flight-price-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10915a98",
   "metadata": {},
   "source": [
    "# <center> Predicting Flight Ticket Prices </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62f69c9",
   "metadata": {},
   "source": [
    "# Table of Contents:\n",
    "* ## [Introduction](#introduction)\n",
    "\n",
    "* ## [Scraped Data EDA](#eda)\n",
    "\n",
    "* ## [Handling Categorical Data](#cat_data)\n",
    "\n",
    "* ## [Features](#features)\n",
    "\n",
    "* ## [Modeling](#modeling)\n",
    "\n",
    "* ## [Saving The Final Model](#model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a302157f",
   "metadata": {},
   "source": [
    "## Introduction: <a class=\"anchor\" id=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6d5c6e",
   "metadata": {},
   "source": [
    "<b> This is the main notebook for the flight price prediction project. We predict ticket prices for upcoming flights to help customers in selecting the optimum time for travel and the cheapest flight to the desired destination. The data of the project which is used to train the models is scraped from [Kayak](http://www.kayak.com) which is detailed in the \"kayak-scraper\" notebook. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3b1502",
   "metadata": {},
   "source": [
    "### Importing necessarry libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "312fbc11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle as pkl\n",
    "\n",
    "sns.set()\n",
    "\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, KFold, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd855d2",
   "metadata": {},
   "source": [
    "## Scraped Data EDA: <a class=\"anchor\" id=\"eda\"></a>\n",
    "### Loading the scraped data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fbb7fe38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.read_csv(\"data/PAR_NYC.csv\")\n",
    "df_2 = pd.read_csv(\"data/PAR_SVO.csv\")\n",
    "df_3 = pd.read_csv(\"data/SVO_NYC.csv\")\n",
    "df_4 = pd.read_csv(\"data/SVO_RUH.csv\")\n",
    "df_5 = pd.read_csv(\"data/NYC_PAR.csv\")\n",
    "df_6 = pd.read_csv(\"data/NYC_SVO.csv\")\n",
    "df_7 = pd.read_csv(\"data/RUH_NYC.csv\")\n",
    "df_8 = pd.read_csv(\"data/RUH_PAR.csv\")\n",
    "df_9 = pd.read_csv(\"data/RUH_SVO.csv\")\n",
    "df_10 = pd.read_csv(\"data/SVO_PAR.csv\")\n",
    "df_11 = pd.read_csv(\"data/PAR_RUH.csv\")\n",
    "df_12 = pd.read_csv(\"data/NYC_RUH.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "92e3e085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PAR => NYC route has 14881 trips\n",
      "PAR => SVO route has 2403 trips\n",
      "SVO => NYC route has 4202 trips\n",
      "SVO => RUH route has 2235 trips\n",
      "NYC => PAR route has 5334 trips\n",
      "NYC => SVO route has 1905 trips\n",
      "RUH => NYC route has 7279 trips\n",
      "RUH => PAR route has 553 trips\n",
      "RUH => SVO route has 2725 trips\n",
      "SVO => PAR route has 3314 trips\n",
      "PAR => RUH route has 7327 trips\n",
      "NYC => RUH route has 3205 trips\n"
     ]
    }
   ],
   "source": [
    "print(f\"{df_1['Source'][0]} => {df_1['Destination'][0]} route has {df_1.shape[0]} trips\")\n",
    "print(f\"{df_2['Source'][0]} => {df_2['Destination'][0]} route has {df_2.shape[0]} trips\")\n",
    "print(f\"{df_3['Source'][0]} => {df_3['Destination'][0]} route has {df_3.shape[0]} trips\")\n",
    "print(f\"{df_4['Source'][0]} => {df_4['Destination'][0]} route has {df_4.shape[0]} trips\")\n",
    "print(f\"{df_5['Source'][0]} => {df_5['Destination'][0]} route has {df_5.shape[0]} trips\")\n",
    "print(f\"{df_6['Source'][0]} => {df_6['Destination'][0]} route has {df_6.shape[0]} trips\")\n",
    "print(f\"{df_7['Source'][0]} => {df_7['Destination'][0]} route has {df_7.shape[0]} trips\")\n",
    "print(f\"{df_8['Source'][0]} => {df_8['Destination'][0]} route has {df_8.shape[0]} trips\")\n",
    "print(f\"{df_9['Source'][0]} => {df_9['Destination'][0]} route has {df_9.shape[0]} trips\")\n",
    "print(f\"{df_10['Source'][0]} => {df_10['Destination'][0]} route has {df_10.shape[0]} trips\")\n",
    "print(f\"{df_11['Source'][0]} => {df_11['Destination'][0]} route has {df_11.shape[0]} trips\")\n",
    "print(f\"{df_12['Source'][0]} => {df_12['Destination'][0]} route has {df_12.shape[0]} trips\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7368c341",
   "metadata": {},
   "source": [
    "### Defining functions to clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e01fdee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert duration to numerical format in minutes\n",
    "def clean_duration(duration):\n",
    "    duration = list(duration)\n",
    "    duration_hours = []\n",
    "    duration_mins = []\n",
    "    for i in range(len(duration)):\n",
    "        duration_hours.append(int(duration[i].split(sep = \"h\")[0])) # Extract hours from duration\n",
    "        duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1])) # Extracts only minutes from duration\n",
    "\n",
    "    d = []\n",
    "    for i in range(len(duration)):\n",
    "        d.append(duration_hours[i]*60+duration_mins[i])\n",
    "        \n",
    "    return d\n",
    "\n",
    "# convert price to numerical format in USD\n",
    "def clean_price(price):\n",
    "    price = price.str.replace(',','',regex=True)\n",
    "    price = price.str.replace('SAR','',regex=True)\n",
    "    price = price.str.strip()\n",
    "    price = round(pd.to_numeric(price)/3.75,2)\n",
    "    return price\n",
    "\n",
    "# convert date to datetime format\n",
    "def clean_date(date):\n",
    "    date = pd.to_datetime(date)\n",
    "    return date\n",
    "\n",
    "# get price quantile to deal with outliers\n",
    "def get_price_quantile(price):\n",
    "    Q1 = price.quantile(0.25)\n",
    "    Q3 = price.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_lim = Q1 - 1.5 * IQR\n",
    "    upper_lim = Q3 + 1.5 * IQR\n",
    "    return (lower_lim,upper_lim)\n",
    "\n",
    "# get average of each airline\n",
    "def get_avg_per_airline(x):\n",
    "    # average for trips with multiple airlines\n",
    "    multiple_airlines = x[x[\"Airline\"].str.contains(\",\")]\n",
    "    b = list(multiple_airlines[\"Airline\"].str.split(\",\"))\n",
    "    d = [] # Airline 1\n",
    "    e = [] # Airline 2\n",
    "    for i in range(len(b)):\n",
    "        d.append(b[i][0])\n",
    "        e.append(b[i][1])\n",
    "    for i in range(len(e)):\n",
    "        e[i] = e[i].strip()\n",
    "    m_airlines = list(set(d)) + list(set(e))\n",
    "    column_names = [\"Airline\", \"Average Price\"]\n",
    "    t_ = pd.DataFrame(columns = column_names)\n",
    "    for airline in m_airlines:\n",
    "        t = pd.DataFrame(x[x[\"Airline\"].str.contains(airline)][\"Airline\"])\n",
    "        t[\"Average Price\"] = x[x[\"Airline\"].str.contains(airline)][\"Price\"].mean()\n",
    "        t_ = t_.append(t)\n",
    "    t__ = t_.groupby(\"Airline\",as_index = False)[\"Average Price\"].mean()\n",
    "    k = multiple_airlines.copy()\n",
    "    k = k.merge(t__, on = \"Airline\", how = \"left\")\n",
    "    \n",
    "    # average for trips with single airlines\n",
    "    single_airlines = x[~x[\"Airline\"].str.contains(\",\")]\n",
    "    avg_per_airline = single_airlines.groupby(\"Airline\", as_index = False)[\"Price\"].mean()\n",
    "    avg_per_airline = avg_per_airline.rename(columns={\"Price\" : \"Average Price\"})\n",
    "    temp = single_airlines.copy()\n",
    "    temp = temp.merge(avg_per_airline, on='Airline', how =\"left\")\n",
    "    \n",
    "    temp_1 = temp.groupby(\"Airline\", as_index = False)[\"Average Price\"].mean()\n",
    "    k_1 = k.groupby(\"Airline\", as_index = False)[\"Average Price\"].mean()\n",
    "    k_temp = pd.concat([k_1,temp_1])\n",
    "    y = x.merge(k_temp, on = \"Airline\")\n",
    "    \n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9b4ce84d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs_raw = [df_1,df_2,df_3,df_4,df_5,df_6,df_7,df_8,df_9,df_10,df_11,df_12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "11331c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning the data\n",
    "dfs = []\n",
    "for df in dfs_raw:\n",
    "    df.drop_duplicates() # drop duplicate rows\n",
    "    df[\"Duration\"] = clean_duration(df[\"Duration\"]) # convert duration to numerical minutes format\n",
    "    df[\"Price\"] = clean_price(df[\"Price\"]) # convert price to numerical format in USD\n",
    "    df[\"Date\"] = clean_date(df[\"Date\"]) # convert date to datetime format\n",
    "    dfs.append(get_avg_per_airline(df)) # get average per airline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce3b7c0",
   "metadata": {},
   "source": [
    "### Studying outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "101a17af",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplots for each route\n",
    "k=0\n",
    "figure, axis = plt.subplots(4, 3, figsize=(15,15))\n",
    "for i in range(4):\n",
    "    for j in range(3):\n",
    "        axis[i,j].boxplot(dfs[k]['Price'])\n",
    "        axis[i,j].set_title(f\"{dfs[k]['Source'][0]} TO {dfs[k]['Destination'][0]}\")\n",
    "        k += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681fa046",
   "metadata": {},
   "source": [
    "#### It is apparent that all routes have outliers, so we'll deal with them by IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1752261c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get quantile to deal with outliers\n",
    "lower = []\n",
    "upper = []\n",
    "for df in dfs:\n",
    "    x = get_price_quantile(df['Price'])\n",
    "    lower.append(x[0])\n",
    "    upper.append(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "68008836",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop outliers\n",
    "k = 0\n",
    "for df in dfs:\n",
    "    low = df['Price'] < lower[k]\n",
    "    up = df['Price'] > upper[k]\n",
    "    df['Price'] = df['Price'][~(low|up)]\n",
    "    df.dropna(inplace=True)\n",
    "    df.reset_index(drop = True,inplace=True)\n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "cbed5070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot for each route after dealing with outliers\n",
    "k=0\n",
    "figure, axis = plt.subplots(4, 3, figsize=(15,15))\n",
    "for i in range(4):\n",
    "    for j in range(3):\n",
    "        axis[i,j].boxplot(dfs[k]['Price'])\n",
    "        axis[i,j].set_title(f\"{dfs[k]['Source'][0]} TO {dfs[k]['Destination'][0]}\")\n",
    "        k += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a9e2aab",
   "metadata": {},
   "source": [
    "#### Now the data is much better, the total dropped data is 5,266 rows out of 55,363."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "feb203a8",
   "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>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Average Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TAP AIR PORTUGAL</td>\n",
       "      <td>PAR</td>\n",
       "      <td>NYC</td>\n",
       "      <td>770</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>379.47</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>441.343760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TAP AIR PORTUGAL</td>\n",
       "      <td>PAR</td>\n",
       "      <td>NYC</td>\n",
       "      <td>770</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>379.47</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>441.343760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TAP AIR PORTUGAL</td>\n",
       "      <td>PAR</td>\n",
       "      <td>NYC</td>\n",
       "      <td>810</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>379.47</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>441.343760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAP AIR PORTUGAL</td>\n",
       "      <td>PAR</td>\n",
       "      <td>NYC</td>\n",
       "      <td>890</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>379.47</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>441.343760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TAP AIR PORTUGAL</td>\n",
       "      <td>PAR</td>\n",
       "      <td>NYC</td>\n",
       "      <td>1030</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>379.47</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>441.343760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>SAS, Pegasus Airlines</td>\n",
       "      <td>NYC</td>\n",
       "      <td>RUH</td>\n",
       "      <td>1300</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>930.40</td>\n",
       "      <td>2022-04-13</td>\n",
       "      <td>838.732500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3047</th>\n",
       "      <td>Qatar Airways, SAUDIA</td>\n",
       "      <td>NYC</td>\n",
       "      <td>RUH</td>\n",
       "      <td>1420</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>1018.40</td>\n",
       "      <td>2022-04-13</td>\n",
       "      <td>1972.813714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3048</th>\n",
       "      <td>Qatar Airways, MEA</td>\n",
       "      <td>NYC</td>\n",
       "      <td>RUH</td>\n",
       "      <td>1415</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>1049.87</td>\n",
       "      <td>2022-04-13</td>\n",
       "      <td>1133.548015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3049</th>\n",
       "      <td>Emirates, Turkish Airlines</td>\n",
       "      <td>NYC</td>\n",
       "      <td>RUH</td>\n",
       "      <td>1100</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>1053.60</td>\n",
       "      <td>2022-04-13</td>\n",
       "      <td>1023.900221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>Lufthansa, MEA</td>\n",
       "      <td>NYC</td>\n",
       "      <td>RUH</td>\n",
       "      <td>1115</td>\n",
       "      <td>3 stops</td>\n",
       "      <td>1139.73</td>\n",
       "      <td>2022-04-13</td>\n",
       "      <td>1023.714222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50097 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Airline Source Destination  Duration Total stops  \\\n",
       "0               TAP AIR PORTUGAL    PAR         NYC       770     1 stop    \n",
       "1               TAP AIR PORTUGAL    PAR         NYC       770     1 stop    \n",
       "2               TAP AIR PORTUGAL    PAR         NYC       810     1 stop    \n",
       "3               TAP AIR PORTUGAL    PAR         NYC       890     1 stop    \n",
       "4               TAP AIR PORTUGAL    PAR         NYC      1030     1 stop    \n",
       "...                          ...    ...         ...       ...         ...   \n",
       "3046       SAS, Pegasus Airlines    NYC         RUH      1300    2 stops    \n",
       "3047       Qatar Airways, SAUDIA    NYC         RUH      1420    2 stops    \n",
       "3048          Qatar Airways, MEA    NYC         RUH      1415    2 stops    \n",
       "3049  Emirates, Turkish Airlines    NYC         RUH      1100    2 stops    \n",
       "3050              Lufthansa, MEA    NYC         RUH      1115    3 stops    \n",
       "\n",
       "        Price       Date  Average Price  \n",
       "0      379.47 2022-02-01     441.343760  \n",
       "1      379.47 2022-02-01     441.343760  \n",
       "2      379.47 2022-02-01     441.343760  \n",
       "3      379.47 2022-02-01     441.343760  \n",
       "4      379.47 2022-02-01     441.343760  \n",
       "...       ...        ...            ...  \n",
       "3046   930.40 2022-04-13     838.732500  \n",
       "3047  1018.40 2022-04-13    1972.813714  \n",
       "3048  1049.87 2022-04-13    1133.548015  \n",
       "3049  1053.60 2022-04-13    1023.900221  \n",
       "3050  1139.73 2022-04-13    1023.714222  \n",
       "\n",
       "[50097 rows x 8 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat all routes into one dataframe\n",
    "df = pd.concat(dfs)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "62b2a0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline          0\n",
       "Source           0\n",
       "Destination      0\n",
       "Duration         0\n",
       "Total stops      0\n",
       "Price            0\n",
       "Date             0\n",
       "Average Price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b55b03",
   "metadata": {},
   "source": [
    "## Handling Categorical Data: <a class=\"anchor\" id=\"cat_data\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d7446238",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# the Airline column will be replaced by the average price per airline.\n",
    "df.drop(\"Airline\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "a9f0ecf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAR    23054\n",
       "NYC     9411\n",
       "RUH     9140\n",
       "SVO     8492\n",
       "Name: Source, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# source\n",
    "df[\"Source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b2eff4ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a5da3b2040>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yd+7cTJgwoaciAwAAAAA70GO3Nk+cODEPP/xwzjzzzFRWVubkk0/OaaedlhEjRuTiiy9OW1tbJk6cmFNOOSVJMnv27MycOTOtra059thjc/755ydJrrjiisyYMSM333xzRo0alWuvvbanIgMAAAAAO9CjcyRefPHFufjii7cZO+GEE3L33Xe/Zt9jjjkmd95552vGR48enTlz5vRYRgAAAACgtB67tRkAAAAAOHAoEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQUlXRAQCAbS1atDCNjYv36Njm5qZ0d7TlxnuX7ONUpT23ekMq1m9JQ8NVe/wc48aNz4knTtqHqQAAgH1FkQgAvUxj4+I0PfV4/mzowN0+dtRB/ZIMTHdnx74PVsLLedtXPbNHxz+/blOSKBIBAKCXUiQCQC/0Z0MH5qKJxxQdo6y+fv+jRUcAAAB2whyJAAAAAEBJikQAAAAAoCRFIgAAAABQkjkS2Wf2ZpXRvdXc3JS2LR1peujuQs5flLbWVWluXrdXK6TuDaurAgAAQN+hSOwBRRZqRWpubsqmTZtSUVncX6vNrSsLO3dRNm9pz+NPPln283Z3dqS5uanP/V1XngIAANBXKRJ7QGPj4jz2xJPpN2BY0VHKq+Kg9Bt0UNEpKJOKJG1Jnnim75S3XZvXJokiEQAAgD5JkdhD+g0YlkFjlA1wINnYtLDoCAAAAFAYi60AAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACXtUpHY1dWVb37zm/n85z+f1tbWfOMb30hnZ2dPZwMAAAAAeoldKhK/+tWv5vHHH8/DDz+cJPnVr36Vr3zlKz0aDAAAAADoPXapSPzNb36TWbNmpaamJrW1tbn11lvzwAMP9HQ2AAAAAKCX2KUisaqqKv36/deu1dXVqaqq6rFQAAAAAEDvsktt4NFHH53vfve76ezszFNPPZVvfetbOeaYY3o6GwAAAADQS+zSFYmXX355/vM//zOrVq3KtGnTsnHjxlx22WU9nQ0AAAAA6CV26YrE2trafOpTn8qXv/zltLa2prm5OcOHD+/pbAAAAABAL7FLVyTOmTMnn/70p5Mka9asycUXX5w77rijR4MBAAAAAL3HLhWJP/zhD/P9738/SXLYYYdl7ty5+c53vtOjwQAAAACA3mOXisTOzs7U1tZufTx48OBUVFT0WCgAAAAAoHfZpSLxDW94Q2bPnp1nnnkmzzzzTK6//vq8/vWv7+FoAAAAAEBvsUtF4pVXXpmlS5fmzDPPzNlnn52lS5fmH/7hH3o4GgAAAADQW+zSqs2HHHJIvva1r/V0FgAAAACgl9ppkfilL30pl19+eS666KLtbv/617/eI6EAAAD2hTVrVmf48BFFxwCAA8JOi8QTTjghSTJ58uSyhAEAANhXHn/80cya9Y+ZMeOLOfroY4qOAwD7vZ0WiSeddFKSZO7cufn2t79dlkAAAAB7q7OzM7fc8tIdVLfc8vV8+cvXpLKysuBUALB/26XFVjZs2JCNGzf2dBYAAIB9YuHCe7N+/bokyfr167Jw4c8KTgQA+79dWmxl4MCBee9735s3vvGNGTRo0NZxcyQCAAC9zbp1a3PXXXekra0tSdLW1pa77ro948aNz9ChQwtOBwD7r5JF4uOPP55Jkybl3e9+d0aOHFmOTAAAAHussXFxOju7thnr6upKY+PinHzy+wpKBQD7v50WiT/60Y/S0NCQMWPGpLm5ObNnz8573vOecmUDAADYbePGvSt33XVHOjr+a6xfv34ZN258caEA4ACw0zkS58yZk5/85Ce544478vWvfz3/+q//Wq5cAAAAe2To0KE566xzUlNTkySprq7JWWd90G3NALCXSi628rrXvS5J8ta3vjVr1qzp8UAAAAB7a9KkyRky5KXicOjQoZk06eSCEwHA/m+nRWJFRcU2jysrK3s0DAAAwL5QWVmZCy64KElywQUX+S4DAPvALq3a/LJXF4sAAAC9xaJFC9PYuDhr167JypUt6ezsTFKRa675SkaMODjDhg3PuHHjc+KJk4qOCgD7pZ0WiY899lje9ra3bX28efPmvO1tb0t3d3cqKiry+9//vscDAgAA7IrGxsV5aumfMmLkwTnk0PrXbH9q6Z+SRJEIAHtop0Xiz3/+83LlAAAA2GsjRh6cUz5++na3Lbjt7jKnAYADy06LxNGjR5crBwAAAADQi5VctRkAAAAAQJEIAAAAAJSkSAQAAAAAStrpHIkAAAC91aJFC9PYuHjr4+bmpnR0dexwUZXVL6zK+n7r0tBwVZJk3LjxVnAGgN3Qo0Xi1772tfz0pz9NkkycODGXXnppFi9enK985Stpa2vL+973vlxyySVJkkceeSSXX355XnzxxRx//PG58sorU1VVleeffz7Tp0/PqlWrcsQRR2T27Nk56KCDejI2AADQC7y6KHy15uambNq8KVXV236tWfXCyh0e097Vnj8tfTIdWzrS3Ny00+dXNALAtnqsSFy8eHF+/etf56677kpFRUUuvPDCzJ8/P7Nnz86cOXMyatSofPKTn8z999+fiRMnZvr06bn66qszduzYXHbZZbn99tszbdq0XHnllZk2bVpOO+203Hjjjbnpppsyffr0nooNAAD0Eo2Ni/Pk00+ktm7IdrdXjxiQ6gzYq3O80Lp8u+OtLeuTRJEIAK/QY0ViXV1dZsyYkerq6iTJn//5n2fp0qUZM2ZMDjvssCTJlClTsmDBghx55JHZvHlzxo4dmySZOnVqbrjhhpxzzjl58MEHc+ONN24dP++88xSJAADQR9TWDclbP3BC2c/70I9+U/ZzAkBv12NF4lFHHbX1z0uXLs1Pf/rTnHfeeamrq9s6Xl9fn+XLl2fFihXbjNfV1WX58uVZs2ZNamtrU1VVtc347jj44Nq9fCW7r3//yrKfEyiP/v0rU1c3uOgYHOD6969Me9EhCuJnDHiloj9Xe08CgG31+GIrTzzxRD75yU/m0ksvTWVlZZYuXbp1W3d3dyoqKtLV1ZWKiorXjL/8z1d69eNSVq1qTVdX9169ht3V3t5Z1vMB5dPe3pmWlg1Fx+AA15f/O+JnDHilot8PvScB0Bft7Jdo/XryxEuWLMnHPvaxfO5zn8tZZ52VkSNHpqWlZev2lpaW1NfXv2Z85cqVqa+vz4gRI7Jhw4Z0dnZusz8AAAAAUF49ViQuW7Ysn/nMZzJ79uycdtppSZK3vOUtefrpp9PU1JTOzs7Mnz8/EyZMyOjRo1NTU5MlS5YkSebNm5cJEyakf//+Of7443PPPfckSebOnZsJEyb0VGQAAAAAYAd67NbmW265JW1tbZk1a9bWsQ996EOZNWtWLr744rS1tWXixIk55ZRTkiSzZ8/OzJkz09rammOPPTbnn39+kuSKK67IjBkzcvPNN2fUqFG59tpreyoyAAAAALADPVYkzpw5MzNnztzutrvvvvs1Y8ccc0zuvPPO14yPHj06c+bM2ef5AAAAAIBd16NzJAIAAAAABwZFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlFRVdAAA6I0WLVqYxsbFhZy7ubkp3e2b8/X7Hy3k/EV5fu3GVLzYlIaGqwo5/7hx43PiiZMKOTcAAOwPFIkAsB2NjYvT9KfHMmpwTdnPPXJAkgE16e7sLPu5i/TSv+vubFmxtOznXrahLUkUiQAAsBOKRADYgVGDa3LhuMOKjkEZfLPxmaIjAABAr2eORAAAAACgJEUiAAAAAFCSIhEAAAAAKMkciQAAQI9ZtGhhGhsX79Gxzc1N2dK5JQ/96Df7OFVprS3r07x6816tJG81eAAONIpEAACgxzQ2Ls6TTz+egSNqd/vYyqH9MzD901nAKvYDRxyUJHlu3fN7dPym1a1JrAYPwIFFkQgAAPSogSNqc9T7jys6Rlk9Mf/hoiMAwD5njkQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAACwH1i69KmiI9DHKRIBAAAAermFC+/NP/7jzCxc+LOio9CHKRIBAAAAerEtW7bku9/9dpLku9/9VrZs2VJwIvoqRSIAAABAL3bTTddv8/jmm28oKAl9nSIRAAAAoJd65pmmPPzwQ9uM/cd//D7PPvtMQYnoyxSJAAAAAL3U9773nR2Mf7vMSUCRCAAAANBrVVRUFB0BtlIkAgAAAPRSU6ZM3e746advfxx6kiIRAAAAoJd65pmlr7kqsaKiIs3NTQUloi9TJAIAAAD0UuPGvSv9+lVuM9a/f3XGjRtfUCL6MkUiAAAAQC81dOjQDBw4cOvjiop+mTr1gxk6dGiBqeirqooOAAAAAMCOdXZ2ZGBNTZKko6srkyadXHAi+ipFIgAAAECvVpHDRo5MkmzqSiorK0vsDz3Drc0AAAAAvdSiRQvT1rY5z7zwQp554YWsXNmSRYsWFh2LPsoViQAAAAC9zKJFC9PYuPil1Zm7u9Pd3Z0k2bJ5U+644/tpbFyccePG58QTJxWclL5EkQgAAABQsJeLw5c1Nzdl8+ZNqamqSnVVVfL/isTqqqp0d7Tn8ccfTXNz09ZjlIqUgyIRAAAAoGCNjYvT9KcnMur/rcY8ctDAZNDAEkclW1auyLJ165JEkUiPUyQCAAAA9AKjhg7Nhe96z24f980HftUDaeC1FIkAAAAAPezVty6/WnNzU7rbt+xRKbhs3bpUbNyUhoardriPW5/ZFxSJAAAAAD2ssXFxlj75REbW1m53e311dVJdna6Ozt1+7tcd9NJzbn5h2Xa3v9DamsStz+w9RSIAAABAGYysrc1Hx7617Of99h8eKvs5OTApEnvI+jUt2Vz1n0XHAPahLWtaksMOKToGAABQkFK3J+9Mc3NTura0FVLqvdC6If2at+z01udS3BpNokgEAAAA2CWNjYuz9InHUz+w9GrKr3ZIv37JgIHp6ujogWQ7Vz/gpbwbn31mj45fsWlTErdG08NFYmtraz70oQ/l61//eg499NAsXrw4X/nKV9LW1pb3ve99ueSSS5IkjzzySC6//PK8+OKLOf7443PllVemqqoqzz//fKZPn55Vq1bliCOOyOzZs3PQQQf1ZOR9ZsjwugwafWzRMYB9aGPHC0VHAAAAClY/cGCmHXl00THK6ntPPl50BHqJHisS/+M//iMzZ87M0qVLkySbN2/OZZddljlz5mTUqFH55Cc/mfvvvz8TJ07M9OnTc/XVV2fs2LG57LLLcvvtt2fatGm58sorM23atJx22mm58cYbc9NNN2X69Ok9FRkAAADo5fbm9uK91dzclK62zX2uWFuxaWP6NTft1a3Re8Nt1b1HjxWJt99+e6644opceumlSZKHH344Y8aMyWGHHZYkmTJlShYsWJAjjzwymzdvztixY5MkU6dOzQ033JBzzjknDz74YG688cat4+edd54iEQAAdlPRX7rbOtryxPyHCzl/UTauak3zOl+6oSc0Ni7O048/lrrq6rKfe0SS9K8u5PbkIh3Svzrp6krr0qfLfu6WLVuSuK26t+ixIvFLX/rSNo9XrFiRurq6rY/r6+uzfPny14zX1dVl+fLlWbNmTWpra1NVVbXN+O46+ODtL6vek/r3ryz7OYHy6N+/MnV1g4uOQRn071+ZLUWHoKz8fHMg+/3vG/PEU4+levjuz+m11wZXpDoD0tHVt750Vw8fkO4kTWuay37uLWs2pX//ypxzzpllPzfltWDBgtx///1Fxyi7Z55pStJddAzKpjvPPNOUa6/9ctFBymrixIk55ZRTio7xGmVbbKWrqysVFRVbH3d3d6eiomKH4y//85Ve/XhXrFrVmq6u8r7BtLd3lvV8QPm0t3empWVD0TEoA+/lfY+fbw5k7e2dqR4+MH/2348qOgpl8PzPn/Ce1kf84hf35enHH80hlX3rYpZhSVJZme7OvvULir7qkMrKZMuWrH700aKjlM3Kzs60t3fm7W9/VyHn39kv18tWJI4cOTItLS1bH7e0tKS+vv414ytXrkx9fX1GjBiRDRs2pLOzM5WVlVv3BwAAAF5ySGVlzhg8rOgYwD40b8PaoiPsUL9ynegtb3lLnn766TQ1NaWzszPz58/PhAkTMnr06NTU1GTJkiVJknnz5mXChAnp379/jj/++Nxzzz1Jkrlz52bChAnligsAAAAAvELZrkisqanJrFmzcvHFF6etrW2be71nz56dmTNnprW1Nccee2zOP//8JMkVV1yRGTNm5Oabb86oUaNy7bXXlisuAAAAAPAKPV4k3nfffVv/fMIJJ+Tuu+9+zT7HHHNM7rzzzteMjx49OnPmzOnRfAAAAABAaWW7IhEAAIq2aNHCNDYuLjpG2TU3N6WtY3Oe//kTRUehDNrWbErzhqY0NFxVdJSyGjdufE48cVLRMQAOaIpEAAD6jMbGxXn8qcdSObS66CjldVBSmep0dFmRvi+oHFqd9nTlT6ueLjpK2XSu25IkikSAHqZIBACgT6kcWp2hE/6s6BjAPrTufz9fdASAPqFsqzYDAAAAAPsvVyQCAADAfmrFunV5aEtH0TGAfWjFphczuOgQO+CKRAAAAACgJFckAgAAwH6qfujQvHXwsKJjAPtQ84beW9e5IhEAAAAAKEmRCAAAAACUpEgEAAAAAEpSJAIAAAAAJSkSAQAAAICSFIkAAAAAQEm9dz1pAHqFRYsWprFxcdExyq65uSnd7W35ZuMzRUehDJZtaEvF5qY0NFxVdJSyGjdufE48cVLRMQAA2E8oEgHYqcbGxVn65KMZWdu3/pNRX52kujJdXR1FR6EMXndQZZL2bH7hyaKjlM0LrS/93VYkAgCwq/rWt0IA9sjI2qp8/LgRRccA9qHbHl5ddAQAAPYz5kgEAAAAAEpSJAIAAAAAJSkSAQAAAICSFIkAAAAAQEmKRAAAAACgJEUiAAAAAFBSVdEBAAAAgD2zsrMz8zasLToGsA+t7OzM4KJD7IAiEQAAAPZD48aNLzpCIZqbm9K5eVPqqmuKjkIZtGxpS+WAgTn88DFFRymbwem9P9+KRAAAANgPnXjipJx44qSiY5RdQ8NVaV36dM4ZNbroKJTBHcueS+3hY/L5z3+h6CjEHIkAAAAAwC5QJAIAAAAAJSkSAQAAAICSFIkAAAAAQEmKRAAAAACgJEUiAAAAAFBSVdEBAAAAAHZHy5YtuWPZc0XHoAxatmxJbdEh2EqRCAAAAOw3xo0bX9i5m5ub0tW2OfUDBxWWoQgrNm1Mv5oBOfzwMWU/d22K/f+cbSkSAQAAgP3GiSdOyoknTirk3A0NV2Xjs89k2pFHF3L+onzvyccz6NDD8vnPf6HoKBRMkQgAQJ+ybuXatD9SUXQMYB/auHJtcnDRKQAOfIrEHtK1eW02Ni0sOgawD3VtXpvkkKJjAAAAQCEUiT2gr96739zclM1b2tN/0Iiio1AG7RtXZ0B1/0LmyCjOIX325xvgQDL0kGEZ+qZRRccA9qF1Ld1FRwDoExSJPaDI+RqK1NBwVZ5+fnUOOeZ9RUehDFY++tMc/mcjzJHRR7ywal1+81Rn0TGAfeiFVa15/ciiUwAAsD9RJAIAAADsohWbNuV7Tz5edIyyWrFpU15fdAh6BUUiACWNPHhoTniDaQvgQPJoa2XREQBgv7M3Ux01Nzela0tbRtYO3oeJds0LrRvSr7pmj6emen367jRubEuRCAAAALAL9mYqs4aGq7L5hWX56Ni37uNUpX37Dw9lwMhRpqZir/UrOgAAAAAA0Pu5IhEAAACgDF5obc23//BQIed9fdnPyoFIkQgAQJ/SuW5L1v3v54uOAexDneu2JAcXnQJ2rtQcg83NTelu35JRQ4fu9nMvW7cuFf2rdzgH4ut34fywKxSJ7FPtG1dn5aM/LToGZdC+cXUSi28AsH/pq1+impubsmnzxlRUmdmoL+ju6MrAAYP2eFGF/dLBfffnm/1HqfkVGxquypaVK3Lhu96z28/9zQd+lepD6s2BSI9TJLLPFPkf7ubmprRt6UhNbd/6NWRb66rUVFcV9CFxhA9rAOx39maS/P3ZokUL09i4uJBzr127JuvXr9+jY9vaNqeruyuV/cv/taWzvSP9KvqlpmbAHj/HkCFDMmzY8H2YateNGze+T/5dB6BnKRLZZ4r8YN7QcFWaX1ibMW89vZDzF6Xpobtz+MhhfusEAOzU/lqgNjRclSeffiK1dUPKfu7WlvU58oijfM4CgFdQJAIAAL3Srswn1t7VnqH1u3/V37oVa9K/X/8d39lR+zp3XwDAqygSAQCAXmlX5hNbuXFV3j1t96+2/PX3FuaQQQe74hDoVZatW5dvPvCrPTpuzCH1PZAItqVIBAAAACjYq6+Cbm5uSndHe/5sxPYXuXx+9epUVL10ZfWYQ+pdRU1ZKBI5YLS1rkrTQ3cXHaOs2lpXJRlWdAwAgMKsW7Emv/7ewj067pDX962F+oDe7dVXYTc0XJX2tavz6cmnbHf/m+5dkP7DRriymrJSJHJA2JvfvDQ3N2VLe0cGDanbh4l2zcb1LanuvzerLg/zWycAoM/a3tU7HV0dGTFy+wXh6hdWparfS5+9Dnn9wT5HAb3e86tX56Z7F+xw25hh279aEXqKIpEDwt6sRNjQcFWeb1mfN40/ex+nKu2RxXfmz+qG+A0SAMAe2N7VO08t/dNOjzn88DE+ewH7hZd/2dHc3JS2zZtS3b9/kmRLe3tqBgzMmDcc6RcilJ0iEQAAOCC88kt3Z3dH6v7spYUHWp5fkcqKqrzh9X/uSzew33j5lyWLFi3MnDm3paKiIknS1d2d00+fmsmTTy04IX2RIhGAkl5o7chtD68uOgawD73Q2pHXFx0C9rGXv3Q3NFyVp5ueesWWClciAvutE0+clO9855Ztxn760/mKRAqhSIS8NFfhI4vvLOS8qRtS9vPC7uirV240Nzelu31zRg2uKToKZbBsQ1sq+g/Yizlr9z+vT9/9+ebAN27c+Dz++GN5fulzSZLuru4cd9xbC04FsGcefPDfkySb2tq2jq1fvzZLlvw2b3/7XxYViz6qoru7u7voED1p1arWdHUd0C+RvbRo0cI0Ni7e4fbm5qa0d3Rm8LD63X7uDWtXpH9V5U6/mI4bN36P53cEek5Dw1XZsmJpLhx3WNFRKINvNj6T6vrXu1oJDiB//dfTtnlcUzMgN998a0FpAPbcJZd8KuvWrXvN+NChw3LddTcVkIgDXV3d4B1uc0UifV6phVoaGq7Kn556aofbS3EbDQBAed1//8LXjLW1bc6vfvXLvOc97y0gEcCeO++8j+fGG/95O+MfK3sWUCRCCa++7evlKxSHHTxyu/uvXfXC1qsQ60fUum0MAKDMfvjD7253/Pvfn6NIBPY7b3/7X6ampiZtr7i1uaZmgNuaKYQiEUp49RWLDQ1XZdW6jZn4/o9td//7538rBw8d5CpEAICCnHvuR3Lbbf+63XGA/c26dWvz6lnpuru7sm7dugwdOrSgVPRVikTYA+tWv5D7539rh9sOHvqG8gYCesSyDW35ZuMzRcegDJZtaMuY3Z8KF+il3vOe9+Z73/vOa67ecTUisD9qbFz8mrUfuru709i4OCef/L6CUtFXKRJhN718q3Jzc1M2b96Uqv4vreja0d6WAQMG5g1HvMHtzHAAKPLn+KX3l42pqexXWIYitHV2ZcCAQYWsnDym3grGcKCZNeufc8kln3rF4+sKTAOw58aNe1fuuuuOdHT811i/fv18dqEQVm2GPbRo0cJ85zu3Jvmvv1//43+cl8mTTy0uFHBAKLWa/M40Nzelu6Mto0fseKW1nvLc6g2pqKrZqyLQSvbAvtTQcFUee+yRvPGNf5HPf35m0XEA9tjPfnZP7rrrjrS1taW6uiZTp37Q1Yj0mJ2t2qxIhL1wwQUf3mauisGDh+b6628uMBHQ1zU0XJWOtcvymclvL/u5b7x3SaqGjTJHLNCr/PznP81//+++bAP7t87Ozlx22efS0rIidXX1+fKXr0llZWXRsThA7axIdGsz7KEHH/z310x4u2HDuixZ8lurZwGFem51a268d0kh5x0zrOynBdgpJSJwIKisrMwFF1yUWbP+MRdccJESkcIoEmEP/du/3bbd8e9851ZFIlCYUnPlrF27JuvXr9/6uKOjPZ2dnenq6tru/hUVFamqqkpVVf8kyZAhQzJs2PDt7jtmmHkGAQB6ytFHH5Nrrvlahg8fUXQU+jBFIuyh4457ax544H+/Zvwtbxlb/jAA/8+JJ07aozkG//ZvP5nW1g2vGT/kkPo0NFigAACgN1AiUrS+tRwk7ENnn33udsc/8IHtjwP0ZiNHjtru+LBhQ8ucBAAA6K0UibCHhg4dmrPOOmebsbPO+mCGDvWlG9j/nH/+Bdsd/8hHtj8OAAD0PYpE2Aunnnp6ampqkiQ1NTU59dQpBScC2DOHHnpYjjtu7DZjxx331hx66GHFBAIAAHodRSLshcrKylxyyeeTJJdc8nkrZwH7tU9/+v9LRUVFkpcWWfn0p/9nwYkAAIDeZL8oEn/yk5/k1FNPzcknn5zvfve7RceBbby8ctbRRx9TdBSAvVJdXZ1p085Pkkyb9tFUV1cXnAgAAOhNKrq7u7uLDrEzy5cvz7nnnpsf//jHqa6uzoc+9KFce+21OfLII3fp+FWrWtPV1atfIgD0KkuXPpXXv/4NRccAAAAKUFc3eIfbev0ViYsXL8473/nODBs2LIMGDcrkyZOzYMGComMBwAFLiQgAAGxPVdEBSlmxYkXq6uq2Pq6vr8/DDz+8y8cffHBtT8QCAAAAgD6l1xeJXV1dWyd+T5Lu7u5tHpfi1mYAAAAA2DX79a3NI0eOTEtLy9bHLS0tqa+vLzARAAAAAPQ9vb5IHD9+fH7zm99k9erV2bRpU372s59lwoQJRccCAAAAgD6l19/a/LrXvS6XXHJJzj///LS3t+fss8/OcccdV3QsAAAAAOhTKrq7uw/oCQTNkQgAAAAAu2a/niMRAAAAACieIhEAAAAAKEmRCAAAAACU1OsXW9lb/fpVFB0BAAAAAPZ7B/xiKwAAAADA3nNrMwAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACUpEgEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQUlXRAaA3e/bZZ3PKKafkz//8z1NRUZH29vbU19fnK1/5SkaOHJmLL744S5cuzU9+8pOtxzQ2Nuaiiy7K4YcfniTp6urKiy++mE984hM599xzi3opAFmwYEH+5V/+JR0dHenu7s4ZZ5yRoUOHZsGCBbnlllu22ffv//7v86Y3vSnnn39+li5dmoaGhjz55JOpqanJEUcckUsvvTSHHXZYQa8E6OueffbZTJo0Kbfeemve9a53bR0/6aSTctRRR6WmpiY33HDD1vFf//rXueKKKzJv3rzU1tZm7ty5mTNnTjo6OtLV1ZVzzjkn559/fhEvBWCb753Jf32HPPPMMzN16tScf/75ue+++7Y55o1vfGMee+yxNDY25mtf+1rmzJmzzfNt7xjYFxSJUEJ9fX3mzZu39fGsWbPy1a9+NTNnzsz//b//N3V1dfn973+ft73tbVv3efOb37zNG/kjjzySs88+O1OmTEltbW1Z8wMkyfLly9PQ0JAf//jHGT58eF588cV85CMfyac//en84Q9/yKpVq3LwwQcnSTZt2pRf/vKXufTSS7Ny5cqcf/75+bu/+7ucfvrpSZJ58+bl3HPPzd13350RI0YU+bKAPqx///75whe+kLvvvnubz1ef+9znctFFF2XhwoWZNGlSNm7cmH/4h3/Il7/85dTW1uaHP/xhfvCDH+Qb3/hG6uvrs379+vz1X/91Bg4cmHPOOafAVwT0Za/+3rl8+fJMnjw5p512WoGp4LXc2gy7ady4cXniiSfyk5/8JO94xzty8skn5wc/+MFOj3nuuecycODAVFdXlyklwLbWrFmT9vb2bN68OUly0EEHZdasWTnqqKPyV3/1V7nnnnu27vuLX/wi73znOzN8+PB8//vfz/jx47eWiElyxhln5O1vf3u+//3vl/11ALysvr4+48ePT0NDwzbjgwYNytVXX52rrroqGzduzA033JCTTjop48aNS5LcfPPNmT59eurr65MkQ4YMSUNDQ44++uiyvwaAHWlpaUl3d3fWrFlTdBTYhisSYTe0t7fn3nvvzdixY/PjH/84n/3sZ3P00Ufn+uuvz2WXXZZhw4YlSf74xz/mjDPOyKZNm7Ju3bqMGzcut956qyIRKMwxxxyTSZMm5a/+6q/ypje9KePGjcuUKVMyZsyYfOADH8g111yTj3zkI0mSuXPn5uMf/3iS5P/8n/+TiRMnvub53vGOd+TXv/51WV8DwKvNmDEjU6ZMyQMPPLDNLc7jx4/Pu9/97vz93/99nnrqqdxxxx1JktWrV2fZsmX5i7/4i22e5+XbCQGKsmLFipxxxhlpa2vLmjVr8t/+23/L1772tYwcObLksS9//3xZe3t7T0alj3NFIpTw8hv6GWeckdNPPz3d3d350Ic+lBdeeCHjx4/PqFGj8qY3vSlz587desyb3/zmzJs3L/Pnz8873/nODB48OMcdd1xxLwIgyZVXXpn77rsv5557bp5//vl88IMfzM9+9rO84x3vyJo1a/LMM8+kpaUlS5cuzfjx45MkFRUV6ezsfM1ztbe3p6KiotwvAWAbtbW1ueqqq/KFL3whra2t22ybMWNGHnjggcycOTMDBgxIkvTr99LXn5qamrJnBdiZl29tvueee3LGGWeku7s773rXu7a+b71Sd3f3Np/DXv7++fL//uVf/qWc0eljFIlQwstv6PPmzctPf/rTNDQ05K677sqWLVsyefLknHTSSXn66ae3e3tzdXV1rr766vzyl7/c5rZBgHJbtGhR7rnnnrzuda/LBz7wgVx33XWZOXNm7rzzzlRUVOTMM8/M/PnzM3/+/JxxxhlbP7Qed9xx+cMf/vCa53vooYfy5je/ucyvAuC13v3ud2/3Fufa2toMGTIko0eP3jo2bNiwHHbYYfnjH/+4zb6//e1vM3v27LLkBdiZfv365dJLL83y5ctzyy23ZMiQIdmwYcM2+6xatSpDhw4tKCF9nSIRdtOWLVvyk5/8JN/61rdy33335b777svChQvT0tKSxsbG1+w/ePDgXHzxxfnqV7+6dW4ygHIbMGBArrnmmjz77LNJXvpN9iOPPJI3velNSZKzzjorP//5z7NgwYJMnTp163HTpk3LkiVLtpn8e+7cufn9739vJXqg15gxY0Z+/etfZ8WKFSX3veCCCzJr1qy0tLQkeel251mzZmXMmDE9HRNgl1RVVeXSSy/NTTfdlE2bNmXMmDG59957t27/4Q9/mBNOOKHAhPRlikTYTffdd19Gjx6dt7zlLVvHamtrc8455+xw0ZVzzjkngwYNym233VaumADbeOc735m/+Zu/yUUXXZTJkyfnlFNOSWVlZT7zmc8kSUaNGpXhw4dn0KBBOfTQQ7ceN3z48Hz3u9/NwoULc8opp2Ty5Mn5xS9+ke9///tWbAZ6jZdvcd6VecHOPffcnHnmmfnrv/7rnH766fnoRz+aqVOnWrEZ6FUmTJiQt771rbn++uvzT//0T/ne976X008/Pe973/vyxBNP5Itf/GLREemjKrq7u7uLDgEAAAAA9G6uSAQAAAAASlIkAgAAAAAlKRIBAAAAgJIUiQAAAABASYpEAAAAAKCkqqIDAACwf/nDH/6Qa665JmvXrk13d3dGjhyZz3/+8znqqKOKjgYAQA+q6O7u7i46BAAA+4ctW7bkPe95T2699dYce+yxSZJ58+bluuuuy8KFC1NZWVlwQgAAeoorEgEA2GWbNm3Khg0bsnHjxq1jp59+empra9PZ2Zk777wzc+bMSb9+/XLIIYfkC1/4Qo444ojMmDEjRx11VC644IIk2ebxSSedlOOOOy6PPfZYPvvZz+bII4/MF7/4xaxevTr9+vXLpz71qZx66qlZvnx5/vEf/zHLli1Le3t7TjvttFx00UVF/asAAOhzFIkAAOyyoUOHZvr06bnwwgtzyCGH5G1ve1vGjRuX0047LUuWLMk3v/nN/PCHP8yIESPy4x//OJ/5zGfyv/7X/yr5vEcddVT++Z//OUly1lln5eyzz86HP/zhLFu2LB/5yEcyYcKETJ8+PR/72Mdy0kknpa2tLZ/4xCdy+OGH59RTT+3hVw0AQGKxFQAAdtPHP/7xPPDAA5k5c2bq6uryr//6rznzzDPzy1/+MqeeempGjBiRJJk6dWqWL1+eZ599tuRzHn/88UmStWvX5tFHH80555yTJBk1alR+8YtfpF+/fnnwwQdz/fXX54wzzsgHP/jBLFu2LI8++mjPvVAAALbhikQAAHbZkiVL8tBDD+XCCy/Me9/73rz3ve/NZz/72bz//e/Pfffdl9NOO22b/bu7u9PR0ZGKioq8cmru9vb2bfYbNGhQkqSq6qWPpxUVFVu3PfXUU6mrq0t3d3d+8IMfZODAgUmS1atXp6ampkdeJwAAr+WKRAAAdtmIESNy880353e/+93WsZaWlrS2tubCCy/MPffck9WrVydJfvSjH2XYsGEZM2ZMhg8fnj/+8Y9JkuXLl+e3v/3tdp+/trY2xx57bObOnZskWbZsWc4999xs3rw5Y8eOzW233ZYkWb9+fc4999wsXLiwB18tAACv5IpEAAB22RFHHJEbb7wx1113XV544YXU1NRk8ODB+fKXv5wJEyaks7MzH/3oR9PV1ZURI0bkG9/4Rvr165ePfOQj+bu/+7tMnjw5hx56aN75znfu8BzXXHNNrrzyysyZMycVFRX50pe+lLq6usyePTtXXXVVpkyZki1btuT9739/Tj/99DK+egCAvq2i+5X3mAAAAAAAbIdbmwEAAACAkhSJAAAAAEBJikQAAAAAoCRFIgAAAABQkiIRAAAAAChJkQgAAAAAlKRIBAAAAABKUiQCAAAAACX9/xNMEZMrsDX1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source vs price\n",
    "sns.catplot(y = \"Price\", x= \"Source\", data = df.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "39508f3a",
   "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>Source_PAR</th>\n",
       "      <th>Source_RUH</th>\n",
       "      <th>Source_SVO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_PAR  Source_RUH  Source_SVO\n",
       "0           1           0           0\n",
       "1           1           0           0\n",
       "2           1           0           0\n",
       "3           1           0           0\n",
       "4           1           0           0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performing OneHotEncoding on Source since it's nominal categorical data  \n",
    "source =df[[\"Source\"]]\n",
    "source =pd.get_dummies(source, drop_first=True)\n",
    "source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "cf024fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NYC    24926\n",
       "RUH    11046\n",
       "PAR     8042\n",
       "SVO     6083\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# destination\n",
    "df[\"Destination\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "254be453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a58a3f8790>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e9bGqXsxBP9fU1JhHH388g/cY3fsXrxqWqqqks6ur969doKo9RqUzyeJnVvb6tds3vHhNRSIAAAAMDGYk9oAiZ+YVqaWlOZs2txdTJNLr2jeszJDqwYXNhiyKWZgAAAD0Z2Yk9rKmpsY88tjjGTRkZNFRelfFnqmoSTo6B9aswIGqomZk2pI8tvT5oqP0mq5Nq5OYhQkAAMDApEjsIYOGjMweE5UN0J9saF5QdAQAAAAojLs2AwAAAABlKRIBAAAAgLIUiQAAAABAWYpEAAAAAKAsRSIAAAAAUJYiEQAAAAAoS5EIAAAAAJSlSAQAAAAAylIkAgAAAABlKRIBAAAAgLIUiQAAAABAWYpEAAAAAKAsRSIAAAAAUJYiEQAAAAAoS5EIAAAAAJSlSAQAAAAAylIkAgAAAABlbVeR2NXVle9///s555xzsm7dunz3u99NZ2dnT2cDAAAAAPqI7SoSv/71r+fRRx/Ngw8+mCT5t3/7t1x66aU9GgwAAAAA6Du2q0j89a9/ncsuuyw1NTWpra3Nddddl/vvv7+nswEAAAAAfcR2FYlVVVUZNOi/nlpdXZ2qqqoeCwUAAAAA9C3b1Qbuv//+ueGGG9LZ2Zknn3wyP/zhD3PAAQf0dDYAAAAAoI/YrhmJ559/fv7f//t/WbFiRU4++eRs2LAh5513Xk9nAwAAAAD6iO2akVhbW5vPfOYz+drXvpZ169alpaUlo0aN6ulsAAAAAEAfsV0zEq+//vp89rOfTZKsWrUqp59+eubMmdOjwQAAAACAvmO7isSf/vSnufHGG5MkEyZMyO23354f/ehHPRoMAAAAAOg7tqtI7OzsTG1tbffxsGHDUlFR0WOhAAAAAIC+ZbuKxDe84Q2ZPXt2li5dmqVLl+bb3/52Xve61/VwNAAAAACgr9iuIvGiiy7KkiVLcswxx+T444/PkiVL8g//8A89HA0AAAAA6Cu2667Ne+21V77zne/0dBYAAAAAoI/aZpH41a9+Neeff35OO+20rT5+9dVX90goAKA4q1atzKhRo4uOAQAA9DHbLBLf8573JEmmTZvWK2EAgGI9+ujDueyyf8y5516Q/fc/oOg4AABAH7LNIvGII45Iktx+++3553/+514JBAAUo7OzM9de++Jqg2uvvTpf+9o3UllZWXAqAACgr9ium62sXbs2GzZs6OksAECBFiy4Oy+8sCZJ8sILa7JgwT0FJwIAAPqS7brZytChQ/P+978/f/mXf5k99tije9weiQDQP6xZszq33TYnbW1tSZK2trbcdtvNmTRpckaMGFFwOgAAoC8oWyQ++uijmTp1ag499NCMHTu2NzIBAL2sqakxnZ1dW4x1dXWlqakxRx75gYJSAQAAfck2i8Sf/exnaWhoyMSJE9PS0pLZs2fnfe97X29lAwB6yaRJ781tt81JR8d/jQ0aNCiTJk0uLhQAANCnbHOPxOuvvz533nln5syZk6uvvjrXXHNNb+UCAHrRiBEjcuyxJ6SmpiZJUl1dk2OP/bBlzQAAQLeyN1vZe++9kyRvf/vbs2rVqh4PBAAUY+rUaRk+/MXicMSIEZk69ciCEwEAAH3JNovEioqKLY4rKyt7NAwAUJzKysr87d+eliT52789zd/7AADAFrbrrs0veXmxCAD0DwsXLsjNN9+QTZs2ZdCgQfnBD76XadOOyuGHTy06GgAA0Edss0h85JFH8o53vKP7eNOmTXnHO96RUqmUioqK/O53v+vxgABAz2tqakxFqSt/OXF8kqTludY0NTUqEgEAgG7bLBJ/8Ytf9FYOAKBg+42ty9kfPyFJ8vUfzCk4DQAA0Ndss0gcP358b+UAAAAAAPqwsndtBgAAAAB4TTdbAQD6l4ULF6SpqTEtLc1JV0f3kuaW51qTQavS0HBxJk2abK9EAABAkQgAA1lTU2NaljyRfetHJ0m6ujqTpPu4ZckTSaJIBAAAFIkAMJC8NAPxJS0tzUlK23hFKS0tzWlouDhJzE4EAIABrEeLxO985zv5+c9/niQ57LDDcvbZZ6exsTGXXnpp2tra8oEPfCBnnnlmkuShhx7K+eefn/Xr1+eQQw7JRRddlKqqqjzzzDM566yzsmLFirz+9a/P7Nmzs+eee/ZkbADYbb28KHy5lpbmtG3amOrBlVuML132/KuftHNzmhc/ls3tnWlpad7m+RWNAADQf/VYkdjY2Jhf/epXue2221JRUZFPfvKTueuuuzJ79uxcf/31GTduXD796U/nvvvuy2GHHZazzjorl1xySQ4++OCcd955ufnmm3PyySfnoosuysknn5yjjjoqV1xxRa688sqcddZZPRUbAHZrTU2NaX7ysYwfXbvVx/cZXp0Mr96pa3Ssfnar40+vXJfEMmgAAOiveqxIrKury7nnnpvq6hc/rPzFX/xFlixZkokTJ2bChAlJkhkzZmT+/Pl54xvfmE2bNuXggw9Okhx33HG5/PLLc8IJJ+SBBx7IFVdc0T3+0Y9+VJEIANswfnRtPjftnb1+3SvuXtTr1wQAAHpPjxWJb3rTm7p/vWTJkvz85z/PRz/60dTV1XWP19fXZ9myZVm+fPkW43V1dVm2bFlWrVqV2traVFVVbTH+WowZs/UZGT1p8MuWiwH9x+DBlamrG1Z0DHhVgwdXpqPg6/saAQCA/qnHb7by2GOP5dOf/nTOPvvsVFZWZsmSJd2PlUqlVFRUpKurKxUVFa8Yf+nff+7lx+WsWLEuXV3b2kR+12tv7+zV6wG9p729M62ta4uOAa+q6L+DfI0AAMDubVsTAwb15IUXLVqUU089NV/60pdy7LHHZuzYsWltbe1+vLW1NfX19a8Yf/7551NfX5/Ro0dn7dq16ezs3OL5AAAAAEDv6rEi8dlnn83nPve5zJ49O0cddVSS5G1ve1sWL16c5ubmdHZ25q677sqUKVMyfvz41NTUZNGiF/dWmjt3bqZMmZLBgwfnkEMOybx585Ikt99+e6ZMmdJTkQEAAACAV9FjS5uvvfbatLW15bLLLuseO/HEE3PZZZfl9NNPT1tbWw477LBMnz49STJ79uzMmjUr69aty5vf/OaccsopSZILL7ww5557bq666qqMGzcu3/zmN3sqMgAAAADwKnqsSJw1a1ZmzZq11cfuuOOOV4wdcMABueWWW14xPn78+Fx//fW7PB8AAAAAsP16dI9EAAAAAKB/UCQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAAAAAChLkQgAAAAAlKVIBAAAAADKUiQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAAAAAChLkQgAAAAAlKVIBAAAAADKUiQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAAAAAChLkQgAAAAAlKVIBAAAAADKUiQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAAAAAChLkQgAAAAAlKVIBAAAAADKUiQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAAAAAChLkQgAAAAAlKVIBAAAAADKUiQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAADot+bO/VnREQCg36gqOgAAsKWFCxekqalxh17b0tKcUkdbrrh70S5OVd7TK9em4oXNaWi4eIfPMWnS5Bx++NRdmAoYyC688MtZurQ5v/vdolx00deKjgMAuz1FIgD0MU1NjWl+8tHsM2Loa37tuD0HJRmaUmfHrg9Wxkt521cs3aHXP7NmY5IoEoFdYuXKlVm6tDlJsnTpkqxcuTKjR48uOBUA7N4UiQDQB+0zYmhOO+yAomP0qqvve7joCEA/cs45/9/Ljr+Qa675UUFpAKB/sEciAADQr8yff2c6Ozu3GOvs7Mg99/yfghIBQP+gSAQAAPqVW2+ds9XxW275aS8nAYD+RZEIAAD0K8cf/z9e0zgAsH0UiQAAQL9y5JFHvWKssrJqq+MAwPZTJAIAAP3OG97wxi2OGxr+dzFBAKAfUSQCAAD9zuDBg7t/PWHC6zJ69OgC0wBA/1BVdID+6oVVrdlU9f+KjgHsQptXtSYT9io6BgDwGowZs1cuuuhrRccAgH7BjEQAAKDfWb16VZJkw4YNWbhwQcFpAKB/MCOxhwwfVZc9xr+56BjALrSh47miIwAA22HhwgVpbV2eikEV2dS2MXPm3JgkOfzwqQUnA4DdmxmJAABAv9LU1JjB1YMz4fX7ZcLr90tXqTNNTY1FxwKA3Z4ZiQAAQL+z9/ix+ejppyZJfvxPPyw0CwD0F2YkAgAAAABlKRIBAAAAgLIUiQAAAABAWYpEAAAAAKAsRSIAAAAAUJYiEQAAAAAoS5EIAAAAAJSlSAQAAAAAylIkAgAAAABlKRIBAAAAgLJ6tEhct25d/vt//+956qmnkiSNjY2ZMWNGjjzyyHzrW9/qft5DDz2U4447LtOmTcv555+fjo6OJMkzzzyTj3zkI5k+fXo+85nPZP369T0ZFwAA2I0tXLggDQ0Xp6WlOcuefi4//qcf5sf/9MMse/q5tLQ0p6Hh4ixcuKDomACw2+qxIvEPf/hDTjrppCxZsiRJsmnTppx33nm58sorM2/evPzpT3/KfffdlyQ566yzcsEFF+Tuu+9OqVTKzTffnCS56KKLcvLJJ2f+/Pk56KCDcuWVV/ZUXAAAYDfX1NSYxc1PZOTYURk9bq+0d3Wkvasjo8ftlZFjR2Vx8xNpamosOiYA7LaqeurEN998cy688MKcffbZSZIHH3wwEydOzIQJE5IkM2bMyPz58/PGN74xmzZtysEHH5wkOe6443L55ZfnhBNOyAMPPJArrriie/yjH/1ozjrrrJ6KDADdFi5cUNiHzZaW5pTaN+Xq+x4u5PpFeWb1hlSsf3HGUBEmTZqcww+fWsi1gV1nzLi6zPzU8Vt9bO41t/RyGgDoX3qsSPzqV7+6xfHy5ctTV1fXfVxfX59ly5a9Yryuri7Lli3LqlWrUltbm6qqqi3GX6sxY2p38B3suMGDK3v9mkDvGDy4MnV1w4qOQS/43e+a0vzEIxk3rKbXrz12SJIhNSl1dvb6tYv04u91KZuXL+n1az+7ti2DB1fmhBOO6fVrA7vO4MGVyebyz/F3OQDsmB4rEl+uq6srFRUV3celUikVFRWvOv7Sv//cy4+3x4oV69LVVdrx4DugvX1gffCDgaS9vTOtrWuLjkEvaG/vzLhhNfnkpAlFR6EXfL9pqa9v6Ae25/twX+vA7mzJkifzute9oegY9HPb+oFbr921eezYsWltbe0+bm1tTX19/SvGn3/++dTX12f06NFZu3ZtOv9zNsZLzwcAAAAYaBYsuDv/+I+zsmDBPUVHYQDrtRmJb3vb27J48eI0Nzdn3333zV133ZUPfehDGT9+fGpqarJo0aK8853vzNy5czNlypQMHjw4hxxySObNm5cZM2bk9ttvz5QpU3orLgAA0Me9fD/blpbmdJY6XnUvxBXPtmZ1xaruvVjtjQrsLjZv3pwbb7w+SXLjjT/K+953eKqrqwtOxUDUazMSa2pqctlll+X000/PBz/4wbzhDW/I9OnTkySzZ8/OpZdemunTp2fDhg055ZRTkiQXXnhhbr755nzwgx/Mb3/723zhC1/orbgAAEAf19TUmCeXPJ6VG1dm5caVqa0blhH1o9LR2bHVf0bUj0pt3bCs3LgyTy553B2cgd3G1Vf/U7q6upK8uHXcd7/7nYITMVD1+IzEe++9t/vX73nPe3LHHXe84jkHHHBAbrnllT81HD9+fK6//voezQcAAOy+Ru49Okd87AOv+XX3Xv/zHkgDsOstXdqcf//3RVuM/f73v81TTy3Nvvvaz5ve1WszEgEAAAB4bW6/fevbNdx225xeTgK9uEciALunl+8/NVC0tDSn1N6W7zctLToKveDZtW2p2NTcvW/aQGF/OADo+yZMmJjf/37RK8aPPfaEAtIw0CkSAdimpqbGLHn84YytHVh/ZdRXJ6muTFdXR9FR6AV771mZpD2bnnu86Ci95rl1L/6/rUgEgL7tkUceytCamuy7995JkqeWLUvl4MGWNVOIgfWpEIAdMra2Kh9/6+iiYwC70A8eXFl0BABgG15aGdTS0vyKxzo7O9PQcLHVBfQ6RSIAAABAH9PU1JiWxU9k/Ji9kiSl9hdXE4wfPSZJ0rL4iSRWF9C7FIkAAECfVG6f3paW5nR0te/QHZhXL1uZdYPWbnNvVDN9gKKNH7NXTj965lYf+6c75vZyGlAkAgAAfVRTU2OeWPJYhtWN2OrjQ8YMTTI0HTuwn21t3fAkyfL1y7f6+NrWNUnM9AGAP6dIBAAA+qxhdSPyrhPe1+vXfWDOv/X6NQGgr1MkAgAAPabc8uRtaWlpTnvn5kJKvbXL12RT5cZtLn0ux9JoAPobRSIAANBjmpoa8/jix7LHmNrX/NqqkdWpSnU6Ojt7INm2Df3PvM+88OwOvX7DinVJLI0GoH9RJPaQrk2rs6F5QdExgF2oa9PqJHsVHQMAdjt7jKnNAUe/vegYverhO35fdAQA2OUUiT1g0qTJRUcoREtLczZtbs/gPUYXHYVe0L5hZYZUD85++00sOkov2mvAfn0DAACAIrEHHH741AG5hKGh4eIsfmZl9jrgA0VHoRc8//DPs98+o3POOV8pOgoAAADQCwYVHQAAAAAA6PsUiQAAAABAWZY2AwBAP7dw4YI0NTUWcu2WluZs7mwbcDcf2bBibVpWb05Dw8WFXH/SpMkDcrslAHqWIhEAAPq5pqbGPPbko6keNbT3Lz5sUAZnaNo7O3r/2gUaPHJoSkmaVy3t9WtvXrUxSRSJAOxyikQAABgAqkcNzb5HvqnoGPSCp+55rOgIAPRT9kgEAAAAAMpSJAIAAAAAZSkSAQAAAICyFIkAAAAAQFmKRAAAAACgLHdtBqCs51asya+f7Cw6BrALPbdiXV43tugUAMBLFi5ckKamxu7jlpbmlDo78k93zN3q859a8XwqVq9OQ8PFSZJJkybn8MOn9kpWBi5FIgAAA8bLP6QNFC0tzWnr2JSn7nms6Cj0grZVG9Kytrm7XBgolCjs7pqaGtP85OPZZ+TIJMm42j2TJF0d7Vt9/j4jRiRJ2lc+n2dWr04SXwP0OEUiAGWNHTMi73nD6KJjALvQw+sqi45QiKamxjz65COpHFFddJTetWdSmep0dHUUnYReUDmiOu3pyhMrFhcdpdd0rtmcRInC7m+fkSPz6fe/9v+Pv/svC3ogDbySIhEAgAGlckR1RkzZp+gYwC605l+fKToCwICgSAQAAADoYeW212hpaU6po32HZhc+s3p1Ktat3+aWBpb/sysoEgEAAAB6WFNTY5qfeCxjhw3b6uN7D6lJUpOujte+DcXY2tokSdvy57b6+HNr1yax/J+dp0gEAAAA6AVjhw3Lxw/5616/7g9++5tevyb906CiAwAAAAAAfZ8iEQAAAAAoy9JmAAAAgO1Q7oYp29LS0pyu9s2FLDN+du3aDNrUts2bsZTjZi0kikQAAACA7dLU1Jgljz+avYfu8ZpfW1dZmVQOTVf7a7+Zys7ae8jQJMnGp5/aodcv27ghiZu1oEhkF2vfsDLPP/zzomPQC9o3rEwyuugY9JLn1nXkBw+uLDoGsAs9t64jrys6BADsgJ2ZFbizWlqak1Ihly5W6cX3vjMzGneG2ZB9hyKRXWbSpMmFXbulpTltmztSUzumsAxFaFu3IjXVVdlvv4kFXH10of/N6T0D9b9zS0tzSu2bMm5YTdFR6AXPrm1LxeAhBf15WozXZeB+fQOwe2tqasySxx5JXXXvf582Jkmqq9PV0fuzCotUV12dlEpZ37yk16/durktidmQfYUikV3m8MOnFvaF3dBwcVqeW52Jbz+6kOsXpfn3d2S/sSNzzjlfKToK/ViRX9tFami4OJuXL8knJ00oOgq94PtNS1NdP9GfpwCwm6irrskJ++xbdAx6wZxndmw5Nj3DXZsBAAAAgLLMSAQAYEBZ8/zqtD9UUXQMYBfa8Pzq/1xzOrAUuVdgkVpamtPZ1mam2gCxvK0tlQXuz1iUvrovpCIRAAAAdkNNTY1Z/OjD2auysugovWpUklQOSldHe9FR6AV7VQ5K2jdn7ROPFR2l1zzf2Zmkb+4LqUgEgFfx7Nq2fL9padEx6AXPrm3LxPqiU9BbRuw1MiMOHFd0DGAXWtM6EG+j+6K9Kiszc9jIomMAu9DctauLjvCqFIn0G23rVqT593cUHaNXta1bkWRk0TGgXyr6TvSl9k3ZZ+QehWUowjOrNxR25+SJ9e5gDAAA5SgS6Rd25sNfS0tzNrd3ZI/hdbsw0fbZ8EJrqgdX7cSH5pE++EIPKfpO9O0rlua0ww4o5PpFufq+hzN4zAR3TgYAgD5KkUi/sDMf+BsaLs4zrS/kwMnH7+JU5T3UeEv2qRvuQzPwCs+s2Zir73u46Bi96pk1GzNxAG6UDwAAuwtFIgD0MTs7y7rU0Zbxo4ftwkTb5+mVa1NRVbPDs6wnjrG8GABeq+Vr1uT3mzuKjgHsQss3rk/vfze/fRSJANDH7Ows647Vz+Zz0965i1OVd8Xdi1I1cpxZ1gAA0E8pEgEAAGA3VT9iRN7urs3Qr7Ss7bt1Xd9NBr1owwuteajxlkKum7rhvX5doH97euW6XHH3okKuO3Fkr18WAADoJYpEBrxy+3G1tDSnvaMzw0bWv+Zzr129PIOrKl99v7C64fYDA3ap7fkzrdS5OfvuNfI1n/up51enorL6Vf9MmzjSHofsHjrXbM6af32m6BjALtS5ZnPihl0APU6RyIBXbi+yhoaLs3zluhwy9eTXfO7fLvhJ6kfX2i8M6DXb82day+LHd+jcFanIfvtN9Gcau7WBWna3tDRnU8em1Izao+go9IK2VRsypGrIDt/8arfkhl0AvUKRCAADyMs/ZLW0NKdt08bUVG/9W4K2zR2pGTI0++03MfsNq/Mhjd3eztzMaHfW0HBxmlctzb5HvqnoKPSCp+55LPuNmuAHPwDscopE2A5rVy/Pbxf8ZIdeVz+6tgcSAeyYl5coCxcuSFNTY1pampOujuw3ti5J0vJcazKoKm/a/42ZNGnygCxeAACALSkSoYyXz75ZvXpVXnjhhbS1bUqpVErV4JokSUd7WyoqKlJTMyTDhw/PyJGjUj+61uwdoE97qVhsaLg4LUue2OIxy5gBAIA/p0iEMl5tCVRDw8VZs25TPnD8p5IkP7/lmoyoHeJDN7BbmjRpch555KE81vLiDShKsdcUAACwJUUi7ISVrc/m57dc0/3rEbWvLzgRwI45/PCp+clP/jkdHR3dY29969sLTATsaptXbcxT9zxWdAx6weZVG5NRRacAoD9SJMIOemn2zsrnn02SVFYOMnsH2K39eYmYJOec84Vcc82PCkoD7EpFfo/S0tKczZ1t2WPMsMIyFGHDirWprqwp5s7Jo8wqH0ie7+zM3LWri44B7ELPd3amr/6tqUiEHXT44VPz4x//IHV7j0+S1A6tdjMCYLc1f/6drxjr7OzIPff8nxx55FEFJAJ2pSLvVt3QcHGeeeHZHHD0wJrl/PAdv88+w8fZ9oYeNVAL45aW5nRu2pT6mpqio9ALlre1pXLIkGJ+MFOQYem7X9+KRNhJrcueTpJs2GOPgpMA7Lhbb52z1fFbbvmpIhHYaRtWrMvDd/y+6Bi9asOKdcnwolPQ3xX5Q4IiNTRcnPXNS3LCPvsWHYVeMOeZp7KnmwD2GYpE2AmVlZXZ3LYpSbK5bVM2btyYoUOHFpwK4LU7/vj/kZtuumGr4wA7Y2dmVLS0NKe9c3OG1Y/YhYm2z9rlazK4snrHZ8AM77uzSQBgRykSYSe8fD+xf/zHWbn00m8UlAZgxx155FGZM+emdHZ2do9VVlaZjQjstJ2ZMdXQcHGWr1+ed53wvl2cqrwH5vxb6vesNwMGAP6MIhF20H/8x59SKpW2GFu27Nk8/PB/5IAD/qqgVAA7rqHh2/n7v//8nx3/7+LCAPynta1r8sCcfyvkuvV71vf6dYHt07q5LXOeearoGPSC1s1t2bPoEHRTJMIOuvbaq7Y6fs01V+Yb3/hOL6cB2HmjR4/OhAkTs3RpcyZMeF1Gjx5ddCRggCu3NLilpTmbNm1MVfVr/1jTsbkjQ4YMfdWly/V71luaDH1U0Xei72pry94DbI/8ZRs2ZFBNMXei3zO2iuhLKkovn1LVz6xYsS5dXf36LVKQ00//VNavX/+K8T333DP/9E/XFJAIYNeYO/dnmTnzQ0XHAChr4cIFaWpq7D5evXpVXnjhhbS1bUpXV1cqKiqSJKVSKRUVgzJkyJAMHz48I0eOSvLiB9OBeKMKYMc1NFycJY8/mr2HDrAiceOGvO6N+9vuYYCoqxv2qo8pEmEHLVr0m1xxxf9+xfjnPveFvPOdf937gQAASPJiwfijH12bikH/WSR2lVJTMzRXXXVtwcmA3d3Lf4DxWrS0NKerfXPGDXv1kqanPLt2bQYN3okbSMUPXwaSbRWJljbDDnrnO/86w4YNz9q1L3SPDRs2XIkIAFCwPfd8cTet0p9NKGhr25hFi37jezVgp+zsDaSan3hsFyfaPhVJ9ttvohmF7DRFIuyEr351ds444++2OAYAoFg//vEPtjr+ox9dp0gEClNun7+Xtmd4SUdHezo7O9PV1bXV51dUVKSqqipVVYOTZIutG15uYv1Y+wyySygSYSfU1tbmkEP+Or/97W9yyCGTUltbW3QkAIAB761vfXvuv/9fXzH+trcd3PthAP7Tjs5m/NSnPpbOzs5XjA8ePDhXX/3DXZAMtp8iEXbSZz/7hTQ2/lsmT35f0VEAAEhy/PEnbbVI/NCHTiogDcDOGT9+37S0NL9ifOzYcQWkYaAbVHQA6A+UiAAAfceIESNy7LEnbDF27LEfzogRIwpKBLDjPvnJz76mcehJikQAAKDf+eAHj05NTU2SpKamJh/84IyCEwHsmH33nZA3v/mtW4wddNDbsu++EwpKxECmSAQAAPqdysrKnHnmOUmSM888J5WVlQUnAthxp5/+xS2OP//5MwtKwkC3WxSJd955Zz74wQ/myCOPzA033FB0HAAAYDew//4H5Bvf+E723/+AoqMA7JTq6up85CP/M0nykY+cmurq6oITMVBVlEqlUtEhtmXZsmU56aSTcuutt6a6ujonnnhivvnNb+aNb3zjdr1+xYp16erq028RAAAAoKwlS57M6173hqJj0M/V1Q171cf6/IzExsbGvPvd787IkSOzxx57ZNq0aZk/f37RsQAAAAB6lRKRolUVHaCc5cuXp66urvu4vr4+Dz744Ha/fsyY2p6IBQAAAAADSp8vEru6ulJRUdF9XCqVtjgux9JmAAAAANg+u/XS5rFjx6a1tbX7uLW1NfX19QUmAgAAAICBp88XiZMnT86vf/3rrFy5Mhs3bsw999yTKVOmFB0LAAAAAAaUPr+0ee+9986ZZ56ZU045Je3t7Tn++OPz1re+tehYAAAAADCgVJRKpX69gaA9EgEAAABg++zWeyQCAAAAAMVTJAIAAAAAZSkSAQAAAICy+vzNVnbWoEEVRUcAAAAAgN1ev7/ZCgAAAACw8yxtBgAAAADKUiQCAAAAAGUpEgEAAACAshSJAAAAAEBZikQAAAAAoCxFIgAAAABQliIRAAAAAChLkQgAAAAAlKVIBAAAAADKqio6APRVTz31VKZOnZrrrrsu733ve7vHjzjiiLzpTW9KTU1NLr/88u7xX/3qV7nwwgszd+7c1NbW5vbbb8/111+fjo6OdHV15YQTTsgpp5xSxFsByFNPPZXp06fnL/7iL5IkXV1dWb9+fY455pgcd9xxOeWUU3Lvvfdu8Zq//Mu/zCOPPJKmpqZ85zvfyfXXX7/F+bb2GoDeMn/+/Hzve99LR0dHSqVSZs6cmREjRmT+/Pm59tprt3jul7/85Rx44IE55ZRTsmTJkjQ0NOTxxx9PTU1NXv/61+fss8/OhAkTCnonAFt+r1ZRUZH29vbU19fn0ksvzdixY3P66adnyZIlufPOO7tf09TUlNNOOy377bdfkv/6/u5Tn/pUTjrppKLeCv2cIhG2YfDgwfnKV76SO+64I7W1td3jX/rSl3LaaadlwYIFmTp1ajZs2JB/+Id/yNe+9rXU1tbmpz/9aW666aZ897vfTX19fV544YV84hOfyNChQ3PCCScU+I6Agay+vj5z587tPl62bFmmTZuWo446qsBUAK/dsmXL0tDQkFtvvTWjRo3K+vXr87GPfSyf/exn8+///u9ZsWJFxowZkyTZuHFj/uVf/iVnn312nn/++Zxyyin5+7//+xx99NFJkrlz5+akk07KHXfckdGjRxf5toAB7uXfq1122WX5+te/nlmzZuU//uM/UldXl9/97nd5xzve0f2cgw46aIsf9j700EM5/vjjM2PGjC0+w8KuYmkzbEN9fX0mT56choaGLcb32GOPXHLJJbn44ouzYcOGXH755TniiCMyadKkJMlVV12Vs846K/X19UmS4cOHp6GhIfvvv3+vvweAV9Pa2ppSqZRVq1YVHQXgNVm1alXa29uzadOmJMmee+6Zyy67LG9605vyN3/zN5k3b173c3/5y1/m3e9+d0aNGpUbb7wxkydP7i4Rk2TmzJl55zvfmRtvvLHX3wfAtkyaNCmPPfZY7rzzzrzrXe/KkUcemZtuummbr3n66aczdOjQVFdX91JKBhozEqGMc889NzNmzMj999+/xRLnyZMn59BDD82Xv/zlPPnkk5kzZ06SZOXKlXn22WfzV3/1V1uc56XlhABFWb58eWbOnJm2trasWrUqb3nLW/Kd73wnY8eOLfvaP/3pT5k5c2b3cXt7e09GBdimAw44IFOnTs3f/M3f5MADD8ykSZMyY8aMTJw4MR/60IfyjW98Ix/72MeSJLfffns+/vGPJ0n++Mc/5rDDDnvF+d71rnflV7/6Va++B4BtaW9vz913352DDz44t956a774xS9m//33z7e//e2cd955GTlyZJL/+h5t48aNWbNmTSZNmpTrrrtOkUiPMSMRyqitrc3FF1+cr3zlK1m3bt0Wj5177rm5//77M2vWrAwZMiRJMmjQi19WNTU1vZ4VYFteWi4zb968zJw5M6VSKe9973u7/9z6c6VSKRUVFd3HBx10UObOndv9z/e+973ejA7wChdddFHuvffenHTSSXnmmWfy4Q9/OPfcc0/e9a53ZdWqVVm6dGlaW1uzZMmSTJ48OUlSUVGRzs7OV5yrvb19iz/zAIrw0g99Z86cmaOPPjqlUiknnnhinnvuuUyePDnjxo3LgQcemNtvv737NS99j3bXXXfl3e9+d4YNG5a3vvWtxb0J+j1FImyHQw89dKtLnGtrazN8+PCMHz++e2zkyJGZMGFC/vSnP23x3N/85jeZPXt2r+QF2JZBgwbl7LPPzrJly3Lttddm+PDhWbt27RbPWbFiRUaMGFFQQoBtW7hwYebNm5e99947H/rQh/Ktb30rs2bNyi233JKKioocc8wxueuuu3LXXXdl5syZ3T8weetb35p///d/f8X5fv/73+eggw7q5XcBsKWXfug7d+7c/PznP09DQ0Nuu+22bN68OdOmTcsRRxyRxYsXb3V5c3V1dS655JL8y7/8yxbbO8CupkiE7XTuuefmV7/6VZYvX172uX/7t3+byy67LK2trUleXO582WWXZeLEiT0dE2C7VFVV5eyzz86VV16ZjRs3ZuLEibn77ru7H//pT3+a97znPQUmBHh1Q4YMyTe+8Y089dRTSV6cRf3QQw/lwAMPTJIce+yx+cUvfpH58+fnuOOO637dySefnEWLFm1xM4Pbb789v/vd79zhFOhzNm/enDvvvDM//OEPc++99+bee+/NggUL0tramqamplc8f9iwYTn99NPz9a9/vXsPWdjVFImwnV5a4rw9+4KddNJJOeaYY/KJT3wiRx99dP7n//yfOe6449yxGehTpkyZkre//e359re/nf/1v/5XfvKTn+Too4/OBz7wgTz22GO54IILio4IsFXvfve78/nPfz6nnXZapk2blunTp6eysjKf+9znkiTjxo3LqFGjsscee2Tfffftft2oUaNyww03ZMGCBZk+fXqmTZuWX/7yl7nxxhvdsRnoc+69996MHz8+b3vb27rHamtrc8IJJ7zqTVdOOOGE7LHHHvnBD37QWzEZYCpKpVKp6BAAAAAAQN9mRiIAAAAAUJYiEQAAAAAoS5EIAAAAAJSlSAQAAAAAylIkAgAAAABlKRIBAAa4p556KgceeGBmzpyZmTNnZsaMGTnxxBMzb968HT7nnDlzcsMNNyRJbrzxxnzve9/b4XM9+OCDueCCC5Ikf/zjH3PGGWfs8LkAANhxVUUHAACgeEOGDMncuXO7j59++umceuqpqayszLRp017z+RYtWpQ3velNSZKTTjppp7I9/vjjWbZsWZLkLW95Sy6//PKdOh8AADtGkQgAwCuMHz8+Z5xxRq699tq8//3vz+zZs/PAAw+ks7Mzf/VXf5VZs2altrY2P/nJT3LTTTdl8ODBqampyT/+4z9m8eLFuffee3P//fdnyJAhWblyZVatWpULLrggRxxxRI499tj8+te/zrPPPpuZM2fmC1/4Qrq6uvK1r30tf/jDH7J+/fqUSqVccskl2WeffXL55Zdn7dq1+fKXv5xjjjkmF198ce66666sXbs2F110UR5++OFUVFTkfe97X774xS+mqqoqb3nLW/J3f/d3uf/++7N8+fJ88pOfzMknn1z0bysAwG7N0mYAALbqgAMOyKOPPprvfe97qayszK233po77rgj9fX1mT17djo7O/O1r30t3//+9/Ozn/0sH/7wh7No0aL8t//233LEEUfk1FNPzUc+8pFXnHfDhg3dBeR1112XpUuX5g9/+EOWL1+en/70p5k3b16OPfbYXHPNNRk3blzOOOOMHHLIIbn00ku3OM8ll1ySkSNH5s4778zPfvazPPLII7nuuuuSJJs3b86oUaNy00035fLLL8+ll16atra2Xvl9AwDor8xIBABgqyoqKjJkyJAsXLgwa9euTWNjY5Kkvb09Y8aMSWVlZaZPn54TTzwxhx9+eA499NAcdthhZc87derUJMnee++dMWPGZM2aNXn729+eESNG5KabbsrSpUvT1NSUPffcc5vn+dd//dfceOONqaioSHV1dU488cT88z//c/7u7/5ui+u8+c1vzubNm7Nhw4bU1NTszG8JAMCApkgEAGCr/vjHP2b//ffPunXrct5553WXhOvXr++e3Td79uw8+uijaWxszPe+973MnTs33/72t7d53j8v8yoqKlIqlbJw4cJ89atfzcc//vFMnTo1b3jDG3LHHXds8zxdXV2pqKjY4rijo+MV13npOaVS6TW8ewAAXs7SZgAAXmHx4sW58sor84lPfCKHHnpobrjhhmzevDldXV35yle+km9+85tZuXJlDjvssIwcOTKnnnpqvvCFL+SPf/xjkqSysnKLUq+c+++/P+9///tz8skn56CDDsovf/nLdHZ2bvNchx56aH784x+nVCpl8+bNufnmmzN58uRd8xsAAMArmJEIAEA2bdqUmTNnJkkGDRqUmpqafPGLX8zhhx+ed7/73WloaMixxx6bzs7OHHjggTn33HNTW1ubz3zmMzn11FMzZMiQVFZW5pJLLkmSTJkyJZdddtl2X//EE0/Ml770pcyYMSMdHR1573vfm3vuuSddXV05+OCDc8UVV+Tzn/98Pvaxj3W/ZtasWbnkkksyY8aMtLe3533ve19OO+20XfsbAwBAt4qSNR4AAAAAQBmWNgMAAAAAZSkSAQAAAICyFIkAAAAAQFmKRAAAAACgLEUiAAAAAFCWIhEAAAAAKEuRCAAAAACUpUgEAAAAAMr6/wEYkuM/HDMqYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# destination vs price\n",
    "sns.catplot(y = \"Price\", x= \"Destination\", data = df.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "7a205f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>Destination_PAR</th>\n",
       "      <th>Destination_RUH</th>\n",
       "      <th>Destination_SVO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Destination_PAR  Destination_RUH  Destination_SVO\n",
       "0                0                0                0\n",
       "1                0                0                0\n",
       "2                0                0                0\n",
       "3                0                0                0\n",
       "4                0                0                0"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performing OneHotEncoding on Destination since it's nominal categorical data\n",
    "destination = df[[\"Destination\"]]\n",
    "destination = pd.get_dummies(destination, drop_first=True)\n",
    "destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c851770a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 stop      27466\n",
      "2 stops     18665\n",
      "3 stops      1988\n",
      "nonstop      1978\n",
      "Name: Total stops, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1 stop ', '2 stops ', 'nonstop ', '3 stops '], dtype=object)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total stops\n",
    "print(df[\"Total stops\"].value_counts())\n",
    "df[\"Total stops\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "935598c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing LabelEncoder on Total stops since it's ordinal categorical data\n",
    "df.replace({\"nonstop \":0, \"1 stop \": 1, \"2 stops \": 2, \"3 stops \":3}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1cf55917",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.concat([df,source,destination], axis=1).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "ac035b57",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
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       "</table>\n",
       "<p>50097 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Source Destination  Duration  Total stops    Price       Date  \\\n",
       "0        PAR         NYC       770            1   379.47 2022-02-01   \n",
       "1        PAR         NYC       770            1   379.47 2022-02-01   \n",
       "2        PAR         NYC       810            1   379.47 2022-02-01   \n",
       "3        PAR         NYC       890            1   379.47 2022-02-01   \n",
       "4        PAR         NYC      1030            1   379.47 2022-02-01   \n",
       "...      ...         ...       ...          ...      ...        ...   \n",
       "50092    NYC         RUH      1300            2   930.40 2022-04-13   \n",
       "50093    NYC         RUH      1420            2  1018.40 2022-04-13   \n",
       "50094    NYC         RUH      1415            2  1049.87 2022-04-13   \n",
       "50095    NYC         RUH      1100            2  1053.60 2022-04-13   \n",
       "50096    NYC         RUH      1115            3  1139.73 2022-04-13   \n",
       "\n",
       "       Average Price  Source_PAR  Source_RUH  Source_SVO  Destination_PAR  \\\n",
       "0         441.343760           1           0           0                0   \n",
       "1         441.343760           1           0           0                0   \n",
       "2         441.343760           1           0           0                0   \n",
       "3         441.343760           1           0           0                0   \n",
       "4         441.343760           1           0           0                0   \n",
       "...              ...         ...         ...         ...              ...   \n",
       "50092     838.732500           0           0           0                0   \n",
       "50093    1972.813714           0           0           0                0   \n",
       "50094    1133.548015           0           0           0                0   \n",
       "50095    1023.900221           0           0           0                0   \n",
       "50096    1023.714222           0           0           0                0   \n",
       "\n",
       "       Destination_RUH  Destination_SVO  \n",
       "0                    0                0  \n",
       "1                    0                0  \n",
       "2                    0                0  \n",
       "3                    0                0  \n",
       "4                    0                0  \n",
       "...                ...              ...  \n",
       "50092                1                0  \n",
       "50093                1                0  \n",
       "50094                1                0  \n",
       "50095                1                0  \n",
       "50096                1                0  \n",
       "\n",
       "[50097 rows x 13 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1c2b841f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop date since it'll not be used as a feature\n",
    "final_df.drop([\"Source\",\"Destination\",\"Date\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4050cb8b",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>50097 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Duration  Total stops    Price  Average Price  Source_PAR  Source_RUH  \\\n",
       "0           770            1   379.47     441.343760           1           0   \n",
       "1           770            1   379.47     441.343760           1           0   \n",
       "2           810            1   379.47     441.343760           1           0   \n",
       "3           890            1   379.47     441.343760           1           0   \n",
       "4          1030            1   379.47     441.343760           1           0   \n",
       "...         ...          ...      ...            ...         ...         ...   \n",
       "50092      1300            2   930.40     838.732500           0           0   \n",
       "50093      1420            2  1018.40    1972.813714           0           0   \n",
       "50094      1415            2  1049.87    1133.548015           0           0   \n",
       "50095      1100            2  1053.60    1023.900221           0           0   \n",
       "50096      1115            3  1139.73    1023.714222           0           0   \n",
       "\n",
       "       Source_SVO  Destination_PAR  Destination_RUH  Destination_SVO  \n",
       "0               0                0                0                0  \n",
       "1               0                0                0                0  \n",
       "2               0                0                0                0  \n",
       "3               0                0                0                0  \n",
       "4               0                0                0                0  \n",
       "...           ...              ...              ...              ...  \n",
       "50092           0                0                1                0  \n",
       "50093           0                0                1                0  \n",
       "50094           0                0                1                0  \n",
       "50095           0                0                1                0  \n",
       "50096           0                0                1                0  \n",
       "\n",
       "[50097 rows x 10 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "65dfc2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50097, 10)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "4db701a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Duration           0\n",
       "Total stops        0\n",
       "Price              0\n",
       "Average Price      0\n",
       "Source_PAR         0\n",
       "Source_RUH         0\n",
       "Source_SVO         0\n",
       "Destination_PAR    0\n",
       "Destination_RUH    0\n",
       "Destination_SVO    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b244184c",
   "metadata": {},
   "source": [
    "## Features: <a class=\"anchor\" id=\"features\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39fd1cb",
   "metadata": {},
   "source": [
    "#### After constructing the final dataframe with all numerical values, now we can analyze the features that will be used for the regressions models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "b6ad32ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Duration', 'Total stops', 'Price', 'Average Price', 'Source_PAR',\n",
       "       'Source_RUH', 'Source_SVO', 'Destination_PAR', 'Destination_RUH',\n",
       "       'Destination_SVO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "1d33c3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_df[['Duration', 'Total stops', 'Average Price', 'Source_PAR',\n",
    "       'Source_RUH', 'Source_SVO', 'Destination_PAR', 'Destination_RUH',\n",
    "       'Destination_SVO']]\n",
    "\n",
    "y = final_df[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "720cfefd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,18))\n",
    "\n",
    "sns.heatmap(final_df.corr(),annot= True, cmap = \"coolwarm\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "ad192d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03999422, 0.01570177, 0.74317697, 0.08991898, 0.00889242,\n",
       "       0.01328784, 0.02215941, 0.05354494, 0.01332344])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting feature importance to the target variable \"Price\".\n",
    "selection =ExtraTreesRegressor()\n",
    "selection.fit(X,y)\n",
    "selection.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "da98cd80",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting graph of important features\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_,index = X.columns)\n",
    "feat_importances.nlargest(20).plot(kind=\"barh\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0106f5",
   "metadata": {},
   "source": [
    "## Modeling: <a class=\"anchor\" id=\"modeling\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e742a8d4",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "bb03f631",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 60% Train - 20% Val - 20% Test\n",
    "\n",
    "X_train_or, X_test, y_train_or, y_test = train_test_split(X, y, test_size=0.2)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train_or, y_train_or, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156f3ea4",
   "metadata": {},
   "source": [
    "### Defining a function to get metrics for val set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "441256e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics(model):\n",
    "    print(f'Train score {model.score(X_train, y_train)}')\n",
    "    print(f'Val score {model.score(X_val, y_val)}')\n",
    "    print(\"MAE:\" , metrics.mean_absolute_error(y_val,model.predict(X_val)))\n",
    "    print(\"MSE:\" , metrics.mean_squared_error(y_val,model.predict(X_val)))\n",
    "    print(\"RMSE:\" , np.sqrt(metrics.mean_squared_error(y_val,model.predict(X_val))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae28b62",
   "metadata": {},
   "source": [
    "### Baseline Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "61e08ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.8040357223322144\n",
      "Val score 0.7891035984538434\n",
      "MAE: 225.09235539537735\n",
      "MSE: 152995.68380136558\n",
      "RMSE: 391.14662698451787\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "score = lr.score(X_val, y_val)\n",
    "get_metrics(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796b82ff",
   "metadata": {},
   "source": [
    "### Polynomial "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "41a9f354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "Degree 1\n",
      "Train score 0.8040357223322144\n",
      "Val score 0.7891035984538438\n",
      "MAE: 225.0923553953841\n",
      "MSE: 152995.68380136523\n",
      "RMSE: 391.14662698451747\n",
      "--------------------\n",
      "Degree 2\n",
      "Train score 0.8569208072557248\n",
      "Val score 0.8475711159764316\n",
      "MAE: 179.19068173029146\n",
      "MSE: 110580.17667106038\n",
      "RMSE: 332.5359780099897\n",
      "--------------------\n",
      "Degree 3\n",
      "Train score 0.8770210319280359\n",
      "Val score 0.8667875960721851\n",
      "MAE: 161.85095797669118\n",
      "MSE: 96639.5001542935\n",
      "RMSE: 310.8689436953996\n",
      "--------------------\n",
      "Degree 4\n",
      "Train score 0.8829392209905675\n",
      "Val score 0.8723399701473494\n",
      "MAE: 152.20393140466717\n",
      "MSE: 92611.50696843152\n",
      "RMSE: 304.321387628986\n",
      "--------------------\n",
      "Degree 5\n",
      "Train score 0.8879647071090535\n",
      "Val score 0.8712998916720526\n",
      "MAE: 149.72369720260875\n",
      "MSE: 93366.03628409792\n",
      "RMSE: 305.55856440966915\n"
     ]
    }
   ],
   "source": [
    "for degree in [1,2,3,4,5]:\n",
    "    poly = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
    "    poly.fit(X_train, y_train)\n",
    "    print(\"-\"*20)\n",
    "    print(\"Degree\", degree)\n",
    "    get_metrics(poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca27f6e",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "dffff082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.8038454319902063\n",
      "Val score 0.7889638512580056\n",
      "MAE: 223.49066191496019\n",
      "MSE: 153097.06399386658\n",
      "RMSE: 391.2761991149814\n"
     ]
    }
   ],
   "source": [
    "lasso_model = Lasso()\n",
    "lasso_model.fit(X_train, y_train)\n",
    "get_metrics(lasso_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb6de57d",
   "metadata": {},
   "source": [
    "### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "04e98a59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.8040357065848055\n",
      "Val score 0.7891037101749157\n",
      "MAE: 225.07772121553117\n",
      "MSE: 152995.6027528426\n",
      "RMSE: 391.1465233807436\n"
     ]
    }
   ],
   "source": [
    "ridge_model = Ridge()\n",
    "ridge_model.fit(X_train, y_train)\n",
    "get_metrics(ridge_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c33c050",
   "metadata": {},
   "source": [
    "### ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "9206bad8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.7895996502819659\n",
      "Val score 0.7752654436876839\n",
      "MAE: 224.4110277527439\n",
      "MSE: 163034.6315286663\n",
      "RMSE: 403.77547167784513\n"
     ]
    }
   ],
   "source": [
    "EN_model = ElasticNet(alpha=1)\n",
    "EN_model.fit(X_train, y_train)\n",
    "EN_model.score(X_val, y_val)\n",
    "get_metrics(EN_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4cd1ed9",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "f37e3449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.9648778537711422\n",
      "Val score 0.9448134490695079\n",
      "MAE: 61.717733027545194\n",
      "MSE: 40035.31608101726\n",
      "RMSE: 200.0882707232417\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train,y_train)\n",
    "get_metrics(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4212c44a",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "a2d8a503",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train.values)\n",
    "X_val_scaled = scaler.transform(X_val.values)\n",
    "X_test_scaled = scaler.transform(X_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e5e64211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR\n",
      "Train score 0.8040357223322144\n",
      "Val score 0.7891035984538433\n",
      "MAE: 225.09235539537684\n",
      "MSE: 152995.68380136567\n",
      "RMSE: 391.146626984518\n",
      "--------------------------------------------------\n",
      "Polynomial - Degree 1\n",
      "Train score -0.3079480631703013\n",
      "Val score -0.3153015545186666\n",
      "MAE: 756.619594657905\n",
      "MSE: 954191.0590377726\n",
      "RMSE: 976.8270363978326\n",
      "--------------------------------------------------\n",
      "Polynomial - Degree 2\n",
      "Train score -6.619866701696714\n",
      "Val score -6.708250742830492\n",
      "MAE: 1819.2993972374343\n",
      "MSE: 5591983.005236949\n",
      "RMSE: 2364.737407247779\n",
      "--------------------------------------------------\n",
      "Polynomial - Degree 3\n",
      "Train score -6.396139020230291\n",
      "Val score -6.597515038614633\n",
      "MAE: 1978.6373777102278\n",
      "MSE: 5511649.321667562\n",
      "RMSE: 2347.6902099015456\n",
      "--------------------------------------------------\n",
      "Polynomial - Degree 4\n",
      "Train score -67.38613658531676\n",
      "Val score -65.94440159195507\n",
      "MAE: 5158.441261042131\n",
      "MSE: 48565098.4234209\n",
      "RMSE: 6968.8663657312945\n",
      "--------------------------------------------------\n",
      "Polynomial - Degree 5\n",
      "Train score -1.2998648856939954\n",
      "Val score -1.3170926486628711\n",
      "MAE: 977.4048403689628\n",
      "MSE: 1680944.632598231\n",
      "RMSE: 1296.512488408126\n",
      "--------------------------------------------------\n",
      "Lasso\n",
      "Train score 0.8040183311451377\n",
      "Val score 0.7891286832351017\n",
      "MAE: 224.60280304137635\n",
      "MSE: 152977.48594102522\n",
      "RMSE: 391.12336409504513\n",
      "--------------------------------------------------\n",
      "Ridge\n",
      "Train score 0.8040357214700532\n",
      "Val score 0.7891039650109529\n",
      "MAE: 225.0937616466228\n",
      "MSE: 152995.41788096476\n",
      "RMSE: 391.14628706018004\n",
      "--------------------------------------------------\n",
      "ElasticNet\n",
      "Train score 0.7354476290670592\n",
      "Val score 0.7243019152118472\n",
      "MAE: 304.4466319557438\n",
      "MSE: 200006.3381624778\n",
      "RMSE: 447.2206817248927\n",
      "--------------------------------------------------\n",
      "Random Forest\n",
      "Train score -1.0693924886375732\n",
      "Val score -1.0832919574956343\n",
      "MAE: 885.6332189325535\n",
      "MSE: 1511332.9353093393\n",
      "RMSE: 1229.362816791422\n"
     ]
    }
   ],
   "source": [
    "# function to get metrics for scaled features\n",
    "def scaled_metrics(model):\n",
    "    print(f'Train score {model.score(X_train_scaled, y_train)}')\n",
    "    print(f'Val score {model.score(X_val_scaled, y_val)}')\n",
    "    print(\"MAE:\" , metrics.mean_absolute_error(y_val,model.predict(X_val_scaled)))\n",
    "    print(\"MSE:\" , metrics.mean_squared_error(y_val,model.predict(X_val_scaled)))\n",
    "    print(\"RMSE:\" , np.sqrt(metrics.mean_squared_error(y_val,model.predict(X_val_scaled))))\n",
    "\n",
    "    \n",
    "## Baseline: Linear Regression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train_scaled, y_train)\n",
    "score = lr.score(X_val_scaled, y_val)\n",
    "print(\"LR\")\n",
    "scaled_metrics(lr)\n",
    "print(\"-\"*50)\n",
    "\n",
    "## Polynomial\n",
    "\n",
    "for degree in [1,2,3,4,5]:\n",
    "    poly = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
    "    poly.fit(X_train, y_train)\n",
    "    print(\"Polynomial - Degree\", degree)\n",
    "    scaled_metrics(poly)\n",
    "    print(\"-\"*50)\n",
    "\n",
    "## Lasso\n",
    "\n",
    "lasso_model = Lasso()\n",
    "lasso_model.fit(X_train_scaled, y_train)\n",
    "print(\"Lasso\")\n",
    "scaled_metrics(lasso_model)\n",
    "print(\"-\"*50)\n",
    "\n",
    "## Ridge\n",
    "\n",
    "ridge_model = Ridge()\n",
    "ridge_model.fit(X_train_scaled, y_train)\n",
    "print(\"Ridge\")\n",
    "scaled_metrics(ridge_model)\n",
    "print(\"-\"*50)\n",
    "\n",
    "## ElasticNet\n",
    "\n",
    "EN_model = ElasticNet(alpha=1)\n",
    "EN_model.fit(X_train_scaled, y_train)\n",
    "EN_model.score(X_val_scaled, y_val)\n",
    "print(\"ElasticNet\")\n",
    "scaled_metrics(EN_model)\n",
    "print(\"-\"*50)\n",
    "\n",
    "## Random Forest\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train,y_train)\n",
    "print(\"Random Forest\")\n",
    "scaled_metrics(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cc932b",
   "metadata": {},
   "source": [
    "#### Feature scaling was of no use, it did not improve anything.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f89831d",
   "metadata": {},
   "source": [
    "**<br>From the above analysis, we can see that the random forest model performed the best with:</br>**\n",
    "\n",
    "**Train score 0.9648778537711422**\n",
    "**<br>Val score 0.9448134490695079</br>**\n",
    "**<br>MAE: 61.717733027545194</br>**\n",
    "**<br>MSE: 40035.31608101726</br>**\n",
    "**<br>RMSE: 200.0882707232417</br>**\n",
    "\n",
    "**So, we'll select it as our model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "4b048733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.963236185218782\n",
      "Test score 0.9458897010690771\n",
      "MAE: 61.86501612022038\n",
      "MSE: 40409.86746202512\n",
      "RMSE: 201.02205715300278\n"
     ]
    }
   ],
   "source": [
    "# retraining the random forest model on train + val, and scoring on test\n",
    "\n",
    "X_train_val = pd.concat([X_train,X_val])\n",
    "y_train_val = pd.concat([y_train,y_val])\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train_val,y_train_val)\n",
    "\n",
    "print(f'Train score {rf.score(X_train_val, y_train_val)}')\n",
    "print(f'Test score {rf.score(X_test, y_test)}')\n",
    "print(\"MAE:\" , metrics.mean_absolute_error(y_test,rf.predict(X_test)))\n",
    "print(\"MSE:\" , metrics.mean_squared_error(y_test,rf.predict(X_test)))\n",
    "print(\"RMSE:\" , np.sqrt(metrics.mean_squared_error(y_test,rf.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "af4b6f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_val_pred = rf.predict(X_train_val)\n",
    "y_test_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "c6a4e708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_test_pred,alpha =0.2,color=\"DarkBlue\")\n",
    "plt.title('Actual vs. Predicted Airline Prices')\n",
    "plt.xlabel('Predicted Airline Prices')\n",
    "plt.ylabel('Actual Airline Prices');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421d1f3c",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "c8711a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# randomized search CV\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "f339bb12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "dc57bc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "af883f54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=  21.6s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=  21.5s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=  22.4s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=  23.0s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=1000; total time=  20.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   6.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=100; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=100; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=100; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=100; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=100; total time=   0.8s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=200; total time=   3.5s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=200; total time=   3.4s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=200; total time=   3.6s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=200; total time=   3.4s\n",
      "[CV] END max_depth=25, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=200; total time=   3.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=100; total time=   1.7s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=10, n_estimators=200; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=10, n_estimators=200; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=10, n_estimators=200; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=10, n_estimators=200; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=10, min_samples_split=10, n_estimators=200; total time=   1.6s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=200; total time=   3.6s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=200; total time=   3.6s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=200; total time=   3.6s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=200; total time=   3.6s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=10, min_samples_split=2, n_estimators=200; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   5.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   5.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   5.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   5.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=600; total time=   5.7s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   6.6s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   6.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   6.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   6.5s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   6.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   3.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   2.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   2.7s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train_val,y_train_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "cccb8a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "56390fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3665c015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,prediction,alpha =0.2,color=\"DarkBlue\")\n",
    "plt.title('Actual vs. Predicted Airline Prices')\n",
    "plt.xlabel('Predicted Airline Prices')\n",
    "plt.ylabel('Actual Airline Prices');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "c76e13d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 64.19388289923229\n",
      "MSE: 41409.9342490973\n",
      "RMSE: 203.49431011479732\n"
     ]
    }
   ],
   "source": [
    "print(\"MAE:\" , metrics.mean_absolute_error(y_test,prediction))\n",
    "print(\"MSE:\" , metrics.mean_squared_error(y_test,prediction))\n",
    "print(\"RMSE:\" , np.sqrt(metrics.mean_squared_error(y_test,prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85d0981",
   "metadata": {},
   "source": [
    "#### Therefore, hyperparameter tuning did not improve the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e56e3e3a",
   "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>Predicted Price</th>\n",
       "      <th>Actual Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2676.530000</td>\n",
       "      <td>2676.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182.571321</td>\n",
       "      <td>248.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>254.362293</td>\n",
       "      <td>265.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>447.386056</td>\n",
       "      <td>490.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>643.470000</td>\n",
       "      <td>643.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10015</th>\n",
       "      <td>2682.130000</td>\n",
       "      <td>2682.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10016</th>\n",
       "      <td>1605.962800</td>\n",
       "      <td>1608.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10017</th>\n",
       "      <td>233.286587</td>\n",
       "      <td>316.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10018</th>\n",
       "      <td>1533.600000</td>\n",
       "      <td>1533.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10019</th>\n",
       "      <td>657.900585</td>\n",
       "      <td>686.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10020 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Predicted Price  Actual Price\n",
       "0          2676.530000       2676.53\n",
       "1           182.571321        248.53\n",
       "2           254.362293        265.07\n",
       "3           447.386056        490.93\n",
       "4           643.470000        643.47\n",
       "...                ...           ...\n",
       "10015      2682.130000       2682.13\n",
       "10016      1605.962800       1608.00\n",
       "10017       233.286587        316.00\n",
       "10018      1533.600000       1533.60\n",
       "10019       657.900585        686.93\n",
       "\n",
       "[10020 rows x 2 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.DataFrame({\n",
    "    \"Predicted Price\" : rf.predict(X_test),\n",
    "    \"Actual Price\" : y_test,\n",
    "}).reset_index(drop = True)\n",
    "\n",
    "test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f134f34",
   "metadata": {},
   "source": [
    "## Saving The Final Model: <a class=\"anchor\" id=\"model\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "e75f71cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "file = open('rf_flight_prediction.pkl', 'wb')\n",
    "pkl.dump(rf, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "e40d7102",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the model\n",
    "model = open('rf_flight_prediction.pkl','rb')\n",
    "rf_flight_prediction = pkl.load(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "b59bb8bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.9458897010690771\n",
      "MAE: 61.86501612022038\n",
      "MSE: 40409.86746202512\n",
      "RMSE: 201.02205715300278\n"
     ]
    }
   ],
   "source": [
    "print(f'R2 score {metrics.r2_score(y_test,rf_flight_prediction.predict(X_test))}')\n",
    "print(\"MAE:\" , metrics.mean_absolute_error(y_test,rf_flight_prediction.predict(X_test)))\n",
    "print(\"MSE:\" , metrics.mean_squared_error(y_test,rf_flight_prediction.predict(X_test)))\n",
    "print(\"RMSE:\" , np.sqrt(metrics.mean_squared_error(y_test,rf_flight_prediction.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91bcaf45",
   "metadata": {},
   "source": [
    "### Therefore, the final model is able to predict flight ticket prices within around  ≈ $61.87"
   ]
  }
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