Flight_Fare_Prediction.ipynb

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      "source": [
        "**`ML Flight Fare Prediction (Kaggle Problem Statement)`**"
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      "source": [
        "In this notebook, I have made an attempt to develop a model to solve the Kaggle Problem Statement - Flight Fare Prediction using ML Regression. Here, I have implemented various ML regression models and finally decided upon Random Forest Regressor for predictions."
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        "--------------------------------------------------------------------------"
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        "Importing the libraries and the dataset"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
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        "id": "s7P-Xoy0zCrY"
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        "train_data = pd.read_excel('Data_Train.xlsx')"
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              "       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",
              "\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  \n",
              "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  "
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              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
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            "<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",
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            " 9   Additional_Info  10683 non-null  object\n",
            " 10  Price            10683 non-null  int64 \n",
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              "Airline            0\n",
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              "        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",
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              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              0\n",
              "Dep_Time           0\n",
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              "Total_Stops        0\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
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          },
          "metadata": {},
          "execution_count": 45
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pre-process & Perform Featurization of \"Date_of_Journey\""
      ],
      "metadata": {
        "id": "_x3443I87mui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = train_data.copy()"
      ],
      "metadata": {
        "id": "o2fyHNu1zY_x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
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      "metadata": {
        "colab": {
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        "id": "SFP65J3P0Lpx",
        "outputId": "107964b7-cfff-4c7c-dcfe-b825c0495a5f"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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    },
    {
      "cell_type": "code",
      "source": [
        "def change_into_datetime(col):\n",
        "    data[col]= pd.to_datetime(data[col])"
      ],
      "metadata": {
        "id": "Z8z_Tz9h0L2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aRoXt2_0L5x",
        "outputId": "189a4be2-6ac8-4f21-acee-5f790b96888a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in ['Date_of_Journey','Dep_Time','Arrival_Time']:\n",
        "    change_into_datetime(feature)"
      ],
      "metadata": {
        "id": "oLM_3Fy80L8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEjWEjFM0L-p",
        "outputId": "1bd9cf3d-a1be-4506-b121-116dff01d6f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Date_of_Journey'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--MkhsFn0RlJ",
        "outputId": "f89eb43a-faa6-4be8-efdd-1ba32baea4fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-01-03 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Date_of_Journey'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYe9m4fi0R1B",
        "outputId": "aff8b93d-ef2c-435a-c5a6-d6a055e49257"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-12-06 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "lets do Feature Engineering of \"Date_of_Journey\" & fetch day,month,year !"
      ],
      "metadata": {
        "id": "0jvVBoKV7vmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Journey_day']= data['Date_of_Journey'].dt.day"
      ],
      "metadata": {
        "id": "CT-Jtivl0R4L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Journey_month']= data['Date_of_Journey'].dt.month"
      ],
      "metadata": {
        "id": "vd0mTmOo0R6p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Journey_year'] = data['Date_of_Journey'].dt.year"
      ],
      "metadata": {
        "id": "Lsrsn8mr0R9G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
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        "id": "N-p7ehNz0R_h",
        "outputId": "5f5cf4ee-edac-4cfb-d430-7e1c35202354"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "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 2023-02-22 22:20:00 2023-03-22 01:10:00   2h 50m    non-stop   \n",
              "1 2023-02-22 05:50:00 2023-02-22 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  "
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop('Date_of_Journey', axis = 1, inplace =True)"
      ],
      "metadata": {
        "id": "GQxfP4E70SCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
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          "height": 260
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        "id": "RR_ZbRrL0SEx",
        "outputId": "4624183f-3617-4448-8cff-fc5a8b6730f8"
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      "execution_count": null,
      "outputs": [
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              "     Airline    Source Destination                  Route            Dep_Time  \\\n",
              "0     IndiGo  Banglore   New Delhi              BLR → DEL 2023-02-22 22:20:00   \n",
              "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2023-02-22 05:50:00   \n",
              "\n",
              "         Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
              "0 2023-03-22 01:10:00   2h 50m    non-stop         No info   3897   \n",
              "1 2023-02-22 13:15:00   7h 25m     2 stops         No info   7662   \n",
              "\n",
              "   Journey_day  Journey_month  Journey_year  \n",
              "0           24              3          2019  \n",
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              "      <td>IndiGo</td>\n",
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              "      <td>BLR → DEL</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets try to clean Dep_Time & Arrival_Time & featurize it."
      ],
      "metadata": {
        "id": "42dhJxkR7y0N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Engineering"
      ],
      "metadata": {
        "id": "uQ-yaPtC0s4_"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "TeX5r7bt0SHZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "extract_hour_min(data, 'Dep_Time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "2TgsMT4s0vnf",
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "     Airline    Source Destination                  Route        Arrival_Time  \\\n",
              "0     IndiGo  Banglore   New Delhi              BLR → DEL 2023-03-22 01:10:00   \n",
              "1  Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR 2023-02-22 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_Timeminute  \n",
              "0          2019             22              20  \n",
              "1          2019              5              50  "
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              "                                                     [key], {});\n",
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        "extract_hour_min(data, 'Arrival_Time')"
      ],
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        "id": "6P_DzYDr0wfw",
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      "execution_count": null,
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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",
              "\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_Timeminute  Arrival_Time_hour  \\\n",
              "0          2019             22              20                  1   \n",
              "1          2019              5              50                 13   \n",
              "\n",
              "   Arrival_Timeminute  \n",
              "0                  10  \n",
              "1                  15  "
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              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
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              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-637d9183-3a31-43e5-bddc-9e8c7863e130')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-637d9183-3a31-43e5-bddc-9e8c7863e130 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-637d9183-3a31-43e5-bddc-9e8c7863e130');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "lets analyse when will most of the flights will take-off"
      ],
      "metadata": {
        "id": "zozaLyQY76Fn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def flight_dep_time(x):\n",
        "    if (x > 4 ) and (x < 8 ):\n",
        "        return 'Early Morning'\n",
        "    elif (x > 8) and (x < 12):\n",
        "        return 'Morning'\n",
        "    elif (x > 12) and (x < 16):\n",
        "        return 'Noon'\n",
        "    elif (x > 16) and (x < 20):\n",
        "        return 'Evening'\n",
        "    elif (x > 20) and (x < 24):\n",
        "        return 'Night'\n",
        "    else:\n",
        "        return 'late night'"
      ],
      "metadata": {
        "id": "p2vwi4Cp00Uv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dep_Time_hour'].apply(flight_dep_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OK8fJRXt1GPu",
        "outputId": "7415bfbf-3ec4-4538-ab38-11b00c740f71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                Night\n",
              "1        Early Morning\n",
              "2              Morning\n",
              "3              Evening\n",
              "4           late night\n",
              "             ...      \n",
              "10678          Evening\n",
              "10679       late night\n",
              "10680       late night\n",
              "10681          Morning\n",
              "10682          Morning\n",
              "Name: Dep_Time_hour, Length: 10682, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dep_Time_hour'].apply(flight_dep_time).value_counts().plot(kind = 'bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "uYxkBzZe1LBm",
        "outputId": "70e51f23-5e06-4149-def3-d50e7511fc6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f125a84d8b0>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pre-process Duration Feature & extract meaningful features"
      ],
      "metadata": {
        "id": "C__elT8L8ECl"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "tOSozSiT1Ga6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Duration'] = data['Duration'].apply(preprocess_duration)"
      ],
      "metadata": {
        "id": "AntR0a3p1GdH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Duration']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1Rsk8rp1Gfu",
        "outputId": "ce7dad9e-ec55-43d5-854d-57d005408530"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Duration'][0].split(' ')[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XIGPl34H0SKB",
        "outputId": "e3718730-129e-4f4e-ba08-d23384aab9a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2h'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int(data['Duration'][0].split(' ')[0][0:-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdvSSRsP0SM-",
        "outputId": "edabd549-e460-4176-9b01-ca665c85c5b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int(data['Duration'][0].split(' ')[1][0:-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkT0eLs31yAs",
        "outputId": "c495bc08-8331-4eb6-b764-17c5dc4f68dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Duration'].apply(lambda x:int(data['Duration'][0].split(' ')[0][0:-1]))"
      ],
      "metadata": {
        "colab": {
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              "     Airline    Source Destination                  Route Duration  \\\n",
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        "id": "2bPxhzoS2QRT"
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              "     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",
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          "height": 386
        },
        "id": "NsM3Q_v32T58",
        "outputId": "9146b525-017b-4496-a0d0-846af2376f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f125a7dce20>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Duration'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsg93wIn2VI7",
        "outputId": "c4f5ec69-c691-4b97-a193-fe43f08542ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2h 50m', '7h 25m', '19h 0m', '5h 25m', '4h 45m', '2h 25m',\n",
              "       '15h 30m', '21h 5m', '25h 30m', '7h 50m', '13h 15m', '2h 35m',\n",
              "       '2h 15m', '12h 10m', '26h 35m', '4h 30m', '22h 35m', '23h 0m',\n",
              "       '20h 35m', '5h 10m', '15h 20m', '2h 55m', '13h 20m', '15h 10m',\n",
              "       '5h 45m', '5h 55m', '13h 25m', '22h 0m', '5h 30m', '10h 25m',\n",
              "       '5h 15m', '2h 30m', '6h 15m', '11h 55m', '11h 5m', '8h 30m',\n",
              "       '22h 5m', '2h 45m', '12h 0m', '16h 5m', '19h 55m', '3h 15m',\n",
              "       '25h 20m', '3h 0m', '16h 15m', '15h 5m', '6h 30m', '25h 5m',\n",
              "       '12h 25m', '27h 20m', '10h 15m', '10h 30m', '1h 30m', '1h 25m',\n",
              "       '26h 30m', '7h 20m', '13h 30m', '5h 0m', '19h 5m', '14h 50m',\n",
              "       '2h 40m', '22h 10m', '9h 35m', '10h 0m', '21h 20m', '18h 45m',\n",
              "       '12h 20m', '18h 0m', '9h 15m', '17h 30m', '16h 35m', '12h 15m',\n",
              "       '7h 30m', '24h 0m', '8h 55m', '7h 10m', '14h 30m', '30h 20m',\n",
              "       '15h 0m', '12h 45m', '10h 10m', '15h 25m', '14h 5m', '20h 15m',\n",
              "       '23h 10m', '18h 10m', '16h 0m', '2h 20m', '8h 0m', '16h 55m',\n",
              "       '3h 10m', '14h 0m', '23h 50m', '21h 40m', '21h 15m', '10h 50m',\n",
              "       '8h 15m', '8h 35m', '11h 50m', '27h 35m', '8h 25m', '20h 55m',\n",
              "       '4h 50m', '8h 10m', '24h 25m', '23h 35m', '25h 45m', '26h 10m',\n",
              "       '28h 50m', '25h 15m', '9h 20m', '9h 10m', '3h 5m', '11h 30m',\n",
              "       '9h 30m', '17h 35m', '5h 5m', '25h 50m', '20h 0m', '13h 0m',\n",
              "       '18h 25m', '24h 10m', '4h 55m', '25h 35m', '6h 20m', '18h 40m',\n",
              "       '19h 25m', '29h 20m', '9h 5m', '10h 45m', '11h 40m', '22h 55m',\n",
              "       '37h 25m', '25h 40m', '13h 55m', '8h 40m', '23h 30m', '12h 35m',\n",
              "       '24h 15m', '1h 20m', '11h 0m', '11h 15m', '14h 35m', '12h 55m',\n",
              "       '9h 0m', '7h 40m', '11h 45m', '24h 55m', '17h 5m', '29h 55m',\n",
              "       '22h 15m', '14h 40m', '7h 15m', '20h 10m', '20h 45m', '27h 0m',\n",
              "       '24h 30m', '20h 25m', '5h 35m', '14h 45m', '5h 40m', '4h 5m',\n",
              "       '15h 55m', '7h 45m', '28h 20m', '4h 20m', '3h 40m', '8h 50m',\n",
              "       '23h 45m', '24h 45m', '21h 35m', '8h 5m', '6h 25m', '15h 50m',\n",
              "       '26h 25m', '24h 50m', '26h 0m', '23h 5m', '7h 55m', '26h 20m',\n",
              "       '23h 15m', '5h 20m', '4h 0m', '9h 45m', '8h 20m', '17h 25m',\n",
              "       '7h 5m', '34h 5m', '6h 5m', '5h 50m', '7h 0m', '4h 25m', '13h 45m',\n",
              "       '19h 15m', '22h 30m', '16h 25m', '13h 50m', '27h 5m', '28h 10m',\n",
              "       '4h 40m', '15h 40m', '4h 35m', '18h 30m', '38h 15m', '6h 35m',\n",
              "       '12h 30m', '11h 20m', '7h 35m', '29h 35m', '26h 55m', '23h 40m',\n",
              "       '12h 50m', '9h 50m', '21h 55m', '10h 55m', '21h 10m', '20h 40m',\n",
              "       '30h 0m', '13h 10m', '8h 45m', '6h 10m', '17h 45m', '21h 45m',\n",
              "       '3h 55m', '17h 20m', '30h 30m', '21h 25m', '12h 40m', '24h 35m',\n",
              "       '19h 10m', '22h 40m', '14h 55m', '21h 0m', '6h 45m', '28h 40m',\n",
              "       '9h 40m', '16h 40m', '16h 20m', '16h 45m', '1h 15m', '6h 55m',\n",
              "       '11h 25m', '14h 20m', '12h 5m', '24h 5m', '28h 15m', '17h 50m',\n",
              "       '20h 20m', '28h 5m', '10h 20m', '14h 15m', '35h 15m', '35h 35m',\n",
              "       '26h 40m', '28h 0m', '14h 25m', '13h 5m', '37h 20m', '36h 10m',\n",
              "       '25h 55m', '35h 5m', '19h 45m', '27h 55m', '47h 0m', '10h 35m',\n",
              "       '1h 35m', '16h 10m', '38h 20m', '6h 0m', '16h 50m', '14h 10m',\n",
              "       '23h 20m', '17h 40m', '11h 35m', '18h 20m', '6h 40m', '30h 55m',\n",
              "       '24h 40m', '29h 50m', '28h 25m', '17h 15m', '22h 45m', '25h 25m',\n",
              "       '21h 50m', '33h 15m', '30h 15m', '3h 35m', '27h 40m', '30h 25m',\n",
              "       '18h 50m', '27h 45m', '15h 15m', '10h 40m', '26h 15m', '36h 25m',\n",
              "       '26h 50m', '15h 45m', '19h 40m', '22h 25m', '19h 35m', '25h 0m',\n",
              "       '26h 45m', '38h 0m', '4h 15m', '25h 10m', '18h 15m', '6h 50m',\n",
              "       '23h 55m', '17h 55m', '23h 25m', '17h 10m', '24h 20m', '28h 30m',\n",
              "       '27h 10m', '19h 20m', '15h 35m', '9h 25m', '21h 30m', '34h 25m',\n",
              "       '18h 35m', '29h 40m', '26h 5m', '29h 5m', '27h 25m', '16h 30m',\n",
              "       '11h 10m', '28h 55m', '29h 10m', '34h 0m', '30h 40m', '30h 45m',\n",
              "       '32h 55m', '10h 5m', '35h 20m', '32h 5m', '31h 40m', '19h 50m',\n",
              "       '33h 45m', '30h 10m', '13h 40m', '19h 30m', '31h 30m', '34h 30m',\n",
              "       '27h 50m', '38h 35m', '42h 5m', '4h 10m', '39h 5m', '3h 50m',\n",
              "       '0h 5m', '32h 30m', '31h 55m', '33h 20m', '27h 30m', '18h 55m',\n",
              "       '9h 55m', '41h 20m', '20h 5m', '31h 50m', '42h 45m', '3h 25m',\n",
              "       '37h 10m', '29h 30m', '32h 20m', '20h 50m', '40h 20m', '13h 35m',\n",
              "       '47h 40m'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "which city has maximum final destination of flights ?"
      ],
      "metadata": {
        "id": "bkxCqVno8RCV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dkpc9uRZ2QVL",
        "outputId": "d9864365-12d0-4fe9-8dd6-e233024e2d79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETsnKNKM2YmL",
        "outputId": "b3babaff-c3ec-4744-f956-d9ce3a41adaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].value_counts().plot(kind = 'bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "PQ25jCMg2aDr",
        "outputId": "812f39e5-af17-4b11-b7a0-00190d7ee9ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f125a79ec10>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAElCAYAAAAIpDLLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYy0lEQVR4nO3df7RdZX3n8feHBESk/M4wmCChkqkDKspERLC2wihBUKijFIuapdDoEi2OzhqhTksFpDhOi8hU21TSRhaKDLUDChUQUETKjyC/Csgi4g9gwAQDiFKowGf+2M8lJ9d7c2+Sc88+Zz+f11p33bOfve89350fn7PPc579PLJNRETUYbO2C4iIiMFJ6EdEVCShHxFRkYR+RERFEvoRERWZ3XYB67PTTjt5/vz5bZcRETFSbrrppodtz5lo31CH/vz581mxYkXbZUREjBRJP55sX7p3IiIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqMtR35G6s+SdcPNDn+9Hphw70+SIiNlau9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIpMO/QlzZJ0s6Svl+3dJV0vaaWkr0jaorQ/r2yvLPvn9/yOE0v73ZIO7vfJRETE+m3Ilf7xwF09258CzrC9B/AIcExpPwZ4pLSfUY5D0p7AUcBewCLgc5JmbVr5ERGxIaYV+pLmAYcCXyjbAg4ELiiHLAeOKI8PL9uU/QeV4w8HzrP9lO0fAiuBfftxEhERMT3TvdL/DPDfgWfL9o7Ao7afLtv3A3PL47nAfQBl/2Pl+OfaJ/iZ50haImmFpBWrV6/egFOJiIipTBn6kg4DVtm+aQD1YHup7YW2F86ZM2cQTxkRUY3Z0zjmAOAtkt4EbAlsA5wJbCdpdrmanwc8UI5/ANgVuF/SbGBb4Gc97WN6fyYiIgZgyit92yfanmd7Ps0HsVfaPhq4CnhbOWwxcGF5fFHZpuy/0rZL+1FldM/uwALghr6dSURETGk6V/qT+RhwnqRTgZuBs0v72cA5klYCa2heKLB9h6TzgTuBp4HjbD+zCc8fEREbaINC3/a3gG+Vx/cywegb208Cb5/k5z8JfHJDi4yIiP7IHbkRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERWZMvQlbSnpBkm3SrpD0idK++6Srpe0UtJXJG1R2p9XtleW/fN7fteJpf1uSQfP1ElFRMTEpnOl/xRwoO29gVcAiyTtB3wKOMP2HsAjwDHl+GOAR0r7GeU4JO0JHAXsBSwCPidpVj9PJiIi1m/K0HfjF2Vz8/Jl4EDggtK+HDiiPD68bFP2HyRJpf0820/Z/iGwEti3L2cRERHTMq0+fUmzJN0CrAIuB34APGr76XLI/cDc8ngucB9A2f8YsGNv+wQ/0/tcSyStkLRi9erVG35GERExqWmFvu1nbL8CmEdzdf6SmSrI9lLbC20vnDNnzkw9TURElTZo9I7tR4GrgNcA20maXXbNAx4ojx8AdgUo+7cFftbbPsHPRETEAExn9M4cSduVx88H3gDcRRP+byuHLQYuLI8vKtuU/Vfadmk/qozu2R1YANzQrxOJiIipzZ76EHYBlpeRNpsB59v+uqQ7gfMknQrcDJxdjj8bOEfSSmANzYgdbN8h6XzgTuBp4Djbz/T3dCIiYn2mDH3btwGvnKD9XiYYfWP7SeDtk/yuTwKf3PAyIyKiH6ZzpR9DZv4JFw/0+X50+qEDfb6ImDmZhiEioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqktCPiKhIQj8ioiIJ/YiIiiT0IyIqMmXoS9pV0lWS7pR0h6TjS/sOki6XdE/5vn1pl6TPSlop6TZJ+/T8rsXl+HskLZ6504qIiIlM50r/aeCjtvcE9gOOk7QncAJwhe0FwBVlG+AQYEH5WgJ8HpoXCeAk4NXAvsBJYy8UERExGFOGvu0HbX+vPH4cuAuYCxwOLC+HLQeOKI8PB77oxnXAdpJ2AQ4GLre9xvYjwOXAor6eTURErNcG9elLmg+8Erge2Nn2g2XXQ8DO5fFc4L6eH7u/tE3WHhERAzLt0Je0NfAPwIdt/7x3n20D7kdBkpZIWiFpxerVq/vxKyMiophW6EvanCbwz7X91dL809JtQ/m+qrQ/AOza8+PzSttk7euwvdT2QtsL58yZsyHnEhERU5jO6B0BZwN32f7Lnl0XAWMjcBYDF/a0v7uM4tkPeKx0A10KvFHS9uUD3DeWtoiIGJDZ0zjmAOBdwO2SbiltfwycDpwv6Rjgx8CRZd8lwJuAlcATwHsAbK+RdApwYznuZNtr+nIWERExLVOGvu1rAE2y+6AJjjdw3CS/axmwbEMKjIiI/skduRERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREVmt11AxHjzT7h4oM/3o9MPHejzRbQpV/oRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFpgx9ScskrZL0Lz1tO0i6XNI95fv2pV2SPitppaTbJO3T8zOLy/H3SFo8M6cTERHrM50r/b8HFo1rOwG4wvYC4IqyDXAIsKB8LQE+D82LBHAS8GpgX+CksReKiIgYnClD3/bVwJpxzYcDy8vj5cARPe1fdOM6YDtJuwAHA5fbXmP7EeByfv2FJCIiZtjG9unvbPvB8vghYOfyeC5wX89x95e2ydp/jaQlklZIWrF69eqNLC8iIiayyR/k2jbgPtQy9vuW2l5oe+GcOXP69WsjIoKND/2flm4byvdVpf0BYNee4+aVtsnaIyJigDZ2auWLgMXA6eX7hT3tH5R0Hs2Hto/ZflDSpcBpPR/evhE4cePLjhhdmTo62jRl6Ev6MvC7wE6S7qcZhXM6cL6kY4AfA0eWwy8B3gSsBJ4A3gNge42kU4Aby3En2x7/4XBERMywKUPf9jsm2XXQBMcaOG6S37MMWLZB1UVERF/ljtyIiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiGzsNQ0TEr8kUE8MvV/oRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCRTK0dETFMXpo7OlX5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUGHvqSFkm6W9JKSScM+vkjImo20NCXNAv4K+AQYE/gHZL2HGQNERE1G/SV/r7AStv32v434Dzg8AHXEBFRLdke3JNJbwMW2T62bL8LeLXtD/YcswRYUjZ/C7h7YAXCTsDDA3y+Qcv5jbYun1+Xzw0Gf3672Z4z0Y6hWznL9lJgaRvPLWmF7YVtPPcg5PxGW5fPr8vnBsN1foPu3nkA2LVne15pi4iIARh06N8ILJC0u6QtgKOAiwZcQ0REtQbavWP7aUkfBC4FZgHLbN8xyBqm0Eq30gDl/EZbl8+vy+cGQ3R+A/0gNyIi2pU7ciMiKpLQj4ioSEI/IqIiCf1KSNqq7Roion1Dd3NWG8qcQDvT8+dh+yftVdQ/kvYHvgBsDbxI0t7A+2x/oN3KNo2kbWz/XNIOE+23vWbQNfWLpGtsv1bS40DvSAsBtr1NS6XFBpA0B/gYzTxjW4612z6wtaJI6CPpQ8BJwE+BZ0uzgZe3VlR/nQEcTLkfwvatkl7Xbkl98SXgMOAmmr8v9ewz8JttFNUPtl9bvv9G27XMhAlezNbRoRe1c4GvAIcC7wcWA6tbrYiEPsDxwG/Z/lnbhcwU2/dJvZnIM23V0i+2Dyvfd2+7lpnUxXehYy9mkk4BHgTOoXnRPhrYpcXS+m1H22dLOt72t4FvS7qx7aIS+nAf8FjbRcyg+0oXjyVtTvMid1fLNfWVpLnAbqwbjFe3V1F/VPAu9C229+7Z/rykW4E/baugPvtV+f6gpEOB/wdM2B05SAl9uBf4lqSLgafGGm3/ZXsl9dX7gTOBuTTzHF0GHNdqRX0k6VPA7wN3svYdjIGRD326/y70l5KOppli3cA7gF+2W1JfnSppW+CjwFnANsCH2y0poQ/wk/K1RfnqjNI1cKbto9uuZQYdQROMT0155Ojp+rvQP6C5IDmTJvS/W9q64hHbj9H8Hb4eQNIB7ZaUaRg6T9I1wIFl0ZrOkfRPwNtt/6LtWvpF0kfKw71o1pTo6rvQTpP0Pdv7TNU2aNVe6Uv6jO0PS/oaE4wksP2WFsqaCfcC35V0ET1vnUc9OCSdRfP39gRwi6QrWDcY/6it2vpgbNROZ9+FAkjaEjiG5sWtd0jje1srqg8kvQbYH5jT8wIOTffOrHaqWqva0KcZMQDwv1qtYub9oHxtxtow6YIV5ftNdGx6btufaLuGATkH+D7NkOKTaUbvdGGQwRY098XMZt3/cz8H3tZKRT3SvVMJSVsDdKkbpKsme/c5pivvQiXdbPuVkm6z/fIyuuw7tvdru7Z+kLSb7R+3Xcd4NV/pA899sPJnrB3yN3bX48je3NNL0ktprqh2KNsPA+8esnUMNpik21l/MI7ysMauv/scMzak8dHy7/Qh4N+1WE+/PSHp0/x691XuyG3Z2cB/pekmGPmbliawFPiI7asAJP0u8Lc0fY6j7LC2C5gp5UYeACQ9H3iR7btbLGmmLJW0PfAnNF10W5fHXTF2R+5hDNEdudV370i63var265jpki6ddwNMBO2jTJJuwELbH+zhORs24+3XdemkvRmmqv+LWzvLukVwMld6d7pOkk32f5PY91Xpe1G269qs65qr/QljQ2buqq8Bfsq647++F4rhfXfvZL+hLUfXL+TZkRPJ0j6Q2AJTffVi4F5wF8DB7VZV5/8GbAv8C0A27dI6sy0E5J2pDnHA2i66r4DnNKhm9FyR+6Q+Ytx2wt7Hhtotd+tj94LfILmRQ2a/1gjPSRunONogvF6ANv3SOpKv/CvbD82bt6kLr01P4/mzun/UraPpukO+c+tVdRfuSN3mNh+fds1DILtR4BRHrM+lads/9tYMEqaTXeC8Q5JfwDMkrSA5u/x2pZr6qddbJ/Ss32qpN9vrZr+u2OCO3Jb7dqBLKKCpNMkbdezvb2kU9usqR8kfU3SRZN9tV1fH31b0h8Dz5f0BuD/AF9ruaZ++RDNyI+ngC/TjPNu/Uqxjy6TdJSkzcrXkcClbRfVRxeUyQABKFOaL2uxnqaOfJDbjBUe19b6rdKbStLvrG9/7wiRUSZpM5q7Ot9IM9z2UuALrv0f9hDrmU9fwAtYO4PoZsAvujKffrmq/xzwZmAf4M+Bw2zf12pdtf/fkHQb8KqxCbvK6I8Vtvdqt7KYrrJCEbZbHw7XL5IWU2bZLE13AZ+1/cX2qooNVaZk+BvgSeDQYfg3Wm2ffo9zgSsk/V3Zfg+wvMV6+mqSm5geo5nG4NRRHSmhphP/JOCDlG5KSc8AZ9k+uc3aNlUJ/A8DHwG+R3NFvA/waUm2fc76fn6UlHH6C1j35qWRnhZ7gjuqt6L5P3e2pNbvqK7+Sh9A0iLWjhi43HZn+hUl/U+am86+VJqOovlH+BDwWttvbqu2TVEmsjoEWGL7h6XtN4HPA9+wfUab9W0KSdcBR9n+0bj2+cB5HZqm4FiadzPzgFuA/YB/bvuO1U017F2rCX1A0s40w/4M3GB7Vcsl9c36pneVdLvtl7VV26aQdDPwBtsPj2ufA1w2/nOaUSLpTtt7bui+UVPehb4KuM72KyS9BDjN9ltbLq3TMnqnGTFwA83sd0cC10tqfSa8Ppolad+xjfLh0tj0rk+3U1JfbD4+8OG5fv3NW6inn/51I/eNmidtPwkg6Xm2v8/azzBGlqTHJf285+vx3u9t15c+ffg4zQe5q+C5K8VvAhe0WlX/HAssK7NsimbY37GSXkAzmmBUrW9RmFFfMOY/lgEG4wnoxESAxf1luPT/BS6X9AgwdLNSbqixhd+HVfXdO+O7OMoQwFtHtdtjMuXOQMrNIiOvfGg70XqqAra0PbJX+2UuoUkN43S9m6r0g29L83nMqL9oP0fS3sBvl82rbU/0Yj5QCf1m3p2X09z8As0i27fZ/lh7VfWPpOfR3OY+n553dqM+wiVGm5r1m++w/ZK2a5kpko4H/pC1U6D8HrDU9lntVVVx6EvaA9jZ9nclvRV4bdn1KHCu7R+0V13/SPoGzXCxdaaOtj1+7qGIgZJ0IfAh2z9pu5aZULroXmP7l2X7BTSjk1pd66HmPv3PACcC2P4q5dVY0svKvpEcyjiBebYXtV1ExAS2p5lf6AbWXb+5K1NHi3XX6HimtLWq5tDf2fbt4xtt317GQ3fFtZJeNtG5xnCTdBBwre0ujdjp1aUFUybydzSjAf+xbB9Bs2hTq2ru3rnH9oJJ9q20vcega5oJku4E9gB+SDNx19hykKO8nGAVJC0HXgOsoZkS+2rgmjJzaido3QVwtgJmjfoCOJJ2HZtfp6zbMdZ1/B1gru2vt1YcdYf+l4Erbf/tuPZjaW766cQUr5ONBOniCJCukvRCmvtI/hvwQtudeIfeuwCO7ReX6aP/2vZIL4Aj6fvAognuqH4v8HHbL26lsLE6Kg79nYF/pBnTfVNpXghsAfye7Yfaqm0mlIVFeuc36eSHZ10i6Z00w/1eBjwMXAN8x/Y/t1pYn0i6hbIAztgd1KN8l/gYSW+i+VzwUNv3lLYTaBaJOcT2/W3W14krho1h+6fA/pJeD7y0NF9s+8oWy+o7SW+hWSXshcAqYDeaGRszi+jw+wzwA5rlH68af+XYAZ1cAMf2JZKeAv5J0hE0N0juC7xuGLrmqr3Sr4WkW2mWfvym7VeWF7l32j6m5dJiGiTtBbyOpl94AXC37Xe1W1V/lMkAHwXeTbNgzAeAO21/vNXC+kTSb9P0JlwLHDk25UTbqp97pwK/KtMnbyZpM9tXse56wDGkJG0DvIjm3dl8mjtWn13fz4yYE4DVwO3A+4BLgP/RakV90DPHziU06+IeBKwalrl3cqXfcZK+STNU7M+BnWi6eF5le/9WC4splZt7rilfV7fdFxzdkNDvuHIX4L/SvKs7muZq8dxRXTylRpK2sv1E23X0yyQL+zwnw4lnVrUf5NZi7BZw4FlJFwM/y/qxo6EstXc2sDXwojJ51/tsf6DdyjbZYeX7ceX72Epg76QDH+QOu1zpd5Sk/YDTaW7sOYXmP9ZONFf877b9jRbLi2mQdD3N+PyLeoY0/ovtl67/J0eDpJvHL3Yz0aI/0V/5ILe7/jdwGs3soVcCx9r+9zQjQUZ5Hv2qjN3Z2eOZCQ8cTZJ0QM/G/iSTZly6d7prtu3LACSdbPs6ANvfHxsXHUPvvhKElrQ5zXqyd7VcUz8dQ7PAz7Zl+1HgvS3WU4WEfnf1Du0bP2FX+vRGw/uBM4G5wAPAZaztB++CW2zv3bUFfoZd+vQ7qmdlKQHPB8ZGf4z8ylLRDZLuBf4BWGa7S+9ghlpCP2LISPrT9ey27VMGVswMkvQbwFHAe2j68pcB59lu/QamLkvoRwwZSR+doPkFNH3gO9reesAlzbiyRu6XgO2AC4BTbK9st6puSuhHDLFyNXw8TeCfD/yF7VXtVtUfZZ3cQ2mu9OfTDCs+l2Zm0dNs/4f2quuufJAbMYQk7QB8hOYu6uXAPsMwQ2Of3QNcBXza9rU97RdIel1LNXVervQjhoykTwNvBZYCf2X7Fy2XNCMkbd3VcxtmCf2IISPpWZqlLZ9m3eG1Y0tdbtNKYX0i6SzWP/fOHw2wnOqkeydiyNju+l2pK3oefwI4qa1CapQr/YhozUTz78TM6voVRUQMt1x1DlhCPyKiIuneiYiBkvQ4a6/wt2LdKUJG/oPqYZfQj4ioSLp3IiIqktCPiKhIQj8ioiIJ/YiIivx/oEuhYC2zF1oAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].value_counts().plot(kind = 'pie')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "jOkQAOzg2bF7",
        "outputId": "79b0072f-ee17-470c-9bbd-d8d4a5902dc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f125a60f4c0>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].value_counts().plot(kind = 'line')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2k_QL_Gz2cNr",
        "outputId": "fb62093b-49aa-4884-f702-d746ddd8803f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f125a661520>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets Perform Exploratory Data Analysis(Bivariate Analysis) to come up with some business insights"
      ],
      "metadata": {
        "id": "MidfGfBN8VGs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Route']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaiU9j6x2eTq",
        "outputId": "47b249c3-c809-4344-ff00-6cba6fd31cd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Airline']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jvnis58W2fQr",
        "outputId": "0f8065bc-27d8-4ee3-8029-901db0d51aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             IndiGo\n",
              "1          Air India\n",
              "2        Jet Airways\n",
              "3             IndiGo\n",
              "4             IndiGo\n",
              "            ...     \n",
              "10678       Air Asia\n",
              "10679      Air India\n",
              "10680    Jet Airways\n",
              "10681        Vistara\n",
              "10682      Air India\n",
              "Name: Airline, Length: 10682, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Airline'] == 'Jet Airways']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mUn-_nca2gub",
        "outputId": "1f5a27d6-75b5-4ab2-8cfb-9e7a2344a64c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Airline    Source Destination                  Route Duration  \\\n",
              "2      Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   19h 0m   \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",
              "13     Jet Airways   Kolkata    Banglore        CCU → BOM → BLR  12h 10m   \n",
              "...            ...       ...         ...                    ...      ...   \n",
              "10668  Jet Airways     Delhi      Cochin  DEL → JDH → BOM → COK   7h 20m   \n",
              "10672  Jet Airways     Delhi      Cochin  DEL → AMD → BOM → COK  19h 55m   \n",
              "10673  Jet Airways     Delhi      Cochin  DEL → AMD → BOM → COK   15h 0m   \n",
              "10674  Jet Airways  Banglore   New Delhi        BLR → BOM → DEL  24h 45m   \n",
              "10680  Jet Airways  Banglore       Delhi              BLR → DEL    3h 0m   \n",
              "\n",
              "      Total_Stops              Additional_Info  Price  Journey_day  \\\n",
              "2         2 stops                      No info  13882            6   \n",
              "6          1 stop  In-flight meal not included  11087            3   \n",
              "7          1 stop                      No info  22270            3   \n",
              "8          1 stop  In-flight meal not included  11087            3   \n",
              "13         1 stop  In-flight meal not included   9663            5   \n",
              "...           ...                          ...    ...          ...   \n",
              "10668     2 stops                      No info  21219            5   \n",
              "10672     2 stops  In-flight meal not included  11150           27   \n",
              "10673     2 stops                      No info  16704           27   \n",
              "10674      1 stop  In-flight meal not included  11087            3   \n",
              "10680    non-stop                      No info   7229           27   \n",
              "\n",
              "       Journey_month  Journey_year  Dep_Time_hour  Dep_Timeminute  \\\n",
              "2                  9          2019              9              25   \n",
              "6                 12          2019             18              55   \n",
              "7                  1          2019              8               0   \n",
              "8                 12          2019              8              55   \n",
              "13                 9          2019             21              10   \n",
              "...              ...           ...            ...             ...   \n",
              "10668              9          2019             11              40   \n",
              "10672              6          2019             23               5   \n",
              "10673              5          2019             13              25   \n",
              "10674             12          2019             20              35   \n",
              "10680              4          2019              8              20   \n",
              "\n",
              "       Arrival_Time_hour  Arrival_Timeminute  Duration_hours  Duration_mins  \\\n",
              "2                      4                  25               2             50   \n",
              "6                     10                  25               2             50   \n",
              "7                      5                   5               2             50   \n",
              "8                     10                  25               2             50   \n",
              "13                     9                  20               2             50   \n",
              "...                  ...                 ...             ...            ...   \n",
              "10668                 19                   0               2             50   \n",
              "10672                 19                   0               2             50   \n",
              "10673                  4                  25               2             50   \n",
              "10674                 21                  20               2             50   \n",
              "10680                 11                  20               2             50   \n",
              "\n",
              "       Duration_total_mins  \n",
              "2                     1141  \n",
              "6                      931  \n",
              "7                     1266  \n",
              "8                     1531  \n",
              "13                     731  \n",
              "...                    ...  \n",
              "10668                  441  \n",
              "10672                 1196  \n",
              "10673                  901  \n",
              "10674                 1486  \n",
              "10680                  181  \n",
              "\n",
              "[3849 rows x 18 columns]"
            ],
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              "      <th>Airline</th>\n",
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              "  <tbody>\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",
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              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1141</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>931</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",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1266</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BOM → BLR</td>\n",
              "      <td>12h 10m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>9663</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>2019</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>731</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>10668</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → JDH → BOM → COK</td>\n",
              "      <td>7h 20m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>21219</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>40</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>441</td>\n",
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              "      <th>10672</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → AMD → BOM → COK</td>\n",
              "      <td>19h 55m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>11150</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>23</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10673</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → AMD → BOM → COK</td>\n",
              "      <td>15h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>16704</td>\n",
              "      <td>27</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10674</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>24h 45m</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>20</td>\n",
              "      <td>35</td>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1486</td>\n",
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              "    <tr>\n",
              "      <th>10680</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>3h 0m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>7229</td>\n",
              "      <td>27</td>\n",
              "      <td>4</td>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>181</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3849 rows × 18 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Airline'] == 'Jet Airways'].groupby('Route').size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JSizKxR2ipj",
        "outputId": "a9adb870-26f8-4082-fff2-11e59106ed44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Route\n",
              "BLR → BDQ → DEL            8\n",
              "BLR → BOM → DEL          385\n",
              "BLR → BOM → JDH → DEL      3\n",
              "BLR → DEL                382\n",
              "BLR → MAA → DEL           10\n",
              "BOM → BDQ → DEL → HYD      2\n",
              "BOM → DED → DEL → HYD      1\n",
              "BOM → DEL → HYD            5\n",
              "BOM → HYD                207\n",
              "BOM → IDR → DEL → HYD      1\n",
              "BOM → JDH → DEL → HYD      1\n",
              "BOM → UDR → DEL → HYD      1\n",
              "BOM → VNS → DEL → HYD      1\n",
              "CCU → BOM → BLR          930\n",
              "CCU → BOM → PNQ → BLR      4\n",
              "CCU → DEL → BLR          300\n",
              "CCU → GAU → BLR           22\n",
              "DEL → AMD → BOM → COK    141\n",
              "DEL → ATQ → BOM → COK     38\n",
              "DEL → BDQ → BOM → COK     28\n",
              "DEL → BHO → BOM → COK     29\n",
              "DEL → BOM → COK          875\n",
              "DEL → CCU → BOM → COK      1\n",
              "DEL → COK                 34\n",
              "DEL → DED → BOM → COK      2\n",
              "DEL → IDR → BOM → COK     86\n",
              "DEL → IXC → BOM → COK     13\n",
              "DEL → JAI → BOM → COK    207\n",
              "DEL → JDH → BOM → COK     23\n",
              "DEL → LKO → BOM → COK     25\n",
              "DEL → MAA → BOM → COK     16\n",
              "DEL → NAG → BOM → COK     61\n",
              "DEL → UDR → BOM → COK      7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Airline'] == 'Jet Airways'].groupby('Route').size().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vfbN-oR2j0C",
        "outputId": "dacd2d10-5356-403b-c28f-74fe20b0605b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's analyze  on which rout Jet Airways extremly used\n",
        "\n",
        "Airline Vs Price Analysis"
      ],
      "metadata": {
        "id": "Wy9nlepm2pOr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (15,5))\n",
        "sns.boxplot(y = 'Price', x = 'Airline', data = data)\n",
        "plt.xticks(rotation = 'vertical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "G9-94BCm2lAb",
        "outputId": "d28f0335-ccba-4bfd-e271-8fabd5396e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
              " <a list of 12 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (15,5))\n",
        "sns.violinplot(y = 'Price', x = 'Airline', data = data)\n",
        "plt.xticks(rotation = 'vertical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "NM-x3QF822nC",
        "outputId": "b7ebee32-49f7-442f-e6f3-05237ce17f2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
              " <a list of 12 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets Perform Feature-Encoding on Data !"
      ],
      "metadata": {
        "id": "9LON71dL8byU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature encoding (One hot encoding to data)"
      ],
      "metadata": {
        "id": "nN_syRsg3CmZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "-tXRSNK629yR",
        "outputId": "2b5758ec-bb21-4a4a-b6c9-b30a9b9bd3b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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_Timeminute  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_Timeminute  Duration_hours  Duration_mins  Duration_total_mins  \n",
              "0                  10               2             50                  171  \n",
              "1                  15               2             50                  446  \n",
              "2                  25               2             50                 1141  \n",
              "3                  30               2             50                  326  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9050b7a8-4095-4143-8c12-6f943a8a3be5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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_Timeminute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Timeminute</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>171</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>2</td>\n",
              "      <td>50</td>\n",
              "      <td>446</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>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1141</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>2</td>\n",
              "      <td>50</td>\n",
              "      <td>326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9050b7a8-4095-4143-8c12-6f943a8a3be5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9050b7a8-4095-4143-8c12-6f943a8a3be5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9050b7a8-4095-4143-8c12-6f943a8a3be5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(data['Additional_Info'].value_counts()/len(data)*100, 2 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlsML8tt2g8K",
        "outputId": "71887948-3be7-4244-a517-ddff7f7d8bc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "1 Short layover                  0.01\n",
              "Red-eye flight                   0.01\n",
              "2 Long layover                   0.01\n",
              "Name: Additional_Info, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns=['Additional_Info','Route','Duration_total_mins','Journey_year'], axis = 1, inplace =True)"
      ],
      "metadata": {
        "id": "xCtg9aFY3L_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "vJ6XVFSc3M4x",
        "outputId": "f8344a97-25d5-4cad-9143-19e42ab6acbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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_Timeminute  Arrival_Time_hour  \\\n",
              "0              3             22              20                  1   \n",
              "1              1              5              50                 13   \n",
              "2              9              9              25                  4   \n",
              "\n",
              "   Arrival_Timeminute  Duration_hours  Duration_mins  \n",
              "0                  10               2             50  \n",
              "1                  15               2             50  \n",
              "2                  25               2             50  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d23da1f-d6f1-42f0-8834-ab7e72b62877\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<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_Timeminute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Timeminute</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>2</td>\n",
              "      <td>50</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>2</td>\n",
              "      <td>50</td>\n",
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              "  </tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d23da1f-d6f1-42f0-8834-ab7e72b62877 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9d23da1f-d6f1-42f0-8834-ab7e72b62877');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets separate categorical data & numerical data !"
      ],
      "metadata": {
        "id": "Cu9yoTn18kEE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "[col for col in data.columns if data[col].dtype=='object']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUbVFYdb3N4J",
        "outputId": "dc5c794d-cd7e-4c6d-e666-c12517a939ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Airline', 'Source', 'Destination', 'Duration', 'Total_Stops']"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[col for col in data.columns if data[col].dtype!='object']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys3Anfbi3PcB",
        "outputId": "808c8889-3a31-42c4-aa21-b206b24d6b81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Price',\n",
              " 'Journey_day',\n",
              " 'Journey_month',\n",
              " 'Dep_Time_hour',\n",
              " 'Dep_Timeminute',\n",
              " 'Arrival_Time_hour',\n",
              " 'Arrival_Timeminute',\n",
              " 'Duration_hours',\n",
              " 'Duration_mins']"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_col = [col for col in data.columns if data[col].dtype=='object']"
      ],
      "metadata": {
        "id": "M8i1NKpq3Qhx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_col = [col for col in data.columns if data[col].dtype!='object']"
      ],
      "metadata": {
        "id": "4uNKf3WK3SMR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling Categorical Data"
      ],
      "metadata": {
        "id": "-X6USAEN8oW7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Source']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ujxbakH3TBh",
        "outputId": "62ed3a35-d2b5-4955-e9ed-255b17592129"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets apply one-hot encoding on 'Source' feature"
      ],
      "metadata": {
        "id": "YP1I7L3Q8ufl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Source'].unique"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUpzcHAp3UDw",
        "outputId": "110d6ec4-34ff-4341-c07f-d07ef31a6114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.unique of 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>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Source'].apply(lambda x: 1 if x=='Bangalore' else 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsmCGfij3U_4",
        "outputId": "099be2d1-9357-4df1-b214-1ee2f0904df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0\n",
              "1        0\n",
              "2        0\n",
              "3        0\n",
              "4        0\n",
              "        ..\n",
              "10678    0\n",
              "10679    0\n",
              "10680    0\n",
              "10681    0\n",
              "10682    0\n",
              "Name: Source, Length: 10682, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for category in data['Source'].unique():\n",
        "    data['Source_'+category] = data['Source'].apply(lambda x : 1 if x==category else 0)"
      ],
      "metadata": {
        "id": "ZKoRtZdP3W04"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "G9oAsQai3YBY",
        "outputId": "ee876f50-8fef-4df1-81ff-0c6d668b8e65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       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_Timeminute  Arrival_Time_hour  \\\n",
              "0              3             22              20                  1   \n",
              "1              1              5              50                 13   \n",
              "2              9              9              25                  4   \n",
              "\n",
              "   Arrival_Timeminute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                  10               2             50                1   \n",
              "1                  15               2             50                0   \n",
              "2                  25               2             50                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  "
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              "      <td>IndiGo</td>\n",
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              "      <td>New Delhi</td>\n",
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              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
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              "      <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",
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              "      <td>15</td>\n",
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              "      <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",
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              "      <td>4</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perform the Manual encoding"
      ],
      "metadata": {
        "id": "RoDznE5g3bwN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['Airline'])['Price'].mean().sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdJ-znfC3Y-4",
        "outputId": "9319a1b2-0abc-4d16-81d8-092cfb35719a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline\n",
              "Trujet                                4140.000000\n",
              "SpiceJet                              4338.284841\n",
              "Air Asia                              5590.260188\n",
              "IndiGo                                5673.682903\n",
              "GoAir                                 5861.056701\n",
              "Vistara                               7796.348643\n",
              "Vistara Premium economy               8962.333333\n",
              "Air India                             9612.427756\n",
              "Multiple carriers                    10902.678094\n",
              "Multiple carriers Premium economy    11418.846154\n",
              "Jet Airways                          11643.923357\n",
              "Jet Airways Business                 58358.666667\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['Airline'])['Price'].mean().sort_values().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ybk9BFyM3fUO",
        "outputId": "fb872d6e-9154-4e6e-ce53-3cac223ca54d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Performing Target Guided Mean Encoding !"
      ],
      "metadata": {
        "id": "JuXYpgnj8ywL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "airlines = data.groupby(['Airline'])['Price'].mean().sort_values().index"
      ],
      "metadata": {
        "id": "xYFL-bj63gGY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "airlines"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLQINKy63hRQ",
        "outputId": "fe5586e1-2ab6-4200-86d1-d4b3f1a9172b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "{key:index for index, key in enumerate(airlines, 0)}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-Okh1SK3iDI",
        "outputId": "cc3b729a-1789-4e11-dee9-2b6e6b02f4d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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}"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dict1 = {key:index for index, key in enumerate(airlines, 0)}"
      ],
      "metadata": {
        "id": "GKjBUcX93jHA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Airline']=data['Airline'].map(dict1)"
      ],
      "metadata": {
        "id": "R9WTYyP83kOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Airline']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yx21eKDD3lC4",
        "outputId": "f59d3bbd-e76f-4915-cb93-439d8e12fe06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzxKAXJp3lvo",
        "outputId": "b65383a7-5577-4b3a-cfad-9184ea8d06f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].replace('New Delhi', 'Delhi', inplace = True)"
      ],
      "metadata": {
        "id": "ad68bx1n3m1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKKYVsCZ3ni4",
        "outputId": "eabb5549-1220-4cb8-eeef-cd9d30cb0c67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['Destination'])['Price'].mean().sort_values().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geeaJyWq3oQg",
        "outputId": "cc7397de-ea25-4f13-c1bc-21294eec74a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Kolkata', 'Hyderabad', 'Delhi', 'Banglore', 'Cochin'], dtype='object', name='Destination')"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
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      "cell_type": "code",
      "source": [
        "{key:index for index, key in enumerate(dest, 0)}"
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        "id": "t0-zRAEW3q2I",
        "outputId": "4bc8aab3-4922-4fe7-dc08-bf5b15da504a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Kolkata': 0, 'Hyderabad': 1, 'Delhi': 2, 'Banglore': 3, 'Cochin': 4}"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
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    {
      "cell_type": "code",
      "source": [
        "dict2 = {key:index for index, key in enumerate(dest, 0)}"
      ],
      "metadata": {
        "id": "Sr_iJYXi3sAf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Destination'] = data['Destination'].map(dict2)"
      ],
      "metadata": {
        "id": "kQ_Kidc13tKf"
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      "execution_count": null,
      "outputs": []
    },
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        "data.head(2)"
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        "outputId": "21c2fd1e-0622-4c1e-f6d1-1d9aae7cd220"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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_Timeminute  Arrival_Time_hour  \\\n",
              "0              3             22              20                  1   \n",
              "1              1              5              50                 13   \n",
              "\n",
              "   Arrival_Timeminute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                  10               2             50                1   \n",
              "1                  15               2             50                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  "
            ],
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              "\n",
              "  <div id=\"df-466655aa-29d2-4d2f-8a01-1181b78e950e\">\n",
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              "      <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",
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              "      <td>3</td>\n",
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              "      <td>3897</td>\n",
              "      <td>24</td>\n",
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              "      <td>22</td>\n",
              "      <td>20</td>\n",
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              "      <td>10</td>\n",
              "      <td>2</td>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Total_Stops'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63S4B88m3uqo",
        "outputId": "eddf59e7-1a5e-4fd0-d592-c73614a8d95a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stops = {'non-stop': 0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4}"
      ],
      "metadata": {
        "id": "5uPQdu3N3v7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perform Manual Encoding on Total_stops feature¶"
      ],
      "metadata": {
        "id": "HlFuXfVq844q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Total_Stops'] = data['Total_Stops'].map(stops)"
      ],
      "metadata": {
        "id": "1v6_kLgq3w1H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "KyJYItJ93yPH",
        "outputId": "d7169a60-b92c-49da-d9c0-d5584798804e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Airline    Source  Destination Duration  Total_Stops  Price  Journey_day  \\\n",
              "0        3  Banglore            2   2h 50m            0   3897           24   \n",
              "1        7   Kolkata            3   7h 25m            2   7662            5   \n",
              "\n",
              "   Journey_month  Dep_Time_hour  Dep_Timeminute  Arrival_Time_hour  \\\n",
              "0              3             22              20                  1   \n",
              "1              1              5              50                 13   \n",
              "\n",
              "   Arrival_Timeminute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                  10               2             50                1   \n",
              "1                  15               2             50                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  "
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Performing Outlier Detection !"
      ],
      "metadata": {
        "id": "DaMfMmtP9Cnb"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "5Bnicecd3zBX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot(data,'Price')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "qfLlq8bZ35AI",
        "outputId": "898094f4-781e-4efd-c985-bc7dcb706d64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: 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",
            "/usr/local/lib/python3.8/dist-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",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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pjx7FgzXrefSV9/jCx/LqrhEikoc6PNJx90J3H5jiMcDddXotQ2++v5NRFf169PWcZp8YO4RTRpbzw6fWaCw2EelU/t85rJfZuWc/723bw/E9tNdaW2bG3593Ahu2NfDASz37qE1Esi/dAT+lmyzbsB2IRmruDe5d8g7uzpghZcz99Uq21u9n6IA+urmbiKSkI50YuTsvrNvKyMGlDC8vzXU43cbM+PzpIyksMH7xwtvs2a9OBSKSmpJOjJ5fW8eWnXs5Y0xlrkPpdoP6lXDxhFHU1e/lnudqqd/bmOuQRCQPKenEaMFzb1NaXMjJI3vmeGudOXZof774sVGs/3A3X/3Zi2zfvT/XIYlInlHSiUlN7VYWLd/EGcdUUlzYezf7ySPK+UL10bz67jY+f8dzrNXtD0QkSe/99MsjTQecG/9nOUcO7MvZxw/NdThZd8rIQcy/bAKbtu9h6g+f4eZFq6jVXUZFhCwnHTObamarzSxhZrNTzO9jZg+E+UvMrCpp3vWhfLWZTemsTTMbE9pIhDZLOltHXP7fb1ezbMMOrj//REqKDo88f9bYITz592cz5aQjuf0PbzHp+7/nnO//nusfeZ3/fmUDG7c35DpEEckBy9ZtccysEHgTmAysB14iGjx0RVKdK4BT3P3rZjYD+Ct3/6KZjQPuAyYARwFPAseHxVK2aWYPAo+4+/1mdjvwmrvf1t46Ooq9urraa2pqDnkb7NnfxLyn1vDj37/FxROO5v/+1cnc9+K7h9xuT7O9YT9vrN/GW1vqeW9bdB8hgFEV/Zg4poKxR/Snsn8figuNooICigqNogKjqLAg+ltgDCwtZtjAvgzuV4xZ+3da3dvYxAe79rFl514+2LmXD3btZdfeRvoWFzKkfwlHlpdyVHlfhvTv0+Pu2CqS78xsqbtXd1Qnm7/TmQAkwqjUmNn9wHRgRVKd6cCNYfph4EcWfaJMB+53973AOjNLhPZI1aaZrQTOAS4JdeaHdm9rbx3ZuAnda+9u47m36mjY18jbW3fzbKKOD3bt5a/Hj2TuZ07u8MOyNysvLeYTxw3lE8cN5YA7G7fvYd0H9az7oJ5fv7GR3fvS72JdUlhAeb9iykoK6VdSREEBNOxrYs/+A+zYs5+de9LrNVdcaAwb2Jejyks5srwvwwb2oaiwgAKDQjPMDHdnX5Ozv+lAy2NfY7Tb9CkuoE9RAX2KCulTVECBGU40r3nPatnBQkHbHc6IupubgWEUGNF0irKCsO8crvuQxOesYyv5yPDs3ecrm0lnBJD8tX490b14UtZx90Yz2w5UhvIX2iw7IkynarMS2ObujSnqt7eOVqNnm9ksYFZ4usvMVqf9SlMb0ryO/wgPSallO8UtkYuVZi5n26mH0XZKT7a20+jOKmhEgsDd7wTu7K72zKyms8NM0XZKl7ZTerSd0pPL7ZTNq9obgORhh0eGspR1zKwIKAfqOli2vfI6YFBoo+262luHiIjELJtJ5yXguNCrrASYASxsU2chMDNMfw74XbjWshCYEXqejQGOA15sr82wzNOhDUKbj3ayDhERiVnWTq+F6yffBJ4ACoG73X25mX0HqHH3hcBPgZ+HjgJbiZIIod6DRJ0OGoEr3b0JIFWbYZX/BNxvZnOBV0LbtLeOGHTbqbpeTtspPdpO6dF2Sk/OtlPWukyLiIi0dXj8UlFERPKCko6IiMRGSaebdTb0T29kZkeb2dNmtsLMlpvZNaG8wswWm9ma8HdwKDczmxe20etmNj6prZmh/hozm5lUfrqZvRGWmWc9+FeSZlZoZq+Y2WPheZeHcOrqMFE9jZkNMrOHzWyVma00szO1Px3MzK4N/3PLzOw+M+ub9/uTu+vRTQ+izg1vAccAJcBrwLhcxxXD6x4OjA/TA4iGKhoHfA+YHcpnAzeH6fOB3xD9KP8MYEkorwDWhr+Dw/TgMO/FUNfCstNy/boPYXtdB9wLPBaePwjMCNO3A98I01cAt4fpGcADYXpc2Lf6AGPCPlfYm/Y/olFFLg/TJcAg7U8HbaMRwDqgNGk/+mq+70860uleLUP/uPs+oHnon17N3Te6+8theiewkugfYjrRhwfh72fC9HRggUdeIPqN1XBgCrDY3be6+4fAYmBqmDfQ3V/w6L9kQVJbPYqZjQQuAO4Kz41oCKeHQ5W226l5+z0MfCrUbxkmyt3XEQ2uMIFesv+ZWTnwSUIPVHff5+7b0P6UShFQatFvEPsBG8nz/UlJp3ulGvpnRDt1e6VwyH4asAQY5u4bw6xNwLAw3d526qh8fYrynugHwD8CB8LztIdwApKHierK9utpxgBbgJ+F05B3mVkZ2p9acfcNwPeBd4iSzXZgKXm+PynpSLcxs/7Ar4BvufuO5HnhG+Vh3T/fzC4ENrv70lzHkueKgPHAbe5+GlBPdDqthfYnCNe0phMl6aOAMmBqToNKg36nk8KQIUO8qqoq12GIiPQoS5cu/cDdO7xTpQb8TKGqqoruuJ+OiMjhxMze7qyOTq+JiEhslHRERCQ2SjoiIhIbXdPJA7fccguJRPr3sdywIbpV0IgRmfdeHDt2LFdddVXGy4uIZEJJJw8kEgleXbaSpn4VadUv3L0dgE17M3v7CndvzWg5EZFDpaSTJ5r6VdBw4vlp1S1d9ThA2vXbW15EJG66piMiIrFR0hERkdgo6YiISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpdKNbbrmFW265Jddh9AraliK9k8Ze60ZdGSlaOqZtKdI76UhHRERio6QjIiKxUdIREZHYKOmIiEhslHRERCQ2SjoiIhIbJR0REYmNko6IiMRGSUfy1u7du7ngggu49957mTRpEpMnT+a8885j2rRpJBIJ6urquPzyy5k0aRKTJk3iggsu4PLLL2fWrFktZZMmTeKyyy7j29/+NpMmTWL69OnU1dW1rKOuro6rr766VVlb8+bNY9KkSdx6660dxptIJJg2bRqzZs0ikUi0ajeRSHDBBRe0xN3ZOuOWzZiSX3tHampqOOecc1i6dGm7dbIRZz6+H7kSx7ZQ0pG89c4771BfX8+dd94JwP79+9m3bx8NDQ3MnTuX+fPnt/ogq6+vJ5FI8Oabb7ZqZ926dTz33HMAbN++nQULFrTMmz9/Pm+88UarsrYeeeQRAB566KEO4507dy4NDQ28+eabzJ07t1W7c+fOpb6+viXuztYZt2zGlPzaO3LjjTdy4MABbrjhhnbrZCPOfHw/ciWObaGkI3lp9+7d7N27t935tbW1PPbYYxm1/eijj1JXV0ddXR2LFi3C3Vm0aFHKb3fz5s1r9by9o51EIkFtbW2r+JrbrampaZlXW1vL448/3uE645bOdshU8napra1t92inpqaGXbt2AbBr166URzvZiDObr72niWtbaOy1brRhwwYaGhq45pprurRcIpGgYJ9nKaqDFezZQSKxs8txximdsdcOHDiQcfsLFizA3VvaaGpqYsGCBVx77bWt6jUf5TR76KGHuPLKKw9qr71v8U1NTdx4442tyhobGztcZ9zmz5/f6XbIVNvtMnfuXO65556D6rXdRjfccMNBXyqyEWc2X3tPE9e20JFOYGazzKzGzGq2bNmS63AOe+7ZTcKLFy/mySefbEkAjY2NLF68OOP2ko9ykjU2NrZ8g08171DW2V26czu01Xa7tLed2m6jVNssG3Fm87X3NHFtCx3pBO5+J3AnQHV1dUafeCNGjADghz/8YZeWu+aaa1i69v1MVpmRA30HMvaYYV2OM05Tpkzp8PTaoZo8eTLuzuOPP05jYyNFRUVMnjw54/aqqqpSfqAWFRXRt2/flB+ih7rO7nLuued223Zoq+12qaqqSlmvf//+rbZR//79Y4kzm6+9p4lrW+hIR/LSqFGjOq1TUJD57nvppZcyc+bMljYKCwu59NJLD6r32c9+ttXzz3/+8ynbmzNnTsrywsLCg04dFRUVdbjOuKWzHTLVdru0t53abqObbrrpoDrZiDObr72niWtbKOlIXurXrx99+vRpd35VVRUXXnhhRm1Pnz6dyspKKisrmTp1KmbG1KlTqaysPKju1Vdf3ep5qus5AGPHjm31Lb6qqqql3erq6pZ5VVVVnH/++R2uM27pbIdMJW+Xqqoqxo4dm7JedXV1y9FN//79Of3002OJM5uvvaeJa1so6UjeGjVqFGVlZcyaNQuA4uJiSkpKKC0tZc6cOcycObPVh1hZWRljx47l+OOPb9XOmDFjOOusswAoLy9v9Q1u5syZnHzyyR1+q2s+2mnvKKfZnDlzKC0t5fjjj2fOnDmt2p0zZw5lZWUtcXe2zrhlM6bk196RG2+8kYKCgpRHOc2yEWc+vh+5Ese2sGxfsO2JqqurvaampsvLNfcGy/SaTsOJ56dVv3TV4wBp10+1/Ol5fk0n020pIrljZkvdvbqjOjrSERGR2CjpiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERklHRERio7HXulF7v7aWrtO2FOmdlHS60VVXXZXrEHoNbUuR3kmn10REJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISGw2DkycKd2+ldNXjadatA0i7fqp1wbCMlhURORRKOnmgq4NbbtjQCMCIEZkmjmEaUFNEckJJJw9ocEsROVzomo6IiMRGSUdERGKjpCMiIrFR0hERkdiYu+c6hrxjZluAtzuoMgT4IKZwMpHP8Sm2zCi2zCi2zGUS32h3H9pRBSWdDJhZjbtX5zqO9uRzfIotM4otM4otc9mKT6fXREQkNko6IiISGyWdzNyZ6wA6kc/xKbbMKLbMKLbMZSU+XdMREZHY6EhHRERio6QjIiKxUdLpIjObamarzSxhZrOzuJ67zWyzmS1LKqsws8Vmtib8HRzKzczmhZheN7PxScvMDPXXmNnMpPLTzeyNsMw8M7MuxHa0mT1tZivMbLmZXZMv8ZlZXzN70cxeC7HdFMrHmNmS0N4DZlYSyvuE54kwvyqpretD+Wozm5JUfkj7gJkVmtkrZvZYPsVmZrVhm79qZjWhLOfvaVh2kJk9bGarzGylmZ2ZR7GdELZZ82OHmX0rj+K7NvwvLDOz+yz6H8ndPufueqT5AAqBt4BjgBLgNWBcltb1SWA8sCyp7HvA7DA9G7g5TJ8P/AYw4AxgSSivANaGv4PD9OAw78VQ18Ky07oQ23BgfJgeALwJjMuH+EL9/mG6GFgS2nkQmBHKbwe+EaavAG4P0zOAB8L0uPD+9gHGhPe9sDv2AeA64F7gsfA8L2IDaoEhbcpy/p6GZecDl4fpEmBQvsSW4jNiEzA6H+IDRgDrgNKkfe2rudzncv5B3pMewJnAE0nPrweuz+L6qmiddFYDw8P0cGB1mL4DuLhtPeBi4I6k8jtC2XBgVVJ5q3oZxPkoMDnf4gP6AS8DE4l+WV3U9n0EngDODNNFoZ61fW+b6x3qPgCMBJ4CzgEeC+vKl9hqOTjp5Pw9BcqJPjgt32JLEet5wLP5Eh9R0nmXKJEVhX1uSi73OZ1e65rmN7DZ+lAWl2HuvjFMb+LPt/9sL66OytenKO+ycPh9GtERRV7EZ9Hpq1eBzcBiom9i29y9MUV7LTGE+duBygxiTtcPgH8EDoTnlXkUmwO/NbOlZjYrlOXDezoG2AL8zKLTkneZWVmexNbWDOC+MJ3z+Nx9A/B94B1gI9E+tJQc7nNKOj2UR18rctrf3cz6A78CvuXuO5Ln5TI+d29y91OJjiomACfmIo62zOxCYLO7L811LO34hLuPB6YBV5rZJ5Nn5vA9LSI61Xybu58G1BOdrsqH2FqE6yIXAQ+1nZer+MJ1pOlEifsooAyYGnccrWIKh0SSZMiQIV5VVZXrMEREepSlS5fuBua6+7+1V0e3q06hqqqKmpqaXIchItKjhF51Czuqo9NrIiLSXba6+/KOKuhIJyb3LnknZfklE0fFHImISNZs6qyCjnRERCQ2SjoiIhIbJR0REYmNko6IiMRGSUdERGKjpCMiIrFR0hERkdgo6YiISGyUdEREJDZKOiIiEi1fZ7sAAAnfSURBVBslHRERiY2SjoiIxEZJR0REYqOkIyIisdGtDfKYbocgIr2NjnRERCQ2OtLpRXRkJCL5Tkknx9pLFCIivZFOr4mISGyUdEREJDZKOiIiEhslHRERiY06EhzG1NtNROKmIx0REYlN3iYdM7vbzDab2bKksgozW2xma8LfwaHczGyemSXM7HUzG5+0zMxQf42ZzczFaxERkUjeJh3gHmBqm7LZwFPufhzwVHgOMA04LjxmAbdBlKSAG4CJwATghuZEJSIi8cvbpOPufwS2timeDswP0/OBzySVL/DIC8AgMxsOTAEWu/tWd/8QWMzBiUxERGLS0zoSDHP3jWF6EzAsTI8A3k2qtz6UtVfeo2kUAxHpqfL2SKcz7u6Ad1d7ZjbLzGrMrGbLli3d1ayIiCTpaUnn/XDajPB3cyjfABydVG9kKGuv/CDufqe7V7t79dChQ7s9cBER6XlJZyHQ3ANtJvBoUvmloRfbGcD2cBruCeA8MxscOhCcF8pERCQH8vaajpndB0wChpjZeqJeaN8FHjSzrwFvA18I1R8HzgcSwG7gMgB332pm/wd4KdT7jru37ZwgIiIxyduk4+4XtzPrUynqOnBlO+3cDdzdjaGJiEiGetrpNRER6cHy9khHuk9Xu1hrTDYRyRYd6YiISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERqNMd7OujugsInI40ZGOiIjERklHRERio9Nrkjbd3E1EDpWOdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYnNY9F4zs6nAD4FC4C53/26OQ+pV1KtNRNLV65OOmRUCtwKTgfXAS2a20N1X5Day3k/JSETa6vVJB5gAJNx9LYCZ3Q9MB5R0ciSToYLaS1RdbUsJTyS3DoekMwJ4N+n5emBi20pmNguYFZ7uMrPVHbQ5BPig2yLsfvkcX0axfambVt5JO71uu8VEsWUmn2ODzOIb3VmFwyHppMXd7wTuTKeumdW4e3WWQ8pYPsen2DKj2DKj2DKXrfgOh95rG4Cjk56PDGUiIhKzwyHpvAQcZ2ZjzKwEmAEszHFMIiKHpV5/es3dG83sm8ATRF2m73b35YfYbFqn4XIon+NTbJlRbJlRbJnLSnzm7tloV0RE5CCHw+k1ERHJE0o6IiISGyWdLjKzqWa22swSZjY7i+u528w2m9mypLIKM1tsZmvC38Gh3MxsXojpdTMbn7TMzFB/jZnNTCo/3czeCMvMMzPrQmxHm9nTZrbCzJab2TX5Ep+Z9TWzF83stRDbTaF8jJktCe09EDqVYGZ9wvNEmF+V1Nb1oXy1mU1JKj+kfcDMCs3sFTN7LJ9iM7PasM1fNbOaUJbz9zQsO8jMHjazVWa20szOzKPYTgjbrPmxw8y+lUfxXRv+F5aZ2X0W/Y/kbp9zdz3SfBB1RHgLOAYoAV4DxmVpXZ8ExgPLksq+B8wO07OBm8P0+cBvAAPOAJaE8gpgbfg7OEwPDvNeDHUtLDutC7ENB8aH6QHAm8C4fIgv1O8fpouBJaGdB4EZofx24Bth+grg9jA9A3ggTI8L728fYEx43wu7Yx8ArgPuBR4Lz/MiNqAWGNKmLOfvaVh2PnB5mC4BBuVLbCk+IzYR/Ugy5/ER/Th+HVCatK99NZf7XM4/yHvSAzgTeCLp+fXA9VlcXxWtk85qYHiYHg6sDtN3ABe3rQdcDNyRVH5HKBsOrEoqb1UvgzgfJRrbLq/iA/oBLxONQPEBUNT2fSTq1XhmmC4K9azte9tc71D3AaLfiT0FnAM8FtaVL7HVcnDSyfl7CpQTfXBavsWWItbzgGfzJT7+PCJLRdiHHgOm5HKf0+m1rkk1pM6IGNc/zN03hulNwLBO4uqofH2K8i4Lh9+nER1R5EV8Fp2+ehXYDCwm+ia2zd0bU7TXEkOYvx2ozCDmdP0A+EfgQHhemUexOfBbM1tq0bBQkB/v6RhgC/Azi05L3mVmZXkSW1szgPvCdM7jc/cNwPeBd4CNRPvQUnK4zynp9FAefa3IaX93M+sP/Ar4lrvvSJ6Xy/jcvcndTyU6qpgAnJiLONoyswuBze6+NNextOMT7j4emAZcaWafTJ6Zw/e0iOhU823ufhpQT3S6Kh9iaxGui1wEPNR2Xq7iC9eRphMl7qOAMmBq3HEkU9LpmlwPqfO+mQ0HCH83dxJXR+UjU5SnzcyKiRLOL939kXyLD8DdtwFPE50CGGRmzT+GTm6vJYYwvxyoyyDmdHwcuMjMaoH7iU6x/TBPYmv+Voy7bwb+iyhh58N7uh5Y7+5LwvOHiZJQPsSWbBrwsru/H57nQ3znAuvcfYu77wceIdoPc7fPZXLe8nB9EH3jWkv0raH5otlJWVxfFa2v6fw7rS9Mfi9MX0DrC5MvhvIKonPhg8NjHVAR5rW9MHl+F+IyYAHwgzblOY8PGAoMCtOlwDPAhUTfPpMvnF4Rpq+k9YXTB8P0SbS+cLqW6KJpt+wDwCT+3JEg57ERfQMekDT9HNE34py/p2HZZ4ATwvSNIa68iC0pxvuBy/Ls/2EisJzo+qYRdci4Kpf7XM4/yHvag6jnyZtE1wn+OYvruY/oHOx+om96XyM6t/oUsAZ4MmmHNKIb1b0FvAFUJ7XzN0AiPJL/IaqBZWGZH9HmIm0nsX2C6FTB68Cr4XF+PsQHnAK8EmJbBvxLKD8m/OMmwj9cn1DeNzxPhPnHJLX1z2H9q0nqLdQd+wCtk07OYwsxvBYey5uXzYf3NCx7KlAT3tf/JvpQzovYwvJlREcE5UlleREfcBOwKiz/c6LEkbN9TsPgiIhIbHRNR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IjlmZk1hdOJlZvaQmfVrp95zcccm0t2UdERyr8HdT3X3vwD2AV9Pntn8y3F3PysXwYl0JyUdkfzyDDDWzCaZ2TNmthBYAWBmu5ormdk/hfurvGZm3w1lx5rZojBg5zNmlhdjzokkK+q8iojEIRzRTAMWhaLxwF+4+7o29aYRDeI40d13m1lFmHUn8HV3X2NmE4EfE43vJpI3lHREcq803IoBoiOdnwJnEY3JtS5F/XOBn7n7bgB33xpG/D4LeCjpppJ9shu2SNcp6YjkXoNHt2JoERJHfRfaKCC6R8qpndYUySFd0xHpeRYDlzX3cjOzCo/uZ7TOzD4fyszMPprLIEVSUdIR6WHcfRGwEKgJp+X+d5j1JeBrZtY8UvT0HIUo0i6NMi0iIrHRkY6IiMRGSUdERGKjpCMiIrFR0hERkdgo6YiISGyUdEREJDZKOiIiEpv/D7VISMUqSxuGAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting a high level over-view of various ways to deal with outliers"
      ],
      "metadata": {
        "id": "sf6fP7F09Fda"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.where(data['Price']>=35000, data['Price'].median(), data['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwTti38136NP",
        "outputId": "ef8a28e7-2df9-44e2-e371-41b9a2d5d467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3897.,  7662., 13882., ...,  7229., 12648., 11753.])"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Price'] = np.where(data['Price']>=35000, data['Price'].median(), data['Price'])"
      ],
      "metadata": {
        "id": "wPAKIOsy37vG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot(data,'Price')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Rwmbe2FR4ADO",
        "outputId": "cb6dc8ac-7c7d-4110-c3ff-891dc49057a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: 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",
            "/usr/local/lib/python3.8/dist-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",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
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              "   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",
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              "   Journey_month  Dep_Time_hour  Dep_Timeminute  Arrival_Time_hour  \\\n",
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              "   Arrival_Timeminute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                  10               2             50                1   \n",
              "1                  15               2             50                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
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    {
      "cell_type": "code",
      "source": [
        " data.drop(columns=['Source','Duration'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "Ww0lohk34B4m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
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          "height": 176
        },
        "id": "ooDLyrz_4DAX",
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      "execution_count": null,
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        {
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          "data": {
            "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_Timeminute  Arrival_Time_hour  Arrival_Timeminute  \\\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               2             50                0               1   \n",
              "\n",
              "   Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0             0               0              0  \n",
              "1             0               0              0  "
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3oWDU7e4Dxm",
        "outputId": "896b18f0-63f6-400d-ffc9-b3a9a569ee58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_Timeminute          int64\n",
              "Arrival_Time_hour       int64\n",
              "Arrival_Timeminute      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"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performing Feature Selection"
      ],
      "metadata": {
        "id": "dNcgsiQM9Lc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import mutual_info_regression"
      ],
      "metadata": {
        "id": "GxBbkym64Evn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(['Price'], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "XCy5Y-HT4HAe",
        "outputId": "db1e5053-4dcd-4ae3-c696-1a0449762645"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Airline  Destination  Total_Stops  Journey_day  Journey_month  \\\n",
              "0            3            2            0           24              3   \n",
              "1            7            3            2            5              1   \n",
              "2           10            4            2            6              9   \n",
              "3            3            3            1            5             12   \n",
              "4            3            2            1            3              1   \n",
              "...        ...          ...          ...          ...            ...   \n",
              "10678        2            3            0            4              9   \n",
              "10679        7            3            0           27              4   \n",
              "10680       10            2            0           27              4   \n",
              "10681        5            2            0            3              1   \n",
              "10682        7            4            2            5              9   \n",
              "\n",
              "       Dep_Time_hour  Dep_Timeminute  Arrival_Time_hour  Arrival_Timeminute  \\\n",
              "0                 22              20                  1                  10   \n",
              "1                  5              50                 13                  15   \n",
              "2                  9              25                  4                  25   \n",
              "3                 18               5                 23                  30   \n",
              "4                 16              50                 21                  35   \n",
              "...              ...             ...                ...                 ...   \n",
              "10678             19              55                 22                  25   \n",
              "10679             20              45                 23                  20   \n",
              "10680              8              20                 11                  20   \n",
              "10681             11              30                 14                  10   \n",
              "10682             10              55                 19                  15   \n",
              "\n",
              "       Duration_hours  Duration_mins  Source_Banglore  Source_Kolkata  \\\n",
              "0                   2             50                1               0   \n",
              "1                   2             50                0               1   \n",
              "2                   2             50                0               0   \n",
              "3                   2             50                0               1   \n",
              "4                   2             50                1               0   \n",
              "...               ...            ...              ...             ...   \n",
              "10678               2             50                0               1   \n",
              "10679               2             50                0               1   \n",
              "10680               2             50                1               0   \n",
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          "metadata": {},
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        "X =data.drop(['Price'], axis=1)"
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      "execution_count": null,
      "outputs": []
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        "y=data['Price']"
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              "Airline               int64\n",
              "Destination           int64\n",
              "Total_Stops           int64\n",
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              "Source_Chennai        int64\n",
              "Source_Mumbai         int64\n",
              "dtype: object"
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              "dtype('float64')"
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          "metadata": {},
          "execution_count": 143
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              "array([0.98318589, 0.99421019, 0.79815264, 0.1887596 , 0.23740946,\n",
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          "execution_count": 144
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        "imp=pd.DataFrame(mutual_info_regression(X,y), index=X.columns)\n",
        "imp.columns=['importance']"
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      "metadata": {
        "id": "RD2i2Z164Ncu"
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      "execution_count": null,
      "outputs": []
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    {
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        "imp.sort_values(by='importance', ascending = False)"
      ],
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        "id": "E-ppiVul4Oo2",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets build ML Model , then later on we can think of saving it"
      ],
      "metadata": {
        "id": "xFMiJMlO9OS6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build ML Model and Save the Model"
      ],
      "metadata": {
        "id": "jlYQTPUS4Z3P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "7iwSxeIK4Qve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "RYSkwFuG4bue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "F1OOANVo4cl-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ml_model= RandomForestRegressor()"
      ],
      "metadata": {
        "id": "Lzyc9VY84dU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model= ml_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "zI_rhzzA4eNF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "gFIsPAYR4e_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1_o9GTZ4gPW",
        "outputId": "f98b1f3a-014f-4575-d596-0342658b03c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2671,)"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_tF9aZX2hB8",
        "outputId": "ff9c0a6f-baf2-4db9-e709-d37699e242cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2671"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining your own evaluation metric"
      ],
      "metadata": {
        "id": "sKAUKTdF9Vip"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mape(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true))*100"
      ],
      "metadata": {
        "id": "3d-oJPGk2hLv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#mape(y_test, forest.predict(X_test))"
      ],
      "metadata": {
        "id": "amM691Dv5g9r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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('MAPE  :', metrics.mean_squared_error(y_test, y_prediction))\n",
        "    print('MAPE  :', mape(y_test, y_prediction))\n",
        "    sns.distplot(y_test-y_prediction)\n",
        ""
      ],
      "metadata": {
        "id": "4FTB-mhw5jID"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}