayXoxeBaPC2SnEs1bDxYzIq5Q2eNBrsP+x3HSymvaePb94+12t2PHe1tXC06T86+K5zLPUNh4SUURcHT05O0tEmkpU0mLW0yfn43Btojd8fxi3+cYevRq0PuI+WdaG438Mt3z1BZ386T940dNmv0iqFhRKA7s1NC+ejU9SHQIwKHxtBluw776oZ2Nh8qYUKcP+NiB+/MsLOjjatF+RRfPkfx5XNUlhejKAoODjrGjEniscceZ+LEKcTFxd9yWFZipA8TEwPYerSUqWOCCPC23lTMg6WptYtf/OMMtY0dfGfZWJu8H0IMf/fNiuJUQTV/21HAcysnoB4CI7/sNuwVReFvOy+hUatYcbflPoopikJZ6VVyju2hrLiA0uKL1FSWoSgKGo2W8JHxzJ6/nMiY0SydPx1v94H1vS9PjyW3qI6/bi/ge8vHWX2+e0tqaOniF+/m0NDSxXeXJZMQ4W3tkoSdcnVy4IH0GP6UdYH9ZyqYPQSWDbXbsN92rJTzxfU8fHcc3u7mW/y5ubmJS5cKuHDhPOfP53H+fB5NTdfnM3dydiEsIp7R46YSET2K8Mg4HHT/2bdON/A6vN0deXBOLH/ZdpHNh4qHbXdOTWMHv/rnGZraDDz1QDJx4V7WLknYuSmjgzh8rpJ3P7rMyGAPIoJsuzvHLsP+3JU63ttbRFpCAOnjb/8dubGxgUuXLlJQcJFLlwooKLhAZaW+9/8jIkYybdpMRicl06EOwC8w1CK3SM8YG8zlska2HCohOtSTJCsscGJJRRVNvPrvXIwmhe8tH0dMqKe1SxIClUrF44tGs+YvJ3jtgzxWfzXttsbeDxa7C/uq+nbe2HSeUH9XHlvQvwU4Ojo6KCkppqTkCsXFVyguLuLKlSJqaqp724SGhjNq1BiWLr2f+PgE4uIScHe/Pq+5otGw71SpxY5JpVLxSEY8V6ta+OPm8zz7yARCh8map8cvVPFW9gW83HR8d1myTc1EKoSHq44n7h3D2ndO83+bz/OdL1lvoEdf7Crsy2vb+NU/clCp4Mn7knDU/efip6IoNDc3UV5+jbKyUkpKiikuLqK4+Ap6fUVvO51OR0REJOPGjSc2Np74+ARiY+OtPn+1o4OGb96XxM/Wn+YXfz/NMw+lEOZve3Nq95eh28g/PrrM3jMVxIR68uT9SX3ORyKENUSHeLJyXjzrtl3klfdy+ea9STY5J5PdhH2xvplf//MMSk8b907y5tTR3ZSXX/v4TxnXrl2jtfU/MxtqtVrCwyMYNWoMCxYsYuTIKEaOjCYkJHTQJi4aqEBvF/5nRQo/fzeHn/89h6eXJxMZNPRWTSosb+Kv2y9SXtPGPZNGcO/MKJs9WxICYEZyCArw120X+c2/zvLt+8fa3BoHtlXNHero6KCqqpLq6kqqq6uoqqqisrKSi0VXqdDr6eloQjH1kLv5enu1Wk1QUDChoWHcffd8QkPDCAsLIzQ0nNDQMKvOPX27gn1deXbFeH7xjxz+9+1TfGl2NHenhQ+JoWFNbQY+2F/E/rN6vN0d+e6yZBlDL4aMmckh6BzU/GnLBZ7/8zEeW5BIog0NDR56afYFamqqeeSRB+joaO/9nkqlRufigcrBg6DQKFKTYhgRFtIb5kFBwcNmsY5PC/Rx4fmvpvGXbRf55+5CzhbW8qXZMTa7NmptUwc7jpex/2wFRqPC/IkjWDw9EifdsHl6CjsxeVQQ/l7O/CnrAr/4xxlmJgezYEokAV7O1i6tf2FfXFzMs88+S2NjI15eXqxdu5bIyMgb2hiNRl5++WUOHDhw/Sr144+zbNkyS9R8U15e3nz3u89g6IHmbkcK9CbKm9S4uzrx0JxYJo8OtKspb91ddDx5XxL7zlbw/r4rvPz2SZKifJk1LoSkKB+rzwHU1NpF7pU6jp6v4uLVBtRqFVPGBLFwcgSBPsP/5jAxfEWHePLCo2l8sP8Ku09f40CunrSEACYmBjI60ueGa4WDqV9h//zzz7NixQqWLFnCpk2bWL16NW+//fYNbbZs2UJpaSk7d+6ksbGRpUuXMmXKFMLCLLu8X0dXD7tPX6O8to3ymgDKqlsB8PdyZeX8EUxLCrbJiyWDQaVSMXtcKJMSA9l9+ho7T5SR934dzo4aRkX4EB3qychgd4J8XPBw1VnkzdCkXF/ftaaxg2s1bZRVtXC5vInymjYAArycWTJ9JNOSgvH1tL85fsTw5Oig4cE5sWRMHMGHJ8vYd6aC4xeq0WrURAS5EernSqC3C85OWjxcdCTH+KKx8MpVfYZ9XV0d+fn5rFu3DoDMzExeeukl6uvr8fH5T3/U1q1bWbZsGWq1Gh8fH+bOncv27dv5+te/3u9ibufuz7KaVg6fq8TDRUdE0PU5KxIjvAn2dbGZM3lFrerX7Hhajdoid8C6OjuwaNpIFkyJoKi8mTOFtRSVN7H3TDl7z1xv4+Cgxs3JARdHB5wdNTg7anHSadBo1KhU19fhVKlUn/o3KCboUUw4OGhobzfQY1ToNBhp7+qhs6uH9q4e2jp7MBpNvbU467REBrkzNzWc2DBPQv1cB+33pNWob/v3cLu/lzvZ5+1ub6DbcnbUYuz54m0O5BjM1c6c23J21FrstdUXX08nHpwTy7K7oimuaOZCaSPXaloprW7jYmljbzsPV53FbxTsM+z1ej2BgYG9I1A0Gg0BAQHo9fobwl6v1xMS8p9FsIODg6msrBxQMd7eAx9DPc3XjWkp4QN+3GBbOKP/K9tYUoC/B1PG2d5i6oMlLPj2bsjy9b39Yay3u8/B2F5/txUV1r+pKczZztz7tLYAfw8mJVvvtSfj2YQQwg70GfbBwcFUVVVhNBqB6xdiq6urCQ4O/ly7ior/3Hyk1+sJCrLdtVyFEMKe9Bn2vr6+JCYmkpWVBUBWVhaJiYk3dOEAzJ8/nw0bNmAymaivr2fXrl1kZGRYpmohhBADolIURemrUVFREc8++yzNzc14eHiwdu1aoqKiWLVqFd/+9rdJSkrCaDTy4osvcujQIQBWrVrF8uXLLX4AQggh+tavsBdCCDG0yQVaIYSwAxL2QghhByTshRDCDkjYCyGEHZCwt6Di4mKWL19ORkYGy5cvp6SkxNolmdXatWtJT08nPj6eS5cuWbscs2poaGDVqlVkZGSwaNEinnzySerr661dllk98cQTLF68mKVLl7JixQouXLhg7ZIs4ve///2wfI6mp6czf/58lixZwpIlSzhw4MCtH6AIi1m5cqWyceNGRVEUZePGjcrKlSutXJF5nThxQqmoqFDuuusupaCgwNrlmFVDQ4Ny9OjR3q9/9rOfKc8995wVKzK/5ubm3n9/+OGHytKlS61YjWWcO3dO+drXvjYsn6MDPSY5s7eQTyaQy8zMBK5PIJefnz+szg5TU1M/dyf1cOHl5cWkSZN6vx43btwNd4gPB+7u7r3/bm1ttZmJA83FYDDw4osv8sILL1i7FJsgq0NYSH8nkBO2z2Qy8e6775Kenm7tUszuRz/6EYcOHUJRFP70pz9Zuxyz+t3vfsfixYstPs26NT3zzDMoisKECRN4+umn8fD44gWK5MxeiD689NJLuLi48Mgjj1i7FLP73//9X/bu3ctTTz3Fz3/+c2uXYzY5OTmcO3eOFStWWLsUi3nnnXfYvHkz7733Hoqi8OKLL96yvYS9hfR3Ajlh29auXcvVq1f57W9/i9rCi0tY09KlSzl27BgNDQ3WLsUsTpw4QVFREXPmzCE9PZ3Kykq+9rWvcfDgQWuXZjafZIlOp2PFihWcPn36lu2H77PXyvo7gZywXb/+9a85d+4cr732GjqdztrlmFVbWxt6vb736927d+Pp6YmXl2UX0Bgsjz/+OAcPHmT37t3s3r2boKAg3nrrLaZPn27t0syivb2dlpYWABRFYevWrSQmJt7yMTI3jgV90QRyw8XLL7/Mzp07qa2txdvbGy8vL7Kzs61dlllcvnyZzMxMIiMjcXK6vlxiWFgYr732mpUrM4/a2lqeeOIJOjo6UKvVeHp68oMf/IDRo0dbuzSLSE9P54033iAuLs7apZhFWVkZ3/rWtzAajZhMJqKjo/nxj39MQEDAFz5Gwl4IIeyAdOMIIYQdkLAXQgg7IGEvhBB2QMJeCCHsgIS9EELYAQl7Ifpp8+bNPPbYY9YuQ4jbImEvbEp6ejpjx44lJSWF1NRUHnzwQd59911MJpPF9vnss88SHx/Prl27bvj+T37yE+Lj43n//fcBWLx4MX/+85/Nss+VK1eyYcMGs2xLiP6QsBc254033iAnJ4c9e/awatUq3nzzTX70ox9ZdJ+RkZFs2rSp9+uenh62bdvGiBEjLLpfIQaLhL2wWe7u7syZM4ff/va3fPDBB1y6dAmDwcDatWuZPXs2U6dOZfXq1XR2dgJw7NgxZs6cyRtvvMGkSZNIT09n8+bN/dpXeno6p06doqmpCYADBw4QHx+Pn59fb5v333+fhx56qPfr+Ph43n33XebNm0dqaipr1qzhk3sUX331VZ555pnetteuXSM+Pp6enh5+85vfcPLkSV588UVSUlJ6J7AqKiri0UcfZeLEiWRkZLB169bex+/bt48FCxaQkpLCjBkzeOutt27zpyrslYS9sHljx44lKCiIkydP8stf/pLi4mI2btzIzp07qa6uvmEKg9raWhoaGjhw4AA/+9nPWL16NVeuXOlzHzqdjjlz5vRO97Bx40aWLl3a5+P27t3Lv//9bzZv3sy2bdv6Xi0IeOqpp0hNTWX16tXk5OSwevVq2tvbeeyxx8jMzOTw4cP85je/Yc2aNRQWFgLXpyJ+8cUXycnJISsri8mTJ/e5HyE+TcJeDAkBAQE0NTXxr3/9ix/+8Id4eXnh5ubGf/3Xf31uPp7vfOc76HQ6Jk6cyKxZs9i2bVu/9rFkyRI2bdpEc3MzJ06cYO7cuX0+ZtWqVXh4eBASEsKkSZO4ePHibR3f3r17CQ0N5f7770er1TJq1CgyMjLYvn07AFqtlsLCQlpbW/H09By2c9gIy5HFS8SQ8Ml00R0dHdx3332931cU5YaLtx4eHri4uPR+HRISQnV1db/2kZqaSn19Pa+//jqzZ8/unQDtVvz9/Xv/7ezsTFtbW7/29Vnl5eXk5uaSmpra+z2j0cjixYsBeOWVV3j99df51a9+RXx8PN/73vdISUm5rX0J+yRhL2xebm4uVVVVzJ07lzfffJPs7GwCAwNv2ra5uZn29vbewNfr9cTGxvZ7X4sXL+a1117j7bffvqOanZ2de68lwPXupVsJDg4mLS2NdevW3fT/x44dy+uvv053dzfvvPMO3/3ud9m3b98d1Sjsi3TjCJvV2trKnj17ePrpp1m8eDEJCQksW7aMn/zkJ9TV1QHXz/g/20/+6quvYjAYOHnyJHv37mX+/Pn93ufKlStZt24daWlpd1R7YmIiJ06coKKigpaWFv7v//7vhv/38/OjrKys9+vZs2dTUlLCxo0b6e7upru7m9zcXIqKijAYDGzevJmWlhYcHBxwdXUd1gupCMuQZ4ywOf/93/9NSkoKs2bN4o033uDRRx/lpz/9KQDf//73iYiI4IEHHmD8+PF89atfpbi4uPexfn5+eHh4MGPGDJ555hleeOEFoqOj+71vLy8vpkyZcseLb0+bNo0FCxawePFi7rvvPu66664b/v/LX/4yO3bsIC0tjZdffhk3Nzfeeusttm7dyowZM5g+fTq//OUvMRgMAGzatIn09HTGjx/PP/7xD37xi1/cUX3C/sh89mLYOHbsGN///vfZv3+/tUsRwubImb0QQtgBuUAr7MLChQupqKj43PfXrFnTO+JFiOFMunGEEMIOSDeOEELYAQl7IYSwAxL2QghhByTshRDCDkjYCyGEHZCwF0IIO/D/AcBd9jDHqa4oAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X07QMW07AARd",
        "colab_type": "code",
        "outputId": "72e706b9-0e1c-47bd-c507-7071c8b15ee0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        }
      },
      "source": [
        "#correlation matrix\n",
        "corrmat = df_train.corr()\n",
        "f, ax = plt.subplots(figsize=(25, 25))\n",
        "sns.heatmap(corrmat, vmax=.8, square=True,annot=True);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "858XfzyCDt2A",
        "colab_type": "code",
        "outputId": "87bb1b3f-48ea-4a78-d1b7-b1f35e9981af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#Source:- https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "\n",
        "#Validation function\n",
        "n_folds = 5\n",
        "x_train=x_train.to_numpy()\n",
        "y_train=y_train.to_numpy()\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_train,y_train,random_state=42, test_size=0.3)\n",
        "   \n",
        "def rmsle_cv(model):\n",
        "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(X_train)\n",
        "    rmse= np.sqrt(-cross_val_score(model,X_train, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
        "    return(rmse)\n",
        "\n",
        "class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, base_models, meta_model, n_folds=5):\n",
        "        self.base_models = base_models\n",
        "        self.meta_model = meta_model\n",
        "        self.n_folds = n_folds\n",
        "   \n",
        "        # We again fit the data on clones of the original models\n",
        "    def fit(self, X, y):\n",
        "        self.base_models_ = [list() for x in self.base_models]\n",
        "        self.meta_model_ = clone(self.meta_model)\n",
        "        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)\n",
        "        \n",
        "        # Train cloned base models then create out-of-fold predictions\n",
        "        # that are needed to train the cloned meta-model\n",
        "        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n",
        "        for i, model in enumerate(self.base_models):\n",
        "            for train_index, holdout_index in kfold.split(X, y):\n",
        "                instance = clone(model)\n",
        "                self.base_models_[i].append(instance)\n",
        "                instance.fit(X[train_index], y[train_index])\n",
        "                y_pred = instance.predict(X[holdout_index])\n",
        "                out_of_fold_predictions[holdout_index, i] = y_pred\n",
        "                \n",
        "        # Now train the cloned  meta-model using the out-of-fold predictions as new feature\n",
        "        self.meta_model_.fit(out_of_fold_predictions, y)\n",
        "        return self\n",
        "   \n",
        "    #Do the predictions of all base models on the test data and use the averaged predictions as \n",
        "    #meta-features for the final prediction which is done by the meta-model\n",
        "    def predict(self, X):\n",
        "        meta_features = np.column_stack([\n",
        "            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)\n",
        "            for base_models in self.base_models_ ])\n",
        "        return self.meta_model_.predict(meta_features)\n",
        "stacked_averaged_models = StackingAveragedModels(base_models = (extraTreeRegressorModel, randomForestModel, xgbRegressorModel),\n",
        "                                                 meta_model = linearModel)\n",
        "print(\"end\")\n",
        "score = rmsle_cv(stacked_averaged_models)\n",
        "print(\"Stacking Averaged models score: {:.4f} ({:.4f})\".format(score.mean(), score.std()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "end\n",
            "Stacking Averaged models score: 0.1238 (0.0042)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agXb3gmPAqo_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLdwbJCF15cW",
        "colab_type": "code",
        "outputId": "b82f21ab-bad2-4d7e-ea0a-a8312726fd74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "stacked_averaged_models.fit(X_train, y_train)\n",
        "y_pred = stacked_averaged_models.predict(X_test)\n",
        "print(print_accuracy_report(y_test, y_pred, X_test, stacked_averaged_models))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R Squared(Accuracy) 0.9422941332458397\n",
            "Mean Absolute Error: 0.07379490857468819\n",
            "Mean Squared Error: 0.015206536055587555\n",
            "Root Mean Squared Error: 0.12331478441609325\n",
            "Root Mean Squared Log Error 0.012320019651070217\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}