Flight_Price_Prediction.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Flight_Price_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFdzQ7cSJeyb"
      },
      "source": [
        "##Importing the Datasets\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9AtctmgJ6q5"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "training_set = pd.read_excel(\"Data_Train.xlsx\")\n",
        "test_set = pd.read_excel(\"Test_set.xlsx\")\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yp5ybsWMKNQQ"
      },
      "source": [
        "##Cleaning and Preprocessing the Datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weWTV59eXdnR"
      },
      "source": [
        "###Missing Fields/ NaNs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIYSEkpzKU2w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aff5d095-9710-4a05-bdf0-271aaae8d3f2"
      },
      "source": [
        "# Dealing with the Missing Value\n",
        "\n",
        "print(\"Original Length of Training Set : \", len(training_set))\n",
        "\n",
        "training_set = training_set.dropna()\n",
        "\n",
        "print(\"Length of Training Set after dropping NaN: \", len(training_set))\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Length of Training Set :  10683\n",
            "Length of Training Set after dropping NaN:  10682\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajkcXR_MYGSt"
      },
      "source": [
        "##Cleaning Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kj2GMiBXj-X"
      },
      "source": [
        "###Date_of_Journey"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrgBP6PbXre3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2a3290f-cc1e-4ee6-c122-5de5b2755f65"
      },
      "source": [
        "#Cleaning Journey Date \n",
        "\n",
        "#Training Set\n",
        "\n",
        "training_set['Journey_Day'] = pd.to_datetime(training_set.Date_of_Journey, format='%d/%m/%Y').dt.day\n",
        "\n",
        "training_set['Journey_Month'] = pd.to_datetime(training_set.Date_of_Journey, format='%d/%m/%Y').dt.month\n",
        "\n",
        "# Test Set\n",
        "\n",
        "test_set['Journey_Day'] = pd.to_datetime(test_set.Date_of_Journey, format='%d/%m/%Y').dt.day\n",
        "\n",
        "test_set['Journey_Month'] = pd.to_datetime(test_set.Date_of_Journey, format='%d/%m/%Y').dt.month\n",
        "\n",
        "# Compare the dates and delete the original date feature\n",
        "\n",
        "training_set.drop(labels = 'Date_of_Journey', axis = 1, inplace = True)\n",
        "\n",
        "test_set.drop(labels = 'Date_of_Journey', axis = 1, inplace = True)\n",
        "\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQ6WvGUlXzdn"
      },
      "source": [
        "###Duration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4S7ga_N9Xyqu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7da9e4e-393c-4111-b29f-04ca4105ec46"
      },
      "source": [
        "# Cleaning Duration\n",
        "\n",
        "# Training Set\n",
        "\n",
        "duration = list(training_set['Duration'])\n",
        "\n",
        "for i in range(len(duration)) :\n",
        "    if len(duration[i].split()) != 2:\n",
        "        if 'h' in duration[i] :\n",
        "            duration[i] = duration[i].strip() + ' 0m'\n",
        "        elif 'm' in duration[i] :\n",
        "            duration[i] = '0h {}'.format(duration[i].strip())\n",
        "\n",
        "dur_hours = []\n",
        "dur_minutes = []  \n",
        "\n",
        "for i in range(len(duration)) :\n",
        "    dur_hours.append(int(duration[i].split()[0][:-1]))\n",
        "    dur_minutes.append(int(duration[i].split()[1][:-1]))\n",
        "    \n",
        "training_set['Duration_hours'] = dur_hours\n",
        "training_set['Duration_minutes'] =dur_minutes\n",
        "\n",
        "training_set.drop(labels = 'Duration', axis = 1, inplace = True)\n",
        "\n",
        "\n",
        "# Test Set\n",
        "\n",
        "durationT = list(test_set['Duration'])\n",
        "\n",
        "for i in range(len(durationT)) :\n",
        "    if len(durationT[i].split()) != 2:\n",
        "        if 'h' in durationT[i] :\n",
        "            durationT[i] = durationT[i].strip() + ' 0m'\n",
        "        elif 'm' in durationT[i] :\n",
        "            durationT[i] = '0h {}'.format(durationT[i].strip())\n",
        "            \n",
        "dur_hours = []\n",
        "dur_minutes = []  \n",
        "\n",
        "for i in range(len(durationT)) :\n",
        "    dur_hours.append(int(durationT[i].split()[0][:-1]))\n",
        "    dur_minutes.append(int(durationT[i].split()[1][:-1]))\n",
        "  \n",
        "    \n",
        "test_set['Duration_hours'] = dur_hours\n",
        "test_set['Duration_minutes'] = dur_minutes\n",
        "\n",
        "test_set.drop(labels = 'Duration', axis = 1, inplace = True)\n",
        "\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ST_Lqa9pX-Dl"
      },
      "source": [
        "###Departure and Arrival Times"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZPmrUFVX9Dn"
      },
      "source": [
        "#Cleaning Departure and Arrival Times\n",
        "\n",
        "# Training Set\n",
        "\n",
        "\n",
        "training_set['Depart_Time_Hour'] = pd.to_datetime(training_set.Dep_Time).dt.hour\n",
        "training_set['Depart_Time_Minutes'] = pd.to_datetime(training_set.Dep_Time).dt.minute\n",
        "\n",
        "training_set.drop(labels = 'Dep_Time', axis = 1, inplace = True)\n",
        "\n",
        "\n",
        "training_set['Arr_Time_Hour'] = pd.to_datetime(training_set.Arrival_Time).dt.hour\n",
        "training_set['Arr_Time_Minutes'] = pd.to_datetime(training_set.Arrival_Time).dt.minute\n",
        "\n",
        "training_set.drop(labels = 'Arrival_Time', axis = 1, inplace = True)\n",
        "\n",
        "\n",
        "# Test Set\n",
        "\n",
        "\n",
        "test_set['Depart_Time_Hour'] = pd.to_datetime(test_set.Dep_Time).dt.hour\n",
        "test_set['Depart_Time_Minutes'] = pd.to_datetime(test_set.Dep_Time).dt.minute\n",
        "\n",
        "\n",
        "test_set.drop(labels = 'Dep_Time', axis = 1, inplace = True)\n",
        "\n",
        "test_set['Arr_Time_Hour'] = pd.to_datetime(test_set.Arrival_Time).dt.hour\n",
        "test_set['Arr_Time_Minutes'] = pd.to_datetime(test_set.Arrival_Time).dt.minute\n",
        "\n",
        "test_set.drop(labels = 'Arrival_Time', axis = 1, inplace = True)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tpml9l8wggm5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "1b988754-28bf-4fdc-a09e-83aebda8bf0e"
      },
      "source": [
        "training_set.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>Duration_hours</th>\n",
              "      <th>Duration_minutes</th>\n",
              "      <th>Depart_Time_Hour</th>\n",
              "      <th>Depart_Time_Minutes</th>\n",
              "      <th>Arr_Time_Hour</th>\n",
              "      <th>Arr_Time_Minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>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>2</td>\n",
              "      <td>50</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>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>19</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>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>5</td>\n",
              "      <td>25</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>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>4</td>\n",
              "      <td>45</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline    Source  ... Arr_Time_Hour Arr_Time_Minutes\n",
              "0       IndiGo  Banglore  ...             1               10\n",
              "1    Air India   Kolkata  ...            13               15\n",
              "2  Jet Airways     Delhi  ...             4               25\n",
              "3       IndiGo   Kolkata  ...            23               30\n",
              "4       IndiGo  Banglore  ...            21               35\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzomrtWqhLCc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "9f54b7d6-8605-42fd-cfd7-7c835981a14b"
      },
      "source": [
        "test_set.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Journey_Day</th>\n",
              "      <th>Journey_Month</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_minutes</th>\n",
              "      <th>Depart_Time_Hour</th>\n",
              "      <th>Depart_Time_Minutes</th>\n",
              "      <th>Arr_Time_Hour</th>\n",
              "      <th>Arr_Time_Minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>55</td>\n",
              "      <td>17</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → MAA → BLR</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>20</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 → BOM → COK</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>45</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Multiple carriers</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Air Asia</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>24</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>23</td>\n",
              "      <td>55</td>\n",
              "      <td>2</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Airline    Source  ... Arr_Time_Hour Arr_Time_Minutes\n",
              "0        Jet Airways     Delhi  ...             4               25\n",
              "1             IndiGo   Kolkata  ...            10               20\n",
              "2        Jet Airways     Delhi  ...            19                0\n",
              "3  Multiple carriers     Delhi  ...            21                0\n",
              "4           Air Asia  Banglore  ...             2               45\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5L57iDANKaO4"
      },
      "source": [
        "##Preprocessing the Datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pg5zaIgzYOPV"
      },
      "source": [
        "###Classifying the Dependent and Independent Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVLJz0uGYVdO"
      },
      "source": [
        "# Dependent Variable\n",
        "Y_train = training_set.iloc[:,6].values  # 6 is the index of \"Price\" in the Training Set \n",
        "\n",
        "# Independent Variables\n",
        "X_train = training_set.iloc[:,training_set.columns != 'Price'].values # selects all columns except \"Price\"\n",
        "\n",
        "# Independent Variables for Test Set\n",
        "X_test = test_set.iloc[:,:].values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NW8IWSglYbL8"
      },
      "source": [
        "###Encoding Categorical Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZB1jwM-UYaGV"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le1 = LabelEncoder()\n",
        "le2 = LabelEncoder()\n",
        "\n",
        "# Training Set    \n",
        "\n",
        "X_train[:,0] = le1.fit_transform(X_train[:,0])\n",
        "\n",
        "X_train[:,1] = le1.fit_transform(X_train[:,1])\n",
        "\n",
        "X_train[:,2] = le1.fit_transform(X_train[:,2])\n",
        "\n",
        "X_train[:,3] = le1.fit_transform(X_train[:,3])\n",
        "\n",
        "X_train[:,4] = le1.fit_transform(X_train[:,4])\n",
        "\n",
        "X_train[:,5] = le1.fit_transform(X_train[:,5])\n",
        "\n",
        "# Test Set\n",
        "\n",
        "\n",
        "X_test[:,0] = le2.fit_transform(X_test[:,0])\n",
        "\n",
        "X_test[:,1] = le2.fit_transform(X_test[:,1])\n",
        "\n",
        "X_test[:,2] = le2.fit_transform(X_test[:,2])\n",
        "\n",
        "X_test[:,3] = le2.fit_transform(X_test[:,3])\n",
        "\n",
        "X_test[:,4] = le2.fit_transform(X_test[:,4])\n",
        "\n",
        "X_test[:,5] = le2.fit_transform(X_test[:,5])\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSss4r1niWTB"
      },
      "source": [
        "###Data After Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HdXl9v1iIF4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7b044e8-4190-4da1-fcfe-8eadbd4cf070"
      },
      "source": [
        "print(pd.DataFrame(X_train).head())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  0  1  2    3  4  5   6  7   8   9   10  11  12  13\n",
            "0  3  0  5   18  4  8  24  3   2  50  22  20   1  10\n",
            "1  1  3  0   84  1  8   1  5   7  25   5  50  13  15\n",
            "2  4  2  1  118  1  8   9  6  19   0   9  25   4  25\n",
            "3  3  3  0   91  0  8  12  5   5  25  18   5  23  30\n",
            "4  3  0  5   29  0  8   1  3   4  45  16  50  21  35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPDG1PAsYh0O"
      },
      "source": [
        "###Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPmGO6mAKiMa"
      },
      "source": [
        "\n",
        "# Feature Scaling\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc_X = StandardScaler()\n",
        "\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "\n",
        "X_test = sc_X.transform(X_test)\n",
        "\n",
        "#sc_y = StandardScaler()\n",
        "\n",
        "Y_train = Y_train.reshape((len(Y_train), 1)) \n",
        "\n",
        "Y_train = sc_X.fit_transform(Y_train)\n",
        "\n",
        "Y_train = Y_train.ravel()\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfxlQYQbikSw"
      },
      "source": [
        "###Data After Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Qk3rowvindK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36228c47-658b-42ee-84b6-2518a04426d0"
      },
      "source": [
        "print(pd.DataFrame(X_train).head()) # Xtrain after scaling"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         0         1         2   ...        11        12        13\n",
            "0 -0.410805 -1.658359  2.416534  ... -0.234950 -1.800436 -0.890014\n",
            "1 -1.261152  0.890014 -0.973812  ...  1.363607 -0.050909 -0.587094\n",
            "2  0.014369  0.040556 -0.295743  ...  0.031476 -1.363054  0.018745\n",
            "3 -0.410805  0.890014 -0.973812  ... -1.034229  1.407030  0.321664\n",
            "4 -0.410805 -1.658359  2.416534  ...  1.363607  1.115442  0.624584\n",
            "\n",
            "[5 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9fGdPS7i23I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "580043aa-c128-40b3-c065-d6c768240cdb"
      },
      "source": [
        "print(pd.DataFrame(Y_train).head())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          0\n",
            "0 -1.125535\n",
            "1 -0.309068\n",
            "2  1.039783\n",
            "3 -0.622209\n",
            "4  0.914006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_giRsy4KjbZ"
      },
      "source": [
        "## Modelling with Support Vector Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naviX4YjJXTd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "e574e9f1-952c-4579-a61d-8a8f4c2acd84"
      },
      "source": [
        "\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "svr = SVR(kernel = \"rbf\")\n",
        "\n",
        "svr.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred = sc_X.inverse_transform(svr.predict(X_test))\n",
        "\n",
        "\n",
        "pd.DataFrame(Y_pred, columns = ['Price']).to_excel(\"Final_Pred.xlsx\", index = False)\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-2fca7386fc4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#Y_pred = sc_X.inverse_transform(svr.predict(X_test))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mY_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msvr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Final_Pred.xlsx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X, copy)\u001b[0m\n\u001b[1;32m   1020\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m             \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"allow-nan\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m         )\n\u001b[1;32m   1024\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    763\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m                     \u001b[0;34m\"if it contains a single sample.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    766\u001b[0m                 )\n\u001b[1;32m    767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[-0.84602169 -0.12219494  0.93752291 ... -0.33881059  0.11662657\n  0.36625476].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGtFHyIXjP6v"
      },
      "source": [
        "print(pd.DataFrame(Y_pred).head())"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}