ml_flight_price_prediction.ipynb

{
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        "id": "view-in-github"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/samsonajulor/data-science-projects/blob/main/ml_flight_price_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "929b35b4",
      "metadata": {
        "id": "929b35b4"
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      "source": []
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      "cell_type": "markdown",
      "id": "a46b8b2c",
      "metadata": {
        "id": "a46b8b2c"
      },
      "source": [
        "#### import all the necessary libraries !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "c9bc16c8",
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        },
        "id": "c9bc16c8",
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/ml-files\n",
            "           Airline Date_of_Journey    Source Destination  \\\n",
            "0           IndiGo      24/03/2019  Banglore   New Delhi   \n",
            "1        Air India       1/05/2019   Kolkata    Banglore   \n",
            "2      Jet Airways       9/06/2019     Delhi      Cochin   \n",
            "3           IndiGo      12/05/2019   Kolkata    Banglore   \n",
            "4           IndiGo      01/03/2019  Banglore   New Delhi   \n",
            "...            ...             ...       ...         ...   \n",
            "10678     Air Asia       9/04/2019   Kolkata    Banglore   \n",
            "10679    Air India      27/04/2019   Kolkata    Banglore   \n",
            "10680  Jet Airways      27/04/2019  Banglore       Delhi   \n",
            "10681      Vistara      01/03/2019  Banglore   New Delhi   \n",
            "10682    Air India       9/05/2019     Delhi      Cochin   \n",
            "\n",
            "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
            "0                  BLR → DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
            "1      CCU → IXR → BBI → BLR    05:50         13:15   7h 25m     2 stops   \n",
            "2      DEL → LKO → BOM → COK    09:25  04:25 10 Jun      19h     2 stops   \n",
            "3            CCU → NAG → BLR    18:05         23:30   5h 25m      1 stop   \n",
            "4            BLR → NAG → DEL    16:50         21:35   4h 45m      1 stop   \n",
            "...                      ...      ...           ...      ...         ...   \n",
            "10678              CCU → BLR    19:55         22:25   2h 30m    non-stop   \n",
            "10679              CCU → BLR    20:45         23:20   2h 35m    non-stop   \n",
            "10680              BLR → DEL    08:20         11:20       3h    non-stop   \n",
            "10681              BLR → DEL    11:30         14:10   2h 40m    non-stop   \n",
            "10682  DEL → GOI → BOM → COK    10:55         19:15   8h 20m     2 stops   \n",
            "\n",
            "      Additional_Info  Price  \n",
            "0             No info   3897  \n",
            "1             No info   7662  \n",
            "2             No info  13882  \n",
            "3             No info   6218  \n",
            "4             No info  13302  \n",
            "...               ...    ...  \n",
            "10678         No info   4107  \n",
            "10679         No info   4145  \n",
            "10680         No info   7229  \n",
            "10681         No info  12648  \n",
            "10682         No info  11753  \n",
            "\n",
            "[10683 rows x 11 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/ml-files', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "8655a7e9",
      "metadata": {
        "id": "8655a7e9"
      },
      "outputs": [],
      "source": [
        "train_data=pd.read_excel(r'/content/Data_Train.xlsx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa70faf5",
      "metadata": {
        "id": "aa70faf5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3513b78d",
      "metadata": {
        "id": "3513b78d"
      },
      "outputs": [],
      "source": [
        "### why there is a need to append 'r' (raw_string?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c1487c1d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1487c1d",
        "outputId": "2b3a26b9-aa5f-4d87-ed3a-a85af3c5dfe7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hello\tPython\n"
          ]
        }
      ],
      "source": [
        "print('Hello\\tPython')\n",
        "\n",
        "### Now, here 'Hello\\tPython' is a normal string literal, the sequences “\\t” will be treated as escape characters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "4df9ac10",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "4df9ac10",
        "outputId": "90a375b2-d180-4435-f341-96e6a1c07327"
      },
      "outputs": [
        {
          "ename": "SyntaxError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-4f7544c6aa2f>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    '''\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 22-23: truncated \\xXX escape\n"
          ]
        }
      ],
      "source": [
        "print(r'Hello\\xPython')\n",
        "\n",
        "'''\n",
        " we cannot put 'Hello\\xPython' into a normal string literal. What can we do now?\n",
        "\n",
        "This is where raw string come handy.\n",
        "## so just to convert normal string into raw string  , u can think of appending \"r\" or \"R\"!\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
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        "id": "4af752aa",
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              "      <th>Airline</th>\n",
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              "      <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>non-stop</td>\n",
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              "      <td>CCU → IXR → BBI → BLR</td>\n",
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              "      <td>9/06/2019</td>\n",
              "      <td>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 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
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              "    <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 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
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              "    <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 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SpiceJet</td>\n",
              "      <td>24/06/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>09:00</td>\n",
              "      <td>11:25</td>\n",
              "      <td>2h 25m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>12/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>18:55</td>\n",
              "      <td>10:25 13 Mar</td>\n",
              "      <td>15h 30m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>11087</td>\n",
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            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
              "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
              "5     SpiceJet      24/06/2019   Kolkata    Banglore              CCU → BLR   \n",
              "6  Jet Airways      12/03/2019  Banglore   New Delhi        BLR → BOM → DEL   \n",
              "\n",
              "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \\\n",
              "0    22:20  01:10 22 Mar   2h 50m    non-stop                      No info   \n",
              "1    05:50         13:15   7h 25m     2 stops                      No info   \n",
              "2    09:25  04:25 10 Jun      19h     2 stops                      No info   \n",
              "3    18:05         23:30   5h 25m      1 stop                      No info   \n",
              "4    16:50         21:35   4h 45m      1 stop                      No info   \n",
              "5    09:00         11:25   2h 25m    non-stop                      No info   \n",
              "6    18:55  10:25 13 Mar  15h 30m      1 stop  In-flight meal not included   \n",
              "\n",
              "   Price  \n",
              "0   3897  \n",
              "1   7662  \n",
              "2  13882  \n",
              "3   6218  \n",
              "4  13302  \n",
              "5   3873  \n",
              "6  11087  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.head(7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "2b27c166",
      "metadata": {
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        "id": "2b27c166",
        "outputId": "89c11fc6-c755-4325-a242-5c59a6b07fac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10683 entries, 0 to 10682\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Airline          10683 non-null  object\n",
            " 1   Date_of_Journey  10683 non-null  object\n",
            " 2   Source           10683 non-null  object\n",
            " 3   Destination      10683 non-null  object\n",
            " 4   Route            10682 non-null  object\n",
            " 5   Dep_Time         10683 non-null  object\n",
            " 6   Arrival_Time     10683 non-null  object\n",
            " 7   Duration         10683 non-null  object\n",
            " 8   Total_Stops      10682 non-null  object\n",
            " 9   Additional_Info  10683 non-null  object\n",
            " 10  Price            10683 non-null  int64 \n",
            "dtypes: int64(1), object(10)\n",
            "memory usage: 918.2+ KB\n"
          ]
        }
      ],
      "source": [
        "train_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17cbdf6d",
      "metadata": {
        "id": "17cbdf6d"
      },
      "source": [
        "#### Importing dataset\n",
        "    1.Since data is in form of excel file we have to use pandas read_excel to load the data\n",
        "    2.After loading it is important to check null values in a column or a row\n",
        "    3.If it is present then following can be done,\n",
        "        a.Filling NaN values with mean, median and mode using fillna() method\n",
        "        b.If Less missing values, we can drop it as well"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b055d208",
      "metadata": {
        "id": "b055d208"
      },
      "outputs": [],
      "source": []
    },
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      "execution_count": 23,
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        "id": "dd4add6e",
        "outputId": "7fbeb0b4-9773-48e7-d797-d8afc797d097"
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      "outputs": [
        {
          "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"
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          "execution_count": 23,
          "metadata": {},
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      ],
      "source": [
        "train_data.isnull().sum()\n",
        "\n",
        "## train_data.isnull().sum(axis=0)\n",
        "## by-default axis is 0 , ie it computes total missing values column-wise !\n",
        "\n",
        "## train_data.isnull().sum(axis=1) -->> if axis=1 , ie it computes total missing values row-wise !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dda2961e",
      "metadata": {
        "id": "dda2961e",
        "outputId": "a840b54b-71be-4b4f-ca08-2c38487d7ac7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10683, 11)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d57f98b2",
      "metadata": {
        "id": "d57f98b2",
        "outputId": "62ed9978-5965-4722-94b1-0718eafb6824"
      },
      "outputs": [
        {
          "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>9039</th>\n",
              "      <td>Air India</td>\n",
              "      <td>6/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>09:45</td>\n",
              "      <td>09:25 07 May</td>\n",
              "      <td>23h 40m</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No info</td>\n",
              "      <td>7480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
              "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
              "\n",
              "      Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
              "9039  09:25 07 May  23h 40m         NaN         No info   7480  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "### getting all the rows where we have missing value\n",
        "train_data[train_data['Total_Stops'].isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "331fc163",
      "metadata": {
        "id": "331fc163"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "fefda914",
      "metadata": {
        "id": "fefda914"
      },
      "source": [
        "#### as we have 1 missing value , I can directly drop these"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "e6df2912",
      "metadata": {
        "id": "e6df2912"
      },
      "outputs": [],
      "source": [
        "train_data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "6fd3d49e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fd3d49e",
        "outputId": "c50153f1-8090-4c32-82c4-b2f01b273792"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "f0d5e87d",
      "metadata": {
        "id": "f0d5e87d"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "04342e31",
      "metadata": {
        "id": "04342e31"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "e3a9f60a",
      "metadata": {
        "id": "e3a9f60a"
      },
      "source": [
        "## Pre-process & Perform Featurization of \"Date_of_Journey\"\n",
        "    ie pre-process it & extract day,month,year from \"Date_of_Journey\" feature.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34648527",
      "metadata": {
        "id": "34648527"
      },
      "outputs": [],
      "source": [
        "data=train_data.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cac7f3ed",
      "metadata": {
        "id": "cac7f3ed",
        "outputId": "61e2ef03-5a1e-462c-9de0-4919edffe67e"
      },
      "outputs": [
        {
          "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>non-stop</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 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0     IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
              "1  Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "\n",
              "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
              "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
              "1    05:50         13:15   7h 25m     2 stops         No info   7662  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e321d17e",
      "metadata": {
        "id": "e321d17e",
        "outputId": "ecd5d981-6081-4e96-b77d-721b142d3968"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline            object\n",
              "Date_of_Journey    object\n",
              "Source             object\n",
              "Destination        object\n",
              "Route              object\n",
              "Dep_Time           object\n",
              "Arrival_Time       object\n",
              "Duration           object\n",
              "Total_Stops        object\n",
              "Additional_Info    object\n",
              "Price               int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "raw",
      "id": "c47ae740",
      "metadata": {
        "id": "c47ae740"
      },
      "source": [
        "we can see that Date_of_Journey is a object data type,\n",
        "Therefore, we have to convert this datatype into timestamp bcz our \n",
        "model will not be able to understand these string values,it just understand Time-stamp..\n",
        "\n",
        "For this we require pandas to_datetime to convert object data type to datetime dtype."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a49c80b6",
      "metadata": {
        "id": "a49c80b6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8825899e",
      "metadata": {
        "id": "8825899e"
      },
      "outputs": [],
      "source": [
        "def change_into_datetime(col):\n",
        "    data[col]=pd.to_datetime(data[col])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "674605ce",
      "metadata": {
        "id": "674605ce",
        "outputId": "910cc403-0baf-4cdd-b194-3252fd90c6a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
              "       'Additional_Info', 'Price'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5d8b558a",
      "metadata": {
        "id": "5d8b558a"
      },
      "outputs": [],
      "source": [
        "for feature in ['Date_of_Journey','Dep_Time', 'Arrival_Time']:\n",
        "    change_into_datetime(feature)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4119e13",
      "metadata": {
        "id": "c4119e13",
        "outputId": "7028699e-e513-43e1-83f0-b197ecc7dc86"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline                    object\n",
              "Date_of_Journey    datetime64[ns]\n",
              "Source                     object\n",
              "Destination                object\n",
              "Route                      object\n",
              "Dep_Time           datetime64[ns]\n",
              "Arrival_Time       datetime64[ns]\n",
              "Duration                   object\n",
              "Total_Stops                object\n",
              "Additional_Info            object\n",
              "Price                       int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "883f00d8",
      "metadata": {
        "id": "883f00d8",
        "outputId": "b295ff2c-1452-4b54-91bb-787474b043a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Timestamp('2019-01-03 00:00:00')"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Date_of_Journey'].min()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f96b7c99",
      "metadata": {
        "id": "f96b7c99",
        "outputId": "39272b3c-1b3b-489d-c7b6-1369f73b4675"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Timestamp('2019-12-06 00:00:00')"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Date_of_Journey'].max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fb375d31",
      "metadata": {
        "id": "fb375d31"
      },
      "outputs": [],
      "source": [
        "### it means our data belongs to 2019 year only, hence extracting year feature & consider this as a input to my machine learning model makes no sense !\n",
        "### but if we have more than 1 year  , then of-course it may impact !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a76d13c",
      "metadata": {
        "id": "3a76d13c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "922e7916",
      "metadata": {
        "id": "922e7916"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "e492e500",
      "metadata": {
        "id": "e492e500"
      },
      "source": [
        "## lets do Feature Engineering of \"Date_of_Journey\" & fetch day,month,year !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a19d931",
      "metadata": {
        "id": "4a19d931"
      },
      "outputs": [],
      "source": [
        "data['journey_day']=data['Date_of_Journey'].dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2953c1b6",
      "metadata": {
        "id": "2953c1b6"
      },
      "outputs": [],
      "source": [
        "data['journey_month']=data['Date_of_Journey'].dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b0d9342",
      "metadata": {
        "id": "6b0d9342"
      },
      "outputs": [],
      "source": [
        "data['journey_year']=data['Date_of_Journey'].dt.year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7759f5e7",
      "metadata": {
        "id": "7759f5e7",
        "outputId": "4bc18afd-1e72-4076-c184-b5892cb5366a"
      },
      "outputs": [
        {
          "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",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>2022-03-24 22:20:00</td>\n",
              "      <td>2022-03-22 01:10:00</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2022-03-24 05:50:00</td>\n",
              "      <td>2022-03-24 13:15:00</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0     IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1  Air India      2019-01-05   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "\n",
              "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
              "0 2022-03-24 22:20:00 2022-03-22 01:10:00   2h 50m    non-stop   \n",
              "1 2022-03-24 05:50:00 2022-03-24 13:15:00   7h 25m     2 stops   \n",
              "\n",
              "  Additional_Info  Price  journey_day  journey_month  journey_year  \n",
              "0         No info   3897           24              3          2019  \n",
              "1         No info   7662            5              1          2019  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d988395",
      "metadata": {
        "id": "0d988395"
      },
      "outputs": [],
      "source": [
        "data.drop('Date_of_Journey',axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d7fbc13",
      "metadata": {
        "id": "0d7fbc13",
        "outputId": "3bb6c95b-1168-44cd-eef1-aa9d49469a7f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "\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>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</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>2022-03-24 22:20:00</td>\n",
              "      <td>2022-03-22 01:10:00</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</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>2022-03-24 05:50:00</td>\n",
              "      <td>2022-03-24 13:15:00</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline    Source Destination                  Route            Dep_Time  \\\n",
              "0     IndiGo  Banglore   New Delhi              BLR → DEL 2022-03-24 22:20:00   \n",
              "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2022-03-24 05:50:00   \n",
              "\n",
              "         Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
              "0 2022-03-22 01:10:00   2h 50m    non-stop         No info   3897   \n",
              "1 2022-03-24 13:15:00   7h 25m     2 stops         No info   7662   \n",
              "\n",
              "   journey_day  journey_month  journey_year  \n",
              "0           24              3          2019  \n",
              "1            5              1          2019  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3578fc1",
      "metadata": {
        "id": "f3578fc1"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "6a79a6c2",
      "metadata": {
        "id": "6a79a6c2"
      },
      "source": [
        "# Your Task :\n",
        "    Doing Featurization using Map ,Lambda ,Custom function ,string splitting :\n",
        "    \n",
        "    Map ,Lambda -->> \n",
        "https://www.geeksforgeeks.org/python-map-function/\n",
        "    \n",
        "    How to write a function in Python -->> \n",
        "https://www.geeksforgeeks.org/python-functions/\n",
        "    \n",
        "    String splitting ->> \n",
        "https://www.programiz.com/python-programming/methods/string/split\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3945e0e",
      "metadata": {
        "id": "f3945e0e"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "deeafa0c",
      "metadata": {
        "id": "deeafa0c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3e8ecc62",
      "metadata": {
        "id": "3e8ecc62"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2cdca6b4",
      "metadata": {
        "id": "2cdca6b4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "85d03969",
      "metadata": {
        "id": "85d03969"
      },
      "source": [
        "## Lets try to clean Dep_Time & Arrival_Time & featurize it..¶"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "419c541b",
      "metadata": {
        "id": "419c541b"
      },
      "outputs": [],
      "source": [
        "def extract_hour_min(df,col):\n",
        "    df[col+'_hour']=df[col].dt.hour\n",
        "    df[col+'_minute']=df[col].dt.minute\n",
        "    df.drop(col,axis=1,inplace=True)\n",
        "    return df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9661c843",
      "metadata": {
        "id": "9661c843",
        "outputId": "f34a991f-0253-4dc7-c887-be19e594a79f"
      },
      "outputs": [
        {
          "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>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</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>2022-03-22 01:10:00</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</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>2022-03-24 13:15:00</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline    Source Destination                  Route        Arrival_Time  \\\n",
              "0     IndiGo  Banglore   New Delhi              BLR → DEL 2022-03-22 01:10:00   \n",
              "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2022-03-24 13:15:00   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
              "0   2h 50m    non-stop         No info   3897           24              3   \n",
              "1   7h 25m     2 stops         No info   7662            5              1   \n",
              "\n",
              "   journey_year  Dep_Time_hour  Dep_Time_minute  \n",
              "0          2019             22               20  \n",
              "1          2019              5               50  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Departure time is when a plane leaves the gate\n",
        "\n",
        "extract_hour_min(data,'Dep_Time')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce5bdd11",
      "metadata": {
        "id": "ce5bdd11"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5bedc4d",
      "metadata": {
        "id": "c5bedc4d"
      },
      "outputs": [],
      "source": [
        "### lets Featurize 'Arrival_Time' !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0819bb7c",
      "metadata": {
        "id": "0819bb7c",
        "outputId": "1e48d2a6-1ff0-41d9-b20a-8864c6fc9c20"
      },
      "outputs": [
        {
          "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</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>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline    Source Destination                  Route Duration  \\\n",
              "0     IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
              "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
              "\n",
              "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
              "0    non-stop         No info   3897           24              3   \n",
              "1     2 stops         No info   7662            5              1   \n",
              "\n",
              "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "\n",
              "   Arrival_Time_minute  \n",
              "0                   10  \n",
              "1                   15  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extract_hour_min(data,'Arrival_Time')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b3292ae",
      "metadata": {
        "id": "7b3292ae"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "380fa32e",
      "metadata": {
        "id": "380fa32e"
      },
      "source": [
        "## Your Task :\n",
        "Doing Featurization of Arrival_Time using Map ,Lambda ,Custom function  ,for loop  :\n",
        "\n",
        "Map ,Lambda -->> \n",
        "https://www.geeksforgeeks.org/python-map-function/\n",
        "\n",
        "How to write a function in Python -->> \n",
        "https://www.geeksforgeeks.org/python-functions/\n",
        "\n",
        "For loop in python:\n",
        "https://www.quora.com/What-does-for-loop-in-python-means"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ea2fde0",
      "metadata": {
        "id": "0ea2fde0"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b638ae5",
      "metadata": {
        "id": "0b638ae5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "feb94bb6",
      "metadata": {
        "id": "feb94bb6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "67776691",
      "metadata": {
        "id": "67776691"
      },
      "source": [
        "## lets analyse when will most of the flights will take-off"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9b0413a6",
      "metadata": {
        "id": "9b0413a6"
      },
      "outputs": [],
      "source": [
        "### Converting the flight Dep_Time into proper time i.e. mid_night, morning, afternoon and evening.\n",
        "\n",
        "def flight_dep_time(x):\n",
        "    '''\n",
        "    This function takes the flight Departure time \n",
        "    and convert into appropriate format.\n",
        "    '''\n",
        "    if ( x> 4) and (x<=8 ):\n",
        "        return 'Early mrng'\n",
        "    \n",
        "    elif ( x>8 ) and (x<=12 ):\n",
        "        return 'Morning'\n",
        "    \n",
        "    elif ( x>12 ) and (x<=16 ):\n",
        "        return 'Noon'\n",
        "    \n",
        "    elif ( x>16 ) and (x<=20 ):\n",
        "        return 'Evening'\n",
        "    \n",
        "    elif ( x>20 ) and (x<=24 ):\n",
        "        return 'Night'\n",
        "    else:\n",
        "        return 'Late night'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2569f96b",
      "metadata": {
        "id": "2569f96b",
        "outputId": "8a86a700-5840-4320-e8f6-8cca0197b946"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "data['Dep_Time_hour'].apply(flight_dep_time).value_counts().plot(kind='bar')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9003a33c",
      "metadata": {
        "id": "9003a33c"
      },
      "source": [
        "# Your Task :\n",
        "    Create Donut & Pie of above  chart :\n",
        "    Pie chart : \n",
        "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.pie.html\n",
        "    \n",
        "    Donut chart : \n",
        "https://www.geeksforgeeks.org/donut-chart-using-matplotlib-in-python/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed40c400",
      "metadata": {
        "id": "ed40c400"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ad79f23",
      "metadata": {
        "id": "3ad79f23"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "33b9e3a7",
      "metadata": {
        "id": "33b9e3a7"
      },
      "source": [
        "## lets use Cufflinks & plotly to make your visuals more interactive !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f7cf3d5",
      "metadata": {
        "id": "3f7cf3d5"
      },
      "outputs": [],
      "source": [
        "## !pip install plotly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "710519c5",
      "metadata": {
        "id": "710519c5"
      },
      "outputs": [],
      "source": [
        "## Lets use Plotly interactive plots directly with Pandas dataframes, but First u need below set-up !\n",
        "\n",
        "import plotly\n",
        "import cufflinks as cf\n",
        "from cufflinks.offline import go_offline\n",
        "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "886e8212",
      "metadata": {
        "id": "886e8212",
        "outputId": "2c1a5708-e4af-4386-9a9c-0e0e58fcd522"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.4.2.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cf.go_offline()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34dcc26f",
      "metadata": {
        "id": "34dcc26f",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
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              "      <th>journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
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              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
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              "  <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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
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              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
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              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
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              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
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              "      <td>9</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
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              "      <td>5h 25m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
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              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
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              "    <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>4h 45m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SpiceJet</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>2h 25m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3873</td>\n",
              "      <td>24</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>15h 30m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>11087</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>18</td>\n",
              "      <td>55</td>\n",
              "      <td>10</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>21h 5m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>22270</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>25h 30m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>11087</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>55</td>\n",
              "      <td>10</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Multiple carriers</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>7h 50m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>8625</td>\n",
              "      <td>27</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
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            "text/plain": [
              "             Airline    Source Destination                  Route Duration  \\\n",
              "0             IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
              "1          Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
              "2        Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
              "3             IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
              "4             IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
              "5           SpiceJet   Kolkata    Banglore              CCU → BLR   2h 25m   \n",
              "6        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL  15h 30m   \n",
              "7        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL   21h 5m   \n",
              "8        Jet Airways  Banglore   New Delhi        BLR → BOM → DEL  25h 30m   \n",
              "9  Multiple carriers     Delhi      Cochin        DEL → BOM → COK   7h 50m   \n",
              "\n",
              "  Total_Stops              Additional_Info  Price  journey_day  journey_month  \\\n",
              "0    non-stop                      No info   3897           24              3   \n",
              "1     2 stops                      No info   7662            5              1   \n",
              "2     2 stops                      No info  13882            6              9   \n",
              "3      1 stop                      No info   6218            5             12   \n",
              "4      1 stop                      No info  13302            3              1   \n",
              "5    non-stop                      No info   3873           24              6   \n",
              "6      1 stop  In-flight meal not included  11087            3             12   \n",
              "7      1 stop                      No info  22270            3              1   \n",
              "8      1 stop  In-flight meal not included  11087            3             12   \n",
              "9      1 stop                      No info   8625           27              5   \n",
              "\n",
              "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "2          2019              9               25                  4   \n",
              "3          2019             18                5                 23   \n",
              "4          2019             16               50                 21   \n",
              "5          2019              9                0                 11   \n",
              "6          2019             18               55                 10   \n",
              "7          2019              8                0                  5   \n",
              "8          2019              8               55                 10   \n",
              "9          2019             11               25                 19   \n",
              "\n",
              "   Arrival_Time_minute  \n",
              "0                   10  \n",
              "1                   15  \n",
              "2                   25  \n",
              "3                   30  \n",
              "4                   35  \n",
              "5                   25  \n",
              "6                   25  \n",
              "7                    5  \n",
              "8                   25  \n",
              "9                   15  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68f30a32",
      "metadata": {
        "id": "68f30a32"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4b267f44",
      "metadata": {
        "id": "4b267f44"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "7da831dd",
      "metadata": {
        "id": "7da831dd"
      },
      "source": [
        "## Pre-process Duration Feature & extract meaningful features "
      ]
    },
    {
      "cell_type": "raw",
      "id": "d008db5e",
      "metadata": {
        "id": "d008db5e"
      },
      "source": [
        "Lets Apply pre-processing on duration column,\n",
        "-->> Once we pre-processed our Duration feature , lets featurize this feature & extract Duration hours and minute from duration..\n",
        "\n",
        "\n",
        "-->> As my ML model is not able to understand this duration as it contains string values , thats why we have to tell our\n",
        "Ml Model that this is Duration_hour & this Duration_is minute.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "613c4872",
      "metadata": {
        "id": "613c4872"
      },
      "outputs": [],
      "source": [
        "def preprocess_duration(x):\n",
        "    if 'h' not in x:\n",
        "        x='0h '+x\n",
        "    elif 'm' not in x:\n",
        "        x=x+' 0m'\n",
        "    return x\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5c54d74",
      "metadata": {
        "id": "a5c54d74"
      },
      "outputs": [],
      "source": [
        "data['Duration']=data['Duration'].apply(preprocess_duration)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9a22fa0",
      "metadata": {
        "id": "d9a22fa0",
        "outputId": "2fc3f793-285d-4ed0-cf50-470bbea70a8e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2h 50m\n",
              "1        7h 25m\n",
              "2        19h 0m\n",
              "3        5h 25m\n",
              "4        4h 45m\n",
              "          ...  \n",
              "10678    2h 30m\n",
              "10679    2h 35m\n",
              "10680     3h 0m\n",
              "10681    2h 40m\n",
              "10682    8h 20m\n",
              "Name: Duration, Length: 10682, dtype: object"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Duration']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a740019",
      "metadata": {
        "id": "5a740019",
        "outputId": "ff1c39d7-0a11-41b9-caed-6e360ab07fd7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2h'"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Duration'][0].split(' ')[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a2d7009b",
      "metadata": {
        "id": "a2d7009b",
        "outputId": "389adf8d-25dd-45c8-bf14-4326ebb04fc9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "int(data['Duration'][0].split(' ')[0][0:-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "717d643e",
      "metadata": {
        "id": "717d643e",
        "outputId": "6ff4c9a3-fafa-4a60-b1d7-a060b9df2161"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "int(data['Duration'][0].split(' ')[1][0:-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "426c5fa0",
      "metadata": {
        "id": "426c5fa0"
      },
      "outputs": [],
      "source": [
        "data['Duration_hours']=data['Duration'].apply(lambda x:int(x.split(' ')[0][0:-1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df3ab960",
      "metadata": {
        "id": "df3ab960"
      },
      "outputs": [],
      "source": [
        "data['Duration_mins']=data['Duration'].apply(lambda x:int(x.split(' ')[1][0:-1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4a56d35",
      "metadata": {
        "id": "d4a56d35",
        "outputId": "9b836631-3be8-434d-b9ac-5d748ad9980a"
      },
      "outputs": [
        {
          "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</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>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline    Source Destination                  Route Duration  \\\n",
              "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
              "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
              "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   19h 0m   \n",
              "\n",
              "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
              "0    non-stop         No info   3897           24              3   \n",
              "1     2 stops         No info   7662            5              1   \n",
              "2     2 stops         No info  13882            6              9   \n",
              "\n",
              "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "2          2019              9               25                  4   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  \n",
              "0                   10               2             50  \n",
              "1                   15               7             25  \n",
              "2                   25              19              0  "
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "74b01fae",
      "metadata": {
        "id": "74b01fae"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1063d644",
      "metadata": {
        "id": "1063d644"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "201c1efb",
      "metadata": {
        "id": "201c1efb"
      },
      "source": [
        "## Lets Analyse whether Duration impacts on Price or not ?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dec8d133",
      "metadata": {
        "id": "dec8d133",
        "outputId": "057fc370-24ff-4668-8e90-69e472c1128e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2*60+50*1'"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'2*60+50*1'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "19e174e8",
      "metadata": {
        "id": "19e174e8",
        "outputId": "2a6c3903-572c-4ede-b141-3e48cdade19e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "170"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "### eval is a in-built function of python which evaluates the “String” like a python expression and returns the result as an integer.\n",
        "eval('2*60+50*1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3e17b67b",
      "metadata": {
        "id": "3e17b67b"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7b6e5f3",
      "metadata": {
        "id": "f7b6e5f3"
      },
      "outputs": [],
      "source": [
        "data['Duration_total_mins']=data['Duration'].str.replace('h','*60').str.replace(' ','+').str.replace('m','*1').apply(eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38578903",
      "metadata": {
        "id": "38578903",
        "outputId": "08bd968b-94eb-4b1f-afbf-8a26dfe8e9c6"
      },
      "outputs": [
        {
          "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Duration_total_mins</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</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>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline    Source Destination                  Route Duration  \\\n",
              "0     IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
              "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
              "\n",
              "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
              "0    non-stop         No info   3897           24              3   \n",
              "1     2 stops         No info   7662            5              1   \n",
              "\n",
              "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Duration_total_mins  \n",
              "0                   10               2             50                  170  \n",
              "1                   15               7             25                  445  "
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec5aec1f",
      "metadata": {
        "id": "ec5aec1f",
        "outputId": "9f5c841f-72c8-4de8-80eb-2a4d9f9534a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x26b97931d90>"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#### It Plot data and regression model fits across a FacetGrid.. (combination of 'regplot` and :class:`FacetGrid)\n",
        "#### its a extended form of scatter plot..\n",
        "\n",
        "sns.lmplot(x='Duration_total_mins',y='Price',data=data)\n",
        "\n",
        "\n",
        "## Conclusion-->> pretty clear that As the duration of minutes increases Flight price also increases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ddb6fdd",
      "metadata": {
        "id": "4ddb6fdd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf033d96",
      "metadata": {
        "id": "cf033d96"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "4171ab52",
      "metadata": {
        "id": "4171ab52"
      },
      "source": [
        "## which city has maximum final destination of flights ?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5fb6fc1f",
      "metadata": {
        "id": "5fb6fc1f",
        "outputId": "a0b6afef-6926-465f-bfec-cc5392b0be7a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Destination'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c31277c1",
      "metadata": {
        "id": "c31277c1",
        "outputId": "7fee6e94-c0d0-4e1b-d91e-1a2f2a6d0344"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Destination'>"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data['Destination'].value_counts().plot(kind='pie')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17b3e551",
      "metadata": {
        "id": "17b3e551"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "Inference->> \n",
        "Final destination of majority of flights is Cochin. There are two values for Delhi destination which needs to be corrected,\n",
        "\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7fda6268",
      "metadata": {
        "id": "7fda6268"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "98c5f454",
      "metadata": {
        "id": "98c5f454"
      },
      "source": [
        "## Lets Perform Exploratory Data Analysis(Bivariate Analysis) to come up with some business insights\n",
        "    Problem Statement-->> on which route Jet Airways is extremely used???"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3295092",
      "metadata": {
        "id": "e3295092",
        "outputId": "0c1e0dff-c984-450c-e7ea-bd5504397403"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0                    BLR → DEL\n",
              "1        CCU → IXR → BBI → BLR\n",
              "2        DEL → LKO → BOM → COK\n",
              "3              CCU → NAG → BLR\n",
              "4              BLR → NAG → DEL\n",
              "                 ...          \n",
              "10678                CCU → BLR\n",
              "10679                CCU → BLR\n",
              "10680                BLR → DEL\n",
              "10681                BLR → DEL\n",
              "10682    DEL → GOI → BOM → COK\n",
              "Name: Route, Length: 10682, dtype: object"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Route']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cf6bbffa",
      "metadata": {
        "id": "cf6bbffa",
        "outputId": "596b354d-816c-4ca0-cda8-63ae7df610e7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Route\n",
              "CCU → BOM → BLR          930\n",
              "DEL → BOM → COK          875\n",
              "BLR → BOM → DEL          385\n",
              "BLR → DEL                382\n",
              "CCU → DEL → BLR          300\n",
              "BOM → HYD                207\n",
              "DEL → JAI → BOM → COK    207\n",
              "DEL → AMD → BOM → COK    141\n",
              "DEL → IDR → BOM → COK     86\n",
              "DEL → NAG → BOM → COK     61\n",
              "DEL → ATQ → BOM → COK     38\n",
              "DEL → COK                 34\n",
              "DEL → BHO → BOM → COK     29\n",
              "DEL → BDQ → BOM → COK     28\n",
              "DEL → LKO → BOM → COK     25\n",
              "DEL → JDH → BOM → COK     23\n",
              "CCU → GAU → BLR           22\n",
              "DEL → MAA → BOM → COK     16\n",
              "DEL → IXC → BOM → COK     13\n",
              "BLR → MAA → DEL           10\n",
              "BLR → BDQ → DEL            8\n",
              "DEL → UDR → BOM → COK      7\n",
              "BOM → DEL → HYD            5\n",
              "CCU → BOM → PNQ → BLR      4\n",
              "BLR → BOM → JDH → DEL      3\n",
              "DEL → DED → BOM → COK      2\n",
              "BOM → BDQ → DEL → HYD      2\n",
              "DEL → CCU → BOM → COK      1\n",
              "BOM → VNS → DEL → HYD      1\n",
              "BOM → UDR → DEL → HYD      1\n",
              "BOM → JDH → DEL → HYD      1\n",
              "BOM → IDR → DEL → HYD      1\n",
              "BOM → DED → DEL → HYD      1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[data['Airline']=='Jet Airways'].groupby('Route').size().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bde5d36c",
      "metadata": {
        "id": "bde5d36c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "673fe808",
      "metadata": {
        "id": "673fe808"
      },
      "source": [
        "## Airline vs Price Analysis\n",
        "    ie finding price distribution & 5-point summary of each Airline.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b531f53",
      "metadata": {
        "id": "8b531f53",
        "outputId": "68c174d7-2f47-4951-8af1-20153ff262f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
              " [Text(0, 0, 'IndiGo'),\n",
              "  Text(1, 0, 'Air India'),\n",
              "  Text(2, 0, 'Jet Airways'),\n",
              "  Text(3, 0, 'SpiceJet'),\n",
              "  Text(4, 0, 'Multiple carriers'),\n",
              "  Text(5, 0, 'GoAir'),\n",
              "  Text(6, 0, 'Vistara'),\n",
              "  Text(7, 0, 'Air Asia'),\n",
              "  Text(8, 0, 'Vistara Premium economy'),\n",
              "  Text(9, 0, 'Jet Airways Business'),\n",
              "  Text(10, 0, 'Multiple carriers Premium economy'),\n",
              "  Text(11, 0, 'Trujet')])"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.boxplot(y='Price',x='Airline',data=data)\n",
        "plt.xticks(rotation='vertical')"
      ]
    },
    {
      "cell_type": "raw",
      "id": "cf8fed04",
      "metadata": {
        "id": "cf8fed04"
      },
      "source": [
        "Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost all are having similar median"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06122d15",
      "metadata": {
        "id": "06122d15"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8ec848c2",
      "metadata": {
        "id": "8ec848c2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd40f764",
      "metadata": {
        "id": "fd40f764"
      },
      "outputs": [],
      "source": [
        "### when we need boxplot + distribution both , its good to consider violinplot.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c09b542",
      "metadata": {
        "id": "3c09b542",
        "outputId": "80b0d4e4-af6f-4bd1-be83-a617b62c6f50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
              " [Text(0, 0, 'IndiGo'),\n",
              "  Text(1, 0, 'Air India'),\n",
              "  Text(2, 0, 'Jet Airways'),\n",
              "  Text(3, 0, 'SpiceJet'),\n",
              "  Text(4, 0, 'Multiple carriers'),\n",
              "  Text(5, 0, 'GoAir'),\n",
              "  Text(6, 0, 'Vistara'),\n",
              "  Text(7, 0, 'Air Asia'),\n",
              "  Text(8, 0, 'Vistara Premium economy'),\n",
              "  Text(9, 0, 'Jet Airways Business'),\n",
              "  Text(10, 0, 'Multiple carriers Premium economy'),\n",
              "  Text(11, 0, 'Trujet')])"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.violinplot(y='Price',x='Airline',data=data)\n",
        "plt.xticks(rotation='vertical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd2ce779",
      "metadata": {
        "id": "fd2ce779"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48288ff2",
      "metadata": {
        "id": "48288ff2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "56714455",
      "metadata": {
        "id": "56714455"
      },
      "source": [
        "## Lets Perform Feature-Encoding on Data !\n",
        "    Applying one-hot on data !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "08f9b468",
      "metadata": {
        "id": "08f9b468"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4392df0b",
      "metadata": {
        "id": "4392df0b"
      },
      "outputs": [],
      "source": [
        "## but lets remove some of the un-necessary features !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32a21c12",
      "metadata": {
        "id": "32a21c12",
        "outputId": "835f6716-f91e-44ba-f7e2-544e81f2f896"
      },
      "outputs": [
        {
          "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Duration_total_mins</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</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>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</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>CCU → NAG → BLR</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline    Source Destination                  Route Duration  \\\n",
              "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
              "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
              "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   19h 0m   \n",
              "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
              "\n",
              "  Total_Stops Additional_Info  Price  journey_day  journey_month  \\\n",
              "0    non-stop         No info   3897           24              3   \n",
              "1     2 stops         No info   7662            5              1   \n",
              "2     2 stops         No info  13882            6              9   \n",
              "3      1 stop         No info   6218            5             12   \n",
              "\n",
              "   journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "2          2019              9               25                  4   \n",
              "3          2019             18                5                 23   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Duration_total_mins  \n",
              "0                   10               2             50                  170  \n",
              "1                   15               7             25                  445  \n",
              "2                   25              19              0                 1140  \n",
              "3                   30               5             25                  325  "
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "468ebc64",
      "metadata": {
        "id": "468ebc64",
        "outputId": "10797653-7865-4e19-bddd-410242005ce5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "No info                         78.11\n",
              "In-flight meal not included     18.55\n",
              "No check-in baggage included     3.00\n",
              "1 Long layover                   0.18\n",
              "Change airports                  0.07\n",
              "Business class                   0.04\n",
              "No Info                          0.03\n",
              "2 Long layover                   0.01\n",
              "1 Short layover                  0.01\n",
              "Red-eye flight                   0.01\n",
              "Name: Additional_Info, dtype: float64"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.round(data['Additional_Info'].value_counts()/len(data)*100,2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b1aad58",
      "metadata": {
        "id": "7b1aad58"
      },
      "outputs": [],
      "source": [
        "# Additional_Info contains almost 80% no_info,so we can drop this column\n",
        "# we can drop Route as well as we have pre-process that column\n",
        "## lets drop Duration_total_mins as we have already extracted \"Duration_hours\" & \"Duration_mins\"\n",
        "\n",
        "data.drop(columns=['Additional_Info','Route','Duration_total_mins','journey_year'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68906bc0",
      "metadata": {
        "id": "68906bc0",
        "outputId": "d526a8be-1bba-4e3c-ec5d-dbeb51e34304"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Source', 'Destination', 'Duration', 'Total_Stops', 'Price',\n",
              "       'journey_day', 'journey_month', 'Dep_Time_hour', 'Dep_Time_minute',\n",
              "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
              "       'Duration_mins'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "007279b1",
      "metadata": {
        "id": "007279b1",
        "outputId": "d2cd80e4-d6b7-4af7-c748-c43e1756d0bc"
      },
      "outputs": [
        {
          "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>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</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>non-stop</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</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 stops</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</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 stop</td>\n",
              "      <td>6218</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline    Source Destination Duration Total_Stops  Price  journey_day  \\\n",
              "0       IndiGo  Banglore   New Delhi   2h 50m    non-stop   3897           24   \n",
              "1    Air India   Kolkata    Banglore   7h 25m     2 stops   7662            5   \n",
              "2  Jet Airways     Delhi      Cochin   19h 0m     2 stops  13882            6   \n",
              "3       IndiGo   Kolkata    Banglore   5h 25m      1 stop   6218            5   \n",
              "\n",
              "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0              3             22               20                  1   \n",
              "1              1              5               50                 13   \n",
              "2              9              9               25                  4   \n",
              "3             12             18                5                 23   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  \n",
              "0                   10               2             50  \n",
              "1                   15               7             25  \n",
              "2                   25              19              0  \n",
              "3                   30               5             25  "
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0737e1e0",
      "metadata": {
        "id": "0737e1e0"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a9277f93",
      "metadata": {
        "id": "a9277f93"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "f8203480",
      "metadata": {
        "id": "f8203480"
      },
      "source": [
        "## Lets separate categorical data & numerical data !\n",
        "    categorical data are those whose data-type is 'object'\n",
        "    Numerical data are those whose data-type is either int of float"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d9623cf",
      "metadata": {
        "id": "2d9623cf"
      },
      "outputs": [],
      "source": [
        "cat_col=[col for col in data.columns if data[col].dtype=='object']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42d346e9",
      "metadata": {
        "id": "42d346e9"
      },
      "outputs": [],
      "source": [
        "num_col=[col for col in data.columns if data[col].dtype!='object']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa183b01",
      "metadata": {
        "id": "aa183b01",
        "outputId": "f3bf3935-da29-48df-d50b-0cc6ad19b922"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Airline', 'Source', 'Destination', 'Duration', 'Total_Stops']"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cat_col"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b397b79d",
      "metadata": {
        "id": "b397b79d"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "450d154d",
      "metadata": {
        "id": "450d154d"
      },
      "source": [
        "## Handling Categorical Data\n",
        "    We are using 2 basic Encoding Techniques to convert Categorical data into some numerical format\n",
        "    if data belongs to Nominal data (ie data is not in any order) -->> OneHotEncoder is used in this case\n",
        "    if data belongs to Ordinal data (ie data is in order ) -->>       LabelEncoder is used in this case\n",
        "\n",
        "    But in real-world , it is not necessary that u have to always One-hot or label , hence we will discuss more interesting approaches to do this !"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a70872b",
      "metadata": {
        "id": "5a70872b"
      },
      "source": [
        "### Lets apply one-hot encoding on 'Source' feature !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8eafc90e",
      "metadata": {
        "id": "8eafc90e"
      },
      "outputs": [],
      "source": [
        "### One-hot from scratch :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e19d367b",
      "metadata": {
        "id": "e19d367b",
        "outputId": "40624eb5-4d63-4844-b729-28e15fa5ea0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'], dtype=object)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Source'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5de2f1d1",
      "metadata": {
        "id": "5de2f1d1",
        "outputId": "9e48f0a7-1076-4714-ef2e-e65236c2ec4e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        Banglore\n",
              "1         Kolkata\n",
              "2           Delhi\n",
              "3         Kolkata\n",
              "4        Banglore\n",
              "           ...   \n",
              "10678     Kolkata\n",
              "10679     Kolkata\n",
              "10680    Banglore\n",
              "10681    Banglore\n",
              "10682       Delhi\n",
              "Name: Source, Length: 10682, dtype: object"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Source']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a3d557d7",
      "metadata": {
        "id": "a3d557d7",
        "outputId": "9354d293-8355-4926-dedf-8b3fa68248ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        1\n",
              "1        0\n",
              "2        0\n",
              "3        0\n",
              "4        1\n",
              "        ..\n",
              "10678    0\n",
              "10679    0\n",
              "10680    1\n",
              "10681    1\n",
              "10682    0\n",
              "Name: Source, Length: 10682, dtype: int64"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Source'].apply(lambda x: 1 if x=='Banglore' else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f912fe6",
      "metadata": {
        "id": "3f912fe6"
      },
      "outputs": [],
      "source": [
        "for category in data['Source'].unique():\n",
        "    data['Source_'+category]=data['Source'].apply(lambda x: 1 if x==category else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd5c28c9",
      "metadata": {
        "id": "fd5c28c9",
        "outputId": "bcc0d350-206f-47b1-8a49-09cc65928f3d"
      },
      "outputs": [
        {
          "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>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</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>non-stop</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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 stops</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline    Source Destination Duration Total_Stops  Price  journey_day  \\\n",
              "0       IndiGo  Banglore   New Delhi   2h 50m    non-stop   3897           24   \n",
              "1    Air India   Kolkata    Banglore   7h 25m     2 stops   7662            5   \n",
              "2  Jet Airways     Delhi      Cochin   19h 0m     2 stops  13882            6   \n",
              "\n",
              "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0              3             22               20                  1   \n",
              "1              1              5               50                 13   \n",
              "2              9              9               25                  4   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                   10               2             50                1   \n",
              "1                   15               7             25                0   \n",
              "2                   25              19              0                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  \n",
              "2               0             1               0              0  "
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad129020",
      "metadata": {
        "id": "ad129020"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "bdd19969",
      "metadata": {
        "id": "bdd19969"
      },
      "source": [
        "## Performing Target Guided Mean Encoding !\n",
        "    ofcourse we can use One-hot , but if we have more sub-categories , it creates curse of dimensionality in ML..\n",
        "    lets use Target Guided Mean Encoding in order to get rid of this.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f64095ee",
      "metadata": {
        "id": "f64095ee"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8a3d47b8",
      "metadata": {
        "id": "8a3d47b8"
      },
      "outputs": [],
      "source": [
        "airlines=data.groupby(['Airline'])['Price'].mean().sort_values().index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3566764f",
      "metadata": {
        "id": "3566764f",
        "outputId": "b54bbbeb-368a-4c64-b9e2-1cc18c1d707b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Trujet', 'SpiceJet', 'Air Asia', 'IndiGo', 'GoAir', 'Vistara',\n",
              "       'Vistara Premium economy', 'Air India', 'Multiple carriers',\n",
              "       'Multiple carriers Premium economy', 'Jet Airways',\n",
              "       'Jet Airways Business'],\n",
              "      dtype='object', name='Airline')"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "airlines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df525826",
      "metadata": {
        "id": "df525826"
      },
      "outputs": [],
      "source": [
        "dict1={key:index for index,key in enumerate(airlines,0)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2495bb45",
      "metadata": {
        "id": "2495bb45",
        "outputId": "d322e4fe-080c-48dc-f443-3b45e9391e08"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Trujet': 0,\n",
              " 'SpiceJet': 1,\n",
              " 'Air Asia': 2,\n",
              " 'IndiGo': 3,\n",
              " 'GoAir': 4,\n",
              " 'Vistara': 5,\n",
              " 'Vistara Premium economy': 6,\n",
              " 'Air India': 7,\n",
              " 'Multiple carriers': 8,\n",
              " 'Multiple carriers Premium economy': 9,\n",
              " 'Jet Airways': 10,\n",
              " 'Jet Airways Business': 11}"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dict1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc1e756e",
      "metadata": {
        "id": "fc1e756e"
      },
      "outputs": [],
      "source": [
        "data['Airline']=data['Airline'].map(dict1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "204d42d1",
      "metadata": {
        "id": "204d42d1",
        "outputId": "eb41bae1-47e7-444e-ed05-f887b85d7f37"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         3\n",
              "1         7\n",
              "2        10\n",
              "3         3\n",
              "4         3\n",
              "         ..\n",
              "10678     2\n",
              "10679     7\n",
              "10680    10\n",
              "10681     5\n",
              "10682     7\n",
              "Name: Airline, Length: 10682, dtype: int64"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Airline']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a478b7df",
      "metadata": {
        "id": "a478b7df",
        "outputId": "d6e388b6-b3c8-44b9-eb49-fc9daf9b89d8"
      },
      "outputs": [
        {
          "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>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline    Source Destination Duration Total_Stops  Price  journey_day  \\\n",
              "0        3  Banglore   New Delhi   2h 50m    non-stop   3897           24   \n",
              "1        7   Kolkata    Banglore   7h 25m     2 stops   7662            5   \n",
              "\n",
              "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0              3             22               20                  1   \n",
              "1              1              5               50                 13   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                   10               2             50                1   \n",
              "1                   15               7             25                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  "
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e1bcfa9",
      "metadata": {
        "id": "1e1bcfa9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a95f51e7",
      "metadata": {
        "id": "a95f51e7",
        "outputId": "3cbd05f8-70fc-41b9-b6fb-1791aa12b0c0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Destination'].unique()"
      ]
    },
    {
      "cell_type": "raw",
      "id": "4b0a2780",
      "metadata": {
        "id": "4b0a2780"
      },
      "source": [
        "Note: till now , Delhi (Capital of India) has one Airport & its second Airport is yet to build in Greater Noida (Jewar)\n",
        "      which is part of NCR , so we will consider New Delhi & Delhi as same ...\n",
        "\n",
        "\n",
        "      but in future , these conditions may change.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb3d6032",
      "metadata": {
        "id": "cb3d6032"
      },
      "outputs": [],
      "source": [
        "data['Destination'].replace('New Delhi','Delhi',inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58d2daf8",
      "metadata": {
        "id": "58d2daf8",
        "outputId": "167af1b9-6290-4d81-b52c-86bcf1f5eff4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Destination'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4e4bedb5",
      "metadata": {
        "id": "4e4bedb5"
      },
      "outputs": [],
      "source": [
        "dest=data.groupby(['Destination'])['Price'].mean().sort_values().index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "37e57fdd",
      "metadata": {
        "id": "37e57fdd",
        "outputId": "ec4e76b6-d858-40ff-e044-8e54284c7546"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Kolkata', 'Hyderabad', 'Delhi', 'Banglore', 'Cochin'], dtype='object', name='Destination')"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa14cdfe",
      "metadata": {
        "id": "fa14cdfe"
      },
      "outputs": [],
      "source": [
        "dict2={key:index for index,key in enumerate(dest,0)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a18e8dfc",
      "metadata": {
        "id": "a18e8dfc",
        "outputId": "6a45d232-9253-43ad-dac1-9f91594926fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Kolkata': 0, 'Hyderabad': 1, 'Delhi': 2, 'Banglore': 3, 'Cochin': 4}"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dict2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63bbe479",
      "metadata": {
        "id": "63bbe479"
      },
      "outputs": [],
      "source": [
        "data['Destination']=data['Destination'].map(dict2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "421332ea",
      "metadata": {
        "id": "421332ea",
        "outputId": "03b54415-b1b9-4abb-8499-61a9591f324b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2\n",
              "1        3\n",
              "2        4\n",
              "3        3\n",
              "4        2\n",
              "        ..\n",
              "10678    3\n",
              "10679    3\n",
              "10680    2\n",
              "10681    2\n",
              "10682    4\n",
              "Name: Destination, Length: 10682, dtype: int64"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Destination']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b32e3ad2",
      "metadata": {
        "id": "b32e3ad2",
        "outputId": "e9978c89-b262-458c-d81d-88f46d826ba8"
      },
      "outputs": [
        {
          "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>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>2</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>3</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline    Source  Destination Duration Total_Stops  Price  journey_day  \\\n",
              "0        3  Banglore            2   2h 50m    non-stop   3897           24   \n",
              "1        7   Kolkata            3   7h 25m     2 stops   7662            5   \n",
              "\n",
              "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0              3             22               20                  1   \n",
              "1              1              5               50                 13   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                   10               2             50                1   \n",
              "1                   15               7             25                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  "
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "80f30cff",
      "metadata": {
        "id": "80f30cff"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "828c5a2d",
      "metadata": {
        "id": "828c5a2d"
      },
      "source": [
        "### Perform Manual Encoding on Total_stops feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "08f2b7f7",
      "metadata": {
        "id": "08f2b7f7",
        "outputId": "ecec3988-05f5-418e-8bae-2dbb61860a8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Total_Stops'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd2f8473",
      "metadata": {
        "id": "dd2f8473"
      },
      "outputs": [],
      "source": [
        "stops={'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b878e20d",
      "metadata": {
        "id": "b878e20d"
      },
      "outputs": [],
      "source": [
        "data['Total_Stops']=data['Total_Stops'].map(stops)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adbf4ec0",
      "metadata": {
        "id": "adbf4ec0",
        "outputId": "a2359349-2253-45b3-d603-9ac14e8f6548"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "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: 10682, dtype: int64"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Total_Stops']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2d2b078",
      "metadata": {
        "id": "d2d2b078"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "78c31588",
      "metadata": {
        "id": "78c31588"
      },
      "source": [
        "## Performing Outlier Detection !\n",
        "    Here the list of data visualization plots to spot the outliers.\n",
        "1. Box and whisker plot (box plot).\n",
        "2. Scatter plot.\n",
        "3. Histogram.\n",
        "4. Distribution Plot.\n",
        "5. QQ plot"
      ]
    },
    {
      "cell_type": "raw",
      "id": "d821ee89",
      "metadata": {
        "id": "d821ee89"
      },
      "source": [
        "CAUSE FOR OUTLIERS\n",
        "* Data Entry Errors:- Human errors such as errors caused during data collection, recording, or entry can cause outliers in data.\n",
        "* Measurement Error:- It is the most common source of outliers. This is caused when the measurement instrument used turns out to be faulty.\n",
        "* Natural Outlier:- When an outlier is not artificial (due to error), it is a natural outlier. Most of real world data belong to this category."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a42563a3",
      "metadata": {
        "id": "a42563a3"
      },
      "outputs": [],
      "source": [
        "def plot(df,col):\n",
        "    fig,(ax1,ax2,ax3)=plt.subplots(3,1)\n",
        "    sns.distplot(df[col],ax=ax1)\n",
        "    sns.boxplot(df[col],ax=ax2)\n",
        "    sns.distplot(df[col],ax=ax3,kde=False)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3762b075",
      "metadata": {
        "id": "3762b075",
        "outputId": "c945b5bc-aeb8-476f-8184-88ba78e83d7c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n",
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot(data,'Price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec73e95a",
      "metadata": {
        "id": "ec73e95a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "d023b303",
      "metadata": {
        "id": "d023b303"
      },
      "source": [
        "### getting a high level over-view of various ways to deal with outliers:\n"
      ]
    },
    {
      "cell_type": "raw",
      "id": "6eeab423",
      "metadata": {
        "id": "6eeab423"
      },
      "source": [
        "\n",
        "Again there are various ways to deal with outliers :\n",
        "\n",
        "\n",
        "1..Statistical imputation , ie impute it with mean , median or mode of data..\n",
        "\n",
        "a..Whenever ur data is Gaussian Distributed ,use 3 std dev approach to remove outliers in such case\n",
        "     ie we will use u+3*sigma & u-3*sigma\n",
        "        data pts greater than upper_boundary( u+3*sigma) are my outliers \n",
        "            & data pts which are less than lower_boundary(u-3*sigma) are my outliers\n",
        "\n",
        "        Above approach is known as Z-score & it has a extended version known as Robust z-score..\n",
        "        Robust Z-score is also called as Median absolute deviation method. \n",
        "        It is similar to Z-score method with some changes in parameters.\n",
        "\n",
        "\n",
        "b..If Features Are Skewed We Use the below Technique which is IQR\n",
        "    Data which are greater than IQR +1.5 IQR and data which are below than IQR - 1.5 IQR are my outliers\n",
        "     where IQR=75th%ile data - 25th%ile data\n",
        "\n",
        "     & IQR +- 1.5 IQR  will be changed depending upon the domain ie it may be IQR + 3IQR \n",
        "\n",
        "\n",
        "       Extended version of above is WINSORIZATION METHOD(PERCENTILE CAPPING)..\n",
        "       This method is similar to IQR method. It says -->> \n",
        "\n",
        "       Data points that are greater than 99th percentile and data points that are below tha 1st percentile \n",
        "       are treated as outliers.\n",
        "\n",
        "\n",
        "\n",
        " c..If we have huge high dimensional data , then it is good to perform isolation forest...\n",
        "     It is a clustering algo which works based on decision tree and it isolate the outliers.\n",
        "     It classify the data point to outlier and not outliers..\n",
        "         If the result is -1, it means that this specific data point is an outlier. \n",
        "         If the result is 1, then it means that the data point is not an outlier.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "So we have tonnes of ways to deal with outliers.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ef6c527f",
      "metadata": {
        "id": "ef6c527f"
      },
      "outputs": [],
      "source": [
        "data['Price']=np.where(data['Price']>=35000,data['Price'].median(),data['Price'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdf46cf4",
      "metadata": {
        "id": "cdf46cf4",
        "outputId": "ce637f76-c7bc-4053-94db-898eeaabc192"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n",
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot(data,'Price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85705896",
      "metadata": {
        "id": "85705896"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5fb40176",
      "metadata": {
        "id": "5fb40176",
        "outputId": "bf2d49cc-7248-4622-dc80-96a791884f24"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "  <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>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>2</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>0</td>\n",
              "      <td>3897.0</td>\n",
              "      <td>24</td>\n",
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              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>3</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2</td>\n",
              "      <td>7662.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
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              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline    Source  Destination Duration  Total_Stops   Price  journey_day  \\\n",
              "0        3  Banglore            2   2h 50m            0  3897.0           24   \n",
              "1        7   Kolkata            3   7h 25m            2  7662.0            5   \n",
              "\n",
              "   journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0              3             22               20                  1   \n",
              "1              1              5               50                 13   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                   10               2             50                1   \n",
              "1                   15               7             25                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  "
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f971252c",
      "metadata": {
        "id": "f971252c"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=['Source','Duration'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d7394b0",
      "metadata": {
        "id": "8d7394b0",
        "outputId": "06e2cab3-8410-452c-9681-563bcec93252"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Price</th>\n",
              "      <th>journey_day</th>\n",
              "      <th>journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
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              "      <th>Duration_mins</th>\n",
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              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline  Destination  Total_Stops   Price  journey_day  journey_month  \\\n",
              "0        3            2            0  3897.0           24              3   \n",
              "1        7            3            2  7662.0            5              1   \n",
              "\n",
              "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
              "0             22               20                  1                   10   \n",
              "1              5               50                 13                   15   \n",
              "\n",
              "   Duration_hours  Duration_mins  Source_Banglore  Source_Kolkata  \\\n",
              "0               2             50                1               0   \n",
              "1               7             25                0               1   \n",
              "\n",
              "   Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0             0               0              0  \n",
              "1             0               0              0  "
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "49939215",
      "metadata": {
        "id": "49939215",
        "outputId": "e5e24b17-1f78-40e5-b729-dd8f453de2e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline                  int64\n",
              "Destination              int64\n",
              "Total_Stops              int64\n",
              "Price                  float64\n",
              "journey_day              int64\n",
              "journey_month            int64\n",
              "Dep_Time_hour            int64\n",
              "Dep_Time_minute          int64\n",
              "Arrival_Time_hour        int64\n",
              "Arrival_Time_minute      int64\n",
              "Duration_hours           int64\n",
              "Duration_mins            int64\n",
              "Source_Banglore          int64\n",
              "Source_Kolkata           int64\n",
              "Source_Delhi             int64\n",
              "Source_Chennai           int64\n",
              "Source_Mumbai            int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bcefc15a",
      "metadata": {
        "id": "bcefc15a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "29058203",
      "metadata": {
        "id": "29058203"
      },
      "source": [
        "## Performing Feature Selection !"
      ]
    },
    {
      "cell_type": "raw",
      "id": "ad8db4ff",
      "metadata": {
        "id": "ad8db4ff"
      },
      "source": [
        "\n",
        "Finding out the best feature which will contribute most to the target variable. \n",
        "Lets get a high level overview of most of the frequently used feature selection technique..\n",
        "\n",
        "\n",
        "Why to apply Feature Selection?\n",
        "To select important features to get rid of curse of dimensionality ie..to get rid of duplicate features\n",
        "\n",
        "\n",
        "ways or technqiues to do it if we have regression use-case\n",
        "a..SelectKBest\n",
        "    Score function:\n",
        "    \n",
        "    For regression: f_regression, mutual_info_regression\n",
        "\n",
        "    f_regression\n",
        "    Its backbone is pearson co-relation.. \n",
        "\n",
        "\n",
        "    mutual_info_regression \n",
        "    Its Backbone is Various statistical test like Chi-sq,Anova & p-value.\n",
        "\n",
        "\n",
        "b..ExtraTreesClassifier\n",
        "   This technique gives you a score for each feature of your data,the higher the score more relevant it is\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22425bea",
      "metadata": {
        "id": "22425bea"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_selection import mutual_info_regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f9bf6b3a",
      "metadata": {
        "id": "f9bf6b3a"
      },
      "outputs": [],
      "source": [
        "X=data.drop(['Price'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63c990a7",
      "metadata": {
        "id": "63c990a7"
      },
      "outputs": [],
      "source": [
        "y=data['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5d806a1",
      "metadata": {
        "id": "e5d806a1",
        "outputId": "068627b6-245d-440b-fcba-ffe336d82d4d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline                int64\n",
              "Destination            int64\n",
              "Total_Stops            int64\n",
              "journey_day            int64\n",
              "journey_month          int64\n",
              "Dep_Time_hour          int64\n",
              "Dep_Time_minute        int64\n",
              "Arrival_Time_hour      int64\n",
              "Arrival_Time_minute    int64\n",
              "Duration_hours         int64\n",
              "Duration_mins          int64\n",
              "Source_Banglore        int64\n",
              "Source_Kolkata         int64\n",
              "Source_Delhi           int64\n",
              "Source_Chennai         int64\n",
              "Source_Mumbai          int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32995d0e",
      "metadata": {
        "id": "32995d0e",
        "outputId": "65a448a9-fe10-47c4-fbf6-0502313d51ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.98286122, 1.00592059, 0.79248293, 0.18016325, 0.24058519,\n",
              "       0.34355597, 0.25470737, 0.3933345 , 0.33968518, 0.46633157,\n",
              "       0.34408044, 0.38267856, 0.45556606, 0.52445805, 0.12874953,\n",
              "       0.19799099])"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mutual_info_regression(X,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "821813f4",
      "metadata": {
        "id": "821813f4"
      },
      "outputs": [],
      "source": [
        "imp=pd.DataFrame(mutual_info_regression(X,y),index=X.columns)\n",
        "imp.columns=['importance']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c3723e4",
      "metadata": {
        "id": "8c3723e4",
        "outputId": "2f8a4dde-fd8a-43d7-dd07-718b9b26840f"
      },
      "outputs": [
        {
          "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>importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Destination</th>\n",
              "      <td>1.005740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline</th>\n",
              "      <td>0.969470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Stops</th>\n",
              "      <td>0.788123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Delhi</th>\n",
              "      <td>0.519929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_hours</th>\n",
              "      <td>0.477345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <td>0.456298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <td>0.400439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Banglore</th>\n",
              "      <td>0.392985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <td>0.348128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_mins</th>\n",
              "      <td>0.346181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <td>0.345226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <td>0.259922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>journey_month</th>\n",
              "      <td>0.233702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Mumbai</th>\n",
              "      <td>0.203205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>journey_day</th>\n",
              "      <td>0.192198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Chennai</th>\n",
              "      <td>0.132871</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     importance\n",
              "Destination            1.005740\n",
              "Airline                0.969470\n",
              "Total_Stops            0.788123\n",
              "Source_Delhi           0.519929\n",
              "Duration_hours         0.477345\n",
              "Source_Kolkata         0.456298\n",
              "Arrival_Time_hour      0.400439\n",
              "Source_Banglore        0.392985\n",
              "Arrival_Time_minute    0.348128\n",
              "Duration_mins          0.346181\n",
              "Dep_Time_hour          0.345226\n",
              "Dep_Time_minute        0.259922\n",
              "journey_month          0.233702\n",
              "Source_Mumbai          0.203205\n",
              "journey_day            0.192198\n",
              "Source_Chennai         0.132871"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "imp.sort_values(by='importance',ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3324378",
      "metadata": {
        "id": "b3324378"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "93b5ad75",
      "metadata": {
        "id": "93b5ad75"
      },
      "source": [
        "## Lets build ML Model  , then later on we can think of saving it.."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52abb121",
      "metadata": {
        "id": "52abb121"
      },
      "source": [
        "#### split dataset into train & test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "43368422",
      "metadata": {
        "id": "43368422"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "29de82e9",
      "metadata": {
        "id": "29de82e9"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e7b472c",
      "metadata": {
        "id": "9e7b472c"
      },
      "source": [
        "#### what we often do in modelling:\n",
        "    a..Initially ,lets build basic random forest model.\n",
        "    b..then later-on , we will try to improve this model using some parameters..\n",
        "    c..Then we will hyper-tune my model to get optimal value of parameters in order to achieve optimal value of params.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6320f23f",
      "metadata": {
        "id": "6320f23f"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f993a63a",
      "metadata": {
        "id": "f993a63a"
      },
      "outputs": [],
      "source": [
        "ml_model=RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a0bc9d2",
      "metadata": {
        "id": "0a0bc9d2"
      },
      "outputs": [],
      "source": [
        "model=ml_model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8dee2a84",
      "metadata": {
        "id": "8dee2a84"
      },
      "outputs": [],
      "source": [
        "y_pred=model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc81c1b9",
      "metadata": {
        "id": "dc81c1b9",
        "outputId": "ba4dd171-d374-4006-e13b-dc6fbb3c888b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([16771.12,  6530.66,  8842.67, ...,  3498.6 ,  6430.97,  6849.96])"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f22bba3",
      "metadata": {
        "id": "8f22bba3",
        "outputId": "be8c2e9f-5915-4f01-df54-3b016de7f813"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2671,)"
            ]
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e287d43c",
      "metadata": {
        "id": "e287d43c",
        "outputId": "61402f6f-f6aa-401b-ba32-6cbe182f0d32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2671"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a55a7d50",
      "metadata": {
        "id": "a55a7d50"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "667416b5",
      "metadata": {
        "id": "667416b5"
      },
      "source": [
        "### How to save ML model into disk"
      ]
    },
    {
      "cell_type": "raw",
      "id": "ba6ba8cc",
      "metadata": {
        "id": "ba6ba8cc"
      },
      "source": [
        "lets try to dump ml model using pickle & joblib..\n",
        "advantage of dumping--\n",
        "imagine in future we have new data ,& lets say we have to predict price on this huge data\n",
        "\n",
        "then just for this new data , we have to execute all the above cells follow the entire pipeline,  then only we are able to predict on this...\n",
        "\n",
        "\n",
        "so to get rid of such issue , will just dump it to reuse it again & again..\n",
        "what does this file store??\n",
        "this save coefficients of our model.. not an entire dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2517e7b8",
      "metadata": {
        "id": "2517e7b8"
      },
      "outputs": [],
      "source": [
        "### !pip install pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9eb7faa0",
      "metadata": {
        "id": "9eb7faa0"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f17bfa1d",
      "metadata": {
        "id": "f17bfa1d"
      },
      "outputs": [],
      "source": [
        "file=open(r'E:\\End-2-end Projects\\Flight_Price\\Datasets/rf_random.pkl','wb')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d668611b",
      "metadata": {
        "id": "d668611b"
      },
      "outputs": [],
      "source": [
        "pickle.dump(model,file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ade9047",
      "metadata": {
        "id": "1ade9047"
      },
      "outputs": [],
      "source": [
        "model=open(r'E:\\End-2-end Projects\\Flight_Price\\Datasets/rf_random.pkl','rb')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f0df3e2",
      "metadata": {
        "id": "7f0df3e2"
      },
      "outputs": [],
      "source": [
        "forest=pickle.load(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3b16596",
      "metadata": {
        "id": "e3b16596",
        "outputId": "f3076ef5-751b-4518-f137-1ca7958e326a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([16771.12,  6530.66,  8842.67, ...,  3498.6 ,  6430.97,  6849.96])"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forest.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d068e8c9",
      "metadata": {
        "id": "d068e8c9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cd0df855",
      "metadata": {
        "id": "cd0df855"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "8886524a",
      "metadata": {
        "id": "8886524a"
      },
      "source": [
        "## Defining your own evaluation metric :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a394304",
      "metadata": {
        "id": "1a394304"
      },
      "outputs": [],
      "source": [
        "def mape(y_true,y_pred):\n",
        "    y_true,y_pred=np.array(y_true),np.array(y_pred)\n",
        "    \n",
        "    return np.mean(np.abs((y_true-y_pred)/y_true))*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4070ae9c",
      "metadata": {
        "id": "4070ae9c",
        "outputId": "581fc97a-664a-4bed-e00d-3b69bc7de788"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "13.168122671175029"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mape(y_test,forest.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "36dc96d8",
      "metadata": {
        "id": "36dc96d8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "372afb7a",
      "metadata": {
        "id": "372afb7a"
      },
      "source": [
        "## How to Automate ML Pipeline :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "927fbeeb",
      "metadata": {
        "id": "927fbeeb"
      },
      "outputs": [],
      "source": [
        "def predict(ml_model):\n",
        "    \n",
        "    model=ml_model.fit(X_train,y_train)\n",
        "    print('Training_score: {}'.format(model.score(X_train,y_train)))\n",
        "    y_prediction=model.predict(X_test)\n",
        "    print('Predictions are : {}'.format(y_prediction))\n",
        "    print('\\n')\n",
        "    \n",
        "    from sklearn import metrics\n",
        "    r2_score=metrics.r2_score(y_test,y_prediction)\n",
        "    print('r2_score: {}'.format(r2_score))\n",
        "    print('MSE : ', metrics.mean_squared_error(y_test,y_prediction))\n",
        "    print('MAE : ', metrics.mean_absolute_error(y_test,y_prediction))\n",
        "    print('RMSE : ', np.sqrt(metrics.mean_squared_error(y_test,y_prediction)))\n",
        "    print('MAPE : ', mape(y_test,y_prediction))\n",
        "    sns.distplot(y_test-y_prediction)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd4ff6f1",
      "metadata": {
        "id": "dd4ff6f1",
        "outputId": "afffa0bf-1653-47c2-c03a-0789f0ca3cff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training_score: 0.9515032800315092\n",
            "Predictions are : [16788.62  6423.72  8848.83 ...  3543.45  6349.43  6898.63]\n",
            "\n",
            "\n",
            "r2_score: 0.8053218217045761\n",
            "MSE :  3789916.8386505432\n",
            "MAE :  1187.3284159521165\n",
            "RMSE :  1946.770874718066\n",
            "MAPE :  13.27313841848225\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\mcr\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning:\n",
            "\n",
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "predict(RandomForestRegressor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5da9be9",
      "metadata": {
        "id": "f5da9be9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d261c70",
      "metadata": {
        "id": "9d261c70"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "f9e17750",
      "metadata": {
        "id": "f9e17750"
      },
      "source": [
        "## how to hypertune ml model\n",
        "    Hyperparameter Tuning or Hyperparameter Optimization\n",
        "    1.Choose following method for hyperparameter tuning\n",
        "        a.RandomizedSearchCV --> Fast way to Hypertune model\n",
        "        b.GridSearchCV--> Slow way to hypertune my model\n",
        "    2.Choose ML algo that u have to hypertune\n",
        "    2.Assign hyperparameters in form of dictionary or create hyper-parameter space\n",
        "    3.define searching &  apply searching on Training data or  Fit the CV model \n",
        "    4.Check best parameters and best score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4781ae5f",
      "metadata": {
        "id": "4781ae5f"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f2a2ec60",
      "metadata": {
        "id": "f2a2ec60"
      },
      "outputs": [],
      "source": [
        "### initialise your estimator\n",
        "reg_rf=RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a81d83b",
      "metadata": {
        "id": "2a81d83b",
        "outputId": "2d003648-e4d7-463f-c5ce-563c3a875a39"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1000., 1040., 1080., 1120., 1160., 1200.])"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.linspace(start=1000,stop=1200,num=6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f62eb95",
      "metadata": {
        "id": "6f62eb95"
      },
      "outputs": [],
      "source": [
        "# Number of trees in random forest\n",
        "n_estimators=[int(x) for x in np.linspace(start=1000,stop=1200,num=6)]\n",
        "\n",
        "# Number of features to consider at every split\n",
        "max_features=[\"auto\", \"sqrt\"]\n",
        "\n",
        "# Maximum number of levels in tree\n",
        "max_depth=[int(x) for x in np.linspace(start=5,stop=30,num=4)]\n",
        "\n",
        "# Minimum number of samples required to split a node\n",
        "min_samples_split=[5,10,15,100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8dcd67f1",
      "metadata": {
        "id": "8dcd67f1"
      },
      "outputs": [],
      "source": [
        "# Create the grid or hyper-parameter space\n",
        "random_grid={\n",
        "    'n_estimators':n_estimators,\n",
        "    'max_features':max_features,\n",
        "    'max_depth':max_depth,\n",
        "    'min_samples_split':min_samples_split\n",
        "    \n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a6e6ccb",
      "metadata": {
        "id": "6a6e6ccb",
        "outputId": "7e979b68-1c62-46ed-b246-43e141f3b704"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'n_estimators': [1000, 1040, 1080, 1120, 1160, 1200],\n",
              " 'max_features': ['auto', 'sqrt'],\n",
              " 'max_depth': [5, 13, 21, 30],\n",
              " 'min_samples_split': [5, 10, 15, 100]}"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "random_grid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d5c803f",
      "metadata": {
        "id": "9d5c803f"
      },
      "outputs": [],
      "source": [
        "rf_Random=RandomizedSearchCV(reg_rf,param_distributions=random_grid,cv=3,verbose=2,n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4548bfc5",
      "metadata": {
        "id": "4548bfc5",
        "outputId": "6e6a6482-523e-4cea-85c3-e571334a5608"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=-1,\n",
              "                   param_distributions={'max_depth': [5, 13, 21, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_split': [5, 10, 15, 100],\n",
              "                                        'n_estimators': [1000, 1040, 1080, 1120,\n",
              "                                                         1160, 1200]},\n",
              "                   verbose=2)"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_Random.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6aebd4a3",
      "metadata": {
        "id": "6aebd4a3",
        "outputId": "955a3608-6137-4599-d81a-ea11fc771075"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'n_estimators': 1040,\n",
              " 'min_samples_split': 5,\n",
              " 'max_features': 'auto',\n",
              " 'max_depth': 13}"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "### to get your best model..\n",
        "rf_Random.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f67fd9c",
      "metadata": {
        "id": "3f67fd9c"
      },
      "outputs": [],
      "source": [
        "pred2=rf_Random.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "022e6400",
      "metadata": {
        "id": "022e6400",
        "outputId": "3ff1e7e2-0fae-4ff5-e200-9f5b66d0f448"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8291428366500607"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "metrics.r2_score(y_test,pred2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ffafab2a",
      "metadata": {
        "id": "ffafab2a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "990f1bef",
      "metadata": {
        "id": "990f1bef"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3fd9d3c3",
      "metadata": {
        "id": "3fd9d3c3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a8216ff4",
      "metadata": {
        "id": "a8216ff4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f0bac112",
      "metadata": {
        "id": "f0bac112"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
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        "9e7b472c",
        "667416b5",
        "8886524a",
        "372afb7a",
        "f9e17750"
      ],
      "include_colab_link": true,
      "name": "ml_flight_price_prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.2 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.2"
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    "vscode": {
      "interpreter": {
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