DecisionTreeExploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Decision Tree Exploration**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Previous Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#import sklearn, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import datasets as Pandas dataframes\n",
    "#NOTE: the filepath may be different for each user\n",
    "main_dataset = pd.read_csv('Datasets/Clean_Dataset.csv')\n",
    "business_df = pd.read_csv('Datasets/business.csv')\n",
    "economy_df = pd.read_csv('Datasets/economy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>airline</th>\n",
       "      <th>flight</th>\n",
       "      <th>source_city</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>stops</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>destination_city</th>\n",
       "      <th>class</th>\n",
       "      <th>duration</th>\n",
       "      <th>days_left</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>SG-8709</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1</td>\n",
       "      <td>5953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>SG-8157</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1</td>\n",
       "      <td>5953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AirAsia</td>\n",
       "      <td>I5-764</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1</td>\n",
       "      <td>5956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Vistara</td>\n",
       "      <td>UK-995</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>5955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Vistara</td>\n",
       "      <td>UK-963</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1</td>\n",
       "      <td>5955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   airline   flight source_city departure_time stops  \\\n",
       "0           0  SpiceJet  SG-8709       Delhi        Evening  zero   \n",
       "1           1  SpiceJet  SG-8157       Delhi  Early_Morning  zero   \n",
       "2           2   AirAsia   I5-764       Delhi  Early_Morning  zero   \n",
       "3           3   Vistara   UK-995       Delhi        Morning  zero   \n",
       "4           4   Vistara   UK-963       Delhi        Morning  zero   \n",
       "\n",
       "    arrival_time destination_city    class  duration  days_left  price  \n",
       "0          Night           Mumbai  Economy      2.17          1   5953  \n",
       "1        Morning           Mumbai  Economy      2.33          1   5953  \n",
       "2  Early_Morning           Mumbai  Economy      2.17          1   5956  \n",
       "3      Afternoon           Mumbai  Economy      2.25          1   5955  \n",
       "4        Morning           Mumbai  Economy      2.33          1   5955  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Unnamed: 0', 'airline', 'flight', 'source_city', 'departure_time', 'stops', 'arrival_time', 'destination_city', 'class', 'duration', 'days_left', 'price']\n",
      "(300153, 12)\n"
     ]
    }
   ],
   "source": [
    "print(list(main_dataset.columns))\n",
    "print(main_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>date</th>\n",
       "      <th>airline</th>\n",
       "      <th>ch_code</th>\n",
       "      <th>num_code</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>from</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>stop</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>to</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>868</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 00m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>624</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 15m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>21:15</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>531</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>24h 45m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t...</td>\n",
       "      <td>20:45</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>42,220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>839</td>\n",
       "      <td>21:25</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>26h 30m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t...</td>\n",
       "      <td>23:55</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>44,450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>544</td>\n",
       "      <td>17:15</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>06h 40m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t...</td>\n",
       "      <td>23:55</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>46,690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    airline ch_code  num_code dep_time   from time_taken  \\\n",
       "0  11-02-2022  Air India      AI       868    18:00  Delhi    02h 00m   \n",
       "1  11-02-2022  Air India      AI       624    19:00  Delhi    02h 15m   \n",
       "2  11-02-2022  Air India      AI       531    20:00  Delhi    24h 45m   \n",
       "3  11-02-2022  Air India      AI       839    21:25  Delhi    26h 30m   \n",
       "4  11-02-2022  Air India      AI       544    17:15  Delhi    06h 40m   \n",
       "\n",
       "                                                stop arr_time      to   price  \n",
       "0                                          non-stop     20:00  Mumbai  25,612  \n",
       "1                                          non-stop     21:15  Mumbai  25,612  \n",
       "2  1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t...    20:45  Mumbai  42,220  \n",
       "3  1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t...    23:55  Mumbai  44,450  \n",
       "4  1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t...    23:55  Mumbai  46,690  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['date', 'airline', 'ch_code', 'num_code', 'dep_time', 'from', 'time_taken', 'stop', 'arr_time', 'to', 'price']\n",
      "(93487, 11)\n"
     ]
    }
   ],
   "source": [
    "print(list(business_df.columns))\n",
    "print(business_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>date</th>\n",
       "      <th>airline</th>\n",
       "      <th>ch_code</th>\n",
       "      <th>num_code</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>from</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>stop</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>to</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>SG</td>\n",
       "      <td>8709</td>\n",
       "      <td>18:55</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 10m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>21:05</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>5,953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>SG</td>\n",
       "      <td>8157</td>\n",
       "      <td>06:20</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 20m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>08:40</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>5,953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>AirAsia</td>\n",
       "      <td>I5</td>\n",
       "      <td>764</td>\n",
       "      <td>04:25</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 10m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>06:35</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>5,956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Vistara</td>\n",
       "      <td>UK</td>\n",
       "      <td>995</td>\n",
       "      <td>10:20</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 15m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>12:35</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>5,955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Vistara</td>\n",
       "      <td>UK</td>\n",
       "      <td>963</td>\n",
       "      <td>08:50</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 20m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>11:10</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>5,955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   airline ch_code  num_code dep_time   from time_taken  \\\n",
       "0  11-02-2022  SpiceJet      SG      8709    18:55  Delhi    02h 10m   \n",
       "1  11-02-2022  SpiceJet      SG      8157    06:20  Delhi    02h 20m   \n",
       "2  11-02-2022   AirAsia      I5       764    04:25  Delhi    02h 10m   \n",
       "3  11-02-2022   Vistara      UK       995    10:20  Delhi    02h 15m   \n",
       "4  11-02-2022   Vistara      UK       963    08:50  Delhi    02h 20m   \n",
       "\n",
       "        stop arr_time      to  price  \n",
       "0  non-stop     21:05  Mumbai  5,953  \n",
       "1  non-stop     08:40  Mumbai  5,953  \n",
       "2  non-stop     06:35  Mumbai  5,956  \n",
       "3  non-stop     12:35  Mumbai  5,955  \n",
       "4  non-stop     11:10  Mumbai  5,955  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "economy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['date', 'airline', 'ch_code', 'num_code', 'dep_time', 'from', 'time_taken', 'stop', 'arr_time', 'to', 'price']\n",
      "(206774, 11)\n"
     ]
    }
   ],
   "source": [
    "print(list(economy_df.columns))\n",
    "print(economy_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300261"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "206774+93487"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>date</th>\n",
       "      <th>airline</th>\n",
       "      <th>ch_code</th>\n",
       "      <th>num_code</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>from</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>stop</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>to</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>868</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 00m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>624</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 15m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>21:15</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>868</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 00m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>887</td>\n",
       "      <td>07:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 05m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>09:05</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>665</td>\n",
       "      <td>08:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 10m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>10:10</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>805</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 10m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>22:10</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>678</td>\n",
       "      <td>09:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 15m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>11:15</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>624</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 15m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>21:15</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>636</td>\n",
       "      <td>14:30</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>03h 30m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IDR\\n\\t\\t\\...</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>441</td>\n",
       "      <td>17:50</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>03h 45m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IXU\\n\\t\\t\\...</td>\n",
       "      <td>21:35</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>868</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 00m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>887</td>\n",
       "      <td>07:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 05m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>09:05</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>665</td>\n",
       "      <td>08:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 10m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>10:10</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>805</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 10m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>22:10</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>624</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>02h 15m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>21:15</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>636</td>\n",
       "      <td>14:30</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>03h 30m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IDR\\n\\t\\t\\...</td>\n",
       "      <td>18:00</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>13-02-2022</td>\n",
       "      <td>Air India</td>\n",
       "      <td>AI</td>\n",
       "      <td>441</td>\n",
       "      <td>17:50</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>03h 45m</td>\n",
       "      <td>1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IXU\\n\\t\\t\\...</td>\n",
       "      <td>21:35</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>25,612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    airline ch_code  num_code dep_time   from time_taken  \\\n",
       "0    11-02-2022  Air India      AI       868    18:00  Delhi    02h 00m   \n",
       "1    11-02-2022  Air India      AI       624    19:00  Delhi    02h 15m   \n",
       "35   12-02-2022  Air India      AI       868    18:00  Delhi    02h 00m   \n",
       "36   12-02-2022  Air India      AI       887    07:00  Delhi    02h 05m   \n",
       "37   12-02-2022  Air India      AI       665    08:00  Delhi    02h 10m   \n",
       "38   12-02-2022  Air India      AI       805    20:00  Delhi    02h 10m   \n",
       "39   12-02-2022  Air India      AI       678    09:00  Delhi    02h 15m   \n",
       "40   12-02-2022  Air India      AI       624    19:00  Delhi    02h 15m   \n",
       "41   12-02-2022  Air India      AI       636    14:30  Delhi    03h 30m   \n",
       "42   12-02-2022  Air India      AI       441    17:50  Delhi    03h 45m   \n",
       "123  13-02-2022  Air India      AI       868    18:00  Delhi    02h 00m   \n",
       "124  13-02-2022  Air India      AI       887    07:00  Delhi    02h 05m   \n",
       "125  13-02-2022  Air India      AI       665    08:00  Delhi    02h 10m   \n",
       "126  13-02-2022  Air India      AI       805    20:00  Delhi    02h 10m   \n",
       "127  13-02-2022  Air India      AI       624    19:00  Delhi    02h 15m   \n",
       "128  13-02-2022  Air India      AI       636    14:30  Delhi    03h 30m   \n",
       "129  13-02-2022  Air India      AI       441    17:50  Delhi    03h 45m   \n",
       "\n",
       "                                                  stop arr_time      to  \\\n",
       "0                                            non-stop     20:00  Mumbai   \n",
       "1                                            non-stop     21:15  Mumbai   \n",
       "35                                           non-stop     20:00  Mumbai   \n",
       "36                                           non-stop     09:05  Mumbai   \n",
       "37                                           non-stop     10:10  Mumbai   \n",
       "38                                           non-stop     22:10  Mumbai   \n",
       "39                                           non-stop     11:15  Mumbai   \n",
       "40                                           non-stop     21:15  Mumbai   \n",
       "41   1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IDR\\n\\t\\t\\...    18:00  Mumbai   \n",
       "42   1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IXU\\n\\t\\t\\...    21:35  Mumbai   \n",
       "123                                          non-stop     20:00  Mumbai   \n",
       "124                                          non-stop     09:05  Mumbai   \n",
       "125                                          non-stop     10:10  Mumbai   \n",
       "126                                          non-stop     22:10  Mumbai   \n",
       "127                                          non-stop     21:15  Mumbai   \n",
       "128  1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IDR\\n\\t\\t\\...    18:00  Mumbai   \n",
       "129  1-stop\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tVia IXU\\n\\t\\t\\...    21:35  Mumbai   \n",
       "\n",
       "      price  \n",
       "0    25,612  \n",
       "1    25,612  \n",
       "35   25,612  \n",
       "36   25,612  \n",
       "37   25,612  \n",
       "38   25,612  \n",
       "39   25,612  \n",
       "40   25,612  \n",
       "41   25,612  \n",
       "42   25,612  \n",
       "123  25,612  \n",
       "124  25,612  \n",
       "125  25,612  \n",
       "126  25,612  \n",
       "127  25,612  \n",
       "128  25,612  \n",
       "129  25,612  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_df.loc[business_df['price'] == '25,612']"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>airline</th>\n",
       "      <th>flight</th>\n",
       "      <th>source_city</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>stops</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>destination_city</th>\n",
       "      <th>class</th>\n",
       "      <th>duration</th>\n",
       "      <th>days_left</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206666</th>\n",
       "      <td>206666</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-868</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206667</th>\n",
       "      <td>206667</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-624</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206701</th>\n",
       "      <td>206701</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-868</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206702</th>\n",
       "      <td>206702</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-887</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206703</th>\n",
       "      <td>206703</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-665</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206704</th>\n",
       "      <td>206704</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-805</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206705</th>\n",
       "      <td>206705</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-678</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206706</th>\n",
       "      <td>206706</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-624</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206707</th>\n",
       "      <td>206707</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-636</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>one</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206708</th>\n",
       "      <td>206708</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-441</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>one</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206789</th>\n",
       "      <td>206789</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-868</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206790</th>\n",
       "      <td>206790</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-887</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206791</th>\n",
       "      <td>206791</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-665</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>zero</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206792</th>\n",
       "      <td>206792</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-805</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206793</th>\n",
       "      <td>206793</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-624</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>zero</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206794</th>\n",
       "      <td>206794</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-636</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>one</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206795</th>\n",
       "      <td>206795</td>\n",
       "      <td>Air_India</td>\n",
       "      <td>AI-441</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>one</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Business</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0    airline  flight source_city departure_time stops  \\\n",
       "206666      206666  Air_India  AI-868       Delhi        Evening  zero   \n",
       "206667      206667  Air_India  AI-624       Delhi        Evening  zero   \n",
       "206701      206701  Air_India  AI-868       Delhi        Evening  zero   \n",
       "206702      206702  Air_India  AI-887       Delhi  Early_Morning  zero   \n",
       "206703      206703  Air_India  AI-665       Delhi  Early_Morning  zero   \n",
       "206704      206704  Air_India  AI-805       Delhi        Evening  zero   \n",
       "206705      206705  Air_India  AI-678       Delhi        Morning  zero   \n",
       "206706      206706  Air_India  AI-624       Delhi        Evening  zero   \n",
       "206707      206707  Air_India  AI-636       Delhi      Afternoon   one   \n",
       "206708      206708  Air_India  AI-441       Delhi        Evening   one   \n",
       "206789      206789  Air_India  AI-868       Delhi        Evening  zero   \n",
       "206790      206790  Air_India  AI-887       Delhi  Early_Morning  zero   \n",
       "206791      206791  Air_India  AI-665       Delhi  Early_Morning  zero   \n",
       "206792      206792  Air_India  AI-805       Delhi        Evening  zero   \n",
       "206793      206793  Air_India  AI-624       Delhi        Evening  zero   \n",
       "206794      206794  Air_India  AI-636       Delhi      Afternoon   one   \n",
       "206795      206795  Air_India  AI-441       Delhi        Evening   one   \n",
       "\n",
       "       arrival_time destination_city     class  duration  days_left  price  \n",
       "206666      Evening           Mumbai  Business      2.00          1  25612  \n",
       "206667        Night           Mumbai  Business      2.25          1  25612  \n",
       "206701      Evening           Mumbai  Business      2.00          2  25612  \n",
       "206702      Morning           Mumbai  Business      2.08          2  25612  \n",
       "206703      Morning           Mumbai  Business      2.17          2  25612  \n",
       "206704        Night           Mumbai  Business      2.17          2  25612  \n",
       "206705      Morning           Mumbai  Business      2.25          2  25612  \n",
       "206706        Night           Mumbai  Business      2.25          2  25612  \n",
       "206707      Evening           Mumbai  Business      3.50          2  25612  \n",
       "206708        Night           Mumbai  Business      3.75          2  25612  \n",
       "206789      Evening           Mumbai  Business      2.00          3  25612  \n",
       "206790      Morning           Mumbai  Business      2.08          3  25612  \n",
       "206791      Morning           Mumbai  Business      2.17          3  25612  \n",
       "206792        Night           Mumbai  Business      2.17          3  25612  \n",
       "206793        Night           Mumbai  Business      2.25          3  25612  \n",
       "206794      Evening           Mumbai  Business      3.50          3  25612  \n",
       "206795        Night           Mumbai  Business      3.75          3  25612  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_dataset.loc[main_dataset['price'] == 25612]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(main_dataset.loc[main_dataset['price'] == 25612]) == len(business_df.loc[business_df['price'] == '25,612'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the cleaned dataset is a combination of the business and economy class flights with a few modifications (~100 dropped flights, prices converted from strings to ints, etc.).\n",
    "\n",
    "However, there are some immediate, glaring issues that should be taken care of:\n",
    "\n",
    "- We need to drop the unnamed column\n",
    "- We should convert the 'stops' (number of stops per flight) from categorical data to numerical data\n",
    "- We should fix some of the names for better readability (e.g. 'days_left' refers to number of days between booking and vacation, but this isn't obvious)\n",
    "\n",
    "Additionally, for quality of life, we may drop some columns we are less interested in working with. This would be done in a new dataframe, however, and will likely be done after we find some sort of test metric for usability (for example, we do not expect flight serial number to be a good predictor of price)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_dataset = main_dataset.drop(columns = ['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['zero' 'one' 'two_or_more']\n"
     ]
    }
   ],
   "source": [
    "print(main_dataset['stops'].unique())\n",
    "def strtonum(ele):\n",
    "    if ele == 'zero':\n",
    "        num = 0\n",
    "            \n",
    "    elif ele == 'one':\n",
    "        num = 1\n",
    "            \n",
    "    else:\n",
    "        num = 2\n",
    "            \n",
    "    return num\n",
    "\n",
    "main_dataset['stops'] = main_dataset['stops'].apply(strtonum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>airline</th>\n",
       "      <th>flight</th>\n",
       "      <th>source_city</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>stops</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>destination_city</th>\n",
       "      <th>class</th>\n",
       "      <th>duration</th>\n",
       "      <th>days_left</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>SG-8709</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Evening</td>\n",
       "      <td>0</td>\n",
       "      <td>Night</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1</td>\n",
       "      <td>5953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>SG-8157</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1</td>\n",
       "      <td>5953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AirAsia</td>\n",
       "      <td>I5-764</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>0</td>\n",
       "      <td>Early_Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1</td>\n",
       "      <td>5956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>UK-995</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>5955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>UK-963</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1</td>\n",
       "      <td>5955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    airline   flight source_city departure_time  stops   arrival_time  \\\n",
       "0  SpiceJet  SG-8709       Delhi        Evening      0          Night   \n",
       "1  SpiceJet  SG-8157       Delhi  Early_Morning      0        Morning   \n",
       "2   AirAsia   I5-764       Delhi  Early_Morning      0  Early_Morning   \n",
       "3   Vistara   UK-995       Delhi        Morning      0      Afternoon   \n",
       "4   Vistara   UK-963       Delhi        Morning      0        Morning   \n",
       "\n",
       "  destination_city    class  duration  days_left  price  \n",
       "0           Mumbai  Economy      2.17          1   5953  \n",
       "1           Mumbai  Economy      2.33          1   5953  \n",
       "2           Mumbai  Economy      2.17          1   5956  \n",
       "3           Mumbai  Economy      2.25          1   5955  \n",
       "4           Mumbai  Economy      2.33          1   5955  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#main_dataset['days_left'] = main_dataset['days_left'].rename('hi') when we think of a name\n",
    "main_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "airline             False\n",
       "flight              False\n",
       "source_city         False\n",
       "departure_time      False\n",
       "stops               False\n",
       "arrival_time        False\n",
       "destination_city    False\n",
       "class               False\n",
       "duration            False\n",
       "days_left           False\n",
       "price               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_dataset.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ExtraTreesRegressor to determine which features play the largest role in determining flight price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "extra_trees_data = main_dataset\n",
    "\n",
    "#Label Encoder encodes target labels with values between 0 and n_classes-1\n",
    "#Labels are then normalized using the encoder\n",
    "encoder = LabelEncoder()\n",
    "extra_trees_data = extra_trees_data.drop(['flight'],axis=1)\n",
    "extra_trees_data['airline'] = encoder.fit_transform(extra_trees_data['airline'])\n",
    "extra_trees_data['source_city'] = encoder.fit_transform(extra_trees_data['source_city'])\n",
    "extra_trees_data['departure_time'] = encoder.fit_transform(extra_trees_data['departure_time'])\n",
    "extra_trees_data['stops'] = encoder.fit_transform(extra_trees_data['stops'])\n",
    "extra_trees_data['arrival_time'] = encoder.fit_transform(extra_trees_data['arrival_time'])\n",
    "extra_trees_data['destination_city'] = encoder.fit_transform(extra_trees_data['destination_city'])\n",
    "extra_trees_data['class'] = encoder.fit_transform(extra_trees_data['class'])\n",
    "\n",
    "X = extra_trees_data.drop('price', axis=1)\n",
    "y = extra_trees_data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>class</td>\n",
       "      <td>0.880371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stops</td>\n",
       "      <td>0.031734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>duration</td>\n",
       "      <td>0.026881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>days_left</td>\n",
       "      <td>0.018426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>airline</td>\n",
       "      <td>0.011287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>destination_city</td>\n",
       "      <td>0.010434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>source_city</td>\n",
       "      <td>0.010425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arrival_time</td>\n",
       "      <td>0.005334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>departure_time</td>\n",
       "      <td>0.005108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Feature  Importance\n",
       "6             class    0.880371\n",
       "3             stops    0.031734\n",
       "7          duration    0.026881\n",
       "8         days_left    0.018426\n",
       "0           airline    0.011287\n",
       "5  destination_city    0.010434\n",
       "1       source_city    0.010425\n",
       "4      arrival_time    0.005334\n",
       "2    departure_time    0.005108"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    'Feature' : X.columns.tolist(),\n",
    "    'Importance' : selection.feature_importances_\n",
    "}\n",
    "\n",
    "selection_output = pd.DataFrame(data).sort_values(by='Importance', ascending=False)\n",
    "selection_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAFlCAYAAAA5w+hdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfy0lEQVR4nO3df7ReVX3n8feHRINIDKOhrkjRa9MoRSJBooI/GEBtran8GKFoUYlas9CqdRwcM7X+6C9XbKZTqtS2qVNpBRVBsJlGQYsgivIjCYQAArYSq+CMiBLRFKrwnT+eE3283iTPDffenXuf92st1j1nn332+Z7nrCQf9tn33lQVkiRJrezVugBJkjTcDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqanZrQsYRvPnz6+RkZHWZUiSNGU2bNjwnaraf6xjhpEGRkZGWL9+fesyJEmaMkm+vqNjvqaRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1JRhpIHNd2xlZOU6Rlaua12KJEnNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlO7HUaSvDvJGRNZzKjx90vy+skav+86b06yT9/+p5LsN9nXlSRJPXvkzEiS2cB+wLjDSJJZ4zzlzcBPwkhVvaiq7hnvdSVJ0u4ZVxhJ8vYktyX5IvDkrm1hkouTbEjyhSQHde1nJ/nrJOu7c36jax/p+m3s/ntW1350174WuBlYBSxMcn2S1d3xf+qr5awky7vtLUnem2QjcHKSX03y5W7885Psu4P7eRPwOOCyJJf1jTW/q/OW7j5uS3JukucnuTLJV5M8o+v/yCR/l+SaJNclOX48n6kkScNu9qAdkxwOvBRY0p23EdgArAFOr6qvJnkm8AHg2O60EeAZwEJ6/+D/MvBt4AVVdV+SRcBHgaVd/6cBh1TV7UlGuu0l3fWP3kWJd1fV05LMBy4Enl9VP0zyNuAtwB+OPqGq3pfkLcAxVfWdMcb8ZeBk4NXAtcBvAc8BjgN+DzgBeDvwuap6dfd655ok/1xVPxz1+a0AVgDMetT+u7gVSZKGx8BhBHgucFFVbQPoZjD2Bp4FnJ9ke785fed8vKoeBL6a5GvAQcDtwFlJlgAPAE/q639NVd2+OzcCnNd9PQI4GLiyq+nhwJd3c8zbq2ozQJKbgEurqpJsphe0AH4VOK5v/czewOOBr/QPVFVr6AU35ixYVLtZjyRJM854wshY9gLu2T57MYbR/+gW8F+B/wcc2p1/X9/xH7JjP+ZnXyvtPer49nMDfLaqXraTsQZ1f9/2g337D/LTzy7AS6rq1gm4niRJQ2c8a0auAE5I8ogkc4EXA9uA25OcDJCeQ/vOOTnJXkkWAr8E3ArMA77VzZi8AtjRgtN7gbl9+18HDk4yp3sd8rwdnHcV8OzuldD2NR1P2kHfsa4zXpcAb0w3DZPksIcwliRJQ2fgMFJVG+m9CtkEfJreGgqAU4HXJNkE3AT0L+D8N+Carv/pVXUfvTUlp3X9D2IHsyFVdTe9Vy03JlldVd8APg7c2H29bgfn3QUsBz6a5AZ6r2gO2smtrQEu3r6AdTf8EfAw4IbuVc4f7eY4kiQNpVRNzvKFJGcD/1RVF0zKBaaxOQsW1YLTzgRgy6plbYuRJGkKJNlQVUvHOrZH/pwRSZI0PB7qAtYdqqrlkzX27khyEfDEUc1vq6pLWtQjSZJ6Ji2M7Gmq6sTWNUiSpJ83NGFkT7L4gHmsd62IJEmAa0YkSVJjhhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1JRhRJIkNWUYkSRJTRlGJElSU4YRSZLUlGFEkiQ1ZRiRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGkQY237GVkZXrGFm5rnUpkiQ1ZxiRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU1NmzCS5INJDt7Nc7ckmb+DY/sleX3f/uOSXLC7dUqSpPHZI8NIklmj96vqt6vq5km43H7AT8JIVd1ZVSdNwnUkSdIYmoSRJJ9MsiHJTUlWdG0/SPJnSTYBR46xf3mSpUlOT7K6b6zlSc7a0bgDWAUsTHJ9ktVJRpLc2Df2J5N8tptdeUOStyS5LslVSR7d9VuY5OLu2l9IctBEfl6SJM1krWZGXl1VhwNLgTcleQzwSODqqjq0qr44xv52nwBO7Ns/BfjYTsbdlZXAv1bVkqp66xjHDwH+C/B04E+AbVV1GPBl4JVdnzXAG7trnwF8YPQgSVYkWZ9k/QPbtg5QliRJw2F2o+u+Kcn2QHEgsAh4gF7Q2G70PgBVdVeSryU5AvgqcBBw5U7Gvfsh1npZVd0L3JtkK/B/uvbNwFOT7As8Czg/yfZz5oxR9xp6oYU5CxbVQ6xJkqQZY8rDSJKjgecDR1bVtiSXA3sD91XVA31dR+/3+xjwm8AtwEVVVTsZ96G6v2/7wb79B+l9fnsB91TVkgm4liRJQ6fFa5p5wPe6wHAQcMRujHERcDzwMn76imZ3x70XmLsbNQBQVd8Hbk9yMkB6Dt3d8SRJGjYtwsjFwOwkX6G3ePSq8Q5QVd8DvgI8oaqueSjjVtXdwJVJbuxfGDtOpwKv6Rbb3kQvKEmSpAGkyuULU23OgkW14LQzAdiyalnbYiRJmgJJNlTV0rGO7ZE/Z0SSJA2PVt9NM+W6b/O9dIxDz+te1UiSpAaGJox0gWNJ6zokSdLPGpowsidZfMA81rtWRJIkwDUjkiSpMcOIJElqyjAiSZKaMoxIkqSmDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJasowIkmSmjKMSJKkpgwjkiSpKcOIJElqyjAiSZKaMoxIkqSmDCOSJKmp2a0LGEab79jKyMp1P9O2ZdWyRtVIktSWMyOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJasowMqAkxyVZ2W2fkOTg1jVJkjQTzKgwkmTSfm5KVa2tqlXd7gmAYUSSpAnQNIwkeWSSdUk2JbkxySlJnpfkuiSbk/xdkjld3y1J5nfbS5Nc3m2/O8mHk1wJfDjJY5Nc1I25Kcmzun4vT3JNkuuT/E2SWTup64VJNnbnX9q1LU9yVjfeccDqbqyFSTb2nbuof7+vfUWS9UnWP7Bt68R9iJIkTXOtZ0ZeCNxZVYdW1SHAxcDZwClVtZjeT4h93QDjHAw8v6peBrwP+HxVHQo8Dbgpya8ApwDPrqolwAPAqWMNlGR/4G+Bl3RjnNx/vKq+BKwF3lpVS6rqX4GtSZZ0XV4FfGj0uFW1pqqWVtXSWfvMG+CWJEkaDq3DyGbgBUnem+S5wAhwe1Xd1h3/e+CoAcZZW1X/3m0fC/wVQFU9UFVbgecBhwPXJrm+2/+lHYx1BHBFVd3ejfHdAa7/QeBV3WzLKcBHBjhHkiTR+HfTVNVtSZ4GvAj4Y+BzO+n+Y34anvYedeyHu7hUgL+vqv+xW4Xu2ieAd9Grf0NV3T1J15EkacZpvWbkccC2qjoHWA0cCYwk+eWuyyuAz3fbW+jNbgC8ZCfDXkr3aifJrCTzuraTkvxC1/7oJE/YwflXAUcleeL2vmP0uReYu32nqu4DLqE3I/Nzr2gkSdKOtX5Nsxi4pnt18i7g9+mtuTg/yWbgQeCvu75/APxFkvX01nzsyO8Cx3TnbwAOrqqbu7E/k+QG4LPAgrFOrqq7gBXAhUk2AeeN0e1jwFu7hbYLu7Zzu3o/M9CdS5IkAFJVrWuYEZKcAcyrqnfsqu+cBYtqwWln/kzbllXLJqkySZLaS7KhqpaOdazpmpGZIslFwEJ6i2clSdI4DHUYSXI1MGdU8yuqavN4xqmqEyeuKkmShstQh5GqemaL6y4+YB7rfS0jSRLQfgGrJEkacoYRSZLUlGFEkiQ1ZRiRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1JRhRJIkNWUYkSRJTRlGJElSU4YRSZLUlGFEkiQ1ZRiRJElNGUYkSVJTs1sXMIw237GVkZXrfqZty6pljaqRJKktZ0YkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1NS4w0iSdyc5Y5znLEnyor7945KsHO+1u3P3S/L6vv3HJblgd8Ya53V/UnOSE5IcPNnXlCRpGEzVzMgS4CdhpKrWVtWq3RxrP+AnYaSq7qyqkx5SdQMYVfMJgGFEkqQJMFAYSfL2JLcl+SLw5K5tYZKLk2xI8oUkB3XtJye5McmmJFckeTjwh8ApSa5PckqS5UnO6vqfneR9Sb6U5GtJTura901yaZKNSTYnOb4rZxWwsBtrdZKRJDd25+yd5ENd/+uSHNO1L09yYVfvV5P86S7u94XddTclubRvjLOSPAs4Dljd1bAwyca+cxf17/e1r0iyPsn6B7ZtHeRjlyRpKOzyJ7AmORx4Kb3ZjdnARmADsAY4vaq+muSZwAeAY4F3Ar9WVXck2a+q/iPJO4GlVfWGbszloy6zAHgOcBCwFrgAuA84saq+n2Q+cFWStcBK4JCqWtKNNdI3zu8AVVWLu3D0mSRP6o4tAQ4D7gduTfL+qvrGGPe7P/C3wFFVdXuSR/cfr6ovdXX8U1Vd0J2zNcmSqroeeBXwodHjVtWa7jNjzoJFNcZHLUnSUBrkx8E/F7ioqrYBdP8Q7w08Czg/yfZ+c7qvVwJnJ/k4cOGAdXyyqh4Ebk7y2K4twHuSHAU8CBwAPHZHA3SeA7wfoKpuSfJ1YHsYubSqtnb3cDPwBODnwghwBHBFVd3ejfPdAer/IPCqJG8BTgGeMcA5kiSJ3f/dNHsB92yfnehXVad3MyXLgA3dzMqu3N+3vT3dnArsDxxeVT9KsoVeCNpd/dd4gIn9vTyfAN4FfA7YUFV3T+DYkiTNaIOsGbkCOCHJI5LMBV4MbANuT3IyQHoO7bYXVtXVVfVO4C7gQOBeYO44a5sHfLsLIsfQm8lgF2N9gV6IoXs983jg1nFe9yrgqCRP7MZ59Bh9fqaGqroPuAT4K8Z4RSNJknZsl2GkqjYC5wGbgE8D13aHTgVek2QTcBOwfYHp6m4B6Y3Al7rzLgMO3r6AdcDazgWWJtkMvBK4pavnbuDKbpHs6lHnfADYqzvnPGB5Vd3POFTVXcAK4MLu3s4bo9vHgLd2i2QX9tX7IPCZ8VxPkqRhlyrXUk6E7mevzKuqd+yq75wFi2rBaWf+TNuWVcsmqTJJktpLsqGqlo51bCLXTQytJBcBC+l9N5EkSRqHoQ4jSa7mp98FtN0rqmrzeMapqhMnripJkobLUIeRqnpm6xokSRp2Qx1GWll8wDzWu0ZEkiTA39orSZIaM4xIkqSmDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJasowIkmSmjKMSJKkpgwjkiSpKcOIJElqyjAiSZKaMoxIkqSmDCOSJKkpw4gkSWrKMCJJkpqa3bqAYbT5jq2MrFz3c+1bVi1rUI0kSW05MyJJkpoyjEiSpKYMI5IkqSnDiCRJampow0iSTyXZbwfHtiSZ321/aUoLkyRpyAztd9NU1YtGtyUJkFH9njVlRUmSNISGYmYkySeTbEhyU5IVXduWJPOTjCS5Nck/ADcCB4469wfd16OTXJ7kgiS3JDm3Cy8kOTzJ57trXJJkwVTfoyRJ09WwzIy8uqq+m+QRwLVJPjHq+CLgtKq6CqDLGGM5DHgKcCdwJfDsJFcD7weOr6q7kpwC/Anw6km4D0mSZpxhCSNvSnJit30gvfDR7+vbg8guXFNV3wRIcj0wAtwDHAJ8tgsxs4BvjT6xm5FZATDrUfuP+wYkSZqpZnwYSXI08HzgyKraluRyYO9R3X444HD3920/QO/zC3BTVR25sxOrag2wBmDOgkU14PUkSZrxhmHNyDzge10QOQg4YoLHvxXYP8mRAEkeluQpE3wNSZJmrGEIIxcDs5N8BVgFDPI6ZmBV9R/AScB7k2wCrgf8DhxJkgY041/TVNX9wK+PcWik+/odems++s8Z6dvet/t6OXB5X/sb+ravB46akIIlSRoywzAzIkmS9mCGEUmS1JRhRJIkNWUYkSRJTc34Bax7osUHzGP9qmWty5AkaY/gzIgkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJasowIkmSmjKMSJKkpgwjkiSpKcOIJElqyjAiSZKaMoxIkqSmDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJamp26wKG0eY7tjKyct1O+2xZtWyKqpEkqS1nRiRJUlOGEUmS1JRhRJIkNWUYkSRJTRlGJElSU9M2jCR5d5IzJnH85UnO2kWfOUn+Ocn1SU5J8nuTVY8kSTOV39r70BwGUFVLAJL8AHhPy4IkSZpuptXMSJK3J7ktyReBJ3dtr01ybZJNST6RZJ8kc5PcnuRhXZ9Hbd9P8qYkNye5IcnHBrzu/t3Y13b/PTvJLwDnAE/vZkbOBx7RbZ87WZ+BJEkzzbSZGUlyOPBSYAm9ujcCG4ALq+pvuz5/DLymqt6f5HJgGfDJ7rwLq+pHSVYCT6yq+5PsN+Dl/wL486r6YpLHA5dU1a8k+W3gjKr6je76P9g+SzJG/SuAFQCzHrX/eG9fkqQZa9qEEeC5wEVVtQ0gydqu/ZAuhOwH7Atc0rV/EPjv9MLIq4DXdu03AOcm+WR3bBDPBw5Osn3/UUn2HU/xVbUGWAMwZ8GiGs+5kiTNZNMpjOzI2cAJVbUpyXLgaICqujLJSJKjgVlVdWPXfxlwFPBi4O1JFlfVj3dxjb2AI6rqvv7GvnAiSZJ203RaM3IFcEKSRySZSy9MAMwFvtWtDzl11Dn/AHwE+BBAkr2AA6vqMuBtwDx6sym78hngjdt3kizZQb8fbV+nIkmSBjNtwkhVbQTOAzYBnwau7Q69A7gauBK4ZdRp5wL/Cfhotz8LOCfJZuA64H1Vdc8Al38TsLRb9HozcPoO+q0BbnABqyRJg0vVzF2+kOQk4PiqekXrWvrNWbCoFpx25k77+Ft7JUkzSZINVbV0rGMzYc3ImJK8H/h14EWta5EkSTs2Y8NIVb1x170gyauA3x3VfGVV/c7EVyVJkkabsWFkUFX1IboFrpIkaeoNfRhpYfEB81jvmhBJkoBp9N00kiRpZjKMSJKkpgwjkiSpKcOIJElqyjAiSZKaMoxIkqSmDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJasowIkmSmjKMSJKkpgwjkiSpKcOIJElqyjAiSZKamt26gGG0+Y6tjKxcN65ztqxaNknVSJLUljMjkiSpKcOIJElqyjAiSZKaMoxIkqSmDCOSJKmpGRFGkrw7yRkTMM5+SV7ft/+4JBc81HElSdKOzYgwMh5JdvbtzPsBPwkjVXVnVZ006UVJkjTEpm0YSfL2JLcl+SLw5K7t8iRLu+35SbZ028uTrE3yOeDSJPsmuTTJxiSbkxzfDbsKWJjk+iSrk4wkubEbY+8kH+r6X5fkmL6xL0xycZKvJvnTKf4oJEma1qblDz1LcjjwUmAJvXvYCGzYxWlPA55aVd/tZkdOrKrvJ5kPXJVkLbASOKSqlnTXGek7/3eAqqrFSQ4CPpPkSd2xJcBhwP3ArUneX1XfGFXzCmAFwKxH7b9b9y1J0kw0XWdGngtcVFXbqur7wNoBzvlsVX232w7wniQ3AP8MHAA8dhfnPwc4B6CqbgG+DmwPI5dW1daqug+4GXjC6JOrak1VLa2qpbP2mTdAuZIkDYdpOTOyEz/mpwFr71HHfti3fSqwP3B4Vf2oe50zuv943N+3/QAz73OVJGnSTNeZkSuAE5I8Islc4MVd+xbg8G57ZwtP5wHf7oLIMfx0JuNeYO4OzvkCvRBD93rm8cCtu30HkiQJmKZhpKo2AucBm4BPA9d2h/4n8Lok1wHzdzLEucDSJJuBVwK3dOPeDVyZ5MYkq0ed8wFgr+6c84DlVXU/kiTpIUlVta5h6MxZsKgWnHbmuM7xt/ZKkqazJBuqaulYx6blzIgkSZo5DCOSJKkpw4gkSWrKb0FtYPEB81jvGhBJkgBnRiRJUmOGEUmS1JRhRJIkNWUYkSRJTRlGJElSU4YRSZLUlGFEkiQ1ZRiRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1JRhRJIkNWUYkSRJTRlGJElSU7NbFzCMNt+xlZGV63b7/C2rlk1gNZIkteXMiCRJasowIkmSmjKMSJKkpgwjkiSpKcOIJElqyjACJHlzkn1a1yFJ0jAyjPS8GTCMSJLUwNCFkSSPTLIuyaYkNyZ5F/A44LIkl3V9XpZkc3f8vX3n/iDJnye5KcmlSfbv2t+U5OYkNyT5WJs7kyRpehq6MAK8ELizqg6tqkOAM4E7gWOq6pgkjwPeCxwLLAGenuSE7txHAuur6inA54F3de0rgcOq6qnA6WNdNMmKJOuTrH9g29bJuTNJkqahYQwjm4EXJHlvkudW1ehk8HTg8qq6q6p+DJwLHNUdexA4r9s+B3hOt30DcG6SlwM/HuuiVbWmqpZW1dJZ+8ybyPuRJGlaG7owUlW3AU+jF0r+OMk7H8pw3ddlwF92416bxB+zL0nSgIYujHSvYbZV1TnAanoB4l5gbtflGuA/J5mfZBbwMnqvZKD3eZ3Ubf8W8MUkewEHVtVlwNuAecC+U3IzkiTNAMP4f/CLgdVJHgR+BLwOOBK4OMmd3bqRlcBlQIB1VfWP3bk/BJ6R5PeBbwOnALOAc5LM6/q/r6rumdI7kiRpGhu6MFJVlwCXjGpeD7y/r89HgY/u4Py3jNH8nDHaJEnSAIbuNY0kSdqzGEbGoapcCyJJ0gQzjEiSpKYMI5IkqamhW8C6J1h8wDzWr1rWugxJkvYIzoxIkqSmDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKYMI5IkqSnDiCRJasowIkmSmjKMSJKkpgwjkiSpKcOIJElqyjAiSZKaMoxIkqSmDCOSJKkpw4gkSWrKMCJJkpoyjEiSpKZmty5gGG2+YysjK9e1LkOSpDFtWbVsSq/nzIgkSWrKMCJJkpoyjEiSpKYMI5IkqSnDSJ8k705yRus6JEkaJoYRSZLU1FCHkSSvTHJDkk1JPjzq2GuTXNsd+0SSfbr2k5Pc2LVf0bU9Jck1Sa7vxlvU4n4kSZqOhjaMJHkK8PvAsVV1KPC7o7pcWFVP7459BXhN1/5O4Ne69uO6ttOBv6iqJcBS4JuTXb8kSTPF0IYR4Fjg/Kr6DkBVfXfU8UOSfCHJZuBU4Cld+5XA2UleC8zq2r4M/F6StwFPqKp/H32xJCuSrE+y/oFtWyfjfiRJmpaGOYzsytnAG6pqMfAHwN4AVXU6vRmVA4ENSR5TVR+hN0vy78Cnkhw7erCqWlNVS6tq6ax95k3VPUiStMcb5jDyOeDkJI8BSPLoUcfnAt9K8jB6MyN0/RZW1dVV9U7gLuDAJL8EfK2q3gf8I/DUKbkDSZJmgKH93TRVdVOSPwE+n+QB4DpgS1+XdwBX0wscV9MLJwCruwWqAS4FNgFvA16R5EfA/wXeMyU3IUnSDJCqal3D0JmzYFEtOO3M1mVIkjSmyfhFeUk2VNXSsY4N82saSZK0BzCMSJKkpgwjkiSpKcOIJElqami/m6alxQfMY/0kLA6SJGk6cmZEkiQ1ZRiRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1JS/tbeBJPcCt7auY8jNB77Tuogh5zNoy8+/vWF7Bk+oqv3HOuBPYG3j1h39GmVNjSTrfQZt+Qza8vNvz2fwU76mkSRJTRlGJElSU4aRNta0LkA+gz2Az6AtP//2fAYdF7BKkqSmnBmRJElNGUYmUZIXJrk1yb8kWTnG8TlJzuuOX51kpEGZM9oAz+AtSW5OckOSS5M8oUWdM9WuPv++fi9JUkn8zoIJNsgzSPKb3Z+Dm5J8ZKprnOkG+Hvo8UkuS3Jd93fRi1rU2ZKvaSZJklnAbcALgG8C1wIvq6qb+/q8HnhqVZ2e5KXAiVV1SpOCZ6ABn8ExwNVVtS3J64CjfQYTY5DPv+s3F1gHPBx4Q1Wtn+paZ6oB/wwsAj4OHFtV30vyC1X17SYFz0ADPoM1wHVV9VdJDgY+VVUjLeptxZmRyfMM4F+q6mtV9R/Ax4DjR/U5Hvj7bvsC4HlJMoU1znS7fAZVdVlVbet2rwJ+cYprnMkG+TMA8EfAe4H7prK4ITHIM3gt8JdV9T0Ag8iEG+QZFPCobnsecOcU1rdHMIxMngOAb/Ttf7NrG7NPVf0Y2Ao8ZkqqGw6DPIN+rwE+PakVDZddfv5JngYcWFXrprKwITLIn4EnAU9KcmWSq5K8cMqqGw6DPIN3Ay9P8k3gU8Abp6a0PYc/gVUCkrwcWAr859a1DIskewH/C1jeuJRhNxtYBBxNb2bwiiSLq+qelkUNmZcBZ1fVnyU5EvhwkkOq6sHWhU0VZ0Ymzx3AgX37v9i1jdknyWx603N3T0l1w2GQZ0CS5wNvB46rqvunqLZhsKvPfy5wCHB5ki3AEcBaF7FOqEH+DHwTWFtVP6qq2+mtb1g0RfUNg0GewWvorduhqr4M7E3v99YMDcPI5LkWWJTkiUkeDrwUWDuqz1rgtG77JOBz5YriibTLZ5DkMOBv6AUR35VPrJ1+/lW1tarmV9VIt1jvKnrPwQWsE2eQv4c+SW9WhCTz6b22+doU1jjTDfIM/g14HkCSX6EXRu6a0iobM4xMkm4NyBuAS4CvAB+vqpuS/GGS47pu/xt4TJJ/Ad4C7PBbHzV+Az6D1cC+wPlJrk8y+i8J7aYBP39NogGfwSXA3UluBi4D3lpVztBOkAGfwX8DXptkE/BRYPmw/Y+p39orSZKacmZEkiQ1ZRiRJElNGUYkSVJThhFJktSUYUSSJDVlGJEkSU0ZRiRJUlOGEUmS1NT/B5+xmOxycEbRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "feature_importance_levels = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feature_importance_levels.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You can see that some features are vastly more 'important' to predicting price\n",
    "- The 'class' of flight (business vs economy) is magnitude more influential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9045476949563359"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using all features\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)\n",
    "reg = LinearRegression().fit(X_train, y_train)\n",
    "reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9057681659877818"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the four least impactful features\n",
    "X1 = X.drop(['departure_time', 'arrival_time', 'destination_city', 'source_city'], axis=1)\n",
    "X1_train, X1_test, y_train, y_test = train_test_split(X1, y, test_size = 0.20)\n",
    "reg1 = LinearRegression().fit(X1_train, y_train)\n",
    "reg1.score(X1_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8940675801077274"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using just 'duration,' 'stops,' and 'class.'\n",
    "X2 = X1.drop(['airline', 'days_left'], axis=1)\n",
    "X2_train, X2_test, y_train, y_test = train_test_split(X2, y, test_size = 0.20)\n",
    "reg1 = LinearRegression().fit(X2_train, y_train)\n",
    "reg1.score(X2_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8799501519915867"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using just 'class'\n",
    "X3 = X2.drop(['duration', 'stops'], axis=1)\n",
    "X3_train, X3_test, y_train, y_test = train_test_split(X3, y, test_size = 0.20)\n",
    "reg1 = LinearRegression().fit(X3_train, y_train)\n",
    "reg1.score(X3_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see what happens if we regress within each 'class' of ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_trees = extra_trees_data[extra_trees_data['class'] == 1]\n",
    "business_trees.get('class').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4469341137792372"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at all features within the the business class\n",
    "XB = business_trees.drop('price', axis=1)\n",
    "y = business_trees['price']\n",
    "XB_train, XB_test, y_train, y_test = train_test_split(XB, y, test_size = 0.20)\n",
    "reg1 = LinearRegression().fit(XB_train, y_train)\n",
    "reg1.score(XB_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "economy_trees = extra_trees_data[extra_trees_data['class'] == 0]\n",
    "economy_trees.get('class').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4615643084014175"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at all features within the the economy class\n",
    "XE = economy_trees.drop('price', axis=1)\n",
    "y = economy_trees['price']\n",
    "XE_train, XE_test, y_train, y_test = train_test_split(XE, y, test_size = 0.20)\n",
    "reg1 = LinearRegression().fit(XE_train, y_train)\n",
    "reg1.score(XE_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second model we considered for our analysis was the Decision Tree Classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, RepeatedKFold, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn import tree\n",
    "from sklearn.tree import export_text\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = extra_trees_data.drop('price', axis=1)\n",
    "y = extra_trees_data['price']\n",
    "\n",
    "feature_names = X.columns\n",
    "labels = [\"\".join(item) for item in y.unique().astype(str)]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of our decision tree is to find a classifier that is able to show the decisions leading up to the price of each ticket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dTree = DecisionTreeClassifier(max_depth = None, random_state = 42)\n",
    "dTree.fit(X_train, y_train)\n",
    "y_pred_dTree = dTree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "print(dTree.get_depth())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To establish a baseline, we first let the classifier run without a max depth to see how large the tree gets naturally based on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(40,50), facecolor='w')\n",
    "\n",
    "# a = tree.plot_tree(dTree,\n",
    "#                    feature_names = feature_names,\n",
    "#                    class_names = labels,\n",
    "#                    rounded = True,\n",
    "#                    filled = True,\n",
    "#                    fontsize = 14\n",
    "#                    )\n",
    "# plt.show()          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_rules = export_text(dTree,\n",
    "                         feature_names = list(feature_names))\n",
    "\n",
    "# with open(\"treeText.txt\", \"w\") as external_file:\n",
    "#     print(tree_rules, file=external_file)\n",
    "#     external_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.9878195047842426\n",
      "Testing Accuracy: 0.6847372699529578\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy:\",metrics.accuracy_score(y_train, dTree.predict(X_train)))\n",
    "print(\"Testing Accuracy:\",metrics.accuracy_score(y_test, y_pred_dTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Unlike the linear regression model, we can see that the decision tree can see how impactful 'class' is and uses it as its first split\n",
    "- Interestingly, the decision tree then splits by desination city and source city which were identified earlier as not being too impactful.\n",
    "- The training accuracy of this classifier is very high, but the testing accuracy is much lower which could indicate that the model does not generalize well.\n",
    "\n",
    "The next step will be to tune the hyperparameters to get the best accuracy without overfitting and we will do so with a GridSearch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 36 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': [10, 40, 60],\n",
       "                         'min_impurity_decrease': [0.0001, 0.001, 0.01],\n",
       "                         'min_samples_split': [20, 40, 60, 80]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param = {\n",
    "    \"max_depth\": [10,40,60],\n",
    "    \"min_samples_split\":[20,40,60,80],\n",
    "    \"min_impurity_decrease\": [0.0001, 0.001, 0.01]\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(DecisionTreeClassifier(random_state = 42), param_grid = param, cv = 10, verbose = 1, n_jobs = -1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 40, 'min_impurity_decrease': 0.0001, 'min_samples_split': 20}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_\n",
    "#{'max_depth': 40, 'min_impurity_decrease': 0.0001, 'min_samples_split': 20}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=40, min_impurity_decrease=0.0001,\n",
       "                       min_samples_split=20, random_state=42)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_\n",
    "# DecisionTreeClassifier(max_depth=40, min_impurity_decrease=0.0001,\n",
    "#                        min_samples_split=20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5068143509607574"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_\n",
    "# 0.5068143509607574"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After doing some research we decided to vary the tree parameters max_depth, min_samples_split, and min_impurity_decrease. We tried many ranges and combinations of these parameters and although this manual search was not exhaustive, we were able to narrow it down to one set of parameters that got the best accuracy within our search.\n",
    "\n",
    "The best combination of the parameters was: \n",
    "{'max_depth': 40, 'min_impurity_decrease': 0.0001, 'min_samples_split': 20}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.5119184057855131\n",
      "Testing Accuracy: 0.5047508628846333\n"
     ]
    }
   ],
   "source": [
    "dTree_best = DecisionTreeClassifier(max_depth = 40, random_state = 42, min_impurity_decrease = 0.0001, min_samples_split = 20)\n",
    "dTree_best.fit(X_train, y_train)\n",
    "y_pred_dTree_best = dTree_best.predict(X_test)\n",
    "print(\"Training Accuracy:\",metrics.accuracy_score(y_train, dTree_best.predict(X_train)))\n",
    "print(\"Testing Accuracy:\",metrics.accuracy_score(y_test, y_pred_dTree_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this tuned classifier we get the accuracies above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we resarched a bit more and found a Regressor option with the Decision Tree so we explored that a bit with the same tuned hyperparameters to see how it compares to our exploration with the LinearRegression earlier. We also wanted to explore how the accuracy would be effected if instead of \"classifying\" each prediction as one of the many prices the model regressing to predict a unique price point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dTree_best = DecisionTreeRegressor(max_depth = 40, random_state = 42, min_impurity_decrease = 0.0001, min_samples_split = 20)\n",
    "dTree_best.fit(X_train, y_train)\n",
    "y_pred_dTree_best = dTree_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41925.11111111, 29979.38461538,  4251.        , ...,\n",
       "        5114.85714286,  5853.53846154,  6864.33333333])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dTree_best.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>price</th>\n",
       "      <th>pred</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7366</td>\n",
       "      <td>7494.750000</td>\n",
       "      <td>-128.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64831</td>\n",
       "      <td>69115.000000</td>\n",
       "      <td>-4284.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6195</td>\n",
       "      <td>6195.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60160</td>\n",
       "      <td>61682.578947</td>\n",
       "      <td>-1522.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6578</td>\n",
       "      <td>6630.500000</td>\n",
       "      <td>-52.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75034</th>\n",
       "      <td>14243</td>\n",
       "      <td>12913.200000</td>\n",
       "      <td>1329.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75035</th>\n",
       "      <td>4500</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75036</th>\n",
       "      <td>5232</td>\n",
       "      <td>5385.461538</td>\n",
       "      <td>-153.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75037</th>\n",
       "      <td>58315</td>\n",
       "      <td>58315.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75038</th>\n",
       "      <td>2877</td>\n",
       "      <td>2683.722222</td>\n",
       "      <td>193.277778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75039 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price          pred         diff\n",
       "0       7366   7494.750000  -128.750000\n",
       "1      64831  69115.000000 -4284.000000\n",
       "2       6195   6195.000000     0.000000\n",
       "3      60160  61682.578947 -1522.578947\n",
       "4       6578   6630.500000   -52.500000\n",
       "...      ...           ...          ...\n",
       "75034  14243  12913.200000  1329.800000\n",
       "75035   4500   4500.000000     0.000000\n",
       "75036   5232   5385.461538  -153.461538\n",
       "75037  58315  58315.000000     0.000000\n",
       "75038   2877   2683.722222   193.277778\n",
       "\n",
       "[75039 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"price\": np.asarray(y_test), \n",
    "        \"pred\": y_pred_dTree_best}\n",
    "out = pd.DataFrame(data)\n",
    "out[\"diff\"] = out[\"price\"] - out[\"pred\"]\n",
    "\n",
    "#Dataframe showcasing the difference in the values\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       price          pred         diff\n",
      "0       7366   7494.750000  -128.750000\n",
      "1      64831  69115.000000 -4284.000000\n",
      "3      60160  61682.578947 -1522.578947\n",
      "4       6578   6630.500000   -52.500000\n",
      "5       4555   5617.000000 -1062.000000\n",
      "...      ...           ...          ...\n",
      "75031   3014   3686.750000  -672.750000\n",
      "75033   6270   5954.764706   315.235294\n",
      "75034  14243  12913.200000  1329.800000\n",
      "75036   5232   5385.461538  -153.461538\n",
      "75038   2877   2683.722222   193.277778\n",
      "\n",
      "[51350 rows x 3 columns]\n",
      "RMSE:  3003.7394485653826\n",
      "MAE:  1220.8235647611773\n"
     ]
    }
   ],
   "source": [
    "#Dataframe showing how many values were not predicted exactly\n",
    "out = out[out.get(\"price\") != out.get(\"pred\")]\n",
    "print(out)\n",
    "\n",
    "#RMSE and MAE value calculation\n",
    "print(\"RMSE: \", np.sqrt(mean_squared_error(y_test, y_pred_dTree_best)))\n",
    "print(\"MAE: \", mean_absolute_error(y_test, y_pred_dTree_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These values are much better than the values we had gotten for the LinearRegression, and in fact the values are almost half of what we found earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bucketed Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we bucketed the prices to see if we could improve accuracy. \"Bucketed\" meaning that we grouped prices in a range and instead of the classifier having to predict exact prices, it would predict a bucket (or a range)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300153, 12157)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_price = extra_trees_data.get(\"price\")\n",
    "len(bucket_price), len(bucket_price.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_trees_data[\"bucket_price\"] = bucket_price\n",
    "bins = np.arange(0, 100000, 1000)\n",
    "extra_trees_data[\"bucket_price\"] = pd.cut(extra_trees_data.bucket_price, bins).apply(lambda x: x.left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_trees_data = extra_trees_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = extra_trees_data.drop('price', axis=1)\n",
    "X = X.drop('bucket_price', axis=1)\n",
    "y = extra_trees_data['bucket_price']\n",
    "\n",
    "feature_names = X.columns\n",
    "labels = [\"\".join(item) for item in y.unique().astype(str)]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 3136 candidates, totalling 31360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:676: UserWarning: The least populated class in y has only 7 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/yw/dn_z452d4wx42yf77kw14b9w0000gn/T/ipykernel_28745/1758172230.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    889\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    836\u001b[0m                     )\n\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 838\u001b[0;31m                 out = parallel(\n\u001b[0m\u001b[1;32m    839\u001b[0m                     delayed(_fit_and_score)(\n\u001b[1;32m    840\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 935\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    936\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    432\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "param = {\n",
    "    \"criterion\": ['gini', 'entropy'],\n",
    "    \"max_depth\": [None, 5, 10, 20, 30, 40, 50, 60],\n",
    "    \"max_features\": [None, 'sqrt', 'log2', 0.2, 0.4, 0.6, 0.8],\n",
    "    \"min_samples_split\":[5, 10, 20, 30, 40, 60, 80],\n",
    "    \"min_impurity_decrease\": [0.00001, 0.0001, 0.001, 0.01]\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(DecisionTreeClassifier(random_state = 42), param_grid = param, cv = 10, verbose = 1, n_jobs = -1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dTree_best = grid.best_estimator_\n",
    "dTree_best.fit(X_train, y_train)\n",
    "y_pred_dTree_best = dTree_best.predict(X_test)\n",
    "print(\"Training Accuracy:\",metrics.accuracy_score(y_train, dTree_best.predict(X_train)))\n",
    "print(\"Testing Accuracy:\",metrics.accuracy_score(y_test, y_pred_dTree_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As predicted, the accuracies are much higher than without the bucketing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = X_test\n",
    "out[\"prediction\"] = y_pred_dTree_best\n",
    "out[\"diff\"] = out[\"prediction\"] - y_pred_dTree_best\n",
    "out[out.get(\"diff\") != 0]\n",
    "out.get(\"diff\").unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"bucket\": np.asarray(y_test), \n",
    "        \"pred\": y_pred_dTree_best}\n",
    "out = pd.DataFrame(data)\n",
    "out = out[out.get(\"bucket\") != out.get(\"pred\")]\n",
    "out.get(\"bucket\")\n",
    "out[\"diff\"] = out[\"bucket\"] - out[\"pred\"]\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# on average, each misclassification was off by just over 1 'bucket'\n",
    "np.mean(out[\"diff\"]) / 1000, np.median(out[\"diff\"]) / 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average each misclassification that our model had was off by just over 1 \"bucket\". For the purposes of our model, this error is still pretty useful because when predicting a price for a ticket one of our buckets gives the user a good estimate of their flight price. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly we began some exploration with RandomForests as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "could not allocate 1472331776 bytes",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12864/1579058882.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m                                       random_state=42)\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mforrest_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\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      9\u001b[0m \u001b[0mforrest_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mforrest_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Training Accuracy:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforrest_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\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[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    385\u001b[0m             \u001b[1;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m             \u001b[1;31m# since correctness does not rely on using threads.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n\u001b[0m\u001b[0;32m    388\u001b[0m                              \u001b[1;33m**\u001b[0m\u001b[0m_joblib_parallel_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'threads'\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    389\u001b[0m                 delayed(_parallel_build_trees)(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1041\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1043\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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[0m\u001b[0;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\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[0m\n\u001b[0;32m   1045\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    859\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    860\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--> 861\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\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    862\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    863\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    777\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\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    780\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\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    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\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    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\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\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\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[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\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[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\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[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\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[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    167\u001b[0m                                                         indices=indices)\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\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    170\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[0;32m    171\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\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\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    901\u001b[0m         \"\"\"\n\u001b[0;32m    902\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 903\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    904\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    905\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\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\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    392\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 394\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\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    395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    396\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.DepthFirstTreeBuilder.build\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.DepthFirstTreeBuilder.build\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.Tree._add_node\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_tree.pyx\u001b[0m in \u001b[0;36msklearn.tree._tree.Tree._resize_c\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32msklearn\\tree\\_utils.pyx\u001b[0m in \u001b[0;36msklearn.tree._utils.safe_realloc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: could not allocate 1472331776 bytes"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forrest_test = RandomForestClassifier(criterion = 'entropy',\n",
    "                                      max_depth = 30,\n",
    "                                      min_impurity_decrease=1e-05,\n",
    "                                      min_samples_split=5,\n",
    "                                      random_state=42)\n",
    "\n",
    "forrest_test.fit(X_train, y_train)\n",
    "forrest_pred = forrest_test.predict(X_test)\n",
    "print(\"Training Accuracy:\",metrics.accuracy_score(y_train, forrest_test.predict(X_train)))\n",
    "print(\"Testing Accuracy:\",metrics.accuracy_score(y_test, forrest_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}