Flight-Delay-Prediction-without-twitter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "source": [
    "#### Pooja Ganesh (pganesh4) \n",
    "#### Srushti Manjunath (srushti5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset has 45 features and target variable - A_delay, which represents delay in aircraft of that sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = pd.read_csv(\"Final.csv\") \n",
    "extracted_col = df_temp[\"Date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"fit.csv\") \n",
    "df=df.join(extracted_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['D_Airport Rating', 'D_Airport On Time Rating',\n",
       "       'D_Airport Service Rating', 'A_Airport Rating',\n",
       "       'A_Airport On Time Rating', 'A_Airport Service Rating', 'D_Delay',\n",
       "       'Duration', 'A_Delay', 'C_Rating', 'C_Market Share', 'C_Load Factor',\n",
       "       'C_On Time Performance Rating', 'D_DewPointC', 'D_WindGustKmph',\n",
       "       'D_cloudcover', 'D_humidity', 'D_precipMM', 'D_pressure', 'D_tempC',\n",
       "       'D_visibility', 'D_winddirDegree', 'D_windspeedKmph', 'D_Time',\n",
       "       'A_DewPointC', 'A_WindGustKmph', 'A_cloudcover', 'A_humidity',\n",
       "       'A_precipMM', 'A_pressure', 'A_tempC', 'A_visibility',\n",
       "       'A_winddirDegree', 'A_windspeedKmph', 'A_Time', 'BLR-BOM', 'BLR-DEL',\n",
       "       'BOM-DEL', 'CCU-DEL', 'DEL-HYD', 'Air Asia', 'Air India', 'Go Air',\n",
       "       'Indigo', 'Spicejet', 'Vistara', 'Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x279728e44f0>,\n",
       "  <matplotlib.lines.Line2D at 0x279728e4850>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x279728e4bb0>,\n",
       "  <matplotlib.lines.Line2D at 0x279728e4f10>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x279728e4160>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x279728ee2b0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x279728ee610>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df['A_Delay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '18-09-2019',\n",
       " '19-09-2019',\n",
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       " '30-09-2019',\n",
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       " '02-10-2019',\n",
       " '03-10-2019',\n",
       " '04-10-2019',\n",
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       " '06-10-2019',\n",
       " '08-10-2019',\n",
       " '09-10-2019',\n",
       " '10-10-2019',\n",
       " '11-10-2019',\n",
       " '12-10-2019',\n",
       " '13-10-2019',\n",
       " '14-10-2019',\n",
       " '15-10-2019',\n",
       " '16-10-2019',\n",
       " '17-10-2019',\n",
       " '18-10-2019',\n",
       " '19-10-2019',\n",
       " '20-10-2019',\n",
       " '21-10-2019',\n",
       " '22-10-2019',\n",
       " '23-10-2019',\n",
       " '24-10-2019',\n",
       " '25-10-2019',\n",
       " '26-10-2019',\n",
       " '27-10-2019',\n",
       " '28-10-2019',\n",
       " '29-10-2019',\n",
       " '30-10-2019',\n",
       " '31-10-2019',\n",
       " '01-11-2019',\n",
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       " '26-11-2019',\n",
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       " '28-11-2019',\n",
       " '29-11-2019',\n",
       " '30-11-2019',\n",
       " '01-12-2019',\n",
       " '02-12-2019',\n",
       " '03-12-2019',\n",
       " '04-12-2019',\n",
       " '05-12-2019',\n",
       " '06-12-2019',\n",
       " '07-12-2019',\n",
       " '08-12-2019',\n",
       " '09-12-2019',\n",
       " '10-12-2019',\n",
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       " '14-12-2019',\n",
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       " '16-12-2019',\n",
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       " '26-12-2019',\n",
       " '27-12-2019',\n",
       " '28-12-2019',\n",
       " '29-12-2019',\n",
       " '30-12-2019',\n",
       " '31-12-2019',\n",
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       " '13-01-2020',\n",
       " '14-01-2020',\n",
       " '15-01-2020',\n",
       " '16-01-2020',\n",
       " '17-01-2020',\n",
       " '18-01-2020',\n",
       " '19-01-2020',\n",
       " '20-01-2020',\n",
       " '21-01-2020',\n",
       " '22-01-2020',\n",
       " '23-01-2020',\n",
       " '24-01-2020',\n",
       " '25-01-2020',\n",
       " '26-01-2020',\n",
       " '27-01-2020',\n",
       " '01-06-2018',\n",
       " '02-06-2018',\n",
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       " '07-06-2018',\n",
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       " '11-06-2018',\n",
       " '12-06-2018',\n",
       " '13-06-2018',\n",
       " '14-06-2018',\n",
       " '15-06-2018',\n",
       " '16-06-2018',\n",
       " '17-06-2018',\n",
       " '18-06-2018',\n",
       " '19-06-2018',\n",
       " '20-06-2018',\n",
       " '21-06-2018',\n",
       " '22-06-2018',\n",
       " '23-06-2018',\n",
       " '24-06-2018',\n",
       " '25-06-2018',\n",
       " '26-06-2018',\n",
       " '27-06-2018',\n",
       " '28-06-2018',\n",
       " '29-06-2018',\n",
       " '30-06-2018',\n",
       " '01-07-2018',\n",
       " '02-07-2018',\n",
       " '03-07-2018',\n",
       " '05-07-2018',\n",
       " '06-07-2018',\n",
       " '07-07-2018',\n",
       " '08-07-2018',\n",
       " '09-07-2018',\n",
       " '10-07-2018',\n",
       " '11-07-2018',\n",
       " '12-07-2018',\n",
       " '13-07-2018',\n",
       " '14-07-2018',\n",
       " '15-07-2018',\n",
       " '16-07-2018',\n",
       " '17-07-2018',\n",
       " '18-07-2018',\n",
       " '19-07-2018',\n",
       " '20-07-2018',\n",
       " '21-07-2018',\n",
       " '22-07-2018',\n",
       " '23-07-2018',\n",
       " '24-07-2018',\n",
       " '25-07-2018',\n",
       " '26-07-2018',\n",
       " '27-07-2018',\n",
       " '28-07-2018',\n",
       " '29-07-2018',\n",
       " '30-07-2018',\n",
       " '31-07-2018',\n",
       " '01-08-2018',\n",
       " ...]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = df['Date'].tolist()\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop unnecessary columns in Indigo Data later "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing only Indigo flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indigo=df[df['Indigo'] == 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D_Airport Rating</th>\n",
       "      <th>D_Airport On Time Rating</th>\n",
       "      <th>D_Airport Service Rating</th>\n",
       "      <th>A_Airport Rating</th>\n",
       "      <th>A_Airport On Time Rating</th>\n",
       "      <th>A_Airport Service Rating</th>\n",
       "      <th>D_Delay</th>\n",
       "      <th>Duration</th>\n",
       "      <th>A_Delay</th>\n",
       "      <th>C_Rating</th>\n",
       "      <th>...</th>\n",
       "      <th>BOM-DEL</th>\n",
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       "      <th>Air Asia</th>\n",
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       "      <th>Indigo</th>\n",
       "      <th>Spicejet</th>\n",
       "      <th>Vistara</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>8002</th>\n",
       "      <td>7.50</td>\n",
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       "      <td>9.2</td>\n",
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       "      <td>7.50</td>\n",
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       "      <td>6.2</td>\n",
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       "      <th>8004</th>\n",
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       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-01-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8005</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-4.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>31-01-2018</td>\n",
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       "    <tr>\n",
       "      <th>8006</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-22.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>01-02-2018</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10295</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>101.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23-01-2020</td>\n",
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       "    <tr>\n",
       "      <th>10296</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>-9.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24-01-2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10297</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25-01-2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10298</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26-01-2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10299</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27-01-2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2298 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       D_Airport Rating  D_Airport On Time Rating  D_Airport Service Rating  \\\n",
       "8002               7.50                       8.6                       9.2   \n",
       "8003               7.50                       8.6                       9.2   \n",
       "8004               7.50                       8.6                       9.2   \n",
       "8005               7.50                       8.6                       9.2   \n",
       "8006               7.50                       8.6                       9.2   \n",
       "...                 ...                       ...                       ...   \n",
       "10295              7.99                       7.3                       9.1   \n",
       "10296              7.99                       7.3                       9.1   \n",
       "10297              7.99                       7.3                       9.1   \n",
       "10298              7.99                       7.3                       9.1   \n",
       "10299              7.99                       7.3                       9.1   \n",
       "\n",
       "       A_Airport Rating  A_Airport On Time Rating  A_Airport Service Rating  \\\n",
       "8002               7.29                       6.2                       9.0   \n",
       "8003               7.29                       6.2                       9.0   \n",
       "8004               7.29                       6.2                       9.0   \n",
       "8005               7.29                       6.2                       9.0   \n",
       "8006               7.29                       6.2                       9.0   \n",
       "...                 ...                       ...                       ...   \n",
       "10295              8.27                       7.8                       9.0   \n",
       "10296              8.27                       7.8                       9.0   \n",
       "10297              8.27                       7.8                       9.0   \n",
       "10298              8.27                       7.8                       9.0   \n",
       "10299              8.27                       7.8                       9.0   \n",
       "\n",
       "       D_Delay  Duration  A_Delay  C_Rating  ...  BOM-DEL  CCU-DEL  DEL-HYD  \\\n",
       "8002       5.0      81.0    -19.0       7.9  ...      0.0      0.0      0.0   \n",
       "8003       3.0      83.0    -19.0       7.9  ...      0.0      0.0      0.0   \n",
       "8004       7.0      75.0    -22.0       7.9  ...      0.0      0.0      0.0   \n",
       "8005      -4.0      76.0    -33.0       7.9  ...      0.0      0.0      0.0   \n",
       "8006       2.0      81.0    -22.0       7.9  ...      0.0      0.0      0.0   \n",
       "...        ...       ...      ...       ...  ...      ...      ...      ...   \n",
       "10295     18.0     101.0    -11.0       7.9  ...      0.0      0.0      1.0   \n",
       "10296     19.0     101.0     -9.0       7.9  ...      0.0      0.0      1.0   \n",
       "10297     11.0     102.0    -18.0       7.9  ...      0.0      0.0      1.0   \n",
       "10298     61.0     118.0     49.0       7.9  ...      0.0      0.0      1.0   \n",
       "10299     -4.0     112.0    -22.0       7.9  ...      0.0      0.0      1.0   \n",
       "\n",
       "       Air Asia  Air India  Go Air  Indigo  Spicejet  Vistara        Date  \n",
       "8002        0.0        0.0     0.0     1.0       0.0      0.0  28-01-2018  \n",
       "8003        0.0        0.0     0.0     1.0       0.0      0.0  29-01-2018  \n",
       "8004        0.0        0.0     0.0     1.0       0.0      0.0  30-01-2018  \n",
       "8005        0.0        0.0     0.0     1.0       0.0      0.0  31-01-2018  \n",
       "8006        0.0        0.0     0.0     1.0       0.0      0.0  01-02-2018  \n",
       "...         ...        ...     ...     ...       ...      ...         ...  \n",
       "10295       0.0        0.0     0.0     1.0       0.0      0.0  23-01-2020  \n",
       "10296       0.0        0.0     0.0     1.0       0.0      0.0  24-01-2020  \n",
       "10297       0.0        0.0     0.0     1.0       0.0      0.0  25-01-2020  \n",
       "10298       0.0        0.0     0.0     1.0       0.0      0.0  26-01-2020  \n",
       "10299       0.0        0.0     0.0     1.0       0.0      0.0  27-01-2020  \n",
       "\n",
       "[2298 rows x 47 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indigo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pooja\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3065: 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",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "df_indigo[[\"day\", \"month\", \"year\"]] = df_indigo[\"Date\"].str.split(\"-\", expand = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_indigo=df_indigo.drop(['Date'], axis=1)\n",
    "#df_indigo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-f9a899c1841b>:1: 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",
      "  df_indigo['year'] = df_indigo['year'].astype(int)\n",
      "<ipython-input-10-f9a899c1841b>:2: 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",
      "  df_indigo['day'] = df_indigo['day'].astype(int)\n",
      "<ipython-input-10-f9a899c1841b>:3: 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",
      "  df_indigo['month'] = df_indigo['month'].astype(int)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D_Airport Rating</th>\n",
       "      <th>D_Airport On Time Rating</th>\n",
       "      <th>D_Airport Service Rating</th>\n",
       "      <th>A_Airport Rating</th>\n",
       "      <th>A_Airport On Time Rating</th>\n",
       "      <th>A_Airport Service Rating</th>\n",
       "      <th>D_Delay</th>\n",
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       "      <th>A_Delay</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-01-2018</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>8005</th>\n",
       "      <td>7.50</td>\n",
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       "      <td>31-01-2018</td>\n",
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       "      <td>26-01-2020</td>\n",
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       "      <td>2020</td>\n",
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       "<p>2298 rows × 50 columns</p>\n",
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      ],
      "text/plain": [
       "       D_Airport Rating  D_Airport On Time Rating  D_Airport Service Rating  \\\n",
       "8002               7.50                       8.6                       9.2   \n",
       "8003               7.50                       8.6                       9.2   \n",
       "8004               7.50                       8.6                       9.2   \n",
       "8005               7.50                       8.6                       9.2   \n",
       "8006               7.50                       8.6                       9.2   \n",
       "...                 ...                       ...                       ...   \n",
       "10295              7.99                       7.3                       9.1   \n",
       "10296              7.99                       7.3                       9.1   \n",
       "10297              7.99                       7.3                       9.1   \n",
       "10298              7.99                       7.3                       9.1   \n",
       "10299              7.99                       7.3                       9.1   \n",
       "\n",
       "       A_Airport Rating  A_Airport On Time Rating  A_Airport Service Rating  \\\n",
       "8002               7.29                       6.2                       9.0   \n",
       "8003               7.29                       6.2                       9.0   \n",
       "8004               7.29                       6.2                       9.0   \n",
       "8005               7.29                       6.2                       9.0   \n",
       "8006               7.29                       6.2                       9.0   \n",
       "...                 ...                       ...                       ...   \n",
       "10295              8.27                       7.8                       9.0   \n",
       "10296              8.27                       7.8                       9.0   \n",
       "10297              8.27                       7.8                       9.0   \n",
       "10298              8.27                       7.8                       9.0   \n",
       "10299              8.27                       7.8                       9.0   \n",
       "\n",
       "       D_Delay  Duration  A_Delay  C_Rating  ...  Air Asia  Air India  Go Air  \\\n",
       "8002       5.0      81.0    -19.0       7.9  ...       0.0        0.0     0.0   \n",
       "8003       3.0      83.0    -19.0       7.9  ...       0.0        0.0     0.0   \n",
       "8004       7.0      75.0    -22.0       7.9  ...       0.0        0.0     0.0   \n",
       "8005      -4.0      76.0    -33.0       7.9  ...       0.0        0.0     0.0   \n",
       "8006       2.0      81.0    -22.0       7.9  ...       0.0        0.0     0.0   \n",
       "...        ...       ...      ...       ...  ...       ...        ...     ...   \n",
       "10295     18.0     101.0    -11.0       7.9  ...       0.0        0.0     0.0   \n",
       "10296     19.0     101.0     -9.0       7.9  ...       0.0        0.0     0.0   \n",
       "10297     11.0     102.0    -18.0       7.9  ...       0.0        0.0     0.0   \n",
       "10298     61.0     118.0     49.0       7.9  ...       0.0        0.0     0.0   \n",
       "10299     -4.0     112.0    -22.0       7.9  ...       0.0        0.0     0.0   \n",
       "\n",
       "       Indigo  Spicejet  Vistara        Date  day  month  year  \n",
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       "8003      1.0       0.0      0.0  29-01-2018   29      1  2018  \n",
       "8004      1.0       0.0      0.0  30-01-2018   30      1  2018  \n",
       "8005      1.0       0.0      0.0  31-01-2018   31      1  2018  \n",
       "8006      1.0       0.0      0.0  01-02-2018    1      2  2018  \n",
       "...       ...       ...      ...         ...  ...    ...   ...  \n",
       "10295     1.0       0.0      0.0  23-01-2020   23      1  2020  \n",
       "10296     1.0       0.0      0.0  24-01-2020   24      1  2020  \n",
       "10297     1.0       0.0      0.0  25-01-2020   25      1  2020  \n",
       "10298     1.0       0.0      0.0  26-01-2020   26      1  2020  \n",
       "10299     1.0       0.0      0.0  27-01-2020   27      1  2020  \n",
       "\n",
       "[2298 rows x 50 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indigo['year'] = df_indigo['year'].astype(int)\n",
    "df_indigo['day'] = df_indigo['day'].astype(int)\n",
    "df_indigo['month'] = df_indigo['month'].astype(int)\n",
    "\n",
    "df_indigo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indigo Data for Only 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D_Airport Rating</th>\n",
       "      <th>D_Airport On Time Rating</th>\n",
       "      <th>D_Airport Service Rating</th>\n",
       "      <th>A_Airport Rating</th>\n",
       "      <th>A_Airport On Time Rating</th>\n",
       "      <th>A_Airport Service Rating</th>\n",
       "      <th>D_Delay</th>\n",
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       "      <th>A_Delay</th>\n",
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       "      <th>Air Asia</th>\n",
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       "      <th>Indigo</th>\n",
       "      <th>Spicejet</th>\n",
       "      <th>Vistara</th>\n",
       "      <th>Date</th>\n",
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       "      <th>month</th>\n",
       "      <th>year</th>\n",
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       "    <tr>\n",
       "      <th>8336</th>\n",
       "      <td>7.50</td>\n",
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       "      <td>9.2</td>\n",
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       "      <td>01-01-2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
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       "    <tr>\n",
       "      <th>8337</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
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       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>02-01-2019</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8338</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>03-01-2019</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8339</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>04-01-2019</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8340</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>05-01-2019</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10268</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27-12-2019</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10269</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28-12-2019</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10270</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29-12-2019</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10271</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-12-2019</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10272</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31-12-2019</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       D_Airport Rating  D_Airport On Time Rating  D_Airport Service Rating  \\\n",
       "8336               7.50                       8.6                       9.2   \n",
       "8337               7.50                       8.6                       9.2   \n",
       "8338               7.50                       8.6                       9.2   \n",
       "8339               7.50                       8.6                       9.2   \n",
       "8340               7.50                       8.6                       9.2   \n",
       "...                 ...                       ...                       ...   \n",
       "10268              7.99                       7.3                       9.1   \n",
       "10269              7.99                       7.3                       9.1   \n",
       "10270              7.99                       7.3                       9.1   \n",
       "10271              7.99                       7.3                       9.1   \n",
       "10272              7.99                       7.3                       9.1   \n",
       "\n",
       "       A_Airport Rating  A_Airport On Time Rating  A_Airport Service Rating  \\\n",
       "8336               7.29                       6.2                       9.0   \n",
       "8337               7.29                       6.2                       9.0   \n",
       "8338               7.29                       6.2                       9.0   \n",
       "8339               7.29                       6.2                       9.0   \n",
       "8340               7.29                       6.2                       9.0   \n",
       "...                 ...                       ...                       ...   \n",
       "10268              8.27                       7.8                       9.0   \n",
       "10269              8.27                       7.8                       9.0   \n",
       "10270              8.27                       7.8                       9.0   \n",
       "10271              8.27                       7.8                       9.0   \n",
       "10272              8.27                       7.8                       9.0   \n",
       "\n",
       "       D_Delay  Duration  A_Delay  C_Rating  ...  Air Asia  Air India  Go Air  \\\n",
       "8336      37.0      83.0     -1.0       7.9  ...       0.0        0.0     0.0   \n",
       "8337       6.0      83.0    -26.0       7.9  ...       0.0        0.0     0.0   \n",
       "8338      18.0      80.0    -18.0       7.9  ...       0.0        0.0     0.0   \n",
       "8339       5.0      77.0    -33.0       7.9  ...       0.0        0.0     0.0   \n",
       "8340     103.0      76.0     64.0       7.9  ...       0.0        0.0     0.0   \n",
       "...        ...       ...      ...       ...  ...       ...        ...     ...   \n",
       "10268      1.0     100.0    -28.0       7.9  ...       0.0        0.0     0.0   \n",
       "10269     49.0     100.0     20.0       7.9  ...       0.0        0.0     0.0   \n",
       "10270     15.0     106.0     -9.0       7.9  ...       0.0        0.0     0.0   \n",
       "10271     79.0     112.0     61.0       7.9  ...       0.0        0.0     0.0   \n",
       "10272      1.0     107.0    -22.0       7.9  ...       0.0        0.0     0.0   \n",
       "\n",
       "       Indigo  Spicejet  Vistara        Date  day  month  year  \n",
       "8336      1.0       0.0      0.0  01-01-2019    1      1  2019  \n",
       "8337      1.0       0.0      0.0  02-01-2019    2      1  2019  \n",
       "8338      1.0       0.0      0.0  03-01-2019    3      1  2019  \n",
       "8339      1.0       0.0      0.0  04-01-2019    4      1  2019  \n",
       "8340      1.0       0.0      0.0  05-01-2019    5      1  2019  \n",
       "...       ...       ...      ...         ...  ...    ...   ...  \n",
       "10268     1.0       0.0      0.0  27-12-2019   27     12  2019  \n",
       "10269     1.0       0.0      0.0  28-12-2019   28     12  2019  \n",
       "10270     1.0       0.0      0.0  29-12-2019   29     12  2019  \n",
       "10271     1.0       0.0      0.0  30-12-2019   30     12  2019  \n",
       "10272     1.0       0.0      0.0  31-12-2019   31     12  2019  \n",
       "\n",
       "[1188 rows x 50 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indigo=df_indigo[df_indigo['year'] == 2019]\n",
    "df_indigo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indigo=df_indigo.drop(['Air Asia','Air India','Go Air','Indigo','Spicejet','Vistara','Date','year'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pol = pd.read_csv(\"pol.csv\") \n",
    "df_sub = pd.read_csv(\"sub.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "   month  day  subjectivity\n",
       "0      1    1      0.437500\n",
       "1      1    2      0.000000\n",
       "2      1    3      0.530952\n",
       "3      1    4      0.346154\n",
       "4      1    5      0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = pd.merge(df_indigo, df_pol, on=['month','day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>D_Airport Rating</th>\n",
       "      <th>D_Airport On Time Rating</th>\n",
       "      <th>D_Airport Service Rating</th>\n",
       "      <th>A_Airport Rating</th>\n",
       "      <th>A_Airport On Time Rating</th>\n",
       "      <th>A_Airport Service Rating</th>\n",
       "      <th>D_Delay</th>\n",
       "      <th>Duration</th>\n",
       "      <th>A_Delay</th>\n",
       "      <th>C_Rating</th>\n",
       "      <th>...</th>\n",
       "      <th>A_windspeedKmph</th>\n",
       "      <th>A_Time</th>\n",
       "      <th>BLR-BOM</th>\n",
       "      <th>BLR-DEL</th>\n",
       "      <th>BOM-DEL</th>\n",
       "      <th>CCU-DEL</th>\n",
       "      <th>DEL-HYD</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.137500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.137500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.186667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   D_Airport Rating  D_Airport On Time Rating  D_Airport Service Rating  \\\n",
       "0               7.5                       8.6                       9.2   \n",
       "1               7.0                       8.5                       8.7   \n",
       "2               7.5                       8.6                       9.2   \n",
       "3               7.0                       8.5                       8.7   \n",
       "4               7.5                       8.6                       9.2   \n",
       "\n",
       "   A_Airport Rating  A_Airport On Time Rating  A_Airport Service Rating  \\\n",
       "0              7.29                       6.2                       9.0   \n",
       "1              7.99                       7.3                       9.1   \n",
       "2              7.29                       6.2                       9.0   \n",
       "3              7.99                       7.3                       9.1   \n",
       "4              7.29                       6.2                       9.0   \n",
       "\n",
       "   D_Delay  Duration  A_Delay  C_Rating  ...  A_windspeedKmph  A_Time  \\\n",
       "0     37.0      83.0     -1.0       7.9  ...              9.0     6.0   \n",
       "1     -1.0     124.0    -27.0       7.9  ...              5.0     8.0   \n",
       "2      6.0      83.0    -26.0       7.9  ...             11.0     6.0   \n",
       "3      3.0     122.0    -25.0       7.9  ...              5.0     8.0   \n",
       "4     18.0      80.0    -18.0       7.9  ...              8.0     6.0   \n",
       "\n",
       "   BLR-BOM  BLR-DEL  BOM-DEL  CCU-DEL  DEL-HYD  day  month  polarity  \n",
       "0      1.0      0.0      0.0      0.0      0.0    1      1 -0.137500  \n",
       "1      0.0      0.0      0.0      1.0      0.0    1      1 -0.137500  \n",
       "2      1.0      0.0      0.0      0.0      0.0    2      1 -0.025000  \n",
       "3      0.0      0.0      0.0      1.0      0.0    2      1 -0.025000  \n",
       "4      1.0      0.0      0.0      0.0      0.0    3      1  0.186667  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined= pd.merge(df_combined, df_sub, on=['month','day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.read_csv(\"volumefinal.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final=df_final.drop(['date','polarity','subjectivity'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>tweet_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  day  tweet_volume\n",
       "0     12   28             1\n",
       "1     12   27             1\n",
       "2     12   24             2\n",
       "3     12   24             2\n",
       "4     12   21             2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined= pd.merge(df_combined, df_final, on=['month','day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['D_Airport Rating', 'D_Airport On Time Rating',\n",
       "       'D_Airport Service Rating', 'A_Airport Rating',\n",
       "       'A_Airport On Time Rating', 'A_Airport Service Rating', 'D_Delay',\n",
       "       'Duration', 'A_Delay', 'C_Rating', 'C_Market Share', 'C_Load Factor',\n",
       "       'C_On Time Performance Rating', 'D_DewPointC', 'D_WindGustKmph',\n",
       "       'D_cloudcover', 'D_humidity', 'D_precipMM', 'D_pressure', 'D_tempC',\n",
       "       'D_visibility', 'D_winddirDegree', 'D_windspeedKmph', 'D_Time',\n",
       "       'A_DewPointC', 'A_WindGustKmph', 'A_cloudcover', 'A_humidity',\n",
       "       'A_precipMM', 'A_pressure', 'A_tempC', 'A_visibility',\n",
       "       'A_winddirDegree', 'A_windspeedKmph', 'A_Time', 'BLR-BOM', 'BLR-DEL',\n",
       "       'BOM-DEL', 'CCU-DEL', 'DEL-HYD', 'day', 'month', 'polarity',\n",
       "       'subjectivity', 'tweet_volume'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_fit = df_combined.drop(['BLR-BOM', 'BLR-DEL',\n",
    "       'BOM-DEL', 'CCU-DEL', 'DEL-HYD'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>D_Airport Rating</th>\n",
       "      <th>D_Airport On Time Rating</th>\n",
       "      <th>D_Airport Service Rating</th>\n",
       "      <th>A_Airport Rating</th>\n",
       "      <th>A_Airport On Time Rating</th>\n",
       "      <th>A_Airport Service Rating</th>\n",
       "      <th>D_Delay</th>\n",
       "      <th>Duration</th>\n",
       "      <th>A_Delay</th>\n",
       "      <th>C_Rating</th>\n",
       "      <th>...</th>\n",
       "      <th>A_tempC</th>\n",
       "      <th>A_visibility</th>\n",
       "      <th>A_winddirDegree</th>\n",
       "      <th>A_windspeedKmph</th>\n",
       "      <th>A_Time</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>tweet_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>0.530952</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>0.530952</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.50</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.29</td>\n",
       "      <td>6.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.128974</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>7.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.008864</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>7.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.008864</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.008864</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.008864</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.008864</td>\n",
       "      <td>0.394621</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>709 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     D_Airport Rating  D_Airport On Time Rating  D_Airport Service Rating  \\\n",
       "0                7.50                       8.6                       9.2   \n",
       "1                7.00                       8.5                       8.7   \n",
       "2                7.50                       8.6                       9.2   \n",
       "3                7.00                       8.5                       8.7   \n",
       "4                7.50                       8.6                       9.2   \n",
       "..                ...                       ...                       ...   \n",
       "704              7.00                       8.5                       8.7   \n",
       "705              7.00                       8.5                       8.7   \n",
       "706              7.99                       7.3                       9.1   \n",
       "707              7.99                       7.3                       9.1   \n",
       "708              7.99                       7.3                       9.1   \n",
       "\n",
       "     A_Airport Rating  A_Airport On Time Rating  A_Airport Service Rating  \\\n",
       "0                7.29                       6.2                       9.0   \n",
       "1                7.99                       7.3                       9.1   \n",
       "2                7.29                       6.2                       9.0   \n",
       "3                7.99                       7.3                       9.1   \n",
       "4                7.29                       6.2                       9.0   \n",
       "..                ...                       ...                       ...   \n",
       "704              7.99                       7.3                       9.1   \n",
       "705              7.99                       7.3                       9.1   \n",
       "706              8.27                       7.8                       9.0   \n",
       "707              8.27                       7.8                       9.0   \n",
       "708              8.27                       7.8                       9.0   \n",
       "\n",
       "     D_Delay  Duration  A_Delay  C_Rating  ...  A_tempC  A_visibility  \\\n",
       "0        6.0      83.0    -26.0       7.9  ...     24.0          10.0   \n",
       "1        3.0     122.0    -25.0       7.9  ...     16.0          10.0   \n",
       "2       18.0      80.0    -18.0       7.9  ...     27.0          10.0   \n",
       "3        8.0     142.0     -1.0       7.9  ...     18.0          10.0   \n",
       "4        5.0      77.0    -33.0       7.9  ...     30.0          10.0   \n",
       "..       ...       ...      ...       ...  ...      ...           ...   \n",
       "704     16.0     112.0    -21.0       7.9  ...     37.0          10.0   \n",
       "705     16.0     112.0    -21.0       7.9  ...     37.0          10.0   \n",
       "706     29.0     107.0      6.0       7.9  ...     35.0          10.0   \n",
       "707     29.0     107.0      6.0       7.9  ...     35.0          10.0   \n",
       "708     29.0     107.0      6.0       7.9  ...     35.0          10.0   \n",
       "\n",
       "     A_winddirDegree  A_windspeedKmph  A_Time  day  month  polarity  \\\n",
       "0               22.0             11.0     6.0    2      1 -0.025000   \n",
       "1              152.0              5.0     8.0    2      1 -0.025000   \n",
       "2               63.0              8.0     6.0    3      1  0.186667   \n",
       "3              281.0              7.0     8.0    3      1  0.186667   \n",
       "4              301.0              7.0     6.0    4      1 -0.128974   \n",
       "..               ...              ...     ...  ...    ...       ...   \n",
       "704            280.0             24.0     8.0    5      9 -0.008864   \n",
       "705            280.0             24.0     8.0    5      9 -0.008864   \n",
       "706            159.0             16.0     7.0    5      9 -0.008864   \n",
       "707            159.0             16.0     7.0    5      9 -0.008864   \n",
       "708            159.0             16.0     7.0    5      9 -0.008864   \n",
       "\n",
       "     subjectivity  tweet_volume  \n",
       "0        0.000000             1  \n",
       "1        0.000000             1  \n",
       "2        0.530952             1  \n",
       "3        0.530952             1  \n",
       "4        0.346154             3  \n",
       "..            ...           ...  \n",
       "704      0.394621             3  \n",
       "705      0.394621             3  \n",
       "706      0.394621             3  \n",
       "707      0.394621             3  \n",
       "708      0.394621             3  \n",
       "\n",
       "[709 rows x 40 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = df_indigo['A_Delay'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering and Machine Learniing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# One Hot Encoding for cataegorical features like route\\nfrom sklearn.preprocessing import LabelEncoder\\n\\ndata = df['route']\\nvalues= np.array(data)\\n\\nlabel_encoder = LabelEncoder()\\ninteger_encoded = label_encoder.fit_transform(values)\\n\\nfrom sklearn.preprocessing import OneHotEncoder\\n\\nonehot_encoder = OneHotEncoder(sparse=False)\\ninteger_encoded = integer_encoded.reshape(len(integer_encoded), 1)\\nonehot_encoded = onehot_encoder.fit_transform(integer_encoded)\\n\\ndf1 = pd.DataFrame.from_records(onehot_encoded)\\ndf = pd.concat((df,df1),axis=1)\\n\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# One Hot Encoding for cataegorical features like route\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "data = df['route']\n",
    "values= np.array(data)\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "integer_encoded = label_encoder.fit_transform(values)\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "onehot_encoder = OneHotEncoder(sparse=False)\n",
    "integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
    "onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n",
    "\n",
    "df1 = pd.DataFrame.from_records(onehot_encoded)\n",
    "df = pd.concat((df,df1),axis=1)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# One Hot Encoding for cataegorical features like carrier\\nfrom sklearn.preprocessing import LabelEncoder\\n\\ndata1 = df['Carrier']\\nvalues1= np.array(data1)\\n\\nlabel_encoder1 = LabelEncoder()\\ninteger_encoded1 = label_encoder.fit_transform(values1)\\n\\nfrom sklearn.preprocessing import OneHotEncoder\\n\\nonehot_encoder1 = OneHotEncoder(sparse=False)\\ninteger_encoded1 = integer_encoded1.reshape(len(integer_encoded1), 1)\\nonehot_encoded1 = onehot_encoder1.fit_transform(integer_encoded1)\\n\\ndf2 = pd.DataFrame.from_records(onehot_encoded1)\\ndf = pd.concat((df,df2),axis=1)\\n\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# One Hot Encoding for cataegorical features like carrier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "data1 = df['Carrier']\n",
    "values1= np.array(data1)\n",
    "\n",
    "label_encoder1 = LabelEncoder()\n",
    "integer_encoded1 = label_encoder.fit_transform(values1)\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "onehot_encoder1 = OneHotEncoder(sparse=False)\n",
    "integer_encoded1 = integer_encoded1.reshape(len(integer_encoded1), 1)\n",
    "onehot_encoded1 = onehot_encoder1.fit_transform(integer_encoded1)\n",
    "\n",
    "df2 = pd.DataFrame.from_records(onehot_encoded1)\n",
    "df = pd.concat((df,df2),axis=1)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "",
    "_uuid": ""
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"fit.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = pd.read_csv(\"Final.csv\") \n",
    "extracted_col = df_temp[\"Date\"]\n",
    "df = pd.read_csv(\"fit.csv\") \n",
    "df=df.join(extracted_col)\n",
    "df[[\"day\", \"month\", \"year\"]] = df[\"Date\"].str.split(\"-\", expand = True)\n",
    "df['year'] = df['year'].astype(int)\n",
    "df['day'] = df['day'].astype(int)\n",
    "df['month'] = df['month'].astype(int)\n",
    "df = df.drop(['Date'], axis=1)\n",
    "data=df[df['year'] == 2019]\n",
    "data=data[data['Indigo'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['D_Airport Rating', 'D_Airport On Time Rating',\n",
       "       'D_Airport Service Rating', 'A_Airport Rating',\n",
       "       'A_Airport On Time Rating', 'A_Airport Service Rating', 'D_Delay',\n",
       "       'Duration', 'A_Delay', 'C_Rating', 'C_Market Share', 'C_Load Factor',\n",
       "       'C_On Time Performance Rating', 'D_DewPointC', 'D_WindGustKmph',\n",
       "       'D_cloudcover', 'D_humidity', 'D_precipMM', 'D_pressure', 'D_tempC',\n",
       "       'D_visibility', 'D_winddirDegree', 'D_windspeedKmph', 'D_Time',\n",
       "       'A_DewPointC', 'A_WindGustKmph', 'A_cloudcover', 'A_humidity',\n",
       "       'A_precipMM', 'A_pressure', 'A_tempC', 'A_visibility',\n",
       "       'A_winddirDegree', 'A_windspeedKmph', 'A_Time', 'BLR-BOM', 'BLR-DEL',\n",
       "       'BOM-DEL', 'CCU-DEL', 'DEL-HYD', 'Air Asia', 'Air India', 'Go Air',\n",
       "       'Indigo', 'Spicejet', 'Vistara', 'day', 'month', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['BLR-BOM', 'BLR-DEL',\n",
    "       'BOM-DEL', 'CCU-DEL', 'DEL-HYD', 'Air Asia', 'Air India', 'Go Air',\n",
    "       'Indigo', 'Spicejet', 'Vistara'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pooja\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99514413 1.20416836 1.5274953  1.86068174 0.96382148 1.16611271\n",
      " 4.58833876 4.1898654  0.         0.         0.         0.\n",
      " 4.14980298 3.15876654 3.71403841 4.01922487 1.49568896 4.07816087\n",
      " 2.97742664 2.33296111 4.15586152 3.45998743 3.10228814 3.87807845\n",
      " 4.21776897 4.2621202  5.10140918 1.49325224 3.46730699 3.79424832\n",
      " 1.6442513  4.12022878 3.86507714 3.37977497 3.75502414 3.881624\n",
      " 0.        ]\n"
     ]
    }
   ],
   "source": [
    "# Feature Importance Score\n",
    "from sklearn import preprocessing\n",
    "\n",
    "X = data.drop('A_Delay', axis =1)\n",
    "y = data['A_Delay'].to_numpy().reshape(-1,1)\n",
    "\n",
    "lab_enc = preprocessing.LabelEncoder()\n",
    "y_encoded = lab_enc.fit_transform(y)\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = ExtraTreesClassifier(n_estimators = 5,criterion ='entropy', max_features = 2)\n",
    "model.fit(X,y_encoded)\n",
    "\n",
    "print(model.feature_importances_ * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(model.feature_importances_, index=X.columns).nlargest(4).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "def plot_feature_importance(importance,names,model_type):\n",
    "\n",
    "#Create arrays from feature importance and feature names\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "\n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "\n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "\n",
    "    #Define size of bar plot\n",
    "    plt.figure(figsize=(10,8))\n",
    "    #Plot Searborn bar chart\n",
    "    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "    #Add chart labels\n",
    "    plt.title(model_type + 'FEATURE IMPORTANCE')\n",
    "    plt.xlabel('FEATURE IMPORTANCE')\n",
    "    plt.ylabel('FEATURE NAMES')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(model.feature_importances_,X.columns,'RANDOM FOREST')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['D_Delay'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-2ae67fce305a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Removing Data Leakage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"D_Delay\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4161\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[1;36m1.0\u001b[0m     \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4162\u001b[0m         \"\"\"\n\u001b[1;32m-> 4163\u001b[1;33m         return super().drop(\n\u001b[0m\u001b[0;32m   4164\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4165\u001b[0m             \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3885\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3886\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3887\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3889\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3919\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3920\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3921\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3922\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5280\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5281\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5282\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5283\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5284\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['D_Delay'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# Removing Data Leakage\n",
    "data = data.drop(\"D_Delay\",axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x2797287aac0>,\n",
       "  <matplotlib.lines.Line2D at 0x2797287ae20>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2797288a1c0>,\n",
       "  <matplotlib.lines.Line2D at 0x2797288a520>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x2797287a730>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2797288a880>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x2797288abe0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['A_Delay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier Removal\n",
    "data = data[data['A_Delay'] < 180]\n",
    "data = data[data['A_Delay'] > -180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the Target\n",
    "plt.hist(data['A_Delay'])\n",
    "plt.title(\"Distribution of Delay\")\n",
    "plt.xlabel(\"Delay (in minutes)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)\n",
    "plt.title(\"Distribution of Delay\")\n",
    "plt.xlabel(\"Delay (in minutes)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlim([0,100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D_Airport Rating</th>\n",
       "      <th>D_Airport On Time Rating</th>\n",
       "      <th>D_Airport Service Rating</th>\n",
       "      <th>A_Airport Rating</th>\n",
       "      <th>A_Airport On Time Rating</th>\n",
       "      <th>A_Airport Service Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>A_Delay</th>\n",
       "      <th>C_Rating</th>\n",
       "      <th>C_Market Share</th>\n",
       "      <th>C_Load Factor</th>\n",
       "      <th>C_On Time Performance Rating</th>\n",
       "      <th>D_DewPointC</th>\n",
       "      <th>D_WindGustKmph</th>\n",
       "      <th>D_cloudcover</th>\n",
       "      <th>D_humidity</th>\n",
       "      <th>D_precipMM</th>\n",
       "      <th>D_pressure</th>\n",
       "      <th>D_tempC</th>\n",
       "      <th>D_visibility</th>\n",
       "      <th>D_winddirDegree</th>\n",
       "      <th>D_windspeedKmph</th>\n",
       "      <th>D_Time</th>\n",
       "      <th>A_DewPointC</th>\n",
       "      <th>A_WindGustKmph</th>\n",
       "      <th>A_cloudcover</th>\n",
       "      <th>A_humidity</th>\n",
       "      <th>A_precipMM</th>\n",
       "      <th>A_pressure</th>\n",
       "      <th>A_tempC</th>\n",
       "      <th>A_visibility</th>\n",
       "      <th>A_winddirDegree</th>\n",
       "      <th>A_windspeedKmph</th>\n",
       "      <th>A_Time</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1.186000e+03</td>\n",
       "      <td>1.186000e+03</td>\n",
       "      <td>1.186000e+03</td>\n",
       "      <td>1.186000e+03</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.00000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>1186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.472530</td>\n",
       "      <td>8.031366</td>\n",
       "      <td>9.008600</td>\n",
       "      <td>7.860388</td>\n",
       "      <td>7.113069</td>\n",
       "      <td>9.042411</td>\n",
       "      <td>103.739460</td>\n",
       "      <td>-15.443508</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>3.970000e+01</td>\n",
       "      <td>9.090000e+01</td>\n",
       "      <td>8.740000e+01</td>\n",
       "      <td>16.969646</td>\n",
       "      <td>15.669477</td>\n",
       "      <td>32.20742</td>\n",
       "      <td>64.261383</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>1009.314503</td>\n",
       "      <td>25.338111</td>\n",
       "      <td>10.229342</td>\n",
       "      <td>182.381956</td>\n",
       "      <td>11.672007</td>\n",
       "      <td>5.124789</td>\n",
       "      <td>16.082631</td>\n",
       "      <td>16.083474</td>\n",
       "      <td>31.545531</td>\n",
       "      <td>56.858347</td>\n",
       "      <td>0.247470</td>\n",
       "      <td>1009.486509</td>\n",
       "      <td>26.827993</td>\n",
       "      <td>10.064081</td>\n",
       "      <td>190.992411</td>\n",
       "      <td>11.955312</td>\n",
       "      <td>6.773187</td>\n",
       "      <td>15.956155</td>\n",
       "      <td>7.178752</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.379501</td>\n",
       "      <td>0.757830</td>\n",
       "      <td>0.208657</td>\n",
       "      <td>0.388122</td>\n",
       "      <td>0.627921</td>\n",
       "      <td>0.049442</td>\n",
       "      <td>23.137937</td>\n",
       "      <td>18.261779</td>\n",
       "      <td>1.768221e-13</td>\n",
       "      <td>5.331319e-13</td>\n",
       "      <td>1.663371e-12</td>\n",
       "      <td>1.492769e-12</td>\n",
       "      <td>6.261725</td>\n",
       "      <td>8.765989</td>\n",
       "      <td>29.33095</td>\n",
       "      <td>20.172525</td>\n",
       "      <td>0.489470</td>\n",
       "      <td>6.360989</td>\n",
       "      <td>5.728117</td>\n",
       "      <td>2.219898</td>\n",
       "      <td>88.612045</td>\n",
       "      <td>6.065286</td>\n",
       "      <td>0.852661</td>\n",
       "      <td>7.218466</td>\n",
       "      <td>12.111951</td>\n",
       "      <td>31.468584</td>\n",
       "      <td>21.446592</td>\n",
       "      <td>0.818116</td>\n",
       "      <td>6.246166</td>\n",
       "      <td>6.003720</td>\n",
       "      <td>2.198797</td>\n",
       "      <td>93.875512</td>\n",
       "      <td>7.368460</td>\n",
       "      <td>0.944890</td>\n",
       "      <td>8.783938</td>\n",
       "      <td>3.544545</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>7.290000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-58.000000</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>3.970000e+01</td>\n",
       "      <td>9.090000e+01</td>\n",
       "      <td>8.740000e+01</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>993.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>7.290000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>3.970000e+01</td>\n",
       "      <td>9.090000e+01</td>\n",
       "      <td>8.740000e+01</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1005.250000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>112.250000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>7.990000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>-19.000000</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>3.970000e+01</td>\n",
       "      <td>9.090000e+01</td>\n",
       "      <td>8.740000e+01</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1011.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>187.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1011.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.990000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>8.270000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>3.970000e+01</td>\n",
       "      <td>9.090000e+01</td>\n",
       "      <td>8.740000e+01</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.990000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>8.270000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>7.900000e+00</td>\n",
       "      <td>3.970000e+01</td>\n",
       "      <td>9.090000e+01</td>\n",
       "      <td>8.740000e+01</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1022.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>1023.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       D_Airport Rating  D_Airport On Time Rating  D_Airport Service Rating  \\\n",
       "count       1186.000000               1186.000000               1186.000000   \n",
       "mean           7.472530                  8.031366                  9.008600   \n",
       "std            0.379501                  0.757830                  0.208657   \n",
       "min            7.000000                  6.200000                  8.700000   \n",
       "25%            7.000000                  7.300000                  8.700000   \n",
       "50%            7.500000                  8.500000                  9.100000   \n",
       "75%            7.990000                  8.600000                  9.200000   \n",
       "max            7.990000                  8.600000                  9.200000   \n",
       "\n",
       "       A_Airport Rating  A_Airport On Time Rating  A_Airport Service Rating  \\\n",
       "count       1186.000000               1186.000000               1186.000000   \n",
       "mean           7.860388                  7.113069                  9.042411   \n",
       "std            0.388122                  0.627921                  0.049442   \n",
       "min            7.290000                  6.200000                  9.000000   \n",
       "25%            7.290000                  6.200000                  9.000000   \n",
       "50%            7.990000                  7.300000                  9.000000   \n",
       "75%            8.270000                  7.800000                  9.100000   \n",
       "max            8.270000                  7.800000                  9.100000   \n",
       "\n",
       "          Duration      A_Delay      C_Rating  C_Market Share  C_Load Factor  \\\n",
       "count  1186.000000  1186.000000  1.186000e+03    1.186000e+03   1.186000e+03   \n",
       "mean    103.739460   -15.443508  7.900000e+00    3.970000e+01   9.090000e+01   \n",
       "std      23.137937    18.261779  1.768221e-13    5.331319e-13   1.663371e-12   \n",
       "min       0.000000   -58.000000  7.900000e+00    3.970000e+01   9.090000e+01   \n",
       "25%      84.000000   -25.000000  7.900000e+00    3.970000e+01   9.090000e+01   \n",
       "50%     109.000000   -19.000000  7.900000e+00    3.970000e+01   9.090000e+01   \n",
       "75%     117.000000   -10.000000  7.900000e+00    3.970000e+01   9.090000e+01   \n",
       "max     199.000000   115.000000  7.900000e+00    3.970000e+01   9.090000e+01   \n",
       "\n",
       "       C_On Time Performance Rating  D_DewPointC  D_WindGustKmph  \\\n",
       "count                  1.186000e+03  1186.000000     1186.000000   \n",
       "mean                   8.740000e+01    16.969646       15.669477   \n",
       "std                    1.492769e-12     6.261725        8.765989   \n",
       "min                    8.740000e+01    -4.000000        1.000000   \n",
       "25%                    8.740000e+01    13.000000        9.000000   \n",
       "50%                    8.740000e+01    18.000000       14.000000   \n",
       "75%                    8.740000e+01    22.000000       20.000000   \n",
       "max                    8.740000e+01    28.000000       81.000000   \n",
       "\n",
       "       D_cloudcover   D_humidity   D_precipMM   D_pressure      D_tempC  \\\n",
       "count    1186.00000  1186.000000  1186.000000  1186.000000  1186.000000   \n",
       "mean       32.20742    64.261383     0.129848  1009.314503    25.338111   \n",
       "std        29.33095    20.172525     0.489470     6.360989     5.728117   \n",
       "min         0.00000     8.000000     0.000000   991.000000    11.000000   \n",
       "25%         4.00000    51.000000     0.000000  1005.250000    21.000000   \n",
       "50%        25.00000    68.000000     0.000000  1011.000000    25.000000   \n",
       "75%        57.00000    81.000000     0.000000  1014.000000    29.000000   \n",
       "max       100.00000    94.000000     7.500000  1022.000000    44.000000   \n",
       "\n",
       "       D_visibility  D_winddirDegree  D_windspeedKmph       D_Time  \\\n",
       "count   1186.000000      1186.000000      1186.000000  1186.000000   \n",
       "mean      10.229342       182.381956        11.672007     5.124789   \n",
       "std        2.219898        88.612045         6.065286     0.852661   \n",
       "min        3.000000         3.000000         0.000000     3.000000   \n",
       "25%       10.000000       101.000000         7.000000     4.000000   \n",
       "50%       10.000000       187.500000        11.000000     5.000000   \n",
       "75%       10.000000       255.000000        15.000000     6.000000   \n",
       "max       20.000000       356.000000        56.000000     9.000000   \n",
       "\n",
       "       A_DewPointC  A_WindGustKmph  A_cloudcover   A_humidity   A_precipMM  \\\n",
       "count  1186.000000     1186.000000   1186.000000  1186.000000  1186.000000   \n",
       "mean     16.082631       16.083474     31.545531    56.858347     0.247470   \n",
       "std       7.218466       12.111951     31.468584    21.446592     0.818116   \n",
       "min      -4.000000        1.000000      0.000000     7.000000     0.000000   \n",
       "25%      11.000000        9.000000      3.000000    40.000000     0.000000   \n",
       "50%      18.000000       13.000000     17.500000    59.000000     0.000000   \n",
       "75%      22.000000       19.000000     61.000000    77.000000     0.000000   \n",
       "max      27.000000       82.000000    100.000000    93.000000    10.700000   \n",
       "\n",
       "        A_pressure      A_tempC  A_visibility  A_winddirDegree  \\\n",
       "count  1186.000000  1186.000000   1186.000000      1186.000000   \n",
       "mean   1009.486509    26.827993     10.064081       190.992411   \n",
       "std       6.246166     6.003720      2.198797        93.875512   \n",
       "min     993.000000    12.000000      3.000000         2.000000   \n",
       "25%    1005.000000    23.000000     10.000000       112.250000   \n",
       "50%    1011.000000    27.000000     10.000000       199.000000   \n",
       "75%    1014.000000    30.000000     10.000000       273.000000   \n",
       "max    1023.000000    46.000000     20.000000       357.000000   \n",
       "\n",
       "       A_windspeedKmph       A_Time          day        month    year  \n",
       "count      1186.000000  1186.000000  1186.000000  1186.000000  1186.0  \n",
       "mean         11.955312     6.773187    15.956155     7.178752  2019.0  \n",
       "std           7.368460     0.944890     8.783938     3.544545     0.0  \n",
       "min           1.000000     6.000000     1.000000     1.000000  2019.0  \n",
       "25%           7.000000     6.000000     8.000000     4.000000  2019.0  \n",
       "50%          10.000000     6.000000    16.000000     7.000000  2019.0  \n",
       "75%          14.000000     8.000000    24.000000    10.000000  2019.0  \n",
       "max          45.000000    10.000000    31.000000    12.000000  2019.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Description post out-lier removal\n",
    "pd.options.display.max_columns = None\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['D_Airport Rating', 'D_Airport On Time Rating',\n",
       "       'D_Airport Service Rating', 'A_Airport Rating',\n",
       "       'A_Airport On Time Rating', 'A_Airport Service Rating', 'Duration',\n",
       "       'A_Delay', 'C_Rating', 'C_Market Share', 'C_Load Factor',\n",
       "       'C_On Time Performance Rating', 'D_DewPointC', 'D_WindGustKmph',\n",
       "       'D_cloudcover', 'D_humidity', 'D_precipMM', 'D_pressure', 'D_tempC',\n",
       "       'D_visibility', 'D_winddirDegree', 'D_windspeedKmph', 'D_Time',\n",
       "       'A_DewPointC', 'A_WindGustKmph', 'A_cloudcover', 'A_humidity',\n",
       "       'A_precipMM', 'A_pressure', 'A_tempC', 'A_visibility',\n",
       "       'A_winddirDegree', 'A_windspeedKmph', 'A_Time', 'day', 'month', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the columns..\n",
    "data.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Air Asia'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Air Asia'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-001e19936443>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# AVERAGE DELAYS OF AIRLINES\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Air Asia'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A_Delay'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m1873\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Air India'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A_Delay'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m3529\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Indigo'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A_Delay'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2296\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Vistara'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A_Delay'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2825\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Air Asia'"
     ]
    }
   ],
   "source": [
    "# AVERAGE DELAYS OF AIRLINES\n",
    "print(abs(data[data['Air Asia'] == 1]['A_Delay']).sum()/1873)\n",
    "print(abs(data[data['Air India'] == 1]['A_Delay']).sum()/3529)\n",
    "print(abs(data[data['Indigo'] == 1]['A_Delay']).sum()/2296)\n",
    "print(abs(data[data['Vistara'] == 1]['A_Delay']).sum()/2825)\n",
    "print(abs(data[data['Spicejet'] == 1]['A_Delay']).sum()/1723)\n",
    "print(abs(data[data['Go Air'] == 1]['A_Delay']).sum()/2487)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(830, 36) (356, 36) (830,) (356,)\n"
     ]
    }
   ],
   "source": [
    "# WE MAKE 4 POSSIBLE SETS OF DATA\n",
    "# 1 - NO CHANGE IN FEATURES, NO CHANGE IN TARGET \n",
    "# 2 - POLYNOMIAL FEATURES OF DEGREE 2, NO CHANGE IN TARGET\n",
    "# 3 - NO CHANGE IN FEATURES, TARGET NORMALIZED\n",
    "# 4 - POLYNOMIAL FEATURES OF DEGREE 2, TARGET NORMALIZED\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.metrics import mean_squared_error\n",
    "PT = PowerTransformer(method = \"yeo-johnson\")\n",
    "X_train,X_test,y_train,y_test = tts(data.drop('A_Delay', axis = 1),data['A_Delay'],test_size = 0.3,random_state = 1)\n",
    "print(np.shape(X_train),np.shape(X_test),np.shape(y_train),np.shape(y_test))\n",
    "\n",
    "PT.fit(y_train.to_numpy().reshape(-1,1))\n",
    "lamb = PT.lambdas_\n",
    "\n",
    "yt_train = PT.transform(y_train.to_numpy().reshape(-1,1))\n",
    "yt_test = PT.transform(y_test.to_numpy().reshape(-1,1))\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "PF = PolynomialFeatures(2);\n",
    "\n",
    "data_p = PF.fit_transform(data.drop('A_Delay',axis = 1))\n",
    "Xp_train,Xp_test,y_train,y_test = tts(data_p,data['A_Delay'],test_size = 0.3,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(830, 36)\n",
      "(356, 36)\n",
      "(830,)\n",
      "(356,)\n",
      "(830, 703)\n",
      "(356, 703)\n",
      "(830, 1)\n",
      "(356, 1)\n"
     ]
    }
   ],
   "source": [
    "# Printing all shapes to be sure!\n",
    "print(X_train.shape) # NO CHANGE APPLIED\n",
    "print(X_test.shape)  # NO CHANGE APPLIED\n",
    "print(y_train.shape) # NO CHANGE APPLIED\n",
    "print(y_test.shape)  # NO CHANGE APPLIED\n",
    "print(Xp_train.shape)# POLYNOMIAL FEATURES\n",
    "print(Xp_test.shape) # POLYNOMIAL FEATURES\n",
    "print(yt_train.shape)# TARGET NORMALIZED\n",
    "print(yt_test.shape) # TARGET NORMALIZED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41.49912479043534\n",
      "120.27652028242049\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-0a4d5500b4bc>:15: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  RFR.fit(X_train,yt_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11760210153940259\n",
      "0.33722832907039724\n",
      "23.676910531607625\n",
      "111.32607925984499\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-0a4d5500b4bc>:27: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  RFR.fit(Xp_train,yt_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.089677389075905\n",
      "0.34334868227494225\n"
     ]
    }
   ],
   "source": [
    "# Applying random forest regression on the data.\n",
    "# Evaluation Metric used is mse.\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# NO CHANGE DATA\n",
    "RFR = RandomForestRegressor(n_estimators = 100, min_samples_split = 10, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(X_train,y_train)\n",
    "print(mean_squared_error(RFR.predict(X_train),y_train))\n",
    "print(mean_squared_error(RFR.predict(X_test),y_test))\n",
    "\n",
    "# TARGET NORMALIZED\n",
    "RFR = RandomForestRegressor(n_estimators = 100, min_samples_split = 10, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(X_train,yt_train)\n",
    "print(mean_squared_error(RFR.predict(X_train),yt_train))\n",
    "print(mean_squared_error(RFR.predict(X_test),yt_test))\n",
    "\n",
    "# POLYNOMIAL FEATURES AND NO NORMALIZATION OF TARGET\n",
    "RFR = RandomForestRegressor(n_estimators = 100, min_samples_split = 10, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(Xp_train,y_train)\n",
    "print(mean_squared_error(RFR.predict(Xp_train),y_train))\n",
    "print(mean_squared_error(RFR.predict(Xp_test),y_test))\n",
    "\n",
    "# BOTH POLYNOMIAL AND NORMALIZATION APPLIED\n",
    "RFR = RandomForestRegressor(n_estimators = 100, min_samples_split = 10, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(Xp_train,yt_train)\n",
    "print(mean_squared_error(RFR.predict(Xp_train),yt_train))\n",
    "print(mean_squared_error(RFR.predict(Xp_test),yt_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pooja\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass k=60 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n",
      "C:\\Users\\Pooja\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(830, 60)\n",
      "(356, 60)\n",
      "(830, 60)\n",
      "(356, 60)\n"
     ]
    }
   ],
   "source": [
    "# SELECTING BEST 60 FEATURES BY SelecKBest\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest ,mutual_info_regression\n",
    "SKB = SelectKBest(mutual_info_regression,60)\n",
    "\n",
    "SKB.fit(Xp_train,y_train)\n",
    "Xp60_train = SKB.transform(Xp_train)\n",
    "Xp60_test = SKB.transform(Xp_test)\n",
    "\n",
    "SKB.fit(Xp_train,yt_train)\n",
    "Xpt60_train = SKB.transform(Xp_train)\n",
    "Xpt60_test = SKB.transform(Xp_test)\n",
    "\n",
    "print(Xp60_train.shape)\n",
    "print(Xp60_test.shape)\n",
    "print(Xpt60_train.shape)\n",
    "print(Xpt60_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(830, 60)\n",
      "(356, 60)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False,  True]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Xp60_train.shape)\n",
    "print(Xp60_test.shape)\n",
    "(Xp60_train == Xpt60_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.8555732730585\n",
      "106.34185064166019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-385453eb382d>:10: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  RFR.fit(Xpt60_train,yt_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1749578823641398\n",
      "0.29372212887635735\n"
     ]
    }
   ],
   "source": [
    "# POLYNOMIAL REDUCED TO 60 FEATURES AND Y WITHOUT NORMALIZATION\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RFR = RandomForestRegressor(n_estimators = 500, min_samples_split = 20, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(Xp60_train,y_train)\n",
    "print(mean_squared_error(RFR.predict(Xp60_train),y_train))\n",
    "print(mean_squared_error(RFR.predict(Xp60_test),y_test))\n",
    "\n",
    "# POLYNOMIAL REDUCED TO 60 FEATURES AND Y WITH NORMALIZATION\n",
    "RFR = RandomForestRegressor(n_estimators = 500, min_samples_split = 20, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(Xpt60_train,yt_train)\n",
    "print(mean_squared_error(RFR.predict(Xpt60_train),yt_train))\n",
    "print(mean_squared_error(RFR.predict(Xpt60_test),yt_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRYING SUPPORT VECTOR MACHINE ON THE DATA\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "svr = SVR(kernel = 'poly',degree = 1, C = 1.0 , max_iter = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9191047825443772\n",
      "0.9958312239696495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pooja\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "svr.fit(Xp60_train,yt_train)\n",
    "print(mean_squared_error(svr.predict(Xp60_train),yt_train))\n",
    "print(mean_squared_error(svr.predict(Xp60_test),yt_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WE TAKE THE PREDICTED VALUES FROM THE SVR AND USE IT AS A FEATURE IN RANDOM FOREST REGRESSION\n",
    "\n",
    "X_train_svm = np.concatenate([Xp60_train,svr.predict(Xp60_train).reshape(-1,1)],axis = 1)\n",
    "X_test_svm = np.concatenate([Xp60_test,svr.predict(Xp60_test).reshape(-1,1)],axis = 1)\n",
    "\n",
    "data_svm = np.concatenate([X_train_svm,X_test_svm],axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-ac552e861caf>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  RFR.fit(X_train_svm,yt_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17546087971975474\n",
      "0.2900338280131972\n"
     ]
    }
   ],
   "source": [
    "# TRAINING RFR ON THE MODIFIED DATASET\n",
    "RFR = RandomForestRegressor(n_estimators = 500, min_samples_split = 20, max_features = 'auto',n_jobs = -1, random_state = 1)\n",
    "RFR.fit(X_train_svm,yt_train)\n",
    "print(mean_squared_error(RFR.predict(X_train_svm),yt_train))\n",
    "print(mean_squared_error(RFR.predict(X_test_svm),yt_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1 = PT.inverse_transform(RFR.predict(X_test_svm).reshape(-1,1)).reshape(-1,)\n",
    "g2 = PT.inverse_transform(yt_test.reshape(-1,1)).reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2712aa60a90>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LOOKING AT THE PREDICTIONS VISUALLY\n",
    "# PREDICTED VS ACTUAL GRAPH\n",
    "# y = x line (ORANGE)\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.plot(np.arange(-40,50),np.arange(-40,50), color = 'orange')\n",
    "plt.scatter(g1,g2, marker = '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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