flight_price_prediction.ipynb

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      "name": "flight_price_prediction.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "# Flight Price Prediction"
      ],
      "metadata": {
        "id": "BNKE7pG10aGp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XMdvnfEx0K-O"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import the Dataset "
      ],
      "metadata": {
        "id": "90ykoSUE0pnH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = pd.read_excel(r\"/content/Data_Train.xlsx\")"
      ],
      "metadata": {
        "id": "XGd3XlUj0loe"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "pd.set_option('display.max_columns', None)"
      ],
      "metadata": {
        "id": "PIYOcCIU0wxU"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "train_data.head()"
      ],
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      "execution_count": 4,
      "outputs": [
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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",
              "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
              "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \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  \n",
              "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
              "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
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              "      <th>Airline</th>\n",
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              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>24/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
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              "      <td>1/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>9/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>12/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>18:05</td>\n",
              "      <td>23:30</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>01/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>16:50</td>\n",
              "      <td>21:35</td>\n",
              "      <td>4h 45m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
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        "outputId": "16258fc6-4249-4d81-ecb6-bf74312a88bd"
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      "execution_count": 5,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10683 entries, 0 to 10682\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Airline          10683 non-null  object\n",
            " 1   Date_of_Journey  10683 non-null  object\n",
            " 2   Source           10683 non-null  object\n",
            " 3   Destination      10683 non-null  object\n",
            " 4   Route            10682 non-null  object\n",
            " 5   Dep_Time         10683 non-null  object\n",
            " 6   Arrival_Time     10683 non-null  object\n",
            " 7   Duration         10683 non-null  object\n",
            " 8   Total_Stops      10682 non-null  object\n",
            " 9   Additional_Info  10683 non-null  object\n",
            " 10  Price            10683 non-null  int64 \n",
            "dtypes: int64(1), object(10)\n",
            "memory usage: 918.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Duration\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mwLS7wQu02Zq",
        "outputId": "6990b61e-c6c9-4cbf-bb34-6a9cc6dbf310"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2h 50m     550\n",
              "1h 30m     386\n",
              "2h 45m     337\n",
              "2h 55m     337\n",
              "2h 35m     329\n",
              "          ... \n",
              "31h 30m      1\n",
              "30h 25m      1\n",
              "42h 5m       1\n",
              "4h 10m       1\n",
              "47h 40m      1\n",
              "Name: Duration, Length: 368, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove all null values\n",
        "train_data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "1GZCrHrr04ot"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().sum()"
      ],
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        "colab": {
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        "id": "egh4S8NR06me",
        "outputId": "e2f2d4bf-cfb3-4099-a5ef-ab2ca67983d9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              0\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        0\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA (Exploratory Data Analysis)"
      ],
      "metadata": {
        "id": "bSPGj7od0-8E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#extract day from dt.day\n",
        "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day"
      ],
      "metadata": {
        "id": "Hjl1qTwz08e7"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#extract month from dt.month\n",
        "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
      ],
      "metadata": {
        "id": "0ucgufId1F14"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head()"
      ],
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        },
        "id": "78lj_Zci1Hbn",
        "outputId": "1119b05a-ea01-41c8-891c-fb37cf183603"
      },
      "execution_count": 11,
      "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",
              "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
              "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
              "\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   \n",
              "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
              "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
              "\n",
              "   Journey_day  Journey_month  \n",
              "0           24              3  \n",
              "1            1              5  \n",
              "2            9              6  \n",
              "3           12              5  \n",
              "4            1              3  "
            ],
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              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
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              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
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              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>24/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
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              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>1/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>9/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>12/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>18:05</td>\n",
              "      <td>23:30</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>01/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>16:50</td>\n",
              "      <td>21:35</td>\n",
              "      <td>4h 45m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8eb99b48-99c2-4e41-9ce8-a03f12f45056')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "    }\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-8eb99b48-99c2-4e41-9ce8-a03f12f45056 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-8eb99b48-99c2-4e41-9ce8-a03f12f45056');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Since we have converted Date_of_Journey column into integers, Now we can drop as it is of no use.\n",
        "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "mEWCVB-31I65"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Departure time\n",
        "# Extracting Hours\n",
        "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
        "\n",
        "# Extracting Minutes\n",
        "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute\n",
        "\n",
        "# drop Dep_Time as it is of no use\n",
        "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "zCHNok4V1O4d"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Arrival time\n",
        "# Extracting Hours\n",
        "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data.Arrival_Time).dt.hour\n",
        "\n",
        "# Extracting Minutes\n",
        "train_data[\"Arrival_min\"] = pd.to_datetime(train_data.Arrival_Time).dt.minute\n",
        "\n",
        "#drop Arrival_Time as it is of no use\n",
        "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "rI1c61PX1taO"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assigning and converting Duration column into list\n",
        "duration = list(train_data[\"Duration\"])\n",
        "\n",
        "for i in range(len(duration)):\n",
        "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
        "        if \"h\" in duration[i]:\n",
        "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
        "        else:\n",
        "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
        "\n",
        "duration_hours = []\n",
        "duration_mins = []\n",
        "for i in range(len(duration)):\n",
        "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
        "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
      ],
      "metadata": {
        "id": "RG64Bjyu2FFS"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding duration_hours and duration_mins list to train_data dataframe\n",
        "\n",
        "train_data[\"Duration_hours\"] = duration_hours\n",
        "train_data[\"Duration_mins\"] = duration_mins"
      ],
      "metadata": {
        "id": "lJWL4THt6B62"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "_f49FWhr6Fhs"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "o640DhqQ6Jtn",
        "outputId": "22db3f71-38cc-4579-d8ac-cc54e8b6e1b1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Airline    Source Destination                  Route Total_Stops  \\\n",
              "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
              "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
              "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
              "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
              "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
              "\n",
              "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
              "0         No info   3897           24              3        22       20   \n",
              "1         No info   7662            1              5         5       50   \n",
              "2         No info  13882            9              6         9       25   \n",
              "3         No info   6218           12              5        18        5   \n",
              "4         No info  13302            1              3        16       50   \n",
              "\n",
              "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
              "0             1           10               2             50  \n",
              "1            13           15               7             25  \n",
              "2             4           25              19              0  \n",
              "3            23           30               5             25  \n",
              "4            21           35               4             45  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec70bc26-1b25-461c-97cb-86910da33af4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr 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>Route</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>Dep_hour</th>\n",
              "      <th>Dep_min</th>\n",
              "      <th>Arrival_hour</th>\n",
              "      <th>Arrival_min</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</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>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
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              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec70bc26-1b25-461c-97cb-86910da33af4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </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",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-ec70bc26-1b25-461c-97cb-86910da33af4 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-ec70bc26-1b25-461c-97cb-86910da33af4');\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": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handle Categorical Data"
      ],
      "metadata": {
        "id": "yGfyE8T68a5L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Airline\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQqOxqtU6Lzl",
        "outputId": "f10e7e08-9195-4e8e-8044-56f48ef77ad7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Jet Airways                          3849\n",
              "IndiGo                               2053\n",
              "Air India                            1751\n",
              "Multiple carriers                    1196\n",
              "SpiceJet                              818\n",
              "Vistara                               479\n",
              "Air Asia                              319\n",
              "GoAir                                 194\n",
              "Multiple carriers Premium economy      13\n",
              "Jet Airways Business                    6\n",
              "Vistara Premium economy                 3\n",
              "Trujet                                  1\n",
              "Name: Airline, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# From graph we can see that Jet Airways have the highest Price.\n",
        "\n",
        "# Airline vs Price\n",
        "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "nvKVqc0_ACEd",
        "outputId": "54e2d284-429c-45c7-8bb1-316ebb9ec7ec"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#One hot encoding\n",
        "Airline = train_data[[\"Airline\"]]\n",
        "\n",
        "Airline = pd.get_dummies(Airline, drop_first= True)\n",
        "\n",
        "Airline.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "ywcidxzFAIWO",
        "outputId": "805f7eea-3e46-44b0-cb8d-5b169991718e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
              "0                  0              0               1                    0   \n",
              "1                  1              0               0                    0   \n",
              "2                  0              0               0                    1   \n",
              "3                  0              0               1                    0   \n",
              "4                  0              0               1                    0   \n",
              "\n",
              "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
              "0                             0                          0   \n",
              "1                             0                          0   \n",
              "2                             0                          0   \n",
              "3                             0                          0   \n",
              "4                             0                          0   \n",
              "\n",
              "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
              "0                                          0                 0   \n",
              "1                                          0                 0   \n",
              "2                                          0                 0   \n",
              "3                                          0                 0   \n",
              "4                                          0                 0   \n",
              "\n",
              "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
              "0               0                0                                0  \n",
              "1               0                0                                0  \n",
              "2               0                0                                0  \n",
              "3               0                0                                0  \n",
              "4               0                0                                0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ad5a53e-e65e-4b82-a504-8ec314fbdc58\">\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_Air India</th>\n",
              "      <th>Airline_GoAir</th>\n",
              "      <th>Airline_IndiGo</th>\n",
              "      <th>Airline_Jet Airways</th>\n",
              "      <th>Airline_Jet Airways Business</th>\n",
              "      <th>Airline_Multiple carriers</th>\n",
              "      <th>Airline_Multiple carriers Premium economy</th>\n",
              "      <th>Airline_SpiceJet</th>\n",
              "      <th>Airline_Trujet</th>\n",
              "      <th>Airline_Vistara</th>\n",
              "      <th>Airline_Vistara Premium economy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ad5a53e-e65e-4b82-a504-8ec314fbdc58')\"\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-4ad5a53e-e65e-4b82-a504-8ec314fbdc58 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-4ad5a53e-e65e-4b82-a504-8ec314fbdc58');\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Source\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWqlbk8YAP96",
        "outputId": "b6b62110-a34a-4746-b9e5-103aa957f985"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Delhi       4536\n",
              "Kolkata     2871\n",
              "Banglore    2197\n",
              "Mumbai       697\n",
              "Chennai      381\n",
              "Name: Source, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Source vs Price\n",
        "\n",
        "sns.catplot(y = \"Price\", x = \"Source\", data = train_data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 4, aspect = 3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "JousbxGdAUmN",
        "outputId": "9aefbe4c-1126-4769-ce8d-5eb0fb6d3631"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#one hot encoding\n",
        "Source = train_data[[\"Source\"]]\n",
        "\n",
        "Source = pd.get_dummies(Source, drop_first= True)\n",
        "\n",
        "Source.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "asSTNbT-AZir",
        "outputId": "e914e95c-9239-45ed-9655-d8bfa6dd60d0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
              "0               0             0               0              0\n",
              "1               0             0               1              0\n",
              "2               0             1               0              0\n",
              "3               0             0               1              0\n",
              "4               0             0               0              0"
            ],
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              "      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-df5579ae-19ec-441f-8266-cfaea3a67b74 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-df5579ae-19ec-441f-8266-cfaea3a67b74');\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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Destination\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uUyvNqqAluv",
        "outputId": "17bd6dc8-5b85-42fd-9d92-aacb0746e983"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cochin       4536\n",
              "Banglore     2871\n",
              "Delhi        1265\n",
              "New Delhi     932\n",
              "Hyderabad     697\n",
              "Kolkata       381\n",
              "Name: Destination, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#one hot encoding\n",
        "Destination = train_data[[\"Destination\"]]\n",
        "\n",
        "Destination = pd.get_dummies(Destination, drop_first = True)\n",
        "\n",
        "Destination.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CiSoGuw_AqMU",
        "outputId": "fb45adab-477e-4873-ad35-12e91e427244"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
              "0                   0                  0                      0   \n",
              "1                   0                  0                      0   \n",
              "2                   1                  0                      0   \n",
              "3                   0                  0                      0   \n",
              "4                   0                  0                      0   \n",
              "\n",
              "   Destination_Kolkata  Destination_New Delhi  \n",
              "0                    0                      1  \n",
              "1                    0                      0  \n",
              "2                    0                      0  \n",
              "3                    0                      0  \n",
              "4                    0                      1  "
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Route\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75tfPo49AvuT",
        "outputId": "7836958a-eaec-44a3-beee-2558cbaf7992"
      },
      "execution_count": 28,
      "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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop column \"Route\" and \"Additional_Info\" as it is of no use \n",
        "train_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "lSdjGZ_NNJNn"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Total_Stops\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1A-c0v5COE2j",
        "outputId": "d00e3825-1d0f-4e28-edbf-ab984d906c87"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1 stop      5625\n",
              "non-stop    3491\n",
              "2 stops     1520\n",
              "3 stops       45\n",
              "4 stops        1\n",
              "Name: Total_Stops, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Label encoding\n",
        "train_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
      ],
      "metadata": {
        "id": "1nQ1-90DOHzj"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "dTzWMPaGOMkt",
        "outputId": "76a1e971-ced1-4586-f10d-109896f64436"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Airline    Source Destination  Total_Stops  Price  Journey_day  \\\n",
              "0       IndiGo  Banglore   New Delhi            0   3897           24   \n",
              "1    Air India   Kolkata    Banglore            2   7662            1   \n",
              "2  Jet Airways     Delhi      Cochin            2  13882            9   \n",
              "3       IndiGo   Kolkata    Banglore            1   6218           12   \n",
              "4       IndiGo  Banglore   New Delhi            1  13302            1   \n",
              "\n",
              "   Journey_month  Dep_hour  Dep_min  Arrival_hour  Arrival_min  \\\n",
              "0              3        22       20             1           10   \n",
              "1              5         5       50            13           15   \n",
              "2              6         9       25             4           25   \n",
              "3              5        18        5            23           30   \n",
              "4              3        16       50            21           35   \n",
              "\n",
              "   Duration_hours  Duration_mins  \n",
              "0               2             50  \n",
              "1               7             25  \n",
              "2              19              0  \n",
              "3               5             25  \n",
              "4               4             45  "
            ],
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0664d415-0e7e-43a3-8935-2237223fad8e 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-0664d415-0e7e-43a3-8935-2237223fad8e');\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#concatenate all dataframes\n",
        "data_train = pd.concat([train_data, Airline, Source, Destination], axis = 1)"
      ],
      "metadata": {
        "id": "cpuPVrL0OSIS"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "1uxndiziOfI0"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "i-5BVeqnOhc7",
        "outputId": "15305a48-9012-4a4d-fdd6-b7215a253586"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Total_Stops  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
              "0            0   3897           24              3        22       20   \n",
              "1            2   7662            1              5         5       50   \n",
              "2            2  13882            9              6         9       25   \n",
              "3            1   6218           12              5        18        5   \n",
              "4            1  13302            1              3        16       50   \n",
              "\n",
              "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \\\n",
              "0             1           10               2             50   \n",
              "1            13           15               7             25   \n",
              "2             4           25              19              0   \n",
              "3            23           30               5             25   \n",
              "4            21           35               4             45   \n",
              "\n",
              "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
              "0                  0              0               1                    0   \n",
              "1                  1              0               0                    0   \n",
              "2                  0              0               0                    1   \n",
              "3                  0              0               1                    0   \n",
              "4                  0              0               1                    0   \n",
              "\n",
              "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
              "0                             0                          0   \n",
              "1                             0                          0   \n",
              "2                             0                          0   \n",
              "3                             0                          0   \n",
              "4                             0                          0   \n",
              "\n",
              "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
              "0                                          0                 0   \n",
              "1                                          0                 0   \n",
              "2                                          0                 0   \n",
              "3                                          0                 0   \n",
              "4                                          0                 0   \n",
              "\n",
              "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
              "0               0                0                                0   \n",
              "1               0                0                                0   \n",
              "2               0                0                                0   \n",
              "3               0                0                                0   \n",
              "4               0                0                                0   \n",
              "\n",
              "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
              "0               0             0               0              0   \n",
              "1               0             0               1              0   \n",
              "2               0             1               0              0   \n",
              "3               0             0               1              0   \n",
              "4               0             0               0              0   \n",
              "\n",
              "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
              "0                   0                  0                      0   \n",
              "1                   0                  0                      0   \n",
              "2                   1                  0                      0   \n",
              "3                   0                  0                      0   \n",
              "4                   0                  0                      0   \n",
              "\n",
              "   Destination_Kolkata  Destination_New Delhi  \n",
              "0                    0                      1  \n",
              "1                    0                      0  \n",
              "2                    0                      0  \n",
              "3                    0                      0  \n",
              "4                    0                      1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-90226b78-1ead-441d-8de2-2d08a047aee6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
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              "      <th>Airline_Multiple carriers</th>\n",
              "      <th>Airline_Multiple carriers Premium economy</th>\n",
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              "      <th>Airline_Vistara</th>\n",
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              "      <th>Source_Chennai</th>\n",
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              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Mumbai</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
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              "      <td>25</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>6218</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>13302</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90226b78-1ead-441d-8de2-2d08a047aee6')\"\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-90226b78-1ead-441d-8de2-2d08a047aee6 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-90226b78-1ead-441d-8de2-2d08a047aee6');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test Dataset"
      ],
      "metadata": {
        "id": "ZWLKNsWsOwx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = pd.read_excel(r\"/content/Test_set.xlsx\")"
      ],
      "metadata": {
        "id": "OlTxAjQXOjSc"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l05iTCjAPAC4",
        "outputId": "d310ed83-5057-4386-a898-1aefaeaa3eb3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2671 entries, 0 to 2670\n",
            "Data columns (total 10 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Airline          2671 non-null   object\n",
            " 1   Date_of_Journey  2671 non-null   object\n",
            " 2   Source           2671 non-null   object\n",
            " 3   Destination      2671 non-null   object\n",
            " 4   Route            2671 non-null   object\n",
            " 5   Dep_Time         2671 non-null   object\n",
            " 6   Arrival_Time     2671 non-null   object\n",
            " 7   Duration         2671 non-null   object\n",
            " 8   Total_Stops      2671 non-null   object\n",
            " 9   Additional_Info  2671 non-null   object\n",
            "dtypes: object(10)\n",
            "memory usage: 208.8+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.dropna(inplace = True)\n",
        "print(test_data.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QL6KnR2FP9D0",
        "outputId": "3a726b9d-daad-489c-8450-8a3f8a6c9fc1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Airline            0\n",
            "Date_of_Journey    0\n",
            "Source             0\n",
            "Destination        0\n",
            "Route              0\n",
            "Dep_Time           0\n",
            "Arrival_Time       0\n",
            "Duration           0\n",
            "Total_Stops        0\n",
            "Additional_Info    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA of test set"
      ],
      "metadata": {
        "id": "xUu9gc06QFEh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Date_of_Journey\n",
        "test_data[\"Journey_day\"] = pd.to_datetime(test_data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day\n",
        "test_data[\"Journey_month\"] = pd.to_datetime(test_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month\n",
        "test_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "0j-SmhAlQA2n"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dep_Time\n",
        "test_data[\"Dep_hour\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.hour\n",
        "test_data[\"Dep_min\"] = pd.to_datetime(test_data[\"Dep_Time\"]).dt.minute\n",
        "test_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "PuUCeoARQJfo"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Arrival_Time\n",
        "test_data[\"Arrival_hour\"] = pd.to_datetime(test_data.Arrival_Time).dt.hour\n",
        "test_data[\"Arrival_min\"] = pd.to_datetime(test_data.Arrival_Time).dt.minute\n",
        "test_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "aM4uHe8lQLKZ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Duration\n",
        "duration = list(test_data[\"Duration\"])\n",
        "\n",
        "for i in range(len(duration)):\n",
        "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
        "        if \"h\" in duration[i]:\n",
        "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
        "        else:\n",
        "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
        "\n",
        "duration_hours = []\n",
        "duration_mins = []\n",
        "for i in range(len(duration)):\n",
        "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
        "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
      ],
      "metadata": {
        "id": "AUUvvxkAQMve"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding Duration column to test set\n",
        "test_data[\"Duration_hours\"] = duration_hours\n",
        "test_data[\"Duration_mins\"] = duration_mins\n",
        "test_data.drop([\"Duration\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "mkN4qyEVQREl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#One hot encoding => Airline\n",
        "print(test_data[\"Airline\"].value_counts())\n",
        "Airline = pd.get_dummies(test_data[\"Airline\"], drop_first= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2aNsK9SQTxj",
        "outputId": "ad6f4e1a-12d7-480f-fd72-7984846fcfa9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jet Airways                          897\n",
            "IndiGo                               511\n",
            "Air India                            440\n",
            "Multiple carriers                    347\n",
            "SpiceJet                             208\n",
            "Vistara                              129\n",
            "Air Asia                              86\n",
            "GoAir                                 46\n",
            "Multiple carriers Premium economy      3\n",
            "Vistara Premium economy                2\n",
            "Jet Airways Business                   2\n",
            "Name: Airline, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#One hot encoding => Source\n",
        "print(test_data[\"Source\"].value_counts())\n",
        "Source = pd.get_dummies(test_data[\"Source\"], drop_first= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX_4dgu7Qbgs",
        "outputId": "96daa3ce-1d4b-447f-8b7f-2f643efd1ac9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Delhi       1145\n",
            "Kolkata      710\n",
            "Banglore     555\n",
            "Mumbai       186\n",
            "Chennai       75\n",
            "Name: Source, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#One hot encoding => Destination\n",
        "print(test_data[\"Destination\"].value_counts())\n",
        "Destination = pd.get_dummies(test_data[\"Destination\"], drop_first = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWltBV2kQk7n",
        "outputId": "5afea0cb-bff1-45dd-c6b6-bf3300392e14"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cochin       1145\n",
            "Banglore      710\n",
            "Delhi         317\n",
            "New Delhi     238\n",
            "Hyderabad     186\n",
            "Kolkata        75\n",
            "Name: Destination, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.drop([\"Route\", \"Additional_Info\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "JprhSX67Qq7f"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
      ],
      "metadata": {
        "id": "NAc2cNfaQty9"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#concatenate\n",
        "data_test = pd.concat([test_data, Airline, Source, Destination], axis = 1)"
      ],
      "metadata": {
        "id": "o2jHIzzCQv4N"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "zikywbwBQzgX"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "ZvF40lkyQ2fo",
        "outputId": "e4677d09-84da-44e7-c41b-1e52b71fc51a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
              "0            1            6              6        17       30             4   \n",
              "1            1           12              5         6       20            10   \n",
              "2            1           21              5        19       15            19   \n",
              "3            1           21              5         8        0            21   \n",
              "4            0           24              6        23       55             2   \n",
              "\n",
              "   Arrival_min  Duration_hours  Duration_mins  Air India  GoAir  IndiGo  \\\n",
              "0           25              10             55          0      0       0   \n",
              "1           20               4              0          0      0       1   \n",
              "2            0              23             45          0      0       0   \n",
              "3            0              13              0          0      0       0   \n",
              "4           45               2             50          0      0       0   \n",
              "\n",
              "   Jet Airways  Jet Airways Business  Multiple carriers  \\\n",
              "0            1                     0                  0   \n",
              "1            0                     0                  0   \n",
              "2            1                     0                  0   \n",
              "3            0                     0                  1   \n",
              "4            0                     0                  0   \n",
              "\n",
              "   Multiple carriers Premium economy  SpiceJet  Vistara  \\\n",
              "0                                  0         0        0   \n",
              "1                                  0         0        0   \n",
              "2                                  0         0        0   \n",
              "3                                  0         0        0   \n",
              "4                                  0         0        0   \n",
              "\n",
              "   Vistara Premium economy  Chennai  Delhi  Kolkata  Mumbai  Cochin  Delhi  \\\n",
              "0                        0        0      1        0       0       1      0   \n",
              "1                        0        0      0        1       0       0      0   \n",
              "2                        0        0      1        0       0       1      0   \n",
              "3                        0        0      1        0       0       1      0   \n",
              "4                        0        0      0        0       0       0      1   \n",
              "\n",
              "   Hyderabad  Kolkata  New Delhi  \n",
              "0          0        0          0  \n",
              "1          0        0          0  \n",
              "2          0        0          0  \n",
              "3          0        0          0  \n",
              "4          0        0          0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec4519aa-2cfc-4652-85a6-783352c79ad2\">\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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              "      <th></th>\n",
              "      <th>Total_Stops</th>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec4519aa-2cfc-4652-85a6-783352c79ad2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ec4519aa-2cfc-4652-85a6-783352c79ad2 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-ec4519aa-2cfc-4652-85a6-783352c79ad2');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Extraction\n",
        "Finding out the best feature which will contribute and have good relation with target variable. "
      ],
      "metadata": {
        "id": "N5lOxkULaIfL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtWxJgRTQ50g",
        "outputId": "4463ff79-eeeb-46f2-f879-cd319bcdcef2"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Total_Stops', 'Price', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
              "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
              "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
              "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
              "       'Airline_Multiple carriers',\n",
              "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
              "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
              "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
              "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
              "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Independent variable => Input\n",
        "X = data_train.loc[:, ['Total_Stops', 'Journey_day', 'Journey_month', 'Dep_hour',\n",
        "       'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n",
        "       'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
        "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
        "       'Airline_Multiple carriers',\n",
        "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
        "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
        "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
        "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
        "       'Destination_Kolkata', 'Destination_New Delhi']]\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "PbVjx29XaSAp",
        "outputId": "64e82fd9-ffa5-48eb-f315-498c1b811f10"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Total_Stops  Journey_day  Journey_month  Dep_hour  Dep_min  Arrival_hour  \\\n",
              "0            0           24              3        22       20             1   \n",
              "1            2            1              5         5       50            13   \n",
              "2            2            9              6         9       25             4   \n",
              "3            1           12              5        18        5            23   \n",
              "4            1            1              3        16       50            21   \n",
              "\n",
              "   Arrival_min  Duration_hours  Duration_mins  Airline_Air India  \\\n",
              "0           10               2             50                  0   \n",
              "1           15               7             25                  1   \n",
              "2           25              19              0                  0   \n",
              "3           30               5             25                  0   \n",
              "4           35               4             45                  0   \n",
              "\n",
              "   Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
              "0              0               1                    0   \n",
              "1              0               0                    0   \n",
              "2              0               0                    1   \n",
              "3              0               1                    0   \n",
              "4              0               1                    0   \n",
              "\n",
              "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
              "0                             0                          0   \n",
              "1                             0                          0   \n",
              "2                             0                          0   \n",
              "3                             0                          0   \n",
              "4                             0                          0   \n",
              "\n",
              "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
              "0                                          0                 0   \n",
              "1                                          0                 0   \n",
              "2                                          0                 0   \n",
              "3                                          0                 0   \n",
              "4                                          0                 0   \n",
              "\n",
              "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
              "0               0                0                                0   \n",
              "1               0                0                                0   \n",
              "2               0                0                                0   \n",
              "3               0                0                                0   \n",
              "4               0                0                                0   \n",
              "\n",
              "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
              "0               0             0               0              0   \n",
              "1               0             0               1              0   \n",
              "2               0             1               0              0   \n",
              "3               0             0               1              0   \n",
              "4               0             0               0              0   \n",
              "\n",
              "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
              "0                   0                  0                      0   \n",
              "1                   0                  0                      0   \n",
              "2                   1                  0                      0   \n",
              "3                   0                  0                      0   \n",
              "4                   0                  0                      0   \n",
              "\n",
              "   Destination_Kolkata  Destination_New Delhi  \n",
              "0                    0                      1  \n",
              "1                    0                      0  \n",
              "2                    0                      0  \n",
              "3                    0                      0  \n",
              "4                    0                      1  "
            ],
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              "      <th>Airline_Vistara</th>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52f6f1f5-ca7d-4e6b-83d9-346068c8077c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-52f6f1f5-ca7d-4e6b-83d9-346068c8077c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dependent/Target variable => Output\n",
        "y = data_train.iloc[:, 1]\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIGV0cVNaUQ_",
        "outputId": "42fe3e48-4bce-4476-9712-e06981847468"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     3897\n",
              "1     7662\n",
              "2    13882\n",
              "3     6218\n",
              "4    13302\n",
              "Name: Price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (18,18))\n",
        "sns.heatmap(train_data.corr(), annot = True, cmap = \"RdYlGn\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6ARdhs42ahiR",
        "outputId": "dbff2379-2e46-41e8-eea8-4fbf914a2de8"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Important feature using ExtraTreesRegressor\n",
        "\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "selection = ExtraTreesRegressor()\n",
        "selection.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idR_QtQtalNf",
        "outputId": "223125cc-458c-43c1-a82c-ce25bfcec429"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(selection.feature_importances_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMSuOt61a4Pv",
        "outputId": "eadac5f9-a0dd-4d7a-c648-31f42bed6213"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.44987533e-01 1.43183191e-01 5.31578758e-02 2.46816624e-02\n",
            " 2.15300855e-02 2.78635802e-02 1.85984286e-02 1.09317103e-01\n",
            " 1.77696228e-02 9.94546159e-03 2.12156143e-03 1.93463824e-02\n",
            " 1.32286450e-01 6.72319833e-02 1.82215916e-02 7.95968405e-04\n",
            " 3.54836114e-03 1.08391814e-04 5.20757680e-03 8.17775165e-05\n",
            " 4.92979027e-04 1.06055439e-02 3.19472982e-03 7.07570783e-03\n",
            " 1.26617213e-02 1.51705564e-02 5.42071370e-03 4.69631454e-04\n",
            " 2.49238273e-02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot graph of feature \n",
        "\n",
        "plt.figure(figsize = (12,8))\n",
        "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
        "feat_importances.nlargest(20).plot(kind='barh')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "Y2ArB9jQa8KT",
        "outputId": "5b443288-0680-4897-ec3e-8e57b650fd9d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fitting model using Random Forest"
      ],
      "metadata": {
        "id": "AM5Gw5L_bcnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "Krrt_s8obXLK"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "reg_rf = RandomForestRegressor()\n",
        "reg_rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ey46eyD-cfDR",
        "outputId": "61c6a816-ca35-44cf-8bd6-7948865939bd"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = reg_rf.predict(X_test)"
      ],
      "metadata": {
        "id": "0y7_TF0uclK-"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg_rf.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWoTVb6icnMY",
        "outputId": "bf83ab5c-ffe6-4552-b105-ba45afed1ab9"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9536750801194166"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg_rf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMxsQenNcpVK",
        "outputId": "9403ba46-9194-4695-be08-b8cde4691ad1"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7984289547810984"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(y_test-y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "NLyv66pUcr7a",
        "outputId": "fdccdf16-bb07-48aa-f829-33ed5ed4c45e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `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",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
        "plt.xlabel(\"y_test\")\n",
        "plt.ylabel(\"y_pred\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "oXbyifaocv5i",
        "outputId": "2bb55298-bc8a-49d1-91c2-b84d339232ca"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/N1u7yuzWG/qYnsuv1OMD99zcknq0g/xN7l3d2j7TNNZ9QK/aMtAopT62k1+gtf7CdvdRSv0+4fzJT2mtS5XLZ4CEUuptlTmVjwNfbWKZaIJ27EXZaJXZc+fSq1a7bfQ/dKcsxRZiP9muR/ORmp8N4K3AFWAKGAcOAH8PbBlolFKvBf418BLwpFIK4BWt9QeUUh8BPqeUilNZigxQ6fE0tEx0j60yL28WODplKbYQ+82WgUZr/Y7qz0qpTwEPa63/qObavyLcG7MlrfWPCAPVRmVPAidbVSa6Q3q5yFBvbNW17Va77SY4CSGuXz1zNB8GRtZc+zQwB/x6w2okxA4M98XJFZ2VYAHbr3bbTXASQly/egLNFeC/Bb5ec+1eQHbci125nvmS02p05TTM7Y5brv6eyatZLs3lmBjrob8nDDhyLIAQzVfPPppfBx5SSj2plPr/lFLfAx4Cfq05VRPdbLNjlKdmdrYaZ6eZl2t/z/FDvRRKLnpqkcVMUY4FEKJFdtyj0Vp/Syl1HHgfcBj4K+CvtNbpZlVOdK/rmS9Z6aHMZMkXHRIxa9Neydrfc+vEIBeuZnjlSoY33jLK3acOy/yMEE1W1z4arfWcUuox4IjW+vvNqZLYD3Y7X1LtofhewOxCHsMwyBdcYpEcjzxVWNerWft7+lJRXndsiPlMifvuOt7YRgkhNrTjoTOl1IRS6u+BF4G/rVz7oFLq882qnOheu01dU+2hLGRLRCMWybhNJGKymC1vmMyzU1LkCLGf1TNH8znC4bJewKlc+xbwnkZXSnS/IyNJ9OQiT794lRcuzHN1Pr+j+ZJq8sxCySVihX++tmVQKHkb9ojkuGYh2q+eQPMm4BNaa58w7T9a6yWgvxkVE91raibDc+fSHB5J0puIki24TM/leMNNw9vOl1R7KImYjeP5ALheQCJmbdhTkeOahWi/euZorgI3E+7uB0Ap9RpgstGVEt2tdoL+4FAKgFzRYXouzx3bPLa6rHmwJ8bF2SyO60MAowPxTZc3y3HNQrRXPYHmAeAvlVL/AbCVUh8Cfgc5VEzUaacLATbbZ/PeN01wRs9Scv2VVWcHh1KSt0yIDlXP8uYvKKXSwC8S5jr7KPC7WuuHm1U50R3WBgzbNJhZKLCQLVGoDIMN9sQ4MJRcecxff/8VHvmHi7i+T8QyiUZMvvejK7zm6CDvPH0D9911fNXzCiE6144CjVLKAh4FflJr/efNrZLoJmsTWV6dz3N2eonlXIlENMJAT4TFTImpmQxj8wmee3mW9FKRXNHDACwTSiUPwzQY7IkwOZPlkacmecNNwzx3Lt2QBJmS0VmI5tpRoNFae0qpY7ThRE6xt1XnYzwv4Afn0swuFnC9sJdiWgYzS0VMwyAetbiSzuP4AX44x08AuJWfbQKyRZdEPEoyZvPoM9OMj/XsOkFm7abPucUCh0eSHBhMSkZnIZqgnjmafwf8sVLq94CLVFaeQZiav9EVE90hvVzENg3OXVomW3CI2hZl18P3YCwVZZEwrbfvB5TcYNPncX0I3ADDCJi6mmVyJksQBBwZ6aEvFQV2niCztpeVK5QBuDSXJxG1V3KgSUZnIRqnnh7K5wnnZc4DZcK9NC7X9tQIsc5wX5zJmSwR28T3A0wTTNPENAwyBQff8/H9gEx++z8jzw/I5sN9MMmYRaHk8fL0Esu5MFjsdCNm7aq3YjncfxOxTS7P5wHJ6CxEo9UTaI5Vvo7XfFVvC7Gh02qUbMGBACzLwPECIqaBZULZ8TBNkyAAf/POzIqoDY4X3vHYob6V69Nz2bo2YlY3fQIr+3Gqmz5BMgcI0Wj1rDq7AKCUMgjPpZnTWu/g7UHsZ+NjvUyMpnj+lfl1Q2OG4WMYkIpZ2z6PQdgTIgi4+Ug/fakoPYkIl9N5FnNlUvHIjhNk1p5lc3g4xcvTSzhu2JOpBqyN9uNUyeIBIepTT66zAaXUF4Ei4ebNglLqi0qpoabVTux5UzMZptO5DedfjABG+uNY1vaBxrYN4lGb3mSES+kcz56d4/J8noHeKHe+5gD33XW8rrNsqmlpepMRjoyEm0YTcWvbzAHXe7yBEPtRPYsB/jPgAW8ALgBHCRcIfAG4r/FVE3vNRp/0z+hZFjLlDe/vA5Zh0JOIUHJcCqXN15S4boDreeRLYJouiahFvugyv1zk9In68pbVbvpMLxc5MJTk/W8+uqNAJcdBC1G/egLNO4GDWutC5fYLSqn/EbjU8FqJjrc2qBwZSW64ryVfdCg7mweQueUiNx7oJR61idoBS7mNFwUEQG8yysRY78pGz2Tc5shIakepa9babVoaOQ5aiPrVE2heBG4EXqi5NgHoRlZIdL6pmQxf+hvN7GKxMqFv4Lo+PckIQ31xehMRMgWHTL5MyQnnYYJNZvNcN2A5X8YwDPpS9qaBxjSgVPaIR1cPs8WjVkvf5Gvnd6pk8YAQW6sn0DwK/E1lnmYKGAc+DHxRKfWx6p201l9obBVFp/nGE68weTWL5/krGyoBSkslSmWPC67PcF+MnngExy1t+VwBML9cYnwsRdkJGOyNsJBZHWxMYKgvSqbg8sNX0vSnYiSiNo7ro6cWuXVisO427HZCv5rUE8KeTL7kbrt4QIj9rp5A82bg5cr3N1eunQPeUvmC8H1DAk2Hq32TnTjUz6039K28yW71Blwte+7lNN4G65EDoOh4BEG4iz8WtRnqi1Mse2QL7rr7V/WnIti2RdFxMAwTywTPD9PPRGyLA4MJTNOgWPYplX1KUY/FbAnH8QkMyOS2DmYbtf+RpybxPJ/FTJnzl5Z55qVZ7n3LUe647eCWj107vzPcF5fjoIXYRj3Lm9+x3X2UUm+9vuqIZlubeyybL6+kXAFWldWmY7mSzvEXT17A84MNg0yV4wYYQDbvEI9Y3HZ0kCvp3JZ1SsQiLGXL5IoOtmWSittk8mFgskwD0zRwXJ9k3Mb1fDI5h4CAaMQkHrGYnM0xNZPZ8Zv9GT2L5/lcmssTsU16EjaFksdfPHmBg8OpbZ9Hjh0Qoj719Gh24q+Bvo0KlFIPAPcTzvOc1Fo/X7n+KuGS6epA+29prb9ZKbuT8GTPBPAq8GGt9cz1lHW77YaE1q2aSkbJF8orRyBvtKLq22cucu7SMvmiQ664ec+kKiDc92JgkM2Xt33M5fkcXuU4PdsyCCrP4fmQq6wsu+WGftLLJRYyJUYG4kTscGW+4/rEqG/VV3q5yGKmTMQ2V54nGbPJVP4dJIgI0ViNTpJpbFH2MHA34dLotT6otX5D5asaZEzgS8CvaK1PAN+lcvbNbsu63U72eNTuiq+qrpqqLVvKlnhxcoGXJhd59uwci5kiC9lwcn8rUdvAMg2ScZtk3Obc5QzeNpnwXC9cLBAQ7vx3vWs9JoMwmLx6OVM5ujkgqNzZcX0c12dirKeuBQHDfXEyBQfbuvbn6ng+PYmIrB4TogkaHWg2HVPRWj+htZ6q47lOA0Wt9ROV2w8CP3OdZV2ttrdiGAapeIRkzF7prcC1o5BrVVdNVcuWsiXOXVrGcX1c36dQ9sgWvU1XjtUyDYOIZRAEMLdUoFj2dtUW0wz/OMPcaAZFx6PouMQiFgvZMkv5sEdy85F+bNusa9XXaTWKZVZSztQErKHeuKweE6IJOiXt/5eVUj9QSn1WKTVQuTZBTe9Haz0HmJVMBLst62rp5SKO4/Hi5ALPnp3jxckFHMdb9Sm9dld8EARk8+WVHGHVssmZLBHLJJt3mF0sEvg7T87t+gFD/XGG+2JgGLsONEYQZgOIRSyGemP4foDj+qjxARJRCwM4NJTEsowd5zirGh/r5d63HAUgUyhjWwZHRlKYllHX8wghdqbRczS7cZfWekopFQP+CPg04bLpthoe7ll1e3S088fte1Mxnj8/RzIWoS8VxXF9zl/J8LrjIyv1Hx3tJe/4/NXfv0p6uYBlGhDAg9/4Ecl4hFTcYno2h+v5+EG4fyUWMXHLOws2vhfguB6Xl0o76gFtxgsgZocpYbJFl0jEYrg/STIVI5WIMLNQ4J/OzXPnyYN86J5bOHa4f8Pn2ex1e/9oL7fdPMb3fniZmYU8Y4NJ3nzy0KbP04n2wt/kbnVz26D727fWjgONUuo/Ag9prZ/b4m5bzdFsqDqcprUuKaU+C3yjUjRJmOam+vtHAF9rPa+U2lVZPfVKp7P4ldVVo6O9zM52fi6rYtHBdX0cK5z0cDwf1/UpFp2V+k/NZPjusxc5MBgnZsGPJxchCJcYT15ewlnTAQkCKG4zL1PLB+YWS5uPodahVHZJRk0KRY/eZIR4xOD5l+eI2CYHBxNkiy5LmSILCzl6Ius759u9bj0Rk/fcfmTVtb3wOsPe+ZvcjW5uG3Rv+0zTWPcBfaWsjuexgG8qpZ5XSv2WUuqGtXfQWtcVppVSKaVUf+VnA/hZoBrIzgAJpdTbKrc/Dnz1Osu6muOFQ0sR26RQdonYJmp8AKdmNr52HueVq1miEQvLMphZLK4LMlCZdKszajQqpXfUNim7AWODCY4d6mM576ysFHP9gN7k+jkoIUTn2XGg0Vr/OnAY+G3CxJovKKX+Vin1UaXUxmGshlLqk0qpi8ANwN8qpX4EHAAeU0r9AHgeOAH8cuX3+cBHCE/1PAu8vfK7d13W7Yb74ti2ya0Tg7zxllFunRhcN1Feu7IsV3DI5MpkC+6OzoNppYhl8O5/Ns7Jm4b52E/dhmkZZPJl7MqeGsf1OTSUlDxjQuwBRrDLgXSl1GuB/wKcBPLAnwC/p7Weblz12uJG4JW9OHRWuxmzNj1Kbdr7hx8/z9X5PJfnc0xe3XojZZVp7OxgskaKRy3eevIgqXiE++46ztRMhi9/6yXml0v4QUDEMujviTHYE+PAUJL77lp//t5eed12Q9q2d3Vr+2qGzo4R7l+8VlbPEyml+pRS/1Ip9XeE+1P+AbgLuA3IEm7YFG1STY+SikeYz5Q2PFslFjH44fk0l2bzdT13zAZ7+2NjGiYetVatJhsf6+Vdtx8BApIxm75klHzR5dylJY6MJFtXMSFE3epZDPBfgZ8kDDAPAg9rrUs15b8BLDW8hmJH1mYEeP3xIabn8nztu+dYzJQry4wDFjIlXC+oq4eibuij5Pq8ciXbtPqvdWgosS5ITs/lOX64j8VsmULJIxm3OTyS3NUxAUKI1qlnefP3gV/VWl/ZqFBr7SulDjSmWmInqsFlcibL3GKBwyNJDgwmuTKf49Ezk/gBlByfwAfLCofAyttnkFlnZqnEUrZMEITLClsxinbL+OC6VDDp5SIHBpMcHEqtXAuCQOZohOhw9STVfGAH96lvPEbsWu18TK4QnmB5aS5PImpz4WqGYtlfSesCYd6wDU5T3pGlbGnlOIBWBBnTZMPgUXsWzFK2xOX5PJl8eBxzPUk1hRCt1SmZAUSdapcpF8seyZhNxDa5PJ9nYbmMQTiBv7LW4zoihLvzbTQNYcCGqWCqmQuuzOc4N71MvuhiGjDYE1uX000I0Tkk0OxRtcuUEzGbbKHMUrbExdlcJUfZtftWsyHvBQZgmBungqkudljMlvGC8NiAW24Y4EBlmbPspxGiM3VCChqxge3S/dcOI/UmIlycyWIYBjHbpNDGel+PqG1iWQZjA4lNh8HGx3oZHUigxgcwjGuJKGQ/jRCdS3o0HWgn6f5Pq1HmFov8o57hh+fTlMoeZcfD8/0903tZKxaxiNoG779zYsv7bZWBWgjReSTQdKCdpPsHyJcclrJl/AAsy8Aw2Pa8mE5mWgYHBlMcHE5teb+1GahzRafuDM5CiNaRobMOlF4uMtQbW3UtGbOZnMny8OPnSS8XmV0skCs4jA0mWMqV8fyAYsndNmNy1DYo73b5WZPEbIODwylO3TxCruhse8plda6mdmjx7lOHZdWZEB1KAk0Hqp1/AVjOlXnp4iILmRJL2RITYz1k8mWWsmWG+w16EjYLmTK+HxBsM3DW3xNlKVvGMIy29n4Mwl6YacBAb5xjh8ITwHc61zI+1iuBRYg9QobOOlDt0FD1SOWlXJnBVJRS2eWZl+ZYzjm4XsDcYpFswQ0TTXoB7jbnjB0/1M+Nh/q4+UhfaxqzCcMAyzQYGUhw29FB+lJRQO4KawgAABgaSURBVOZahOhG0qPpQLVDQ8+enSMRswkCsGyDxawDRnhWSzWoFMqlrZ+wxo8vLOA6HsVWb45ZIxoxGR/rIRmLYFkGQRCsJAG9+9ThDR+z3Uo8IURnkh5Nhxof6+W+u44zcaCH1x0bor8nSibvYpoGjuNu23PZTKHkUHT86zr9shE8P2DiQC8DPdEtk4BW7WQlnhCiM0mPpsNV52sODSW5OJMjYhuUnN1Hid0GqEZz3TAL83y5tGGK/7VqV+IBK9+3WzgghNhes0cLJNB0uCMjSf7iyQt4fngGS9ndu/tkagXUNx9TXYm3nCtzKZ2jUHKJRy1SiWhzKypEl6vNm1g7WrDZ6MJuyNBZB5uayfDcuTRHRlL0JGwitoGx/cP2jHr2vgz3xZlZKPDy9BKO65OI2hRKHnOLBRk+E+I67HTf3vWQQNPBzuhZsrkyZ6eXmJ7L42Mw3Bfb/oF7RD2fmE6rUabnwhNBbcvA8cLFDIdHkpLjTIjrUJs3sarRKZ1k6KyD6ckFXp5eWpm4L5Zc5pfDn1t1Lkwz1dMtHx/rZbg/RqHoUSh7JGIWE2M99KWikuNMiOuwdt8eNH6bgfRoOtTUTIaXp5fw/Gvp/msDi2UZRG0Ts5vG0rZx9EAfA71REjGLQsnj8nyeqwt52XcjxHVoRUon6dF0oKmZDH/2nXN4a1L9m1wLNgOpKAvZ0qojmW0TvNozaDpcvStdjowkefL5y8SjNomoRb7oMr9c5PQJyXEmxG61IqWTBJoO9O0zF5lZWJ/svzaoLGZLqwIRhAeU7ZUOjgF1r3SZnstz0+F+FrIlCiWXZNzmyEiK6bk8d7A+cL37zhvpiUinXYjtNDulkwSaDnT+8jKJqE3EMnC8jbsnm23s3yOdGSyTuvfFpJeLjA0mODCUXLkWBAHp5eKGSzS//tjL3HPqkOyzEaLNJNC02UbDR2BQdFwiERPHW73DMhoxsU0olv1VPZy9pr8nVvdKl60mLTfa0BkYhmzoFKIDtCTQKKUeAO4HbgROaq2fr1w/ATwEDANp4KNa67PNKus0m22U6k3YnLucJ2pbJGPBSlDpSdgM9cXI5B0K5XK7q79rhgHHDvWSL7l1rXQ5rUZ55KlJIAxKtbnR/ubpqfVHKyQiXLyy3JxGCCF2rFUD2A8DdwMX1lx/EPiM1voE8Bngc00u6yibbZTKFh164hFM08AyTXpTUYb7YqjxAXwfIraJZeyV2Zj1UnGbsuszt1isa6VLddJyo9xoG566WXBkRZoQHaAlPRqt9RMASqmVa0qpMeB24D2VS18BPq1UZeyowWVad96uvs0OOCuWfV574yBXFgoUSuGekYODCV65kuXwSJJXL2eI2AZeebvTZ5pnt/t4DKAnESEetUhEwyBbz0qXzSYtN+rt+Bjcc+rQLmophGikds7RjAPTWmsPQGvtKaUuVa4bTSirK9AMD/esuj062vhx/olD/WTzZVLJa/m6svkyY4NJ+vsSHDnYv+q6PZdnoD+BPZsnEYtgmi65YuuzZFpGOPxlmgaWZVIseTsOOiP9MfpSMa4uFDkwlOQ3f/pUQ+o0OtrL4GCK7/3wMjMLecaGe3jzyUMcO9y//YP3qGb8TXaKbm4bdH/71pLFAJtIp7P4ldn20dFeZmcbn0/r1hv6eOSpSfKF8qo5h7tOHuC5c+lV12cXCxSLDo/940UCwgSbtm1hGt7KooBWZAtIRE1sywyHqQKIWAbRVISlnLOjxw/2xYEAz/NZzBQb+u/aEzF5z+1HVm4363XrBNK2vatb22eaxroP6CtlLa5LrSngiFLKAqh8P1y53oyyjrPZnMMdtx1cdX1xuciFKxmuLhQouz6OG5Av+WTz4Zu7VZmuacUwmm2ZGIZBxDYJgLLrY1km9g5SFCSiBgTguD5BEJCsWQgghOhebevRaK1nlFLPAR8CvlT5/mx1LqUZZZ1oszmH6vWpmQyf/drz5AoOlmWAF6z0YIIALIt1GzebxSA8sGx0IB7uys8UCTwI/GBlp2jUNvF9f90+n8GUzehQikLJJRGzGRtYvR9GCNG9WrW8+ZPATwMHgb9VSqW11q8FPg48pJT6N8AC8NGahzWjrGNtlo7ljJ4lVwqPb7ZMA9MwKDnhu3hAaw8yM4xwxVvR8Sg6Hql4hLIb/mwaBuOjCUquT9nx8f1woUIianH/24/z3Lk0yZi9aoiwkbmUhBCdywj2SmKs1rkReKUVczRVtftpat+I3/umCf7m6SmefWmGYjkMLoZhrLyJt8Nwb5ShvjjL+TKL2TKpuEUqEcMyAqZm83h+gAHEoyZ9yRhHD/VwcCjFaTXa1BP8NtKtY+EgbdvLurV9NXM0x4BXa8tkMUAHOKNn8b2AqZnsytDSYE+MM3qWiGXieP617M1t+mBQnYIpuwHpTBHPC4OdHxikYibnLmXwKoE5AAplH9sqE49YpJeLTc+lJIToXBJoOsDkTJbZhTzRSLi3JJsvc3kux9npJcDH89qbw8wgPGwslYiQiFpkCi6maRCPmLiuz7npDE4lyBiAaYLvQ9HxmZrNcfL4cBtrL4RoNwk0HSBfdFZWchXLLpmCix+A43gUSl7Lc5oZxrWOk21Smeg3SMRsiiUP1/Xw/DChpWWZuJUKmsbqDNOuF5AtODIXI8Q+J4GmAyRiFvmCi+NeW7JsGgZ+4OO3YagsFjExCXskGAaGEWAaAYuZEiUnDDIGYUCy7TUHsplQ3dFjAK+9cUiGzITY5yTQdICjB/qIRXJcmc+znHcwDYNoxMSrWcrcKqYBEdvCNMC0TBzHA9MkFrGIWAb5UrjMzaj0Xkrla8ve/ABsIxw6A+hLRnhHzQZKIcT+JKdCdYDTapRi2aPs+KTiEaIRE9fzNz2LpllMAwZ7o/hBQMnxGOoN08XYpkk8FibCrG7L9INwWK3a4TKARCzczBkQBpmffdct0psRQkiPplMsZctk887KMFTEMvE9l1ZtkzENGOiJcsNYDwOpGIZh4Hg+k1ezvObGQQC+/8LVVcNkrh8+zjLhyEgPp24eaenyZdF49R6vLcROSKBps+oemrLrE43AYs7Fb9FO/1q3nxjh3rceW/em8vDj58kVHV6+uIS/QQ/LD8AkHOq7767jraquaILNzkfa6nhtIXZChs7a7NtnLnLhyjJX0nnmM+0JMiZwdaHI3z0zzdTM6o1kp9Uo+ZLLzGIBd5OhvGQ0HDITe9tm5yOd6dwMTmKPkEDTRlMzGf7pfJqljNPWc2XsiIllwouTC3ztO+dXBZvxsV7ecNMwpS2OAggwJEFmF0gvF+s+XluInZChszaojoM/e3aWfNHFtlrfG4jYYGBWjhwwiUYsDMNgOV/mjJ5dNVQyPZcnFjUplDfubsWiNhNjG6cHF3vHcF+cXNGp63htIXZCejQtNjWT4c++c44fnk+TXirheT7FUmsPLzMN8P0wuMWjNrGoBYQLEBzXW/cJNr1cxDQ3/lOxTIN41JJNmV2gOkxaz/HaQuyEBJoW+/aZi8wsFIAw8WTtLvxmi1jhUJllGURsA6uSwKw3EX6CdTyfiG2t+wQ73BfHMMJDz0xj5UQAINxPc+9bjspkcRfY7HwkeW3F9ZKhsxY7f3kZE1jKlSmUvJbmMfN86E3Y+IQHmBVK4bCdaRiUHY9i2ePAYHLdJ9jTapTv/+gKmbxDMm7juD6u5xO1LV57bJA7bjvYohaIZpPkp6IZJNC0WNnxWc6V8AJwHL81p2KaBn4QEI1YHBxJcXg4RV8qytX5PHPLJcqOCxjcOjHIO24/su6NZnysl/vffpyvfecVckWHqG0y3B9ntD/BvW891oIWCCH2Mgk0LWaaYQp9w2hdTyYWNSk5PuNjKW6dGFy5PjaYoKcnxs+965ZV999o094dtx3k4HBKNvMJIeomgabFSmUvTDnZwvXMvg8x22Kod/XcS77kMja8erXYdpv2JLAIIeolgaZJNkvl4XoBpgGtTGPWl4py96mDXJzLkys6q07xfPPJQ6vuW7tpD1j5vnbJsxBC7JSsOmuCaq8gV3RW9QqmZjJEbLOlQQbglz/wOt5357ENVxQdO9y/6r6yaU8I0WjSo2mCrXoFPYkIC5lSS9L/WwaMDCRWeiI7GfqSTXtCiEaTHk0TbNUrGOiNEos095/dMCBqG/T3Rrnt6OD2D6ghm/aEEI0mgaYJhvvi5EvuqmvVXkG57G2ayqVRgiA8RrknXv/BY7JpTwjRaDJ01gSn1SiPPDUJsDLxPrdYxO3xeXl6uSV18AOYXSpyJZ2rO0jI6jIhRCNJoGmg2pVmUSs8ink+U6JUdrmczm2Zar/RDMKl1F9//BXZuS+EaKuOCDRKqVeBYuUL4Le01t9USt0JfA5IAK8CH9Zaz1Qes6uyZlm7/2RmocD0XI5EzGJ6NocBmKbRsk2aphmmnJlblNViQoj26qQ5mg9qrd9Q+fqmUsoEvgT8itb6BPBd4BMAuy1rpjN6Fs/zmZrJ8vSLM/z41QXKjsfV+QKlskfZ9Vt2OJhR+a8B+K3cGSqEEBvopECz1mmgqLV+onL7QeBnrrOsaS5cXebSbB7H9Sk5HhhQKHvkii6WZWAYBoWyu/0TNUAAeH5AAESbvMJNCCG20xFDZxVfVkoZwBPA7wATwIVqodZ6TillKqWGdlumtZ7faWWG16RmGR3denLc8w0iEZNUIspCpoRtGeSKLp4fYFWSWrZqfqbWiYmBbeu+XfleJm3bm7q5bdD97VurUwLNXVrrKaVUDPgj4NPA19tZoXQ6i1/ZVTk62svsbGbL+9tWmGofyvh+QL7oghFgGWHvwmvuiuZVDMK9NJZpEDHNLeu+k7btVdK2vamb2wbd2z7TNNZ9QF8pa3FdNqS1nqp8LwGfBd4KTAJHq/dRSo0AfqVXstuyppkY62GoN8ZSrkyu6OIHYU8mlbDxGxhkTANsa+v79CYjDPTGGB1MMLNYaNwvF0KIXWh7oFFKpZRS/ZWfDeBngeeAM0BCKfW2yl0/Dny18vNuy5rmyEiSqdkcjusDYeLM6lBZIwfMBnpj9KVim5YbBhwcTjLSHydqm6w+D1MIIVqv7YEGOAA8ppT6AfA8cAL4Za21D3wE+GOl1Fng7cBvA+y2rJleuLCIaYbDVZZpYpomEcvEa3BSs7edPMTrjw9jVY5UjkZMoraxEk5sKzzoxnF9imWPmw73NfT3CyFEvdo+R6O1Pg+8cZOyJ4GTjSxrlnOXlulLRolGLEplj/lMqZIrrPErzfpSUY4d6uX85Qy+F2CaEIsYOB4MpKLkSw4R2+LAYLLuFDRCCNFobQ803SPAqHQQY1GLod4Yi9ly439LEJAvuYwOJXnjiRH+6dw8i9kyAz1RTt00RMkJ5ARMIURHkUDTIMcP9aGnFsGwsS0Dx/NxfR/LALeBo2fzmRLDfXHuPnWY8bFe3nfnscY9uRBCNIEEmgZ55+kbWMiWyOQdlnMOS9kSGOA2eMXZv/yp1zTuCYUQogU6YTFAVxgf6+X+t9/EyePDQBDO1F9nT8aofJlG+NWTkM8FQoi9R965GqiaXv8ffnyFIICSs7vujGUCQbgByjDC82UCAn7yTeONrbAQQrSABJomWM6Vdx1keuMm7zg9QSxi8Ogzl8gXXZJxm3fdfljmY4QQe5IEmiZwdpHTzDTg+KFeRoeSK6vFJLAIIbqBBJoGefqFKzz6zDSzdR5uZhrQE7c5dqSPowf6ZEmyEKLrSKBpgKdfuMJXHzuH7wcsZna2d8Y04PBIipuP9GNZBql4hPvuOt7kmgohROtJoGmAR5+Zpuz4LOedHT8mEbO4+Ug/fakoQRBushRCiG4ky5sbYG6pWFeQidoGI/0J+lJRAPIll+G+eLOqJ4QQbSU9mgYo1nFy5qHhBK7nU3b9lXQy+ZLL3acON7GGQgjRPhJoGiBf9La9jwnc/YbD9KWiXJ3Ps5AtrUsnI4QQ3UgCTQvEIiYnbuinNxkhV3QwLYOfe88JCS5CiH1BAk0LfOz9tzI9l1/Jqiw9GCHEfiKBpslG+qLccdtB7mh3RYQQok1k1VmT/doHT7W7CkII0VYSaBrgC7/9zg2v/7uP3SFDZEKIfU+Gzhpks2AjhBD7nfRohBBCNJUEGiGEEE0lgUYIIURTSaARQgjRVLIYYD0LwmOUa6293U2kbXuTtG3v6sb21bTJWltmBEH9p0F2ubcBj7e7EkIIsUfdBTxRe0ECzXox4A7gMrB9tkwhhBAQ9mQOAU8DpdoCCTRCCCGaShYDCCGEaCoJNEIIIZpKAo0QQoimkkAjhBCiqSTQCCGEaCoJNEIIIZpKAo0QQoimkhQ0W1BKnQAeAoaBNPBRrfXZ9tZqNaXUA8D9wI3ASa3185Xrm9a9GWVNatsw8EXgJqAMnAV+UWs9q5S6E/gckABeBT6stZ6pPK7hZU1q38PAMcAHssCvaa2f64bXrqaNvwf8Wyp/m13yur0KFCtfAL+ltf5mN7StWaRHs7UHgc9orU8AnyF8wTvNw8DdwIU117eqezPKmiEA/kBrrbTWJ4FzwCeUUibwJeBXKnX5LvAJgGaUNdHPa61Paa3fCDwAfKFyvRteO5RStwN3Uvnb7KLXDeCDWus3VL6+2WVtazgJNJtQSo0BtwNfqVz6CnC7Umq0fbVaT2v9hNZ6qvbaVnVvRlkT2zavtX6s5tL3gaPAaaCota7mU3oQ+JnKz80oawqt9VLNzX7A75bXTikVIwxov1RzuStet010c9uumwSazY0D01prD6Dy/VLleqfbqu7NKGu6yqe7XwK+AUxQ04PTWs8BplJqqEllzWzX55VSk8C/B36e7nnt/g/gS1rrV2uudc3rBnxZKfUDpdRnlVIDTap/u9rWcBJoxF7xKcJ5jE+3uyKNpLX+Ba31BPA7wB+2uz6NoJR6M/DPgM+2uy5NcpfW+hRh8l2DLvubbAYJNJubAo4opSyAyvfDleudbqu6N6OsqSoLHm4B/oXW2gcmCYfQquUjgK+1nm9SWdNprb8IvAO4yN5/7d4O3Aa8Upk4vwH4JnAzXfC6VYeqtdYlwmD61ibVv61/k40kgWYTlZUdzwEfqlz6EPCs1nq2fbXama3q3oyyZrZFKfX7hGPV91X+xwY4AySUUm+r3P448NUmljWcUqpHKTVec/teYB7Y86+d1voTWuvDWusbtdY3EgbPnyTsse311y2llOqv/GwAP0v4b7vn/yabSY4J2IJS6lbCJaGDwALhklDd3lqtppT6JPDTwEFgDkhrrV+7Vd2bUdaktr0WeB54CShULr+itf6AUuothCun4lxb9nm18riGlzWhbQeAPwdShOcezQO/qbV+phteuzVtfRX45zpc3rzXX7fjwJ8Rnr1iAT8Gfl1rfXmvt62ZJNAIIYRoKhk6E0II0VQSaIQQQjSVBBohhBBNJYFGCCFEU0mgEUII0VQSaIQQQjSVBBohOpRS6jGl1C804HnuUUpdbESdhNgNCTRCCCGaSjZsCtECSqn/BbhTa31/zbVPAoHW+l9tcP9/D/w24AAu8P9orX+1ssP/U4RpeWaB39Va/2nlMe8nPNdmHFgG/iPwx4QZI2JAvvL0J7TWl5rSUCE2ID0aIVrjS8B7KynlUUrZhHmy/t+N7qy1/t+Ax4Ff1Vr3VIJMCvgW8F+AscrjP6uUek3lYf+J8ATSXuB1wLe11jngfcClyvP0SJARrSaBRogW0FpfJjwh8b+vXHovMKe1PlPH0/xz4FWt9X/WWrta62cJ825Vn9MBXqOU6tNaL2itn2lU/YW4HhJohGidh4APV37+MPDFOh9/FPhvlFKL1S/g5wgTqgLcD7wfuKCU+k7lXBgh2s5udwWE2EceBv5YKfU6wt7J/7rN/ddOoE4B39Fav2ejO2utnwb+O6VUBPhV4E8J52tkIla0lQQaIVpEa11USv1XwjmWp7TWk9s85CpwvOb2XwKfUEp9BPiTyrU3EJ48eo5wCO0vtdZLSqllwK95nmGlVL/WeqlBzRFix2ToTIjWegg4yc6Gzf4v4INKqQWl1Ce11hngJwgXAVwCrgD/J+GKMoCPAK9WgszHCYfV0Fq/CHwFOF8ZcjvcyAYJsR1Z3ixECymlJoAXgYNa6+V210eIVpAejRAtopQygd8A/kSCjNhPZI5GiBao7IG5ClwgXNpcvZ7d5CHv01o/3oq6CdFsMnQmhBCiqWToTAghRFNJoBFCCNFUEmiEEEI0lQQaIYQQTSWBRgghRFNJoBFCCNFU/z8ZA+eMuS5TGQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZt2y3Y2cziP",
        "outputId": "be594f54-0ba9-4039-ff71-6b5f1e142aa8"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1177.1732148851902\n",
            "MSE: 4346286.230330118\n",
            "RMSE: 2084.774863223873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics.r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_bKS_ric2GK",
        "outputId": "20652755-df72-48ff-be89-98e61a5c6935"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7984289547810984"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hyperparameter Tuning\n",
        "We'll use RandomizedCVSearch Method for hyperparameter tuning"
      ],
      "metadata": {
        "id": "_o2ngQzChGTK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "metadata": {
        "id": "pe8fbcCoc8E9"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Randomized Search CV\n",
        "\n",
        "# Number of trees in random forest\n",
        "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
        "# Number of features to consider at every split\n",
        "max_features = ['auto', 'sqrt']\n",
        "# Maximum number of levels in tree\n",
        "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
        "# Minimum number of samples required to split a node\n",
        "min_samples_split = [2, 5, 10, 15, 100]\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [1, 2, 5, 10]"
      ],
      "metadata": {
        "id": "9Dc1xStLhS7t"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the random grid\n",
        "\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf}"
      ],
      "metadata": {
        "id": "0fWdUwM4hU3z"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
      ],
      "metadata": {
        "id": "BtVvZPy-hXO3"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSIHffdUha5-",
        "outputId": "2f9ba2a0-b7b0-466e-aa7e-79282f71fc9d"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.2s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.1s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   9.3s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.5s\n",
            "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   3.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   5.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   5.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   5.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   6.0s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   5.9s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   3.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   3.5s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   3.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   3.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   3.4s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   6.3s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   6.2s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   6.3s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   6.1s\n",
            "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   7.2s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=  10.2s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   9.6s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   9.6s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   9.5s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   9.6s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   9.1s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   8.9s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   8.7s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   8.7s\n",
            "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   9.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.9s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.5s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.5s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.5s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.5s\n",
            "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  11.6s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  11.5s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  11.3s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  11.4s\n",
            "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=  11.5s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
              "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
              "                                        'min_samples_split': [2, 5, 10, 15,\n",
              "                                                              100],\n",
              "                                        'n_estimators': [100, 200, 300, 400,\n",
              "                                                         500, 600, 700, 800,\n",
              "                                                         900, 1000, 1100,\n",
              "                                                         1200]},\n",
              "                   random_state=42, scoring='neg_mean_squared_error',\n",
              "                   verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N2WrGuQFiLEu",
        "outputId": "94967c0f-a336-488e-a110-ea0fa915a966"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 20,\n",
              " 'max_features': 'auto',\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 15,\n",
              " 'n_estimators': 700}"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = rf_random.predict(X_test)"
      ],
      "metadata": {
        "id": "ru9061KrnP2y"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8,8))\n",
        "sns.distplot(y_test-prediction)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "FXZ8kXNBnToS",
        "outputId": "4b50ef58-460a-4acc-958e-2bd8b2e09a56"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `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",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8,8))\n",
        "plt.scatter(y_test, prediction, alpha = 0.5)\n",
        "plt.xlabel(\"y_test\")\n",
        "plt.ylabel(\"y_pred\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "E-1i4FVVnXjM",
        "outputId": "aa0c6f34-f37d-4e6f-f971-81dd5c265bdf"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffg8VD-OnbOL",
        "outputId": "8fa93311-9caf-46fd-e407-59e10c60e8e0"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1164.0478670826647\n",
            "MSE: 4046544.8828179375\n",
            "RMSE: 2011.6025658210763\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save Model using Pickle for further use in web app"
      ],
      "metadata": {
        "id": "8OYMMLB-niha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "# open a file, where you ant to store the data\n",
        "file = open('flight_rf.pkl', 'wb')\n",
        "\n",
        "# dump information to that file\n",
        "pickle.dump(reg_rf, file)"
      ],
      "metadata": {
        "id": "isYmjx8onhda"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = open('flight_rf.pkl','rb')\n",
        "forest = pickle.load(model)"
      ],
      "metadata": {
        "id": "sNxU7TUynr0d"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_prediction = forest.predict(X_test)"
      ],
      "metadata": {
        "id": "O-sKY6sCnu57"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics.r2_score(y_test, y_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uufjO17XnxqB",
        "outputId": "17a402d6-6d9e-4a37-847e-6db8c8515e29"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7984289547810984"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MKK638hBn16H"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}