4.Rmodels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "import statsmodels.api as sm\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "import sqlite3\n",
    "from sklearn import (metrics, linear_model, preprocessing)\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line of Action\n",
    "- subsampling to reduce the size\n",
    "- dealing with data types\n",
    "- numerical data encoding as string\n",
    "- Categorical data encoding as integers\n",
    "- Detecting and dealing with nulls\n",
    "- Checking for Multicollinearity\n",
    "-Checking for Homoscedacity\n",
    "- Normalizing Data\n",
    "-Perform modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data From SQL DataBase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlite3.connect(\"flightdatabase.db\" , detect_types=sqlite3.PARSE_COLNAMES)\n",
    "cur = con.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cur.execute(\"\"\"Select * from finalflights where year < 2019;\"\"\")\n",
    "# df1 = pd.DataFrame(cur.fetchall())\n",
    "# df1.columns = [x[0] for x in cur.description]\n",
    "# df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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>field1</th>\n",
       "      <th>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.50</td>\n",
       "      <td>100.99</td>\n",
       "      <td>429193.0</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.7627</td>\n",
       "      <td>-84.4225</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>98.19</td>\n",
       "      <td>605085.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>38.9047</td>\n",
       "      <td>-77.0163</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>27.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1011.60</td>\n",
       "      <td>65.94</td>\n",
       "      <td>5074252.0</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>103.8558</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1014.78</td>\n",
       "      <td>115.24</td>\n",
       "      <td>3450395.0</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5218</td>\n",
       "      <td>13.4015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1015.60</td>\n",
       "      <td>130.30</td>\n",
       "      <td>10460118.0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8667</td>\n",
       "      <td>2.3333</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>4963</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>New York City</td>\n",
       "      <td>3794306</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.7</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1019.10</td>\n",
       "      <td>100.00</td>\n",
       "      <td>8398748.0</td>\n",
       "      <td>New York City</td>\n",
       "      <td>40.6943</td>\n",
       "      <td>-73.9249</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964</th>\n",
       "      <td>4964</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1295947</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1018.50</td>\n",
       "      <td>81.46</td>\n",
       "      <td>425403.0</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>44.9635</td>\n",
       "      <td>-93.2678</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>4965</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3491734</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>14.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1017.90</td>\n",
       "      <td>82.30</td>\n",
       "      <td>3990456.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>34.1139</td>\n",
       "      <td>-118.4068</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4966</th>\n",
       "      <td>4966</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3491734</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>14.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1017.90</td>\n",
       "      <td>82.30</td>\n",
       "      <td>3990456.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>-37.4600</td>\n",
       "      <td>-72.3600</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4967</th>\n",
       "      <td>4967</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Miami</td>\n",
       "      <td>2267294</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>22.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1019.20</td>\n",
       "      <td>86.01</td>\n",
       "      <td>470914.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.7839</td>\n",
       "      <td>-80.2102</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4968 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      field1          ds           city  PASSENGERS           Type  Month  \\\n",
       "0          0  2010-01-01        Atlanta     2735491       Domestic      1   \n",
       "1          1  2010-01-01     Washington     1929494       Domestic      1   \n",
       "2          2  2010-01-01      Singapore     1681005  International      1   \n",
       "3          3  2010-01-01         Berlin      489487  International      1   \n",
       "4          4  2010-01-01          Paris     4154249  International      1   \n",
       "...      ...         ...            ...         ...            ...    ...   \n",
       "4963    4963  2018-12-01  New York City     3794306       Domestic     12   \n",
       "4964    4964  2018-12-01    Minneapolis     1295947       Domestic     12   \n",
       "4965    4965  2018-12-01    Los Angeles     3491734       Domestic     12   \n",
       "4966    4966  2018-12-01    Los Angeles     3491734       Domestic     12   \n",
       "4967    4967  2018-12-01          Miami     2267294       Domestic     12   \n",
       "\n",
       "      Year  temperature_mean  precipitation  rain_days  pressure  \\\n",
       "0     2010               3.6          137.0        7.0   1019.50   \n",
       "1     2010               0.4           57.0        8.0   1016.70   \n",
       "2     2010              27.5           70.0        8.0   1011.60   \n",
       "3     2010              -4.9           21.0        7.0   1014.78   \n",
       "4     2010               0.6           36.0        7.0   1015.60   \n",
       "...    ...               ...            ...        ...       ...   \n",
       "4963  2018               4.7          175.0       11.0   1019.10   \n",
       "4964  2018              -3.3           39.0        4.0   1018.50   \n",
       "4965  2018              14.8           37.0        2.0   1017.90   \n",
       "4966  2018              14.8           37.0        2.0   1017.90   \n",
       "4967  2018              22.2           40.0        2.0   1019.20   \n",
       "\n",
       "      cost_of_living_index  population     city_ascii      lat       lng  \\\n",
       "0                   100.99    429193.0        Atlanta  33.7627  -84.4225   \n",
       "1                    98.19    605085.0     Washington  38.9047  -77.0163   \n",
       "2                    65.94   5074252.0      Singapore   1.2930  103.8558   \n",
       "3                   115.24   3450395.0         Berlin  52.5218   13.4015   \n",
       "4                   130.30  10460118.0          Paris  48.8667    2.3333   \n",
       "...                    ...         ...            ...      ...       ...   \n",
       "4963                100.00   8398748.0  New York City  40.6943  -73.9249   \n",
       "4964                 81.46    425403.0    Minneapolis  44.9635  -93.2678   \n",
       "4965                 82.30   3990456.0    Los Angeles  34.1139 -118.4068   \n",
       "4966                 82.30   3990456.0    Los Angeles -37.4600  -72.3600   \n",
       "4967                 86.01    470914.0          Miami  25.7839  -80.2102   \n",
       "\n",
       "            country iso2 iso3  \n",
       "0     United States   US  USA  \n",
       "1     United States   US  USA  \n",
       "2         Singapore   SG  SGP  \n",
       "3           Germany   DE  DEU  \n",
       "4            France   FR  FRA  \n",
       "...             ...  ...  ...  \n",
       "4963  United States   US  USA  \n",
       "4964  United States   US  USA  \n",
       "4965  United States   US  USA  \n",
       "4966          Chile   CL  CHL  \n",
       "4967  United States   US  USA  \n",
       "\n",
       "[4968 rows x 19 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = create_dataframe_from_SQL(\"\"\"Select * from finalflights where year < 2019;\"\"\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "field1                  0\n",
       "ds                      0\n",
       "city                    0\n",
       "PASSENGERS              0\n",
       "Type                    0\n",
       "Month                   0\n",
       "Year                    0\n",
       "temperature_mean        0\n",
       "precipitation           0\n",
       "rain_days               0\n",
       "pressure                0\n",
       "cost_of_living_index    0\n",
       "population              0\n",
       "city_ascii              0\n",
       "lat                     0\n",
       "lng                     0\n",
       "country                 0\n",
       "iso2                    0\n",
       "iso3                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values\n",
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping Columns\n",
    "df1.drop(['Year','Month' ], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Coverting the date variable to ordinary values\n",
    "import datetime as dt\n",
    "df1['ds'] = pd.to_datetime(df1['ds'])\n",
    "df1['ds']=df1['ds'].map(dt.datetime.toordinal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot a histogram to view dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.hist(figsize = (20,18));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2623e6d8>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# from ggplot import *\n",
    "sns.distplot(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# checking for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the percentile at 2:  196000.0\n",
      "the percentile at 4:  426590.28\n",
      "the percentile at 6:  496980.31999999995\n",
      "the percentile at 8:  540851.0\n",
      "the percentile at 10:  571955.1\n",
      "the percentile at 12:  610008.9600000001\n",
      "the percentile at 14:  659338.3\n",
      "the percentile at 16:  698749.12\n",
      "the percentile at 18:  747009.0\n",
      "the percentile at 20:  804086.2\n",
      "the percentile at 22:  860822.94\n",
      "the percentile at 24:  930623.52\n",
      "the percentile at 26:  1016491.9800000002\n",
      "the percentile at 28:  1099389.2\n",
      "the percentile at 30:  1164121.7\n",
      "the percentile at 32:  1230964.52\n",
      "the percentile at 34:  1293258.58\n",
      "the percentile at 36:  1364484.7599999998\n",
      "the percentile at 38:  1434382.78\n",
      "the percentile at 40:  1496745.0\n",
      "the percentile at 42:  1550648.2\n",
      "the percentile at 44:  1609077.6\n",
      "the percentile at 46:  1658115.08\n",
      "the percentile at 48:  1721755.24\n",
      "the percentile at 50:  1787230.0\n",
      "the percentile at 52:  1850949.44\n",
      "the percentile at 54:  1931905.0\n",
      "the percentile at 56:  2029171.0000000005\n",
      "the percentile at 58:  2119437.159999999\n",
      "the percentile at 60:  2213988.0\n",
      "the percentile at 62:  2298399.7399999998\n",
      "the percentile at 64:  2390688.4000000004\n",
      "the percentile at 66:  2476226.7800000003\n",
      "the percentile at 68:  2566221.8800000018\n",
      "the percentile at 70:  2656161.0\n",
      "the percentile at 72:  2756651.48\n",
      "the percentile at 74:  2860980.96\n",
      "the percentile at 76:  2974395.0\n",
      "the percentile at 78:  3087472.08\n",
      "the percentile at 80:  3193515.4000000004\n",
      "the percentile at 82:  3334799.1399999987\n",
      "the percentile at 84:  3453912.999999999\n",
      "the percentile at 86:  3622307.2800000003\n",
      "the percentile at 88:  3825408.72\n",
      "the percentile at 90:  4114658.2000000007\n",
      "the percentile at 92:  4843692.0\n",
      "the percentile at 94:  5473031.0\n",
      "the percentile at 96:  6009201.959999999\n",
      "the percentile at 98:  6567183.359999999\n"
     ]
    }
   ],
   "source": [
    "s = list(np.arange(2, 100, 2))\n",
    "for p in s:\n",
    "     print ('the percentile at ' + str(p) + \": \", np.percentile(Y, p))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1c24e54a20>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='temperature_mean' , y = 'PASSENGERS' , data = df1 , kind = 'reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passegners Vs Cost of Living Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1c28185898>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EUjaTqQyx7M8TiQyj8QyxVL4hi4YMz1h1NbG2I0pbNERTyEQp0NrrM4qlbM6MJzk5luDo8FRuEmtz2OSide1cuqGDiUSGnrYIq1rCNIetugxDCiuLuVzEJNIOw7FU4CvNcCzFual0zuMqhgIMI3vRhUIpiFgGpqEwlMr9XzLjopQ3Q0vhhzNDWIbKRUCaw6b3FbFoCZs0hb3vzRGL5pBJU9j07jN7P4YBfd2tnN89K1UO+UBWYNkZKKXUI0B3lad3A8MLuJyFQta9uMi6FxdZt8ew1vqmeby/FceyM1AASqmvAj8PDGqtLylz3rPAk8C/yB5qBnq11p0Lv8rZo5R6Vmt99VKvo1Zk3YuLrHtxWcp1V7vnZc+9k2W255ViuVbxfQ34c+BvK52otf6k/7NS6uPAlQu3LEEQhAXhazTgnrcsy2S01k8A54LHlFIXKKUeUUo9p5R6UinVX+Smu4H7F2WRgiAI80Sj7nnL1YMqxr3Av9NaH1ZKvQv4i+wxAJRS5wFbgMeWaH21cG/lU+oSWffiIuteXOpt3cX2vBv9/1xme15RlmUOCkApdT7wHa31JUqpVmAIeCNwSkRrfVHg/M8AG7XWH1/UhQqCIMwDjbjnrRQPygDGtNZXlDnnFuBji7QeQRCEhaQh9rxlmYMqRGs9ARxVSv0KgPK43P9/pdSFQBfw9BItURAEYd5olD1vWRoopdT9eC/8hUqpE0qpjwL/CvioUupF4FXgg4Gb7Aa+oZdrPFMQhIamUfe8ZZuDEgRBEFY2y9KDEgRBEFY+y85A3XTTTRpPj0++5Eu+5Gs5f1XFCt3zqmLZGajh4eUo4SUIgjA7GnnPW3YGShAEQWgMxEAJgiAIdYkYKEEQBKEuEQMlCIIg1CVioARBEIS6RAyUIAiCUJeIgRIEQRDqEjFQgiAIQl0iBkoQBEGoS8RACYIgCHWJGChBEAShLhEDJQiCUMcMx1JLvYQlQwyUIAhCHXN6PLnUS1gyxEAJgiAIdYkYKEEQhDqnUSefi4ESBEGocxrUPomBEgRBqHca1D6JgRIEQRDqEzFQgiAIdY7koARBEIS6pDHNkxgoQRCEuqdBHSgxUIIgCEJ9IgZKEAShztENGuQTAyUIglDnSIhPEARBEOoIMVCCIAhCXSIGShAEoc6REJ8gCIJQl0iRhCAIglCXiAclCIIgCHWEGChBEIQ6x2lQF0oMlCAIQp1jO2KgBEEQhDrEdt2lXsKSIAZKEAShzmlUD8pa6gUIQqOy/+Ag9zwxwPHROJu6mrn9hj529vcu9bKEOkQMlCAI804pI7T/4CCff+hVQqaisynE4GSSzz/0KneAGClhBhkJ8c0/SqmblFJvKKWOKKU+W+T/NyulfqCUekEp9ZJS6v0LuR5BWEx8IzQ4mcwzQr7RCpmK5rCFUt73kKm454mBpV62UIc4bmN6UAtmoJRSJnA38HPAxcBupdTFBad9Dvim1vpK4BbgLxZqPYKw2JQzQsdH4zSFzLzzm0ImJ0bjS7RaoZ7JOOJBzTfXAEe01gNa6zTwDeCDBedooD37cwdwagHXIwiLSjkjtKmrmUTGyfu/RMZhY1fzYi5RWCY0ag5qIQ3UBuB44PcT2WNBvgB8RCl1AngY+HixO1JK3aaUelYp9ezQ0NBCrFUQ5p1yRuj2G/rIOJp42kZr73vG0dx+Q98SrVaoJ4J7HkiZ+UKgihwrvAzYDXxNa70ReD/wd0qpGWvSWt+rtb5aa311T0/PAixVEOafckZoZ38vd9y8g962KOOJDL1tUe64eYcUSAhA/p4HkGlQD2ohq/hOAJsCv29kZgjvo8BNAFrrp5VSUaAbGFzAdQnCorCzv5c78HJRJ0bjOc/JN0I7+3vFIAlVkbYb04NaSAP1DLBNKbUFOIlXBPHhgnOOAe8BvqaUugiIAhLDE5Yle/cd4r6njjKVdmgJm9x6/Rb27NouRkiYM/G0U/mkFciCGSitta2U+h3ge4AJfFVr/apS6g7gWa31Q8DvAn+tlPokXvjvN7RuUFVEYVmzd98h7nrsCIYCy/ByTXc9dgSAPbu2L/HqhOVOImMv9RKWhAVt1NVaP4xX/BA89vnAz68B717INQjCYnDfU0ezxslLoRrKS2zf99RRMVDCnJlKNaYHJVp8gjAPTKUdjIKyIEN5xwVhrsTT4kEJgjBLWsImE0kbyDdI7VH5iAlzp1FzUOJBCcI8sGNdW03HBaFaFI1roOTyThCqpJz6+KunJzEUBCXTDOUdF4S5oJRq2BCfeFCCUAXlhF/ByzWFTEVTyMx9hUwlOShhzigF8QYtkhAPShCylOpjgnzhV4DmsEU8bXPPEwPs7O+lJWySyOQXSrjay00JwlwwUNn8ZuMhBkoQ8IzTl/cdzv0+kbRzv+/ZtZ3jo3E6m0J5twmqj996/RbueuwItuvmQn2u9o6XQgYWCtWgDJhIZJZ6GUuChPgEAfjKY4fLHq+kPr5n13Y+ceNWmkImtusZr0/cuLVkD1QwZJjOOBwYGOE3vvYMF/3ed9m779A8PjNhuWMoxVgivdTLWBLEgxIEIFNC6sw/fvsNfXz+oVeJp22aQl44r1B9fM+u7VU35d7zxACTyTRjcTtPQTmZcUWBQsjDUDAWFw9KEIQSzFZ9fP/BQXbfe4Drv/QYu+89kCuqeOXkGKMFxgk8vS9DecoUggCeBzXeoCE+8aAEAYhYBqkiitERa/oarlb1cT+MFzJVXuXfHUC8lMsGOI5mSjdm1ZYwE0MpUrZLMuMQDTVW0Y0YKEEAPrbzAu7cdzjPo1HZ4z61FjXc88QAGcdhJGaTdlzCpkF7k8U9TwxQThPZBUIzfKu5Ua5CUahv/MrQsXiGtR2NZaAkxCcIePmeT+7aRnvUwjQU7VGLT+7altvEK/VBFePw4CTDk2lsV2MaCtvVDE+mOTw4SWvEwiw20jOLYczfR3PvvkPcue8wE0kbx9VMJG3u3HdYijGWCUbWQjVioYR4UIKQpVyRQ6U+qGKkbReUF6IBr+HSVZq07ebK0iniSZkKbHf+PKi7979ZNNd19/43xYtaBvh/P41YKCEGShCqoFIfVDE8JQlNMuOg8UKGhgFhU+UMQ7D3ygAiIRPbdWmaY64hGI4sllsDSh4X6otgiK/REAMlCFWwqauZt0ZiTCTy80nnr24teZue1ggjU9ObigYcF7pbI8B0Gbk/6NCfIVWpwbcShcUZJ0YTs74vYelRWQ+qEZt1xUAJQhVc17eKHx0dyYnBZhyHRMZh9zs3l7xNLFVcnuaNwRgX/JeHiZgGq1pCtIRNptIOtqNpi1pzLmAoDEeWIiQZ6GWB70GNTEkOShCEIjz88mnQXpjOD9ehveO+MSmslIslbUwFTkECSGswlCaecYiPOfS0huhpaybj6Kp6qypRGI7saQ0zFJu5uX38xm1zehxhcTCUIho2GY6llnopi45cQwlCFRwdiWOZimhWqTwaMrFMxdERLwe1d98h7nrsCImMg2V4MkgunnFSeAUSQYJGayiW4cx4krTtcM8TA3Nea6Es09qOJjqbvOpEv0LxU4EKRaH+iYZMhiYbz0CJByUI88B9Tx3FUGBly8MN5YUBcxR4UYVFevG0Q9J2sJ25Fy4Uk2VqbwrzZ782N+9MeqmWjtaIJR6UIAjF6etuySqUazQaV2tc7R0Hbx6UUaavqZqicceFeHruBmq2skzlKOYh3vXYEemlWiTaopZ4UIIgFOczN/Xz2//wXJ5EUXPI4DM39QPe3CdvZk++RJECmsMmacdFuxq7gqXKzIMHBbXLMlXivqeOorXOE9X1NQP37Nou3tUC0xqxODTWeNOZxUAJQpZym+xLJ8ZIFvQNJW2Xl06MsbO/lx3r2nj66OiM+7QMWNsRzYXaBieSONoL6QXReA26Rjk3bAmZTM4UtnW1d9xXqvD/31eqAFFkny9aoxaj8Qxp2yVsNU7gq3GeqSCUoVII676njmIa+SPdTUPlVMeffXumcQJvEw+G2r6y+x28dsdNbOyMzjjX0V7FXT1SyvHzFSlKKVUI80NbxKvKHJlqrDCfeFCCwMwiB8d1cVzNl/cd5r6njjKRtAkZkLIdtPaq8kzl5Z6g9DwpR8P9t10743hrxMLA28j9snWVPT6fLEbobbZKFTJRuHraot7fxfBkmnUdTUu8msVDPChBIL/IIeO4eVp4fsl2xvU8Io33PePmj+OohVjaYdOqJprDZrap1mTTqqacwZsP5rOwoT1a3HCWOl6J2YjvNjL+hcvgZHKJV7K4iIESBLwiB98mFQq1WmWUxZssz6qVMlQhUxUdWLipqznnYTiuZirt8Pa5BKfGk/NWGRf0Cg1lZL/PbhjirddvwTQUIVMRsbzvpqG49fotJRUpyilVBNUulPK+h0w1L31gKxHfgxpssEo+MVCCgLcB247Oa3CFyh+QiaR3/sd2XkBheYMCopZR1Eu4rm8VQ7G019AbsIeOq+etfDvoFaYyTm5M/US2sKEW9uzazidu3EpTyMR2PaHcT9y4lT27tpdUpCinVHF8ND5DELeS+G4j0+obqInGMlCSgxKELMUKASpV1fnelp/XCeZ72iMmtsZTicgKzLZFrZyX0NsW5vR4/obji8b65dtzoSXsVQ5mbE/VIshdjx3JW3c1lBpHUuy5V8p1bepqZnAymacXmMg4bOxqrno9jYRlGDSHzYYL8YmBEgRKV5zZrkap0sn+oAEr3MCv+v3vM5G0MVCYSmE7mpGpNLYzQTRssbolwunxlOd5KU+jz9Ppo2QuqtrCgv0HB+loCmV7s/KxDIWaJyNY6rlD+QKNYmoXGUdz+w1987KelUhb1Gq4EJ8YKEGgfMVZU8jEcewZXgh4s518CjfkRNoGpXJGTClwXU3a0WzLju+ArOeWdd+U8gowWsIz50EVjtHwQ4Z3QJ6R8s8LWwa9rWEGC4RibVfnVSAuBH6BhpcDmy7QAM+Y7ezv5Q68XNSJ0TgbpYqvIm3RUMOpSYiBEoQKvPSF93HV73+fc1OZvDCgYtqQFNuQvdJzjaE0KushoSFsGVzXt4ofv3UOQ+Xr8rkaVIl5UNVO9Q2e1xy2GIqlZ4QvHQ2RcjPn50gxbULbdfO8tvlWu1jptEUsjg5PLfUyFhUxUIJQBb1tUUanMrlQnFKeIelp8xpui0kBgWfELENNDzlsCXH+6laeHjhHT2uYyaRNPO3kGZBrzussGnqrdqpv8LzBiWTJJlvXrU1WqZaeqqm0V9oepFzoUqhMWzTEZMpGa50bYrjSEQMlCEzPeSp2HEBrjWF4uSTfG3K0RmvvVsWkgMjeZ1DqyM+zfO7BV+hujaC1t2kHH//Hb4+xd9+hGZt/tVN9/QKEWNLmbJmQkDuj7rA0e/cdyhtPP5G0c78X0+Iz0HjzGvMN0mz7pgQvB+W4mrF4hq6W+lQcmW/kr0UQ8D78xQoK/P6TWNqhq9liZCqDmy1kWN0SynkE5TRghyZTeV7Hzv5eNj3hGZGh2HSRBLp8FZ8fFgSN63rjPKbSDtduWZV33u039PEfH3iR4SJDCoMYVWmse3zlscNljxdq8ZVifUeE3fceWHL1iOWoYtGSbdY9F083jIGSPihBAHasayt7vDVsMhq3CRkGUcsgZBiMxu2ixQyFtERMLlrbRk9bhL9+coCLP/8IPzo6wsDwVE6ZIuuI4WpwHF00FPb0wDnaoyau693GN2YPv3J2hgJDNb6RUaYBuZBSUk4Zt7gWXykOnp1acvWI5api0ZTtfB5PZJZ4JYuHGChBoLTYq388F/NXga/A8XIfpImEjVKKiUSGyZRDPO0QMlVJI+JS3Ls5PhonlfHUrJtCJhHLJGwaOK7OU2C454kB2ptCGKq0oTLVTMWM2VJJc6+QpVaPWK4qFn5j84QYKEFoLMp5CACTKZsNnVEsQ+G4GstQbOiMEvMSLUVL0H3iaYeDZyYYyobcFKC1ynlNxSjm3WzqaibluHnj47X2ZJaChRLHR+PYjotpqKKejWV4pe/VeH8+paScZqtF6LMU6hHLVcUi6huoMiHUlYYYKEGogk1dzVimQV9PK/1r2+nracUyjaqVD8xAQ68Gb4BhqXNLeDe339CHZXgek9Ya19W4aNqiVt462iIWJ8eRgpnjAAAgAElEQVSSGEphFShhmGq616pYKXspSkk5fWznBWU19yqxFOoRm7qaZ0haLQcVi2hYPChBaEgqeQi339BHxtHE016ZbzxtV618oIFkKRetAAUlvZud/b18bOcFGEphuxrLVKxuCRO2zLx1+JWFaM9bipjTz83RkHF0yVL2UuzZtZ1P7tpGe9TCNBTtUYtP7tpWVouvFLN5DeeTubyXS4nv9UkOShAajI/tvKDs8Z39vdxx84684YN33LwjV/l13ZaukvddS8eKP8qjlHezZ9d27vnIVbzz/FWsagmzpbs1bx3gVRxu6IximV7/VSowRt5UnsK6X8o+H+zZtZ1PFRivUhhQ8jVcLCq9l/WKZXgK8hPJxjFQUmYuCFkKe6EKDctLJ8Z49dQ4U2mH8UQmN+4d4P7bf4rd9/zzjLHvIVPl1BSSGaeqardK3k0lBQa/D6o1Ys2oBnSyShW1CtJWki4qxKB4Xs5Prc1PecbsWY4qFkopwqZBooGancVACQLeZm0FjAnkS/Ps3XeIO7ONqRqvMffOQKMqeEYqyNV/8H8Yj2dwtSd15FtAX6xVAWln5lZdqlG3Wm6/oY9PP/BiriijENvVRCxVk6pDMaWM4GypQuNVKqBpu+TKu98aiXH73z9Ha8Rk+5r2ZdGLtNSELYN4AxkoCfEJAvmzk8Cby2RnZyftvvcAX3nscG48O0yPav/Lx4uroANs622juy2cq/wLGwpTeQYqVFBh51fmBRt150IyU34TKyVIW4rJpE1h3YarvePFBiOWozlsEUvZjMQ8453MuMumF2mpCZnGjAKPlYwYKEEgf6Ku42oy2So7Q3lX/KVqHBJlih9uv6GPkGmytiPKhWva2Liqma6WMFu6W2iOWBiBenG/rsFUas6adV/87utl1wW1V/GVCslpZhr3ahiaTKGU93zTjrtsepGWmrCpSDaQByUhPkHA26zv3HeYjJP/4V/dEs4bqlcLO/t7+dCJsZICq/sPDvLRv30WJ+CaZFwN7tw0646OxLMejSrZjFttFZ8vCVQOfzBiLUYq7bh5Ic6BoRjdreFcL9JylCJaDEINFuITAyUIWYpt5SNT6bIbQnBiRaFg6nv6e3ju2Dg9bRE2Z8ViH3j+JJdt7Mwl6a85r3NGYQWUll6aDwxVXZ4rOH+qHKWMeyniaRsDSGWNU8gA29GcHEuytael6rlXjUhYQnyC0Hjc99RRQqaiKWTmVe+5urwk0LoOb9yGX+WWyDi5QoH/9ZPTTCTSeZI6adthzzde4PovPcbuew/wTAmJpeePj8/6ufR1t5Rdt6ury3MVzp8qxdHhWE1Veb1tUW/uFV5RhWkaedJRy1WKaDEImUbF/OJKQgyUIJCfRyncbNMBrTkV+G4ArVmF6VKFAkFZmolEhpGpNFNpO+cZlJKxq1XfLshnbuqnqzlU9pxq8lxBSaBS4TtDwbdfPI1peI2k/lcpIpbB/bddy5qOKJtXNRGxzBnSUb5U08BQjINnJhgYimE7bt1LES0GYUs8qHlDKXWTUuoNpdQRpdRnS5zzq0qp15RSryqlvr6Q6xGEUgSLJMqR04xVsLp1etyGb+AcV5OyndxVbvA+h2PebKaoZeY8g9mw/+Agu+89kPPCCivfdvb38uvXnlf2Pqqp4gtKAq0uMd5hdUsYraHa2Ye+V7epq5m0k3+jtOOysas5J9Vkuxozm0c7OZbMXQzMB5Vew3rFMlRD9UEtmIFSSpnA3cDPARcDu5VSFxecsw34z8C7tdY7gP+wUOsRhHLcev2WbFhs5k5bKM7qfx+JZXKbfEvYxPar/zR5Xb7DsSRaa88r0tDTFpn1OvfuO8Ttf/8cz7x1jtGpNEeHYzPKs/cfHOSB508SLSPkWk0VX1ASaE17lM6maQNhKOhpDbO2owlDVd9466u0X9e3irMT3pysTHa8yNmJFNf1rcqTagrW9uty6ro1sFzHbYAng+XO0+uwHFhID+oa4IjWekBrnQa+AXyw4JzfAu7WWo8CaK3r/y9EWJHs2bWdT9y4tWh4KugFBfugXMipmd96/RYcN7CnZk9sDhlMpTzlieawSXdbmLZo+fAbFJdH2n9wkL2PHSZtu9iuJp52GJxMkbadGeM2Mo5DskyYsJoqvkJJoNawWTTU51cc2q6Lq92iRt7HV2l/+OXToPNDpmjveFCqydGe5uCGzui8jYtfzjkuBVV5+iuFhTRQG4Djgd9PZI8F2Q5sV0r9UCl1QCl1U7E7UkrdppR6Vin17NDQ0AItVxBq58xEEvAMXGvEyPO2LOV5Sx1NIZ78zI3sveVKQqaZJ1JazBAZanqSb5Avfvd1sk4YMK3bNxxL5eVnDg9OMjxZfpru00dHa9LiG42nODE+PT7e1TAUS/PqyXEs06B/TQsZR5OyNZlsdV5h8V9Qpf3oSBzLVESzOatoyMQyFUdH4rNSjq8lZLccclzBPW9y7Fzg+Px5ksuBhTRQxT57ha+sBWwDdgK7gfuUUp0zbqT1vVrrq7XWV/f09Mz7QgUhWIVXDqWmv8DTtvPZ1NWCqRQRyyAaMjBNIy93Ukyk9BevWIdpKEKmImJ535VSRcNvR0eKb6C2S15+Jm27VSnUlhrj7hMMhcXTnlc0Q00CmEpmODQ4lfccwHttgoUThqEIm4rd9x4gZbtkHI3tzPS2alUbrzVktxg5rrkS3PPaOlcF/kc1lAe1kO/ICWBT4PeNwKki5xzQWmeAo0qpN/AM1jMLuC5BmEGwCq+wn8dQgY3Zzy9lf7fy1CACuZPA9+AVbzGR0i3dh0o281ZL8DFCpiKRmSl+W0ilCSDBUFjhpuhdyXs/pxyNqyGSDd95xSIOrmaGwW8OmwxOJomYipSjvcZkHAzDwNWwrafFM+TZxz8xGmdjhUbde54YIG07jMRs0o5L2DRoi1rc88RA0dtU8z7VK43mQS2kgXoG2KaU2gKcBG4BPlxwzrfxPKevKaW68UJ+9R8IFlYcU2kHA68Cr5CQaeSVfets7sRUsLW3NXf8xFgc29XYAbPQHjEq5k727NpelUHqaQ1zYixZcv0+29e0c3Q4xpmJVNFzq+X4aJzOJi9flmekAwSPV1P+nMp4skbrOps4MZrwXi8XIgZ0NYf4zE39QG1q44fOTjCRtDFQmEphO5qRqTS2M1H0/FjaoTmkmEhNv6fVvE/1gDeNealXsXgsWIhPa20DvwN8D3gd+KbW+lWl1B1KqZuzp30PGFFKvQb8APi01npkodYkCKWIWAYZd9r4BFFAR5NFc8Ho2NaoldtQ9+47RCw10yWZSLl5V7xzKW9WqnTcLvgYt9/QR9iqLARbaVx7uTJz/+FKlZ+Xwi/caIuG2NjVRHPIwDAUV27u4k8+dPmslCL8nJdheOFRI1vJUUwp3l/8RMF7Vfg+1Sta51eVrnQWNOiqtX4YeLjg2OcDP2vgU9kvQVgyVjVZxNMz5zUZwJWbu7iubxV//eRALrSlFLgBl6KcKsO5KW/AXCkJn6t+coJHDw5VDPENTpb2iPzHgGkNwC/vK51j8se1l+P2G/r4/EOvEk/blAoWZorkkKqlLRrCNBS9bVHuv+3aWd9P2PJmJPljTXS2lDJcwgD7r1Vwn9fkv4b1ylxe7+WIKEkIAnBqvPTmf/9t1/Lwy6dJZFxChkHUMggZBomMyxe/+zpQXpXBn2hbrLw5lszwv35yOk8i6a7HjtQ87TY4NdfvgyqHP669HMGijuHs5h02DcKmkSs3n0zVHhbzix+GY0lOjCY4dHZiTs2yhWNNLEPR3RZmW29xPcOU4xIy8puuQ0b+a1ivZBwXy2ycbbtxnqkglKHU1uQf9yroNBnX9SrQXBfQucq6cqoM/v8FpYN8RuPexh+USCqlk7dldeky6+Dj+4ZwTYmG4E9VYZx8dvb3cv9t12Ior0LPzI4dj1gmEWt2sabetihnJpKcm8rQ1RxiXUfTnJplC8earO2IEjLNklV/xVRDap2PtVRkHE1YDJQgCEHcbDI/qCRhu9Nhvluv31Kysvs9/V5rxKauZk6OxXn11Dgvnxzn1VPjXlFgwQ1L6eR99ucuIlxCWdx/DJg2hL3tUda0RXLejqI24xSk3KZeKZdVjMmk50WNTqV54+wkZ8aTZBxnVs2yxcr377h5R8l81nv6e3C0t36/l8zR+a9hvZJx3Irq8iuJ+in8F4Q6JmQZZHyjEajfDmU35z27tvPNZ45xcjyVl63parY4M+E1za5tD/P0wLR4rBswdkFKXc3v7O+lr7uFI0NTeUrlwccAzxAOTiZpDlv0tkfpbY9y/NwUkykvfHjfU0drLmW/9fot3PXYEWzXzVXuBeWS7tx3uGq5oxeOjeZVRYYMr4F3eDJNxpmsek1Baqn6OzORpqvZYjxh55TdO5ryX8N6Je24hBrIgxIDJQhA/5oWDp6dKnocoClkkLId3ECln2mQX9lnGOxY355Xbae1zikUPHpwCFNNSyH5CX0NJTf+QmJph/61bSUfA/KLG5pCJidG44wlbEwjKzaazXMBFY1UcHDgurYw5xI2KdstWswR7OUKqrgXUmjIMi4YOjvAMGu4FnJg4fHROBs6m9nYVfo1rFdsRxON1H8ocr5oHFMsCGV45JM72diRn7PZ2BHhkU/uBLzeop7WCM1hM1voYNLTGmHbmvbc+cGybJ9ExslJ9EylHSzTy99EQyYRyyRsKRSe4oLtet8/cePWkoaj0mNAfsjr2DnPOAE4rvdVLs8VpFChIRK26G6NcPNlawG467EjXPaF7+UGH770hffx5h++n5e+8L6yQhbFKtH8MJvWLvsPDvLpB17khWOjnBlP8MKxUT79wIvzJuZazWtYrzSFzbzeu5WOGChBwNuMDdPkgp4WLlnfzgU9LRimmdsUb7+hD9vVONl4nKM1tpsvv1NJoqclbGI70+M4UraD7eiS5dDFqFYGaGd/L9f1rZqRy/IV16uZB1Ws6nAika6q6rAtapWcIVWs3chQ3pdSBl965CCj8QwasExP/3w0nuFLjxys9PJURa1SSvVEIu0QLTNva6UhBkoQqE7h2lfc1tm4XOH+WylZXyo5n7LdqsvMaykI8OWbCvGMlK5YtVZYdXhmPJHzxjKOJmPrkt7YrddvQSk1Q58v+xLOQGf/CVsGA8NTWYOlUCgMpTAUDAzPDMHOhlqLKuqJZMahKdw427bkoASBfFkf8KbfDsdSvDUSZ/e9BxidStHeFGJtR1PunHjanqH3Vi5ZXyw5Pz36fFrHznZd7nvqaNEwX7W5mf0HB8vmgVwNO9YV7xPy2dTVzFsjMSYSdlFvywVSGYeQpZhI2lz2he/lNRt/4sateXkpy/BK9P28Wx7ZnFx3S5jJpI2rNbbt5HJ1hgJzHiUUaimqqCcSGafsxOKVhhgoQSC/8m0ikeHUeAKAiKkYnEzy1sgUGzub8m7jFyD47N13iL96YoB42kEp2NAe4Q9+6bLcRlgsOf/yyfGZ6hUlwm9+TijjOIzHM5weT/D8sVE+tvOCPGPmn1dJLPbV0+Ur5q7rW8WP3zpXMlQHWSNle49S6AVec17+YILu1ggZx2UymW/wDOXpHTquRik1Q3NQZ73Ote21ySqVYyGLMBYKrXXDGajG8RUFoQzBvIQ/ml2h6G2PeuE+w+BsgdRQMLG+d98h/uzRw8SzG6/WcGI8xcfvfz6XxyqWnFfMDBWWKjP3BxGOxDI42tvUXa25e/+beQUE/nlWiX4Zy/DCbpVyUE8PnKOnNVxVY6ivBO83G2utefroaJ7ROjGaIJFxWNsRxTLAMqafu2V4QwljKbu0GmqDT9S1XY3WEF0GDcXzhRgoQSA/L5G0XUKGYn1nNDf9dk17pGxi/b6njhZV+46lpptPiyXn25tmTqMtVWZ+fDTOeDyDq71Ch1TGxXa8n4O5Mn9goZ+7CeJ7K9UoJxwfjVfVhKtgRvOo/1oEjZZlKtK2S29bFNMwsEyDzaua2bG+I28o4dBUBsvIFk3gGz8YmietPH88x5nxZK5JuHAqcT3iVz+KByUIDYgv63PN+avoaA4xNJnKTVxNOy7be1tLJtZLeSMaOHx2Inf/hcn5u37tSj65a1tVZeabuppJ2i5O9krav3+tpx8DpgcWOq4uqv6Qsp2yvVY+rWGTE6OJnFdYDruYdS7AUJ7C+P23Xcs9H7mK3jZvrHsxg+9JK02X4xsV8k+1qMQfOjuRHceh88ZzBF/DesRXbW8kAyU5KEHIsnefNzjQLy7wvA1F2nEZnEyz+52bS/YnlWtODY59KJac39nfW5Wqw+039HFgYKRoXin4GP7AwlJGw9XwqfeU7rXymUo7OEWqFQvReP1VtppuNvYpDGm2RwPThSk+lLCvu4XDgzFUQJ3cH2ZYjP0HB9lz//PE0p7hPTWW4NWTY+zd/Y6ieaWMo3FdjYPOFWEoyoznqBMy2SbmRiozFwMlCEyPfDcUeZtixtE0hUzaohZPD5xjT4nb33r9lrLjLXzmkpzf2d9LNOSpqAcp3FbLDSw0AGWoqgzi4GQqT/miFGb29WoKmbmKvYjpEkvPbMhtD6gglKqk+8xN/Xz6gReZTNrYjotlGHnDDAv53LdfZiKgqu5qmEg5fO7bL/PUZ99T9DZBw6t1abHgeiLtiIEShLrB92jmMgq9Wrw+Ho3jztyM+3paK0rh7Nm1na88dnjGGPVgfcFc50GBlz9KO9lSbf/qv8DF8aWOiuECoapV88iql3tPIpVxim7kxRyPYsYJvMKRSuzs7+VPPnR51SPfT44XnzJc6jh470vwNSxXqVgv5HJQUiQhCEvL3n2H+PK+w0wkbRxXM5G0+fK+wzXPSaqWWMrGcWeONXc1DAzFGI6lKkrhtDeFvMZU05sZFTENDKVyCuTzMQ8qZHqNqyHDIJKdSxV8DJjOdZVK2xhGdR/7LaubcVxNMuOQyOQPc1RMh8Z8gs9hvqjGlNZa9FfNa1iPNGIOSgyUUJd85bHi4bJSxxeStOMyFEtzXd+qsudtX9PO6pYwlqlwtMYyFatbwjm9vrnOg6rmMXx29vdiKEXhnmuq6goaAN5/6bqilYnB0FiQ4HOYC7WWgZfyfkodr/Y1rDekik8Q6oTCUFml43OmzJ4dNg1628I8PXCu7F3cfkMfYSt/cF7Ymh6cV6wPqpZ5UOA1z47GM6Rsl7Bp0Ba18h4jSEvYxDAUTSEz92UYqurBfE8PnGNNe75AbthUuZ/971C5kKIWqpGdCtJcQvqnpcTxSu9TvZLOhfgaZ9uWHJQwbyxmzmi+CVkGadudYacU1eWgwPNarvrJCR566QyOqzENxc2Xrc3lTgrHYCQyTk2Nuv4o95CpSNkudtphKu1w3ZauovmZW6/fwp37DpNxpo2dAm79F+XLy32Oj8bpbo3Q0xbNHZtIpDkxlmBTRzT3HHztvGqwDNh97wGOj8Zpi1horYmlnbyCkeOjcUxFrrw/bBp0t4ZLvv6Xbuji9dPjTCSnJaTaoxYXresoen6l96lesRuwSKKsKVZKvVMptTbw+79WSj2olNqrlCof7xAaCr8KrtpcSiVKNYjOZnprNWxZ3eyNMjeNvM3Wf7xqxjHs3XeIh146g6EgYnlNsg+9dCb3GhTrg/rFK9ahlKqqUfeeJwaIJTPEUvmG7emjoyVf50KDW0shdTGPzzINtvXk94P94uXrgPznUMpehU2DF46Pcno8yetnJjl4Nsap0fyRGm0Ri5NjSeys8bBdzcmxJK2R4tfTt9/QR3tTmC3dnhL9lu4W2pvCJT2iSu9TvZKWHNQM7gHSAEqpG4AvAn8LjAP3LuzShOWEr5xdbS6lEh/becGMTU5ljy8En/25i+hsDqEKPhEZx+Xw2UkmEpmKIaBqXgO/GfjJz9zI/bddy523vINP3Li1qkbd46NxRuMZ73UpcL0KX+f9BwdzQwkNBaFsqC9kqqrfk1JjKT77cxdVfA6f3LWNT+3aRnvUwjQU7VGL7pYQaUejXa+J2MeFvJEaOq8LmZxV1SWqHmpVJ5/vv9XFwu+DaqQqvkohPlNr7Qfefw24V2v9LeBbSqmfLOzShOXEVNpBuzovnGQAU3p2FV3+Br1YIcOd/b38t2xp8+GzE4wlMhhKeXujqs7zmEo7GHjznvzyZbOKuUt7dm2v6nlt6mrmxGjCE4ENLEiR/xh+kYFvBLwpvZqM652TcezckMFylGqmhekwnR+aK/Ucgscu/Nx3vTEahoKCl8RQCq00A8NT9LRF2NAZZTiWzoX41rZGyr6OtaiTz/Z9Wmr8IomoJQbKx1RKWVprG3gPcFsNtxUaCANNoVJarT03hVS7cc8X/ia3+94DOWVzn2KjNQqJWIanZA6eUdOQ0aWT+LVSSklCkx/69IsMjMBI+UKqHfke3Pj37jvEb3/9eeJpB0PB6pbpCrs7sudWwnE1tjvTEKRsJzdSw1eW7+uZnhwbT9v0BnJhc2Gh36eFIuO4RCzDM/ANQqV35H7gcaXUg0ACeBJAKbUVL8wnCEDp3ppqe27qiWLl4IWjNYqxKiv86isv6ILjc2Vnfy+rmovfV/Ax/PX3tEaKGifLUDWHtPwco6/L52oYimWYSGTKVtgF6W2LZMe6z/w/V4PtQk9reMEn3i70+7RQpB3dUOE9qOAFaa3/P6XUo8A64Pt6OghsAB9f6MUJywfb1ZgqX1Wglp6bYix2VaAvQzQ0mWJ4MsXajmk182qKJDAMelpDjExlctVkq1tCqPk00tkptZnAC93Tmv8YvgfS2+55HMExIZahsmrmbk0hLT9v40Iu96U1jEylWdMe5ZWTozMGFvq384+FTa8vq1RvlQJaI1ZZnb55YTHepwUg47gNVSABVYTptNYHihweBH4FeH7eVyQsS1rCXslxOPAht93Zf6D27jvEnfsO565uJ5I2d2a17hbCSAVliNa2Rzg5luTEaIINnRrLNKq6gvcNw9qOaUM2n6Gp/QcHiWU156KWkdWQ05iGkWc8g+XsPW0RhmIpXO1V0JnZ8FA14zaCTKW96kxfp9A3Uq6GE6NxJlMuIVPnKjiD7x2QE9LtiJjY2ru/4EDF5rBJd2t4htFcCPnWhX6fFoqM4zZUiTlULjPfpJS6Ryn1HaXUrUqpZqXUnwKHgPpuGhAWlVuv35IN01Qul66Gu/e/WTTXcvf+N+e81mIEm0Pbm8Js6GzCMhVnJlIVq8J8Fjo0dc8TA6xqCaHwijf8isPReH6FYWFV2/r2iJePQs/6vWkJm7h6ejS9/+YYCsaTNqZRMLCwxP2Mpxz6elo9jyorB9USNvPmQS30QMGFfp8WCtvRC9ZmUa9U8qD+Fngc+J/ATcAB4FXgMq31mQVem7CMmO+qu5RdXDKi1PG5cnw0TmdTKPd7e1OItqjFeCLD/bddW9V9VBOamoua+fHROKtbIkQsk6HJFGnHG6zYnA2LFa4leGyu4dJbr9+SVXv3vCRf0WN9e4RTEymsGhL38bRNd2uYk9mx7mtbI3lGInixANActqoqUqmWBQ8hLhCOqwlZjVMgAZUN1Cqt9ReyP39PKXUWeKfWurIksdBwFFbd7d13aEZeol6VJTZ1NfPWSIyJhJ0rbW5vsjh/dWvlGwcoV+5cSs282go4PzTVFg3lcmPVhqZmWxEZNKjr2sKcS9ikbJf26PT7edkXvkci41StJjE0mWIq7RCxDFY1WbgaetuiOSPxuQdfybtYgOqKVGqhlrL0esHVGtNorBBfxRyUUqqL6ZbAM0CzUqoFINAjJQh5zDWHFDKK6+6FFijCcV3fKn781jmvT0cRGFI4f4Ipc/UMbr+hj08/8CInRxPYrjcnqS1q8XsfuHje1hhk/8HB6blMgcf7iw/nDwL0vSvbnTmwsBg9bRE2Z2WSMo6eET71DXGwzL9SkcpcPNPlgqM1oQYqMYfKBqoDeI58uTC/MEID9R20FRaVYBjJKbJL+TmkagzUx2/cVnQA4Mdv3DYfS53B0wPn6GkNM5n0PCgDryDg7v1v8vTAuXnZ8ArDiFC7Z5DMOKQdN9t865KsMNpiLqG9Lz1ykNF4BtNQWKZXlDEaz/C5b7/MplUtecbgEzduzXucNa1m0dlPUcuoaKCLaRaWyxHN1TNdLmjtNTQ3EpXKzM9fpHUIy5xCj6kU1eaQFltJIiiMOpHIcGrcU2xwXHfeNrzZeAZBvvjd10lkXEKGkaumS2Rcvvjd14uuKzglOKiPCNV5sb4IrL8pKgW2djkxliQSMvONwc07eOkL78u7/fV/tC/PSJkKtvbmh0yLGehac0QLnbOqFyxD5RTNG4VKVXwfCfz87oL/+52FWpSw/ChWdbecCAqjDsdSGCiUUkQss+K4h2qZa/XY0ZF4TipIKYWRbbg9OlLcA1sIzTnfMT4znuSNs5OcGU+ScZwZr83efYc4PZn2BjhaXu+Wo+GtkSkGhmIcPDPBwFCMkanygyCr+ZuabWP1ciNkGhU95pVGpYj+pwI/f6Xg//7tPK9FWMZU6xlVm0Na7Im6QeORdlw0Gq29fAnMz4ZXq6jpXJlKzyxcKDdrqpDCibrJwFTdoNL48GSaw4OTebctZhwBYikvRBnM8xUOgqy1zLyY6notnulyIWSqeZ1WvByotF2oEj8X+10QKlJtDmmxJ+oGjYehvJHg6ztrVJKo8nGCSuC1GKe+7pac8KvX06RxtXe8GH7vUpBaGnRLTdT1w34K73VCQbrgAqWYcfTTJ2HTyDUO97TOHARZ68DC5drXVCviQc1El/i52O9CA1POM/LHLXxq17aqc0iLPlE3QGvERENW7bp+NrzP3NRPV3MIhTe8TgFdzSE+c1N/0fPn2jz99MA51nbkT9RVeEYu6FW5WhMumC3fEjZJ2ZpE9rxEJqscjjcAsn9tO309rXS3RmZ4prWG7BbbM10qmsImo1MZ3DnIhy03KlXx9SulXsL7u7og+zPZ31fW5ckcWM6TZOeLUlV3tRilxZDqqeoAACAASURBVKBUOXKwEmxdRxPDsRTnpjJkHM223ra6KFve2d/Ln2RHglRTPDDXQhO/Mbi7dbrP6tDZSS+c6+sUKdAueVN3AXasa+Ppo6Mz7rNQCaGYZzqXYpKVvHV3NoVIOy4jU+lc6HmlU8lAXbQoq1jG7N13iD979HAuFDKRtPmzRxdOM65emc+qu3J5ppA5+8hyuXLkwkqwnrYoSsFUyuH4aDwXXqoHI1XLGuYysqSYoXBdz4UNByoJHa1nDBN89fRk0Z6olO1WLB+XMvPidDaHATg1lhADBaC1fnuxFrJc+avHB4rG+f/q8YGGMlAwP0oSfml0KaKWwf6Dg7PaeO55YoC07TASm1aLaItaOY8q2KM0mcwwPJlGA5tXNdfNpreYDanFDIWLoqfVIp52yw4TnEo72ZlU0x6Tq13Stqa3LVrWA5Qy8+J0ZP8+T40luHxT5xKvZnEoa6CUUh/Fkzv6k+zvJ4E2PAf/P2mt/3Lhl1gflNoY4iWSlqWOr2SCoU7LUKRsl5CpaurB8au/wtkNMYiloCVizXrjOXR2gomkjYHCVArb0YxMpbGdCbatac/zFoYmU6AgYhq5RP1Sb3qL7Sns7O/lQyfG8rzi9R1RwpbB2o78YY6Fcku+un2wUMLV0Ba1qtY2hOrLzE0FA0OxnNHsbg2vuDLzrqwH9fa5lfW8ylGpSOLfAV8N/D6otW4HeoDdC7aqOmOh1ZVXAr7nk8h4Yxn8snPH0TX14BSr/vIxDYORqTSHz07kHd9/cJDd9x7g+i89xu57D5R8X/wZSsE+IvAGwRVWgiVtzzh2t06HUpa6t6bW6ra5sv/gIA88f5KetggXrW2jpy1CIuMwkchUrJibS4FGrZ+31rDJybEktqNzFx4nx5I1jRNZDjSFTTZ2NfHSibGlXsqiUSkHZWitRwK//yOA1jqplGpauGXVF+VCCIJHsO/FIzt5NXBONT04LWGTWMrG1TPPyzgupqlIOzrn0R4enGQyadPVHKK7NVLWqwhbBom0V3WWm2ukveN+WOn3HnyFgaEpNODg9QC1N81vqflsmQ+ppFoo9ncPEDIUXS2RsuG3ueQkaw3ZKb9+3Z96CKADx1cQV2zq5Pm3ZxafrFSq0eLLobX+QwCllAGsXqhF1RuLvTEsR/yBduWopgenVPUXeMbOdTRRS+dCXfGUje24nJ1IMTiZoilk5vJKhZvZtt62mYrlLaGcYvlLJ8Y4NZ7EMhVaa2zXm0artaatKbTkpeZzlUqqlVJ/9+OJDI98snKYrliBRjU5tFo/b5Mpmw2dUYZj6em8WHuEWMqu5mkuK67Y1Ml3XjrN4MT0xOSVTKUQ3/eVUn9Q5PgdwPcXYD11SaN0qs+Fwr6XIP4x29EVQzyvnp4s+/8K78rYv8JOOV6zqsbziOJphzMTKZ5969yMkNDtN/QRMk3WdkS5cE0bazuihEwzZ3SCXmDINAkZCgUMxdJ10VvjhyGHJpMMDMV4/fQEJ0YTM5QY5ov5/ruvNnRX6+Nu6mrGMo28/ip/+OFKY2jS0zZ84XhjhPkqGahP4/U/HVFKfSv7dQTYCvzHhV9efXD7DX1MJDIcPjvJ66fHOXx2kolEZskbN+uJHevaKp5TTcLbmxNUOjSj8UJyfiNnsLxZBx7DdvWMza+woTNsGjSHDD734CvsvvcAk0l7Zv5LLU5vTTV5tJ39vXzoHRty85RsV5O2Xf77D48uSD60lELDdX2rqsr5FVJtDq1WZYhG+nxu6GyiJWzy+KGhpV7KolDWQGmtp7TWu4H3Al/Lfr1Pa32L1jq28MurHzSAysa1A5tWqb20wQZflvV8mkImTSFPjaBSkUQxeZ4ghoLJpM2RwRiTycyM8QN+GsI0VNkCgkTGYWB4irFEJnc1D9OFFLbjknF1Tv1gIQtjaikK+MfnTuAEXh8NjCdsfu/BV+Z9XcUUGj70jg088PzJWRUMVasQMRtliFKfz5WGZRrsvLCX//Pa2YZQlKikZr5ZKbUZsIEXs1+ZwPGGwL/yM7OboammN7/OlnDR25Q6vlKZTFaO91dTJOFXfxXim6H2qMXa9gi2qzkxmsBUYAT+in0vynY1R4emeP7YaG7z9AfwvXBslOFYmpTtcmYixWunJzgznqQlbOQqz+yAZ6aUp96dtmeqds8HtVTnnRhNFL2PUsfnSqF24NMD52ZdSTibkGE1W/A9TwzQ0RRiW28b/Wvb2dbbRkdTaMUWMb13xxqGJlO8cHzlF0tUKpL4J3KCJjk0Xpl5L7Cy6jhLcHhwknOxNDpb+WW7Dom0193e0xphJJbO+yApoKe1MTq9fcptJP6mZChojZT/kwtWf00EjJ7Gu5rqbA5nBVwVZyeTOI7GMgy061Io0+filbvf/vfP8bGdF/DdV87kBvDlnZfNXRkGNIUMQqaRe2wzO2E3nnaYSjuMxNKzbhQuRS19PKVe58W6lp5LwVC1ChG19ns1Sh+Uz7/o7yVkKr7/6lmuOm9h8o/1QqUQ36Va68uy3y8FfgH4IRAD/kOlO1dK3aSUeiObw/psmfM+pJTSSqmra30Ci8FUysYlW5ac/e5mj08m0kVVdCcT6cVd5DLA1dXlqvbs2s5LX3gfX/uNd9LTGiaaLQ90gbdH4rx+eoLhWIo1bRHWdkS55yNXlZVAyjgud+9/kyODsbwBfIU4rud5BdHaE6gtl9uaK20Ry+vjCYywODmWrGjMF4PC3FhbxJp14US1obta+70apQ/Kpz0a4roLuvnOS6dXfJivqk+AUmob8F+BdwF/CuzRWmcq3MYE7gZ+FjgBPKOUekhr/VrBeW3AHuBHtS9/cfDzEoVupO1ohqYyWIa3+WrthYMMBUNTZV+eFUd71MrzeErxbBU9HL4iRc6LyVbT+eE7x9W5TXxbdkJryin9QXW150kpBaEKvTHeez29ARcTT/cH9M2XF5Ur9PCfgi44HmBTVxPHi4TzNnXNf1tiMU9mPJHJfQ6q0ckrpBotwVq9tEbqg/L50FUb2XP/Czx5ZJif2d6z1MtZMCrloC5RSt0PfAvYB1yitb6vknHKcg1wRGs9oLVOA98APljkvN8H/hhI1rb0xcM0FKaanmejVDb0kw0VGdnJq9GQScQyS16hr2RuvX5LrjChXBVepXEZQUUKH8fVM+a+ZGwX29W8fmaS3/ybZ6pao9aesap01ZkpY+w0cHo8xYGBkXkbnhhLO2zojGKZCkdrLFOxoTNaNF939XnFNdhKHZ8LxTyZjqYQq1vCCzraotZcld8HZRkKx9VYhvf6rcQ+KICv/+gYY1NpmsMmf/LIwaVezoJSyYN6ETiOl4u6BrgmeFWitd5T5rYbsrf1OYHngeVQSl0JbNJaf0cpVbJsXSl1G3AbwObNi1+b0dfdwuHBGJahphWcXZ0bFHd4MIYKqBO4Grb1FB8it1IpVA6YLcFepIxT+n6Cdq6Io1GS5pDBZGrm/dYaKNEwK9X6YqNZ/Abcvp7W3HnF9O0AHj1YvLy41PG5MNdG3dlSq5p5La/fciG453Wv3TDj/y3T4B2bu3j6zRGGY6k8Sa6VRCUD9VFmn38tdhmdu6+sGsWdwG9UuiOt9b3AvQBXX331ogddP3NTP5/4Hy8wkbBzFSPtTVZuUNynH3iRyaSnaGAZRtkhciuZoHLAhZ/7btEx8IXzgAqpRpFiLgR7peZKoWp9pblgvnfoGeBpAd2bL1v7f9l78zi5rvLO+3vura33vbW1lu6WZMmW5X2R7Bhha17sSbADQ4jNm2GWEEyGweAZeDEkcRhPFggkBieEmJgMEBIbogTw++IlsY0QQpKxLFuyZctauiV1a+t9rf3e8/5x61bXdmvpruqq7j7fz8dW162lT92uOs895/k9v4cTA5OcGwtgxPJQtV4Xf/Crl6f9Tqdt1Fzbq4kODgIYmgoRNmRWC6L5dq6wKdTNvNCAVm5MUxI2THxu5xxZ4pzXtXlrxo/s9Wub2HtyiF2v9vOxd3WXZrBlJle7jW873SeEyBXc+oHVCbc7gPMJt+uALcDu2KpsOfC0EOJuKeXBHK8977h1Da9bi08gbt2aRQttIrdU+PiObh594USauvHjO7J/kTK5YBeTQJFb8tqu9Y+9cDzp/U4Eozz6QvIK64m9vUgpk7Y5NQHPHb1kCSJkLO8kRcaru9my+9gAn951mKlQlHDUjMv4c7nMl3PiL6TvVaEBrRyYpsQfMZgORfHHLmCyBah8aK/30dlaw3f3neYjt3bi0kt4ZVcmcuWg9ib8/Pcpd/8yx2u/AmwQQnQKITzAvcDT9p1SynEpZauUcp2Uch1wAKjI4PT4nh5cWnIdlEubURWl1opU0hejXDywcyMP7txAvc8Vb/n+YB7ddT9yaydRQ6blIIqFJjIv7efK13efyqjm/PruU/Hbk8Foxt5hgYhJfZWbDcvq2LyigQ3L6qgvYh3PF599mzF/BGkmNxCMmmR1mV+IrdQrSdMmpWQ6FGVgIsiZET8DE0GmQ9GM4pfZcuv6Vs6PB3n2zYtFe81KItcqKDGRckXKfVm/51LKqBDivwPPY9VL/Z2U8qgQ4hHgoJTy6WzPrySOX5pgLBDBNG31nkEwahA1ins1vtiYbTfXUk4y4SwCiNlgq9szbWemHs/2m/NxWMiF0xZj77DfktdrIlGgGCdbAXWhHXzLQaV11A2EDSZDEfwhyzm/lFy2vI7O1hq+tbeX9161sqS/qxzkClDZzm7OMy+lfAZ4JuXYww6P3ZHr9crFVMggMRZZUmfwh1WAKjZP7O3FrYusSrpKYmVjceTdgYgx51xPpvyWjSXNTw9CoaiBEFDjKX/N1WyphI66wYjBVCjKdCiKMY+1SZoQbFnVwP97+DyvnhnlurVN8/a754Ncn8pGIcT7iBXxCyHeHzsuSGnFsVjZfWzA8ep4KXbNLTWlFkkUE42Z9iFuLbOE3p3wXpxqxarcGhOBCOdGA0RNS2hT58ssksg6noR+XJqwLJue2NtLW62H/rHMVRxmTDVyx6aFW0tTrnY4oajBdMjKK0UK3E0ZD0SK1i7jujVNvPDWJf52Tw/X/cfrivKalUKuqeBnwN3Ar8V+fm/sv18D9pR2aJXBYvXzqlRss9gFEaOEpYYD+MTtGzI+JPF4aq2YWxfomuDOK5YVxew0VVhib93V+dxWHZ/D82o8GhcnFq7zyXy2w4kYJmP+MH0jfs6NBhjzh/MOTudGA/zTwT4e/P7r3PnVnxdtTB6Xxo2dzTz/1kXODE8X7XUrgVwqvv8yXwOpVPoWqZ/XfJBLdp2Jj9zayV/E1G+VjO2Ybue08ukg6/SY/T0jNFS5WdEws104my0qUyYHKbtB5GQoSkdTFUNT4bRckxAQCJucGMjeh6uSKbXaMGqYTIcMpsJRQgXsmhim5NjFCX5xcpj9p4Y5M1K6uWRbdwv7Tg3xrb29PHLPlpL9nvkma4ASQrwXOCKlPBO7/TDwH4AzwCellNl7JywC6irAD20h4lTzA4UVtuaDbYM0n8jY/zwJ+5H5iEIyPeYHX3qpKFtUUUMSNYwkd+ePvNsKgHYh65vnx5MKm2Ws2eP0AnZdKIXM3DAl02ErpxQooPA8EDE4dGaUfaeGOdAzzKg/2XSnucbDtq4Wbi/ylmq9z82vX72KHxzs48GdG2laJN0Ucs2+fwzcDCCE+DXgt4D7gGuAvwHeU9LRVQDZJKGtNW7H+5Y6iY4QkJwTyTaJJ8qyc5HozzffCKC1zhNvFz8XVjdV0zs0xWRwphV9nc9FZ2thr13l0ZMm0/oqF1s7Gtna0RhfYaR+nO1zGF0gohQniqE2tGuVpoJRAhEjbzn4yHSY/aeG+cWpIQ6dHSOckrPubK1he3cL27pa2LSiDk2IkhgB/85tXfzTq/1878AZPnFH5i3nhUZOFZ+U0r6Mez/wLSnlq8CrQoj/VtqhVQZD085781/5javncSQLi0xih3z6QTkJUlLRBFkbG5Yae1LP1CrCdmxYneFKPtO257auZn55eiTmtA5hw2RwKsyHbiyslUI4arK2pTrWjmRmm/DJj94cX2GcGpzJUQhi5sbM+EouNaS0au6mglGmw/kFJSklp4f97Ds1xC9ODnPsYvL2qCbgqtWN8aBULKVnLjYuq2PHZW18Z/9pfue2rjkXAlcCuQKUEELUAn7gDuCvE+5buEZXBRCOmrh1gWHIJP83t1aeGouFQiZHCDsnUgxMidWy3aMzFiiPc3wwJR/x4FOH+NHrF+JbbP5QNKkex8ltoqOpivY6DxOBmRVUfZWL/T0jZDO7TEcyOBmKB6jEbUJ7hXHXV/dw7OJkfOVpdwy2fSWXCoGwJQv3h/OThUcNkyPnxtl3ysonXRhPVkXWeHRu7Gxme3cLN3Y2x/8G88k/vnyW7rZadr8zyOf+5Q0e/c2FfwGdK0B9FXgdmADetl0eYiavF0o8torArQsmgukfYENS9MZ1C53E1YFLs2uZzPhqx5SWCCIbTnLtTFR7dFY3V8OIvyxBKhAx+eKzb8eDzw9ft74SdkweieUfbLGDk9tE32iALSvraa2dueaTUhaegzLBCBscuzgRD3KpW5CbltfydsoVv4wdX+wEY1ZD0yGDqJn7QzYVjPLL0yPsOzXMy73DTKeYDC+r97K9u5Xt3S1s7WiI25+Vk67WGlY2+Nh7YgjTlAt+ZZxLxfd3QojnsbrnHk646wKwJBR+VQ5FOUIwr4WAlU6qKMKU0pqopSQqRd4qvk/cviFvFd9YIMLYufH47YRWQPOCJuDYpSnWPfST5DvstiwSRv2ReKDJtn05PB1KW0HNJr8lmdkmHJgMc98N1jahvfV4oGc44/NK4YZeCRRaq3RhPMD+U8PsOzXM4f7xtNXVZcvq2L6+he3dLXS11sy651SpelUJIbh1Qxs/ONjHT98Z4I7Ny0rye+aLXCq+tcCYlPJc7Pa7gV/HUvH9VemHV35GApnVTabJom0pPRsyiSLApMqtc+QLmbU0mfI1D+zcyP5TQ+zvzd3YMBUJuGIdaecDJ9uk1DRGPvU4A5PhpBxUYnApFHv7s85nbRNuTbACcjozk3k0m1wohKMm06EoU3kEJVNK3rk4Gd+66xlKriNy64Jr1zRZ+aTullm3tXBpGj6PRpVbp8qtl9TY9cpVDTx/9CLf3nd6cQco4AfA+4BxIcTVwD8BfwpchZWP+khph1d+nK56JflNPEuFQkUR2fzTjl6YXU2OwGrnEZ1DP6pSYAspnLYvBdBW60lT8RWeg7LO+abl9cDMNmGqFdBiJGLMBKVUFV0qoYjBobNjVlDqGWYkRQjVUOXm5q5mbulu5bp1TWk+ifmgCYEvFoyqPHpSOUKp0TXB9euaePHtAU4PTbNuAecXc31iq6SUdouM38IyfP3zWC+n10s7tMog20K8UvvNlINCRRGP7+khYhgMTyVvaz2+p6fgq/lEuXlXWy1vnhuvGFdrjRkxjdP2ZZ3PRWutl7a6ueWgIFnZaLsp9I360QX0DE45Pq9SzlchFFJAO+oPc6BnhH2nhnj19CjBlCC2trmabd3W1t3mFfXoBeZuhBB4XVo8IHldWllbzt+wtpnd7wzy5C/P8rl/v7ls45grOVV8CT/fDnwOQEpplvPkzyfZtotU/mmGj9zayddeOknUnBFFRA1JQEbp/vwzaTmoEwOTjPsjaJpl9xM1JUOTYcYDY7PqbgvW6mFoKlgxk60GiISJLpuThFNjwNRt0Hx4+8J4kp/fl547xomBqYIn3UrEMGXclDVVRZmIlJKzI372xfJJb52fSPpcaAK2rGpgW1cLt6xvmdVuiDumIq3y6PhcekUJEuqr3GxaXsffHzjDqsYqPrx9XbmHNCtyBaiXhBA/wBJFNAEvAQghVgAL17xLUXRSJ1+XJpBIEAKXSHeSCEdNDCkxDGlJnWPf7cgctud0YeVyKoEqt07UNNO2hzI5Sdg5olSrnm1dzWnboPmQ6ucnpcQ0ZVY5tUevnMk1FTPu6mBkLaA1TMmbMSn4vlPDnBsLJN1f5da5YV0T29e3clNnMw1VhUnB5zOPVAxu6mzh6PkJjp6fKPdQZk2uAPUp4DeBFcCtUkpby7sc+HwpB1YpCJGe9LaPK5JJnHy3fuF5TCkdnSRMaSZtRxWjbY4pwShx/518sc1Lu1uTk+pO/oSZrHoe39NDOJq8DZoviU01B6dCCA1kltRMpVl6SSnxh2Oy8CwFtNOhKK+cHmXfqSFe7h1J2x5uq/WyvbuF7etbuKqjsaBckCaEtTqKBaT5zCMVg662Ghqq3LzeN1buocyaXDJzCTyV4a4a4B7gX0sxqEpCyMz786Iy5sGK4r7H96Wp7xK/04miCU1o6MKcKRaNuW0X4rjj0kS89iQQMQp6bjE5/cVfBeDOR3dz7FKyCuzYpWkefOoQj957bVZ/wq0djUDyZ+34pQkmglE0rG7O+doR2Y8dng4TNSbivbUcL7aAqQoRluRTQDswEWR/j7VKeu3sWNo2/Pr2Wm6J5ZPWt9fmnQsSQuBzWyskn3vuLdnLjSYEV3U0svfkIMNTIVpmqUAsJ3lfNsVUfB8CPgj0Av9cqkFVEsLBU0dU0H5zJZApOIE1CdvbXImiCY9LYzo0Y1Ya2w2k2qPjz3OyNExJ6hxSDuNYm9TgZPPD1y/w9JFnMExpBSfdGrS9qvzGz07RVudLUzQGYpI/O7chBBk74qYihEAIa2vMlsIbZmbBj5alDcd8kauAVkrJiYGp+NbdyYFksYdLE1yzZsZaqJA+S54EYUOVWy+rsKEUXL26kT0nBvnJGxf48LZ15R5OweSqg9oI3ItlEDsMfB8QUsp3z8PYKgJbnWZvVQEZcwtLnWx1S/Z2XqKTRFutl+Gp8EyeBOv+tc3VaU4HTkisya0Skv8PPnUo6/32asCUJPUu0oCIITN2hI3ElGamlI6rn0xIrLye7bYejproYsbaKBH72mvFPBsf20HJHzYy1iqFoyav943FgtIQQ1PJucV6n4ubuqyAdMO6Jmry3KJ065q1ZRcLSJXw2Sklyxt8LK/38aPXzi2+AAUcA34OvFdKeRJACPFgyUdVQSSq05AyXseyrFZXVkd5EjVJU/FNBiMZbX8mg4VZFkmsyV9QvtXT7mMD/Oj12Tl/2VNz6gVPldtShdXEvAZNmf9KxzClJduvcbOupZbjlybQNIGGIJQSDGyz2Lqq0rdnyOXqMO6P8HKvtUp65fRoWhPCVY1V8XzSlpUNeQUXXRPWll0sIFWCHdF8s7WjgX996xIXxgNJPccWArkC1H/AWkH9VAjxHFY+anFfcqTwwM6N9A5N8ePDF+JXm/VeDa/HlWQEqiiMixOZFWlOx7MhhKXgM2RxxBaF8vienjkFRhFTOdorqIGJIAOTISRWobiW8P7yQUpLsWe7rT++pyfeziNkzARyAVS7dVprPUyVqB9ULleHvhE/vzg1zP5TQxw9P5G0my6AK1bWW0Gpu5XVzVU5t+ASC2R9Hg2vS+10XL6ynn996xJ/+swxHrvvmnIPpyByiSR+CPxQCFGDZXH0ILBMCPEN4IdSykUvkth9bIBXz47j0bWYv5wgELW+/G5dKD++GNs6mxy3+QxTMhGMxotUH9i50XGynY3QwYwFpjXNVfSNBua9Dcdcui7rsRRnxJD4w1EmAxEGYttZdiAp9P2kysztjrPLG1xcGAsQib3gyoYq6qvc+MNR2uuK15wgm6uDYUreOj/BvlND7Ds1TN9oshTc59K4fp3lCn5zVzON1dlXdpVWIFuJtNf5aK318taFhSc3z2vjVko5DfwD8A9CiGbgN4CHWAIqPtvxwLY8EkKiCcHQVIjO1hrlxxfjyfu3OwolEvnLl07wwM6NWdWRAsjT0DyOBOqrPOjjQVyx25F5kvWtbqqmP2WizYUAfLFaqRq3ziN3X8Hje3roSejXNJvRuzQRtzrK1A9qPBBhMhilqdpNnS+W6ypCe/SIYeJ3cHUIhA1eOTPC/lPDHOgZYTzFeb6lxhP3urt2TVNOOfdiFzaUgstX1LP35CDjgUjB9V/lJJdIwgd8DFgPvIHVsHAEeDz236LHdjyAmSSzKSXRsMHwdKgoHVUXC0/evz3+c5rDdww7h+dzUOs5Hc8Xj64RCBsFB7i5cP9tXex3cAl3JKbgs4Ujdr+mTofzlg8uzfIitMnUDwpmTHrn2h49aphMx2ThqUFpcDLEgZgU/NDZ0bSLhe62mvjW3YZltWhZgsxSEzaUgstX1rPnxCC73xngnqtXlXs4eZNrBfUdIIIllLgLuBz4ZKkHVUmEo5Y+17bjSWQujtNLHafCy3zbbDtR63Xl7NpbbGYzucuYqvHurcuTnCXm9u4tUUXP4BRhw0TXBOuanS18ZvO7jLirQzSpvbyUklOD0+w7NcT+UyO8cylZialrgqs7GtjW3cr29S0szyIFV8KG4tPRVEW1R+dnxwcXVYC6XEp5JYAQ4lvAL0s/pMrCrQsCETIWDbbVemblOL0U8Lq0jE7wLg3u++aBeI1PKk4tLPJBSpm2fVTJGKbkpXcGi6YGrXZrjPgj8domu1g38fWzucg7jcHJaihimByOS8GHGZgMJT2vxqtzc6dVMHtDZzO1DlLwxVYgW4loQrChvZY9xxdWI8NcASr+bZdSRpfiXu/GZfX0Dk1xaSL5y6cJaxJWOajMfHxHd1J7cxuPS+e1s1lqpuagcHj74iRmmayO6n0uJmbRU2k8EOUPfvwm17/eyNNHLs5pDKuaqjk5MIm92+Z1aehaspAnk31Snc+VJvaRUjIdnqlVsoPSRCBidZk9Ocwrp0fSVqsrGnyxrbsWrlzV4OhX57VbUbh1fG4lbJgPNi6r43D/OG9dmGDLqoZyDycvcgWoq4QQtvRDAFWx2wLLCam+pKOrAGwFlFsXSVf3poT+0QAbl9WVcXSVRapIoqPBy0TIiPvOeVwaY/5I1hzCbMOLrsHmpsBDbQAAIABJREFU5XWcHJhKa6UwH9yxqS3e8r1Q+kYDaWq22TAwGQQh8OgiXthr2x3ZeaeXe4eTVIFRwyAYNYgaJlJKApGY1VDIiAf7c2MzXWaP9I+lScE3r6hje3cr27pbWNdSnTHYuHUtnkPyqTxSWVjfbuXLf3Z8cHEEKCnlkl9r79jUziPA/X9/MO0+Q8JkoDLcs8tNJgVf/3iIbZ1NcfHEZb//bKxrbPEnJ9OEdy5NopfhStwuRSgn1W6NiCExpcSQMw7xQoA/Ys501E25ArAKnWE8EOHMsB9TWq/x9oWJ+NbdmeHkXQKvS+O6tU0xKXgLzTXpUvCF5vy9FKjzudmyqp6fvTPIx9+9vtzDyYvKsjCuUHZsagch0JBJ6jANGJxeODmPUuIkLy+kdftcnCAklknqfO/w1Xn1+LZZOfFHTLzSCjY2tt1RKGLk7KgbMeHv9vYyNBVif88wo/7kz3VTtZubu6ytu+vWNqXliRKFDT7XwnP+Xiq8a2Mbf/OzHiaCEep9lS83VwEqT0xTpkmXTeaWM1lqdLZUc3JwGpFyzhJrguZSu2SbpOopE3Up8egaxy9NMDZLcUYx7ZkyiVI0YX1Oq9w6pimz/q7vHjiTdNvn0tA0waqGKv7LLeu4ubslfp8tbKh2u5RjwwLiXRvb+fpPT7Hv5BB3bllR7uHkRAWoPNEcXM0XihqmnNgdde/Y1MbQdNiy1UlYcLg0kRToC7H1SSQQMRBYk/JsRQuFMuyP4HVpsw6ID+7M3Aa+WJjSOp8TwQheXct5bq9e3cCqxmp+2TscU9RpBCMGf/nTk3hdGu/evEwJGxYw16xppM7r4mfHBxdEgFLr8DyJOCTenY4vNbZ1NjneZ/c+evrIRX5lfQvXrG6yFGYxOXTElERMkxqPlTyf7YpCxP9nGYvOF5lWLvlQ59XSuuuWgnqfi6GpMD3D/qzBacvKev7ig1dzbjSAz61T7XHFWse7qXJr7Dp0juYaDy/3DPOhv32ZW7/0Evd98wC7jw2U/D0oioNb19i+voU9x4fKPZS8UAEqT6TDxaLT8aXGk/dvzxikqtw6mtBwaRqagBePDfLkR2/m4zu6rXbwusDrEuiaYDJkcOPaxll56Qms5L1Hs67s51rwOx9Mh01u/dJLJf89owFLKp6t5TvAh7etpdbnYmAqSL3Phcel4dItqXq1x0X/qD9eRzUwGUyqo1JBamHwjy+fxa1rnBsL8Nc/PVnu4eREbfHli9N3u/LnwXnDVuvtPjbAf/72K4DVYkEXApduBajJYJT7vnmAQ2dH4ysoU1q5nPoqF/1j+buZJ+ZvXLrAkFabieW13nl3k5gNpoRLs3Bvnw2XLatjTXMV//a2cyD54A1rAFjbXMPAZJBqz8z1ayBi0NFUzeN7ejL2rlKmyQuHdS01AJweztxgs5JQK6g8cdpuV9vwyew+NsCndx2O3zaltYUXiBiEolY4GZgMYsQ6p5pYrtpdbbW01HgLMl3VNWG1otCsALdpeT1dbbW4dM3RtaDSMObJ0LbKLbIGp0Tuv60r7q4upUwylO0b9WfsXaUK1hcOyxt8+NwavUOV/zdTAUpRVL747NuM+SOOTcO8Lo1qjwuvy3Kh1rCc4cG6Srfk4rl/j1UgWs/qpmqkhOmwwbGLE5wYmGRwIsjgVCjna1QC85XBfL0/e6sFXRDfptuxqZ1H7r6C9jof44EI7XU+Hrn7CnZsamd1U3VaI0F7daVYGGhCsLa5htNDagW1aKj1ukgV7GmCBXOlPl/0DvvRhPPOpy0oaKvzIqXVnjxsmPGr9Cq3lteuqbVdaEm74910rRckEDVxKXVlQVS5NR7f05N0bMwfZmAyxKGzo3zx2bfZfWyA+2/rYiIQ4cSlSd6+MM6JS5NMBCJzbtehmF/WtFQzOBUquIP1fKMCVJ7csaktLXlvSuu4Ij9ELHD1DE5xbiwQD/iaEPGr9N99V3feIomBiSCXJoMIIVjbUs3mFQ1sWFaHKSWT8yAxX0xMhU3ePDcGWCupz+w6zImBKaSUSCk5OTjNp3cd5kj/mHUBIdIbIyoWDitjrd/fOl/ZTQxVgMqTixNhGqvSV1E/fP0CDz51qDyDqkC6WmsyBhhrhWP9HDasNuaGlBjSMpZ98qM3s2NTe96ya0NCMGoiJaxq9FGXUBXv1a3aHUVh2MKSx/f0MOYPY0hJxJBWmxkpmQpFeWJvLw1Vbja017FpeT0b2utoqHKnrb4Ulc3KRqvdyXf2n8nxyPKiAlSe9MWau2WyB1nMQeqxF46z9QvP0/35Z9j6hed57IXjWR//2Ts30VTtJtV6zRaT1Ho0PLoWV+7ZLUsSqfe5cOvCMY9l43VpXLumKc3nzefWZ1Xou9SxLyyOX5ogYhLbgrWOR6XVG206bBnL9gxOceziBD2DU0QNU4kkFhh1Pjf1Phfnx+ZuUlxKVIDKk9VN1ZwfCzha2sy1VUIl8tgLx/naSycJRIx4se3XXjqZNUjt2NTOlz9wFTeua6He50LXrEBT63VR73OxrrWWrrbauOKutdYbn9x2Hxvgvm8ewJT5WR5FTZkxJzJe4fvqlY5TR2ProkLQNxLAHzZiSj+DvpEANR5ldbTQWNlYpQLUYmF5vYcRv/PEl6sI0saehBdCFf4Te3vRBLEi25li2yf29ub1/PoqNzeua+b//OcbOPKF93DFygZHBZid93jt7CjBiJnXB9N2RU/NicyXD99CoBCpiK2eTO0cnUiNR8dkJu8ksZSIUyGV81torGysYnAyhD9cuX87JUHLkxePDeJgxweQV3+b3ccGeODJQ0yFjXg/qQM9w2xaXsdn79xUcYWO02Fr5ZSIJshaBJutY6vdW8sfjlLl1glEDCYCEdya4He+e5CoKdE1cOkaUrOCvtX+IfPv6mqt4fE9PTRUuVnRMGNt9Ma58ra+qCS0AnwN7d5mTudbEzAZMtAg3dV/SrWdWWisaqxCAkfPT3DDuuZyDycjagWVJ9NhI2teZENb5jqQxBXT/d97lYmQkTQBSKxOsJ/edbjiVlM1Hj2jcjHbdk6i04AQ1r9ufaar6weuXcXgZIi3L05yYTxIMGLEvPgsp+2oCcGISShqEjWdg5PXpfHZOzfRN+pPy4koZsg3FacLK38IONpESWmtrjK5+mdbdSkqk9XN1pz16pn8W+LMNypA5Yk9WTt9Dc+OplvWpPqWZTMVHfNH+OKzbxdptMXhI7d2Wgly08SUZuxf67gTmQKGnUTffWyAXYfO0VbnZfPyOkxTEoiYRA0Zr2XKB5cm6GypZsemduq8Ls6NBWOrL6EmylQkrG7KbpwrsEQs9greycZQ4txeZiF4HyqSqfW6aK31cPD0SO4HlwkVoPLEnqydyJRYTl1NZEMTVpFrJfHAzo188vb1VLl1oqZlafPJ29dnlYJnChjnxoLUel08vqeHiGFwcTzIO5cmCUWtNuN9MZFEvlNctVvEtxmllJimJBQ14ysvsFYEnnwsKRY5JtARkxQ7IYHEvYE6n/POv6MlpYpPC5K1LTUcPDNasX3tVIDKE3uyLoTU1cRCZGtHI1esbGBFg48rVjawtaMx6+PjV9Iy4b/Y8RMDkwxNhuPBC6z8SLaVaSYmQib+WFK+f8yf0S5IE3Dd2srcV59v8ulqnHj9lG2FDNa51WJGv/bP2YKaonJZ11LNmD/CyQrdGlcBqgAK7d1T69HpH52R5GbDMCVdrTVzGV7RmU1rhamwwapGX9xd3KULVjX6mA4bhKMmCEt9l7vKKTvDMUWlP+zQp8u0rHqUmW9+RE0Z/7vm+pwLYbVH8cTapAghcgY1RWXS1VYLwEsVlv+2UQGqhEyHDYw8VweGhLu2LC/5mAohm+DBidVN1Wm5tlDUpKOpGndsy800LfucYmwqZNuZ6B2ajkvRFc64NIFHT/biS1Vv2nhdGndvXY4pIRS1RCx3b10+L40XFcWnqdrD1o4Gnn3jQrmHkpGSBighxJ1CiHeEECeFEA9luP9/CCHeEkIcEUK8KIRYW8rxFAO3wxnLdLzQIrjvHqgs25HZtFbY1tXM4FQ4bmcUNkwGp8Js62pm47J6Wmo88dXVXMnV7C8UNSt2b30+yfUlN0xJvc+VVDDtpOcJRU1ePTvOupZqtqysZ11LNa+eHa84Baoif371yhUc7h/n5EDlbfOVLEAJIXTg68BdwOXAfUKIy1Me9hpwvZRyK7AL+LNSjadYNFR78j5eqN3OUIXVksymtcL+nhHa6zxJdkbtdZad0f23deFx6Sxv8LGyIXviPh8aq9JtpxKxkv+VjxDQ0eAt2euvyqHic+mCEX8k7syfy1ev0FW1orL5D9d14NYF//ByZV0gQ2lXUDcCJ6WUPVLKMPAUcE/iA6SUP5VS2pfjB4COEo6nKGxor6O52p3gxA3N1ZZ55mJjNq0V+kb9tNR4k+yMrEaE/qQ+Qxcn5t6vKZ9utAvBVEJKOF+E8+FE6kVGKlosjNsClxMDk1kfrxoWLh4+dNMaWmu93LVlBbsO9jOexS2nHJQyQK0C+hJu98eOOfHbwLOZ7hBCfFQIcVAIcXBwcLCIQyycbV3NjAej6JrAG0sSjwejbOtanIqxQlsr5Fp17djUzpMfvZm2urmvGBaTe0EpdyLD0ezWURHTpKnaFZfuh7PU61V7dNWwcB5InPMmx0pfp/S7O7qZDEX51t7KWgmXMkBl2l3J+DUUQvwWcD3w5Uz3Sym/KaW8Xkp5fVtbefsv7e8Zoa3Wk9ORezZUWo8920bIqbVCJl/BXKsu+zmDkwuj4+1iwK0Lqjw6LpH+GROAW9MY9UfjDiFuXaS50dt8TDUsnBcS57y6xtJf/G5eUc9dW5bzd784zZi/ci78Slm80A+sTrjdAZxPfZAQYifwe8C7pJQVP2v1jfrxujQSN0G8Lo2j58fZ+oXnmQ4b1Hj0gmW3GuB2mhVKzGMvHOeJvb1JY39g50b6Rv1peR57O8fJc+8D165yXHUlPmd5vZczI5XtpLwYaK1xs2FZPW9fGMcfTr9ClEDYNBGSeDH5xtjjJ4LR+MpOAy5bXsfWjkYkZ1TDwkXIp3Zu5LmjF/nbn/fwmfdsKvdwgNIGqFeADUKITuAccC/wocQHCCGuAR4H7pRSLggZUJ3XxYmBKXRNxJ0S+kYCmFhXnoltKQpBCOhsmf9tksdeOM5XXzwRn4gmglG++uIJwNquOz08xUQgStgw8ega9VUu1rXU8vieHsJRg+GpmfvqfC6e2NtLW503ybzVH47GV112gh1AEFCTW5ERzIhDNAGttV62dTXzy9Mj8ZqwNAGlBKHB4KSV07Mfr2sCd8wg2YyVQTy+pweXJtCFwECiC4FLm/FaVCxcLltex7+/cgXf/sVpfvvWLpprMgvC5pOSXbJLKaPAfweeB94GfiClPCqEeEQIcXfsYV8GaoF/EkK8LoR4ulTjKRaZnBLsHXvDlISj0mq9UaCM2pTw769cUcyh5sXf7OnBlDN+bCI2lr/Z08O2rmYujoeSev9cHA+xrauZ45cmGJ4OEzWsSSpqSIanw0wGo45J9ETZ+qTq2ZTGXHd43bqIByEhoLnGzXTYiCsrq9w6roQ9Pk1YfxufW0cTgnBMdmo/Hqxap4hhfab/6dV+x7/7iUsL0ylFkcyn7tiAP2Lwtz+vjFxUSf1JpJTPAM+kHHs44eedpfz9pcB2ShiK1fp4dI1QLGccj12yMPWYJqC9zsv+nhEeKPqIZ9h9bIDH9/TQN+pndVM199/WNeMhaM9bsUtwf9jgmTcupLUY0QQ888aFuDOGFpvwhLAKcDVNEIgY8VUSJCfRByaDVHtcXBwPqtUTCRcGRehjZRgSt64hhPUZHPVHWd/mjSsrW2staf9bFyZirUxAIq3PrQRPrDq3b9TPuD+S5n7SNxrA69IwTIkRe54dEMOqhfGC5B9fPpt27MpVDXzr5700VXvipQc2H7ppzXwNDVBOEgWzuqkal64lyahtUuzn8kZKkjrLlgIn2yI7Lkk58x9YE0/vsB9dE/jcevxKW9cEvcN+azKTYEqJxOrbRKwVh7XaiiKl9W/EsDrf3n9bV/y+YBal2FLC59bxupJXNnNCJPyHlSdKVVb6XFpcLBGMmEQMkzqfa6ZUQkr8kcx/n1DUjG/52e3grQ0DFaAWC7df1k7EMNl7oryKaVABqmASJ1l7AnbCrpHKNfdISi/VtXNGtpP4xfEg4ajhuK3UXJV9cb2hvY7WOg8uTWCYEpcmaK3zcMXKhnit03ggQnudj0fuvoJf2djG9Z3NPLhzQ/Em40WEvc06F9wuLenvsarRx1QomvaZ9bgEprQc3zOVSoxMZ99+Tf3zaWJGYKFY+LTX+9ja0cD+nuGyd0pWAapAEotN7QnY4zDhSmm1Vb5iZQNXrmrI+rrjJZTqPvbCcQ70DHNxImT5Axoynjtwmlfa66voaq2JXSHPrJJMaXWyvf+2Lty65Qpx2bI6ljf4cOt6/D1IKTGkJGKYXBgP8P1XzvK5fz7CHz59lHcuVZ6lSrkIRAwCEQPDlHQ0VRUUpGwhhEsTVudcUyat7F26RkdTddpn1jChscqFz61nLJUI5dhrjOcsmclZKhYXt29aRsSQvNw7XNZxKI/8WbBjU3uSYqnrcz/J+Dh7ZWTnY3wuzXFrK5yj2n+2PPbCcb720smkbUerA6qJpgkMCboGppmg/oopur7yG1fzmV2HmQxGiRomLk2jqdodb0//CNbKrH/UT0dTNf/1lnVMh6L88bNvgwTDNDl6fpxXz4yqfFMOdAG/cV0Hf/HCibyf44sJTqS0hAy6JvCHLZFKIGLEt1Yh+TN765deQhfJ1lpelxbfYvbqGn4z8+fR69KIGqa1lR3LQamr3MVHW52XrrYaXjs7xu2XtZdthawCVBHIdgVpb61UuXWaatxcmgilPd6lCfwRky8++3bRpbpP7O21rq5TjhsSNGlfhQs8CW63UdMkbEh2bGrnyx+4KikI3X9bV3yM9rZdIGwQCBv0DE3xh08fjQtIUqnx6PGVVSiqQlYiERN+8Ep6wjobppSWuEJKXLrGx3d0s79nJOPfKpFaj87JwWl0IeJKvHNjQda3We1emmvc+McyByiPLjClQEPExRgmUjWHXIRct6aJf3q1n77RAGuay+MUogJUiXnk7iviE/y6llruu2ENj754Aint7RkNXRMYplmSjrrTYQPNYf3SWudhaDIcFzvYE06ioivxyltKq3PtyHSYQMTAH4py9Pw4+04Ns+/UMP2jyYW3QlhByQpMEDFMxoOG2hJyoH88RL3PFd/3lzlatdhtTdy64OM7unlg58a8VKDxq+EEMQUy8bjAJSDxGkLDUmwKIWip8TAZTKx/c9PZWoticbFxmSWa6RmcUgFqIWMXR2Y6nrodCPD13aeQUqJrpd8c8eoa/gzbhwJw6zrr22sZC4STi3Fr3KxrsSaciGHiDxsEI9YqaSoU4ZXTo+w7NczLPcNMBJOTqG5N4HFpNFS5qfbquDSNUMRgWb2PI+fG4/kLFaMy85FbO/naSyfjnWrzWWlGDMkzb1xga0djXivwyVA0rVRieb03HhhXN1VzYTyAJ1aIbgdKXRN4dOuY3S7FkJKoKZXV0SKkxuuivc7L6eHpso1BBagikC1AZaKrtYbjlyaJmEY88aMBG5cV9yr0sReOZwxOYOWdHrn7CgA+s+swRqyJYNQwiURN7rthNX0jfn5xYojv7j9D35jfqo+KJfQTWd9Wy/buFravb2F0Osxf/vQkXl3Dq1s5N0PCx97Vze9892BR399iJlcH5lSOXZri07sO85UPXJU1SO0+NsBEIMJ0OIrPpbOqsYo6nxt/OEp7nVUndf9tXRw8M5JW2xSOmvhqPdbKzZaWy7n2RlZUMsvqfQX3tSsmKr9ZBJw0T07H79qyPJ5kTvy3mB11H3vheM6Euz2RTQbCBKMmURNChrTMQAem+F9PH+X3f/wmb12cYDIYZTIUxTAtB4Hr1zZx99YVXL68nslQhDfPj2Oakg/esIab1jVxdjTA0QuTnB72c92aBnZsakfTLCuobPlWgRU8Bc5dXRczj75wIu0CIF9Gp8N88dm3He/ffWyAz+w6TCAcxTCt7d++ET9DU8E0QUW9V097vgQGJ0PUV7nZsKyOzSsa2LCsjvoEA2HF4qKhys14IFK2Oje1gioDlpWMN2Uf31VUJ4mvvZg9OEVNq5/SQ/98mGDKIitswl/+NNlLUBNQ43HhcQlWNlTzges6+LPnjjEdihI1JeP+CH/49FH+7a2L/PjwhXieyTAlPz58gc7W43S2VHNycBq3JjJKmfWEnEhrrZuhqaVnhzSXacCQZM1jfum5YwxPhZMunAwJ44Eof/2hrUkrr/HUD0WMkCFVP6glRJVHj2/pusqg5FuC16jlp2/UT2ttclO/YjtJ5LNDdGEswMVJZ2t9PSZy8OjWNk7UNPG5dUb8Ib79i14r/yQEbpclwxj1R/jR6xccvf0eumszjdVuhDaTn9cEtNV6qPHoCCGo8bjY0FZLfZVH5almQbYW98cHpjKu6kNRM21b0F7F2TZMiXOT6ge19CjXRq4KUEWg2pO+HZLt+GxaqRdCtkkqkfd/Y1/W+9c0VREIG4QNiSEhEDG5OB6kzuuid9gfS+QLBAJNWMWi8d8skv/1hw12bGrnKx+4imtWN9FS68Hj0miv87Ks3me1gW+s4rF7r+GhuzYXnINRWGRr2eK0dZjpuMuWjdt32W03BI5WVorFR+KFSjlQAaoIXNXRSG3Knn2tV+eqjsaMj89klzSXL7mUkkDYYGQ6zLmxQN6qm2xxzCUsA9DUK25DEu+8WgipH3CfW6ertYbG2B63bYlkqx5tAcdSohhzQLWnOF/p7tYa4jEq9jnRBWxsr81oZaVabSxO1jRXU+dz8Vs3r513o1hQOaiicP9tXTz89FGW1YuMVfypZHJhcCqqdCIYiUm/IwbBiJmUxAxEjLyk3DVenelQ5mBjShhw6Hp7cTxId1sNJwenEaZVPxUxzKRtxdScqpBw56M/Y3g6TH2Vm8Yqd/w8/e97tqS996U44fncWtyMNe0+l8b69lrePO/c1kIAG5bVF2UsD921mU/vOsyULYzRBLVeFw/dtTlj6YRicTIeiNBY7c79wBKhAlQRmE3AKfRLHo6asWBk1SOZKRFgeCrE/h6rYDZfa6FglpWQyYxDdeLqR0qISpk0gYUTJlWNzOpFE0sKDTAZjLK8wReXN6tmdxa2M3gmDCkZD2QXjUiIG77OFXs7di4XUYqFz5g/TEOVClALnmJfVUYNM24kGgybRM3kaV9KSe/QdNzF4djFSYdXyowtULBXPalBSGBt6URl+mpIF8kT2IGe4Zgs3HqRXDmwYNTk9LAfn0tjWX1p24wsJEJZWpBUuXV+/tnb2fD5n+DQCQOwenU9sHNjxvtcmqXezHQ8E2qlpBgPRFSAUljJSHvLLhA2iGSQYUcNkyP9M9ZCFyeCSffXeHRu7Gxme3crf/yMcz0M2Cq7hI3AlJjS0VRFjUfn+KWpeI8rW3m3PtYDy57Auj//DC4NNKERihrx18+1igtGTc6MBHBpcN83D6Rdoac2S1zK2F53Zo4zm01m7mRSPtdGiYrFS8SQNGQR3pQaFaDKhJSSYGRmlRRycHyYCkZ5uXeEfaeG+OXpkbSc0fJ6n+Xi0N3ClR0NcRXXnzzzdtYAYWJN/h0NXibDBhOBaDwINVS5+N/3bAFwzEMkUuOx8m6amFltFRJXoia8dnaUz+w6zJcTnBB8Lt3RCWOp0RZzechVMJltFeb0THUNoHDC/t6XCxWg5pFswoZELowH4qukI/3jaTLgTcvr4kGps7UmyQrfqiXKLG9PpbHazR+9bytH+sd4Ym8v02GDGo/Ob9/SOeNYvr6Fp49cjLcI/5X1LWnbPrZ/XNQ0Zz3ZBaMmwakw//MHr/Pqw/8XANVeFaBsJgNWvVqt15Xmf5gvHk0QzrAkdepnplBUe5yFVPOBClAlJBS18ke2uCFV2GBjSsk7FyfjQal3KFkm7nFpXLumke3drWzraqal1pt0vxCWerDGq1PjcaHlOeF85QNXAbDr0Dna6rysiSkQdx06x9aORo70j/H0kYuW63qsC+vTRy7S2XqcB3ZuZPexAR7f00PfqJ+VDT6GpkLx+iU91tm1UIb9ETZ8/ieYCNVGPIHBWJfbOza18cPXL8zqNeqr3Uk9oBKPKxRAmpR8z/FBTg2Wr8GoClBFJBITNgTD1gopVdiQSChicOjsGPtODbO/Z5iR6eSJo7HKzc1dLdyyvoVr1zal2cuAZUNS43VR43GlLcNzTe2rm6rYsamd+755ALcu4k0Vqz2uuLLu6PnxeEsQsHJCUdPkib29bO1o5OGnj+LWRVwy3lbnYzoUodbrYmgqPKt6KSAmAlDBKRMXJ5ydP3Ix7s+sAnQ6rlCsaqpi9/EBq2FpGXJRKkDNAcOUcVFDMJJZ2JDIyHSYl2NS8INnRtPyBWtbqtkWC0qbltdn3PvNFpTyJTHH1DfqpzFFpWN7q02HjTSFlyasQt3H9/RkDGwRw2qe19VWyxvnxmc1PkU6Xa1WM8G+HIrHugwmrzbRLGUDCkUmrlzVQDBicmJgis0rilNjVwgqQBWAaUqCUSsgBSIG4SwJabAS2mdG/Ow7aQWlty9MJK0LNAFbOxrY1tXC9u5WVjVVZXwdn9sOSnpRrmK+9pvXxPNIq5uqGZgMxgMNzNgujQcicfGDjSktUURqYJsMRhiYCBKMmoSiJs01atuoWFS7NT575ybA6oabjZ2bnWXh2coGFIpMXL3acsM5eHpEBahKw+4gawekUNRZ2GBjmJI3zo2z79QQ+04Nc34sWQpe7dG5fl0Tt3S3cmNns2ONgcelUet1Uet1FX1p/fs/fpPVe6zCS9sFw25Ln+iCcaR/LC5+sCXfprREEft7RugdmmIslfChAAAcUElEQVQyGCUYNTFNGVcBmqaZMdehmB2JK2WRwxTtxWODjvdtaK/jnYuTyJjaUgjL4WNDe13RxqpYXKxtqWZNczW73xnkP25bN++/XwWoFPIVNiQyHYryyukRq8ts7wiTKSqr9jqvtUpa38JVHY3xduqpuHWNmlhQcnpMvlS5NQIOFZ2NVW4GJoM8/PRRHrn7iqS29ImOAfYqK1Hh95FbO61C0BeO88vTI1axb4IYQgCaps26p5HCws45mqYkYsq428ZkKOpYcAvZfRI/e+cmPrPrMJPBKFHTxKVZbV7s1Vmi6GW1co5QYF0QvfuyNr5/sI9gxMCXIRdeSpZ8gEq0EApm6BbrxMWJIPtjqrvDfWNEU563ob2Wbd0t3NLdwvr2WscrX5emWeo7r6uof/zffVc3j8YaFiaObFmdFyFEkhjiyY/e7DgRPbBzY0ZnAqunlYeJQJSIkTApxhzOpVABai4EIwZCxLbfTBF321jdVE3/qHOH02wlBjs2tfNlB/ui3ccGkkQv8QsYlqYv4lLlH18+m3ZMCEEwYvLFZ4+xcVndvJrGLrkAlai0CxQQkKSUnBiYiueTTqZIL9264JrVjWzrtvJJbXVeh1eytmzslVKprkjsoGKvfkxTUu/VmQpFGbk4gUfXaK31zNpmqG/UT0uNl9ZaH2/GxBB2d2DF3LHPpfXxlPFWLPff1sX+nmHH533k1s6sr+tkX+QkelE+iYrO1hrcuuCdS5NsXDa/28GLPkDZnnbBiJmX0i6RcNTktb5RSwp+ajgtr1Lvc3FzVwvbulu4YV1TktAgFV2zvvy1XhdVeRbSzpWtHY1csbKBvlE/w1MhpsIGLk1DF4KoITk3FmR9W82sXjtRXOF1aQRje05CWNtSaoevyEjr/O7Y1E5Ho49zY8E0IX5LtdvRhy8X2dSciqWNW9dY2VjF+THnlXupWHQByrSl31k87bIx7o9woNdaJb1yeoRgSh6no6mK7d1WUNqysiGr1FsTgmqvbgUlt54zwV1M0rZsJoIYEjQkuj0OmTvp7kSiuMIyfA3MGM9q0OjNXBSqmB2/PDMW//mPfv3KjLmkL8cKr2fD6qbquOglbJh4dOs1O1trizF8xQJneb2Pw/1j8148v+ADVKLSzh+Tfhd6EvtG/Pzi1DD7Tw1x9PxE0tW/JuCKlfVs625le3cLa5qzd721rYZqvC6qPfMblBJ5fE8P4ajB8JQ14UQNicaMoMGjayyv9zIVmp1tTmqLkZWNVQxNhQgbEo+u8eGb1/IXsRxYJpQRbGHYfzdbyCCJKT11nQ3L6ucsaNjW1Zy0dRgxDKbDBh+6MXu+QQkrlgbVHhehyOytzGbLggxQwbiowdq+KzQgGabk6PkZV/DUpLPPrXHDuma2d7dwU2czjdWerK9niQ5iQcmt5201VEqOX5pgIhhFQ1hbeljdcV1CsGm5Vc/gD0dpj5mQzgY7n2Gv1lY2VsWl6rsOncv63BqPzmQZPb4WGpqwgsFH//4g4YTOkB5dpAWFx144zjd+dopAxPI+72iqihdmOwWT7+zrzfh7v7Ov13HbcPexAT75/dfiRsPnRgO8dWE8qc5OsTgIRKL43DraPF9wL7gAFY6as9oL9YejHDxt5ZMO9AynGW621HrYHpOCX7O6KafMe7b+d/OF7Ylnj8ulCSKmxDCl1SI+R9ffQkhNsBumZCClFUgq5VpZLlSq3RqfePJQUnACCBuSTzx5iDf+151sefhZpsIpfcOAvtEA93/vVby6IGxIoqbJ0GQoyT1+2J95Je10HOAPfvwm44GZ+yUwHojyBz9+k59vun3W71VRefSPBmgqg2fjggtQhTA4OdNl9rWzo/FJ22Z9Wy3buq3+SRuW1eZ1dVAMq6H5wOPS4p13hbAClWZKdF0wHogU1CHVaRvHPv7L0yNWs0IpiUriRbvZUEawhRExiffaSmUyZGQMTomEoiaJu7lR03KP/9Jzx2a92ulzkLs7HVcsTPpG/PSPBnjv1hVAZil6vhQqUV9UAUpKyanB6biLw/FLyVJwlya4anUjt8REDsvq89vesq2Gar2VHZQS2dBex+nhKSYCM0nv6moXhlmYDetjLxzn0RdOxJ/TPxrgcN8oH3tXN7sOncOtC1wiex+iTKSuBGx0TVjWSlJm7RyrSCZbcMrG2wV2YlYsLaKGyTNvXMDr0rhmTdO8//4FH6DCUZPD/WNxKfjAZCjp/lqvi5s6m7llfQs3rGumxpvfW/a4NOq8bmq8xfG/m29sld3yBktBODQVYnAqTHudJ+9CzPse38f+3tG04/6IyV++dJI1LdVUe1zJnXnzJOIQ0FxCEjVnBBSFv/LipKu1RgUTxbwipeTHh89zZsTPvTesnncXCVigAWoiEIl1mbWk4P4Ue5cVDVaX2VvWt7JlZX3eAcatW/53NUWwGio3qSo7f9igvc5Da621asxViPngU4cyBiebiCnjdjyGlHh0QcTIvbVn43S9n6qbUMEJ6r06n71zE//526+UeyiKJYIpJc+9eZFXz4zy7sva2NrRWJZxLLgAdXbEz/u/sS9JoiyAzSvq2N7dyvb1Laxtrs47CV8qq6FKINE14NYvvVRQIWY+TfECEYNqjwuPrhE1rSAVKrDubCmyuqkqa55m8/I6emJNKztbqnnors0lV8XV+zJ36q33LbgpQjFHghGD77/SxzuXJrm5q4U7Ni8r21gW3KcvEDEwJXhdGtetbeKW7hZu6mqhuSa7FDyRcrg6lINEccNEIIJhmvEVFMy01ZgtEUPiD0djlkkzhbqKzGgCWmo8/O97tmRdDT37qdvmcVQWV6xs4J2LE4wFIpjSGmtjlZvLls9/iwVF+Tg/FuD7B/sYngpxz9UruamzpazjWXABqqHKzR//+hauXdOIt4AVjxarVar1zb+rQzlIdZKIGiYDk5azQ0uNtygyc9sF/cTApKUSVMIGR3QNNrTVxldDOpBJk1euyyU7Z9lU40lru6JYeBSqlgtHTf7qpRN842enaK7x8L2P3MT27tYSjS5/FlyAWl7vY1t3flG9UlwdykFqbVJbrCB3OmTg0gqTmTthbyHe980DcV++xdJFV4hYb6sirQq/9eEbks71J3duyOi08cmdGxxfo6XalbEuyel4PmzrtJRZqTnLfD4fGplzibPN3ipXivLwet8YD/3zEY5dnOT916zi4fdentOcYL5YcAEqF5Xo6lAOMpl/ttZ6GQ9E+Plni1NE+dgLx3lib6/lWBHbvlos/J//ZAWUzod+MmehhltLV0qmus0n9dpy4NWH38N1jzyfFIxaql28+vB7WPfQT7KOQQCXLavh2KXp+LFtnU08ef/2+O1Up/PHXjjOA0+95ji+y5bXZVQWXra8cMfr3ccGkvwFUwuJFcVnZDrMnz13jKde6aO9zssTH76enZeXL9+UiUURoCrd1aEcrG6qTquD8rgEhmkJJnJdoeaSd+sQ7zcF1kpjsMzmsG6Nom0x2udlVaOP/rHMrhhaQmdasFSgmerBPnF75lWRU68tmAn+qcHh1YffU/B7aa318JUCJ/rHXjjOV188EV9BTgSjfPXFE/FxQ+4GiIXwpeeOMTIdxpTW584wTSLTcyskVmTGMCX/+MuzfOX5d5gKRfnIrZ18cucG6nzz7xSRiwWtpa7y6LTWeVnTXM3yBh91PrcKTjG2dTVzcTyEP2zlEqbDBqP+KBPBKP2jAV7uHeaBJw+x+9hAxuc/mGWrCUDXhdWzqIAxCZH7A+fK8ufL9qfdtKyWE3/yq3mPJd+C6z/69Sup9+rx360Jy/9OxF7D4xLomkAIwcd3dPM/dm6g3mcVdNf7XPyPnRsKboFhB4eJYBTDlPHg8NgLxx2f43ReNeDg7/+7gif5v9nTk7a9aUrruI3dAPGaNU2saKjimjVNs17xnByYwpAznycJGNI6rigeh86Ocs/X9/IHP3qTzSvqePaTv8Lv/9rlFRmcYAGuoIQQtNR6qfEszALa+eKZNyyZuFMAMSVMhAx+/0dvsPehO9Luf2Dnxqxu5E5OEKkI7DbwxK+wvS4t46pEE/DAHRv425/3WE0WY2qyGo/O7/xKF/t7RjgxMJnWxsOtwUN3bQYsdWc2VwtNWJ+hT96+nu/s63XM6djs2NTOY/ddm5abOdI/5rg9N9ueTDbZgoPTa3/KIad1U2cTW7/wfN7biDaptYVOx50aIBZKxCHZ53RcURjDUyG+9NwxfnCwn2X1Xh677xreu3VFxeflF1yAcuuChqrKjPaVxMnBqbxWN+cctq+KQUeDlz9639a0yR3g07sOM+4PE41ZL7l1wSfevZ4Hdm5ka0djxmT9A1gFxKk1WhETfvx6Pzs2tWPkqMMyJXg1yf6ekaw5nUQyTcI7NrXPORA5kW9wSCRTTuuKFXW8fHo06zbdbJhNwFOUByklPzjYx588c4zpUJT7b+viE3dsoDZPR51yszBGqSiYfK3xSnl92j8ecrzC/soHrnJUjGW7Kv/x4cwFxD8+fIFH74VoHm8oZMBrZ0cAZpXTKTdO+anUnNblDz9X8EosH+yC3mIFPEVpODU4xef/5Q1e7h3hxnXN/PH7trBhnlu2zxUVoBSzwqXlHwQzMdutIacdn0J3goL5RLISkepxmKqmy0Y+4gWb2azECqUYAU9RXEwpefxnp/jzfz2Oz63xxfdfyQevX70g8/MqibPEyfYBqHYohK5262xor0NjRsGmyI9MBrz7e0e57/F9eT0/H/HCfFPMgKeYG2P+MN/a28ufPnuM2ze188L/fBf33rhmQQYnUAFq0VLlzu9Pm61m5WPv6kpTzmnCOv7ZOzfRUuvBm0WokucQlhROBrzZjHkTmY9VkWJh8tb5CR576QTnxgL82Qe28o3funZOHbMrgZJOIUKIO4UQ7wghTgohHspwv1cI8f3Y/S8LIdaVcjxLid99V3dcQZeIrlnbcz6XRlutJ2vNygM7N/KpO5Jl05+6w5JNJ0qMnQxFnep/5sJmh4DqdNyJljJ0B1U4U+vJPBU5HVfMIKXkp+8M8L2Xz9Ba6+UT717PB69fXfEKvXwoWQ5KCKEDXwf+HdAPvCKEeFpK+VbCw34bGJVSrhdC3At8CfjNUo1pKZFJ1XXHpjYuToTztrKxX8cpv5CYR3JK3BebXMWh2zqbcq5GqlyCP//g1UUfm2L2/NWHruN3vvtKUqG1W7OOK5yJGia7DvVzpH+cq1c38r5rVuFeROU3pRRJ3AiclFL2AAghngLuARID1D3AF2I/7wL+SgghpOoHXhSyBZeF+rvslZuTAvDJ+7en5Xk2LauhqcZXUGAuJ++7ekXGdidOxxcDOza187cfvqEgL8ClQnONJ6P5ayhq8LvfO8SR/nE+857L+G87uhfFqimRUgaoVUBfwu1+4Canx0gpo0KIcaAFGCrhuBQLnFwKwHwVcZXKo/deCxzi6SMXMUyJrgnu3rqcR++9lh++nt1zL5Fsga6YJBY2z4ViFf0uBUJRg//2vUO8dGyAP3nflQW7ly8UShmgMoXy1JVRPo9BCPFR4KMAa9Yszj+EQpHIo/dey6P3ph/vaPDSPx7KeDzTazgFulw4BTeXSK41y1TYrJg72eY8KSWf/5c3efHYAH/8vi2LNjgBiFLtpgkhtgFfkFK+J3b7cwBSyj9NeMzzscfsF0K4gItAW7Ytvuuvv14ePHiwJGNWKOaDTM7jp7+Yv4/grX/6QlKQ6mjwsvdzO4sytkQefGp2wU2RN3ntx6XOed/Zd5o/fPoon9q5gU8t3PqzvN57KQOUCzgO3AGcA14BPiSlPJrwmI8DV0opPxYTSbxfSvnBbK+rApRCoVgkFByg3ugf531//Qt2XNbON//jdQu2vok833vJtvhiOaX/DjyP1Z3h76SUR4UQjwAHpZRPA98C/l4IcRIYATJsaigUCoXCMCW/96M3aKrx8OcfvGohB6e8KanVkZTyGeCZlGMPJ/wcBH6jlGNQKBSKxcBTr5zlSP84X7v36iVjmL14BPMKhUKxSIkYJn/901Ncv7aJu69aWe7hzBsqQCkUCkWF88wbFzg3FuBj71p8tU7ZUAFKoVAoKpwfHOxjXUs1ty+xOjEVoBQKhaKCiZqSAz0j/OrWFUtCGJGIClAKhUJRwUwFoxim5D1XLC/3UOYdFaAUCoWigvGHo9R4dK5Y2VDuocw7KkApFApFBeMPG2ztaERfYtt7oAKUQqFQVDShqMnGZbXlHkZZUAFKoVAoKhhTSta01JR7GGVBBSiFQqGocFY0LOzW7bNFBSiFQqGocJprPOUeQllQAUqhUCgqnHrf0vDeS0UFKIVCoahwvO6lOVUvzXetUCgUCwiPvjSn6qX5rhUKhWIB4XUtzal6ab5rhUKhWEB4VIBSKBQKRSWiApRCoVAoKhKVg1IoFApFxdFW58WlApRCoVAoKo3l9UvTRQJUgFIoFApFhaIClEKhUCgqEhWgFAqFQlGRqAClUCgUiopEBSiFQqFQVCQqQCkUCoWiIlEBSqFQKBQViQpQCoVCoahIVIBSKBQKRUWiApRCoVAoKhIVoBQKhUJRkagApVAoFIqKREgpyz2GghBCDAJn8nx4KzBUwuGUCjXu+UWNe35R47YYklLemetBQojn8nncYmTBBahCEEIclFJeX+5xFIoa9/yixj2/qHEr8kVt8SkUCoWiIlEBSqFQKBQVyWIPUN8s9wBmiRr3/KLGPb+ocSvyYlHnoBQKhUKxcFnsKyiFQqFQLFBUgFIoFApFRbIoApQQYrUQ4qdCiLeFEEeFEJ+MHW8WQvybEOJE7N+mco81E0IIXQjxmhDi/4vd7hRCvBwb9/eFEJ5yjzEVIUSjEGKXEOJY7LxvWwjnWwjxYOwz8qYQ4kkhhK8Sz7cQ4u+EEANCiDcTjmU8v8LiMSHESSHEESHEtRU27i/HPidHhBA/FEI0Jtz3udi43xFCvKc8o8487oT7Pi2EkEKI1tjtijnfi51FEaCAKPA/pZSbgZuBjwshLgceAl6UUm4AXozdrkQ+CbydcPtLwKOxcY8Cv12WUWXna8BzUspNwFVY46/o8y2EWAU8AFwvpdwC6MC9VOb5/jaQWpzpdH7vAjbE/vso8I15GmMmvk36uP8N2CKl3AocBz4HEPuO3gtcEXvOXwsh9PkbahLfJn3cCCFWA/8OOJtwuJLO96JmUQQoKeUFKeWh2M+TWJPlKuAe4Duxh30H+PXyjNAZIUQH8KvAE7HbArgd2BV7SMWNWwhRD9wGfAtAShmWUo6xAM434AKqhBAuoBq4QAWebynlHmAk5bDT+b0H+K60OAA0CiFWzM9Ik8k0binlv0opo7GbB4CO2M/3AE9JKUNSyl7gJHDjvA02eYyZzjfAo8D/AySqySrmfC92FkWASkQIsQ64BngZWCalvABWEAPayzcyR76K9QUwY7dbgLGEL3Q/VrCtJLqAQeD/xLYmnxBC1FDh51tKeQ74CtbV8AVgHHiVyj/fNk7ndxXQl/C4Sn4P/xV4NvZzRY9bCHE3cE5KeTjlrooe92JiUQUoIUQt8M/Ap6SUE+UeTy6EEL8GDEgpX008nOGhlVYL4AKuBb4hpbwGmKbCtvMyEcvZ3AN0AiuBGqztmlQq7XznYiF8ZhBC/B7Wdvw/2IcyPKwixi2EqAZ+D3g4090ZjlXEuBcbiyZACSHcWMHpH6SU/xI7fMleesf+HSjX+By4BbhbCHEaeAprq+mrWFsGrthjOoDz5RmeI/1Av5Ty5djtXVgBq9LP906gV0o5KKWMAP8CbKfyz7eN0/ntB1YnPK7i3oMQ4j8Bvwb833Km+LKSx92NdSFzOPb97AAOCSGWU9njXlQsigAVy9t8C3hbSvkXCXc9/f+3d6chWlVxHMe/vzAGRREXJKRSs7JVJxMzshLbfeGbXBFcMMEWohUrzSWwqN6FkiBIYKSipElIYWNqWSqV45JtStJGlBhi2Ib8enHO5NM4+zw5d6b/Bx7mcu659/7vYZ7ncO65nD8wLW9PA94427E1xPaTts+33Z80WbzF9hTgXWBcrlbEuH8EvpU0KBfdAhyk4O1NerQ3QlKX/D9TE3eh27tEfe27EZia3y4bARyveRRYBJLuBOYAY22fLNm1EZgkqULSANJLB7vbIsbabO+33cd2//z9/A4Ymv/3C93eHYrtdv8BRpKG2PuA6vwZQ5rPqQK+yn97tnWsDdzDKODNvH0R6Yt6CFgLVLR1fHXEWwl8lNt8A9CjPbQ3sAj4HDgArAQqitjewCrSPNlfpB/HmfW1L+mR01LgMLCf9JZikeI+RJqzqfluLiupPzfH/QVwV5HirrX/CNC7aO3d0T+x1FEIIYRC6hCP+EIIIXQ80UGFEEIopOigQgghFFJ0UCGEEAopOqgQQgiFFB1UCCGEQooOKhSKpEpJY1p4bIWkdyRVS5pYT52tkobl7U2lqR/qqNtX0rr69rcwxtmSpjbzmH9iDuH/pFPjVUI4qyqBYcCmFhx7DXCu7cqmVLbdYEdo+wdOrzBRFraXlfN8IXRkMYIKZSdpak7ktlfSSkn9JFXlsipJF+Z645USB+6VtD0nCnwGmNjIKKinpA35fDslDZbUB3gVqMzHDmxCnEck9Zb0vKT7SsoXSnpUUv+aBHaSpkt6XdJbSgkDXyipP1PSl3mks1zSkgauuVDSY3l7a7727nz8jbm8s6TV+f7WAJ1Ljr9d0oeSPpG0VlJXSd2VEv4NynVWSZrV2P2HUHTRQYWyknQlafma0baHkJIxLiHlzxlMWsn6pVx9PnBHrjfW9p+5bI3tSttr6rnMImBPPt9T+dw/AfcA7+VjDzcj7NVAaWc4gbTkUW2Vud7VpE70Akl9gadJiTJvAy5rxnUBOtkeDjwELMhl9wIn8/0tBq4FUMroOg+41fZQ0lJTj9g+DjwAvCJpEtDD9vJmxhFC4UQHFcptNLDO9lEA28eA64HX8v6VpLUTAXaQflRnkbLbNtXIfB5sbwF6Sere0oBt7wH65DmnIcAvtr+po2qV7eO2fyctMtuPlGBvm+1jTiuk19WxNaRm5f2Pgf55+ybSaBDb+0jrHULqBK8AdkiqJi0Y2y/X20xaF24pqaMOod2LOahQbqLx3DgGsD1b0nWkjMLVkpo0d8R/k49nHWm+6TzSiKouf5RsnyJ9f+qKpTlqzllzvhp13Y+AzbYnn7FDOge4HPgN6Ela8DSEdi1GUKHcqoAJknpBmi8CPiClEwGYAryf9w20vcv2fOAoKcfOCaBbI9fYns+DpFHAUbc+QeXqHOM4Tqd/b4rdwM2SeuScUne3Mg749/1dBQzO5TuBGyRdnPd1kXRp3vcw8BkwGVihlB8thHYtRlChrGx/KmkxsE3SKWAP8CDpR/NxUqr4Gbn6i5IuIY0MqoC9pJxNT+RHWM/VMw+1kJRufh9wktM5klobdzdSiu8m5/ax/b2kZ4FdpKR1B0mp5FvjZU7fXzU5R5LtnyVNB1ZJqsh150mC9FhvuO0TkraT5qoWnHHmENqRSLcRQitJ6mr71zyCWg+ssL2+reMKob2LR3whtN7CPOI7AHxNSuAYQmilGEGFwpI0g/Saeqkdtu9vwrHrgQG1iufYfrtc8TVy/bnA+FrFa20vPhvXD6EjiA4qhBBCIcUjvhBCCIUUHVQIIYRCig4qhBBCIUUHFUIIoZD+Bg4LwA6D7k/sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='cost_of_living_index' , y = 'PASSENGERS' , data=df1, kind = 'reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precipitation Vs Passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1c22c70908>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='precipitation' , y = 'PASSENGERS' , data = df1 , kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c24378828>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df0.corr() , annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check categrical variables be considered as continous or categirical?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------- city_ascii -----------------------------\n",
      "['levels for the variable city_ascii:', 'Portland', 'London', 'Barcelona', 'Los Angeles', 'Salt Lake City', 'Croatia', 'Philadelphia', 'Chicago', 'Miami', 'New York City', 'San Francisco', 'Seoul', 'Singapore', 'Amsterdam', 'St. Louis', 'Reykjavik', 'Paris', 'Atlanta', 'Delhi', 'Taipei', 'Orlando', 'Phuket', 'Minneapolis', 'Seattle', 'Honolulu', 'Tampa', 'Boston', 'Denver', 'Hong Kong', 'Dubai', 'Rome', 'Tokyo', 'Detroit', 'Athens', 'Phoenix', 'Charlotte', 'Houston', 'Berlin', 'San Diego', 'Las Vegas', 'Washington', 'Dallas']\n",
      "['# of levels for the variable city_ascii:', 216, 216, 216, 216, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108]\n",
      "['# of categories for the variable city_ascii:', 42]\n",
      "-------------------------------------------------\n",
      "-------------------- city -----------------------------\n",
      "['levels for the variable city:', 'Portland', 'London', 'Barcelona', 'Los Angeles', 'Salt Lake City', 'Croatia', 'Philadelphia', 'Chicago', 'Miami', 'New York City', 'San Francisco', 'Seoul', 'Singapore', 'Amsterdam', 'St. Louis', 'Reykjavik', 'Paris', 'Atlanta', 'Delhi', 'Taipei', 'Orlando', 'Phuket', 'Minneapolis', 'Seattle', 'Honolulu', 'Tampa', 'Boston', 'Denver', 'Hong Kong', 'Dubai', 'Rome', 'Tokyo', 'Detroit', 'Athens', 'Phoenix', 'Charlotte', 'Houston', 'Berlin', 'San Diego', 'Las Vegas', 'Washington', 'Dallas']\n",
      "['# of levels for the variable city:', 216, 216, 216, 216, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108]\n",
      "['# of categories for the variable city:', 42]\n",
      "-------------------------------------------------\n",
      "-------------------- country -----------------------------\n",
      "['levels for the variable country:', 'United States', 'Spain', 'Greece', 'Japan', 'Italy', 'Korea, South', 'Singapore', 'Croatia', 'India', 'Canada', 'Iceland', 'Taiwan', 'France', 'Germany', 'Chile', 'United Arab Emirates', 'Netherlands', 'Hong Kong', 'United Kingdom', 'Thailand', 'Venezuela']\n",
      "['# of levels for the variable country:', 2808, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108]\n",
      "['# of categories for the variable country:', 21]\n",
      "-------------------------------------------------\n",
      "-------------------- iso3 -----------------------------\n",
      "['levels for the variable iso3:', 'USA', 'THA', 'ISL', 'ITA', 'JPN', 'NLD', 'CHL', 'CAN', 'IND', 'SGP', 'ESP', 'TWN', 'FRA', 'DEU', 'KOR', 'GRC', 'GBR', 'ARE', 'HRV', 'VEN', 'HKG']\n",
      "['# of levels for the variable iso3:', 2808, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108]\n",
      "['# of categories for the variable iso3:', 21]\n",
      "-------------------------------------------------\n",
      "-------------------- iso2 -----------------------------\n",
      "['levels for the variable iso2:', 'US', 'GB', 'CA', 'GR', 'JP', 'HR', 'TH', 'IN', 'IT', 'CL', 'HK', 'ES', 'NL', 'FR', 'TW', 'KR', 'DE', 'SG', 'VE', 'IS', 'AE']\n",
      "['# of levels for the variable iso2:', 2808, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108]\n",
      "['# of categories for the variable iso2:', 21]\n",
      "-------------------------------------------------\n",
      "-------------------- Type -----------------------------\n",
      "['levels for the variable Type:', 'Domestic', 'International']\n",
      "['# of levels for the variable Type:', 2916, 2052]\n",
      "['# of categories for the variable Type:', 2]\n",
      "-------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "x = ['city_ascii', 'city', 'country', 'iso3', 'iso2', 'Type']\n",
    "\n",
    "for j in range(len(x)):\n",
    "    field = x[j]\n",
    "    print ('-------------------- ' + field + ' -----------------------------')\n",
    "    z = ['levels for the variable '+ field + ':']\n",
    "    s = df1[field].value_counts()\n",
    "    ind = list(s.index)\n",
    "    z.extend(ind)\n",
    "    print (z)\n",
    "    \n",
    "    z = ['# of levels for the variable '+ field + ':']\n",
    "    s = list(df1[field].value_counts())\n",
    "    z.extend(s)\n",
    "    print (z)\n",
    "    \n",
    "    z = ['# of categories for the variable '+ field + ':']\n",
    "    z.extend([len(s)])\n",
    "    print (z)\n",
    "    print ('-------------------------------------------------')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check for Numerical Variables and Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iso2', 'Type', 'city', 'city_ascii', 'iso3', 'country']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = list(set(df1.columns) - set(df1._get_numeric_data().columns))\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['field1',\n",
       " 'ds',\n",
       " 'PASSENGERS',\n",
       " 'temperature_mean',\n",
       " 'precipitation',\n",
       " 'rain_days',\n",
       " 'pressure',\n",
       " 'cost_of_living_index',\n",
       " 'population',\n",
       " 'lat',\n",
       " 'lng']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = list(df1._get_numeric_data().columns)\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Plot of Correlation Matrics Using Heatmap and Check for Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'PASSENGERS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2406bd30>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 15\n",
    "cols = df1[num_cols].corr().nlargest(k , target)[target].index\n",
    "cm = df1[cols].corr()\n",
    "plt.figure(figsize = (10 ,6))\n",
    "sns.heatmap(cm , annot = True , cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop('field1' ,1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4968 entries, 0 to 4967\n",
      "Data columns (total 16 columns):\n",
      "ds                      4968 non-null int64\n",
      "city                    4968 non-null object\n",
      "PASSENGERS              4968 non-null int64\n",
      "Type                    4968 non-null object\n",
      "temperature_mean        4968 non-null float64\n",
      "precipitation           4968 non-null float64\n",
      "rain_days               4968 non-null float64\n",
      "pressure                4968 non-null float64\n",
      "cost_of_living_index    4968 non-null float64\n",
      "population              4968 non-null float64\n",
      "city_ascii              4968 non-null object\n",
      "lat                     4968 non-null float64\n",
      "lng                     4968 non-null float64\n",
      "country                 4968 non-null object\n",
      "iso2                    4968 non-null object\n",
      "iso3                    4968 non-null object\n",
      "dtypes: float64(8), int64(2), object(6)\n",
      "memory usage: 621.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a copy of the dataframe\n",
    "df0 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>field1</th>\n",
       "      <th>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>733773</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>3.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.50</td>\n",
       "      <td>100.99</td>\n",
       "      <td>429193.0</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.7627</td>\n",
       "      <td>-84.4225</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>733773</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>0.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>98.19</td>\n",
       "      <td>605085.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>38.9047</td>\n",
       "      <td>-77.0163</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>733773</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>27.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1011.60</td>\n",
       "      <td>65.94</td>\n",
       "      <td>5074252.0</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>103.8558</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>733773</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1014.78</td>\n",
       "      <td>115.24</td>\n",
       "      <td>3450395.0</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5218</td>\n",
       "      <td>13.4015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>733773</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>0.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1015.60</td>\n",
       "      <td>130.30</td>\n",
       "      <td>10460118.0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8667</td>\n",
       "      <td>2.3333</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>4963</td>\n",
       "      <td>737029</td>\n",
       "      <td>New York City</td>\n",
       "      <td>3794306</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>4.7</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1019.10</td>\n",
       "      <td>100.00</td>\n",
       "      <td>8398748.0</td>\n",
       "      <td>New York City</td>\n",
       "      <td>40.6943</td>\n",
       "      <td>-73.9249</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964</th>\n",
       "      <td>4964</td>\n",
       "      <td>737029</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1295947</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1018.50</td>\n",
       "      <td>81.46</td>\n",
       "      <td>425403.0</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>44.9635</td>\n",
       "      <td>-93.2678</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>4965</td>\n",
       "      <td>737029</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3491734</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>14.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1017.90</td>\n",
       "      <td>82.30</td>\n",
       "      <td>3990456.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>34.1139</td>\n",
       "      <td>-118.4068</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4966</th>\n",
       "      <td>4966</td>\n",
       "      <td>737029</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3491734</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>14.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1017.90</td>\n",
       "      <td>82.30</td>\n",
       "      <td>3990456.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>-37.4600</td>\n",
       "      <td>-72.3600</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4967</th>\n",
       "      <td>4967</td>\n",
       "      <td>737029</td>\n",
       "      <td>Miami</td>\n",
       "      <td>2267294</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>22.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1019.20</td>\n",
       "      <td>86.01</td>\n",
       "      <td>470914.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.7839</td>\n",
       "      <td>-80.2102</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4968 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      field1      ds           city  PASSENGERS           Type  \\\n",
       "0          0  733773        Atlanta     2735491       Domestic   \n",
       "1          1  733773     Washington     1929494       Domestic   \n",
       "2          2  733773      Singapore     1681005  International   \n",
       "3          3  733773         Berlin      489487  International   \n",
       "4          4  733773          Paris     4154249  International   \n",
       "...      ...     ...            ...         ...            ...   \n",
       "4963    4963  737029  New York City     3794306       Domestic   \n",
       "4964    4964  737029    Minneapolis     1295947       Domestic   \n",
       "4965    4965  737029    Los Angeles     3491734       Domestic   \n",
       "4966    4966  737029    Los Angeles     3491734       Domestic   \n",
       "4967    4967  737029          Miami     2267294       Domestic   \n",
       "\n",
       "      temperature_mean  precipitation  rain_days  pressure  \\\n",
       "0                  3.6          137.0        7.0   1019.50   \n",
       "1                  0.4           57.0        8.0   1016.70   \n",
       "2                 27.5           70.0        8.0   1011.60   \n",
       "3                 -4.9           21.0        7.0   1014.78   \n",
       "4                  0.6           36.0        7.0   1015.60   \n",
       "...                ...            ...        ...       ...   \n",
       "4963               4.7          175.0       11.0   1019.10   \n",
       "4964              -3.3           39.0        4.0   1018.50   \n",
       "4965              14.8           37.0        2.0   1017.90   \n",
       "4966              14.8           37.0        2.0   1017.90   \n",
       "4967              22.2           40.0        2.0   1019.20   \n",
       "\n",
       "      cost_of_living_index  population     city_ascii      lat       lng  \\\n",
       "0                   100.99    429193.0        Atlanta  33.7627  -84.4225   \n",
       "1                    98.19    605085.0     Washington  38.9047  -77.0163   \n",
       "2                    65.94   5074252.0      Singapore   1.2930  103.8558   \n",
       "3                   115.24   3450395.0         Berlin  52.5218   13.4015   \n",
       "4                   130.30  10460118.0          Paris  48.8667    2.3333   \n",
       "...                    ...         ...            ...      ...       ...   \n",
       "4963                100.00   8398748.0  New York City  40.6943  -73.9249   \n",
       "4964                 81.46    425403.0    Minneapolis  44.9635  -93.2678   \n",
       "4965                 82.30   3990456.0    Los Angeles  34.1139 -118.4068   \n",
       "4966                 82.30   3990456.0    Los Angeles -37.4600  -72.3600   \n",
       "4967                 86.01    470914.0          Miami  25.7839  -80.2102   \n",
       "\n",
       "            country iso2 iso3  \n",
       "0     United States   US  USA  \n",
       "1     United States   US  USA  \n",
       "2         Singapore   SG  SGP  \n",
       "3           Germany   DE  DEU  \n",
       "4            France   FR  FRA  \n",
       "...             ...  ...  ...  \n",
       "4963  United States   US  USA  \n",
       "4964  United States   US  USA  \n",
       "4965  United States   US  USA  \n",
       "4966          Chile   CL  CHL  \n",
       "4967  United States   US  USA  \n",
       "\n",
       "[4968 rows x 17 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['field1', 'ds', 'city', 'PASSENGERS', 'Type', 'temperature_mean',\n",
       "       'precipitation', 'rain_days', 'pressure', 'cost_of_living_index',\n",
       "       'population', 'city_ascii', 'lat', 'lng', 'country', 'iso2', 'iso3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0.drop('field1' , 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ds', 'city', 'PASSENGERS', 'Type', 'temperature_mean', 'precipitation',\n",
       "       'rain_days', 'pressure', 'cost_of_living_index', 'population',\n",
       "       'city_ascii', 'lat', 'lng', 'country', 'iso2', 'iso3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Dummies for Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iso2', 'Type', 'city', 'city_ascii', 'iso3', 'country']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4968, 17)\n"
     ]
    },
    {
     "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",
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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>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>Type_International</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>733773</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>3.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.50</td>\n",
       "      <td>100.99</td>\n",
       "      <td>429193.0</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.7627</td>\n",
       "      <td>-84.4225</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>733773</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>0.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>98.19</td>\n",
       "      <td>605085.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>38.9047</td>\n",
       "      <td>-77.0163</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733773</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>27.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1011.60</td>\n",
       "      <td>65.94</td>\n",
       "      <td>5074252.0</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>103.8558</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733773</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1014.78</td>\n",
       "      <td>115.24</td>\n",
       "      <td>3450395.0</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5218</td>\n",
       "      <td>13.4015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>733773</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>0.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1015.60</td>\n",
       "      <td>130.30</td>\n",
       "      <td>10460118.0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8667</td>\n",
       "      <td>2.3333</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ds        city  PASSENGERS           Type  temperature_mean  \\\n",
       "0  733773     Atlanta     2735491       Domestic               3.6   \n",
       "1  733773  Washington     1929494       Domestic               0.4   \n",
       "2  733773   Singapore     1681005  International              27.5   \n",
       "3  733773      Berlin      489487  International              -4.9   \n",
       "4  733773       Paris     4154249  International               0.6   \n",
       "\n",
       "   precipitation  rain_days  pressure  cost_of_living_index  population  \\\n",
       "0          137.0        7.0   1019.50                100.99    429193.0   \n",
       "1           57.0        8.0   1016.70                 98.19    605085.0   \n",
       "2           70.0        8.0   1011.60                 65.94   5074252.0   \n",
       "3           21.0        7.0   1014.78                115.24   3450395.0   \n",
       "4           36.0        7.0   1015.60                130.30  10460118.0   \n",
       "\n",
       "   city_ascii      lat       lng        country iso2 iso3  Type_International  \n",
       "0     Atlanta  33.7627  -84.4225  United States   US  USA                   0  \n",
       "1  Washington  38.9047  -77.0163  United States   US  USA                   0  \n",
       "2   Singapore   1.2930  103.8558      Singapore   SG  SGP                   1  \n",
       "3      Berlin  52.5218   13.4015        Germany   DE  DEU                   1  \n",
       "4       Paris  48.8667    2.3333         France   FR  FRA                   1  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# city_ascii = 'city_ascii' + '_' + df0.city_ascii.astype(str)\n",
    "# df_city_ascii = pd.get_dummies(city_ascii)\n",
    "\n",
    "# city = 'city' + '_' + df0.city.astype(str)\n",
    "# df_city = pd.get_dummies(city)\n",
    "\n",
    "# country = 'country' + '_' + df0.country.astype(str)\n",
    "# df_country = pd.get_dummies(country)\n",
    "\n",
    "# iso3 = 'iso3' + '_' + df0.iso3.astype(str)\n",
    "# df_iso3 = pd.get_dummies(iso3)\n",
    "\n",
    "# iso2 = 'iso2' + '_' + df0.iso2.astype(str)\n",
    "# df_iso2 = pd.get_dummies(iso2)\n",
    "\n",
    "Type = 'Type'+ '_' + df0.Type.astype(str)\n",
    "df_Type = pd.get_dummies(Type , drop_first=True)\n",
    "\n",
    "\n",
    "for x in [df_Type]:\n",
    "    df0 = pd.concat([df0 , x] , axis = 1)\n",
    "    \n",
    "print(df1.shape)\n",
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0.drop(columns = {'city' ,'city_ascii','country','iso2','iso3'} , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0.drop('Type' , 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modelling with Statsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome = 'PASSENGERS'\n",
    "x_cols = ['temperature_mean','precipitation','rain_days','pressure','cost_of_living_index','population','lat','lng']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnew = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnew.drop('field1' , 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# some brief preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-1.465700</td>\n",
       "      <td>0.675845</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.898949</td>\n",
       "      <td>1.077087</td>\n",
       "      <td>-0.536458</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>-0.054228</td>\n",
       "      <td>-0.551928</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-1.817862</td>\n",
       "      <td>-0.267331</td>\n",
       "      <td>0.245274</td>\n",
       "      <td>0.312224</td>\n",
       "      <td>0.902155</td>\n",
       "      <td>-0.509404</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.271057</td>\n",
       "      <td>-0.459175</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.164511</td>\n",
       "      <td>-0.114065</td>\n",
       "      <td>0.245274</td>\n",
       "      <td>-0.756452</td>\n",
       "      <td>-1.112693</td>\n",
       "      <td>0.177998</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>-2.108274</td>\n",
       "      <td>1.806009</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-2.401130</td>\n",
       "      <td>-0.691761</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>-0.090101</td>\n",
       "      <td>1.967369</td>\n",
       "      <td>-0.071767</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>1.132480</td>\n",
       "      <td>0.673189</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-1.795852</td>\n",
       "      <td>-0.514915</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.081725</td>\n",
       "      <td>2.908256</td>\n",
       "      <td>1.006397</td>\n",
       "      <td>Paris</td>\n",
       "      <td>0.901257</td>\n",
       "      <td>0.534574</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds        city  PASSENGERS           Type  Month  Year  \\\n",
       "0  2010-01-01     Atlanta     2735491       Domestic      1  2010   \n",
       "1  2010-01-01  Washington     1929494       Domestic      1  2010   \n",
       "2  2010-01-01   Singapore     1681005  International      1  2010   \n",
       "3  2010-01-01      Berlin      489487  International      1  2010   \n",
       "4  2010-01-01       Paris     4154249  International      1  2010   \n",
       "\n",
       "   temperature_mean  precipitation  rain_days  pressure  cost_of_living_index  \\\n",
       "0         -1.465700       0.675845   0.029375  0.898949              1.077087   \n",
       "1         -1.817862      -0.267331   0.245274  0.312224              0.902155   \n",
       "2          1.164511      -0.114065   0.245274 -0.756452             -1.112693   \n",
       "3         -2.401130      -0.691761   0.029375 -0.090101              1.967369   \n",
       "4         -1.795852      -0.514915   0.029375  0.081725              2.908256   \n",
       "\n",
       "   population  city_ascii       lat       lng        country iso2 iso3  \n",
       "0   -0.536458     Atlanta -0.054228 -0.551928  United States   US  USA  \n",
       "1   -0.509404  Washington  0.271057 -0.459175  United States   US  USA  \n",
       "2    0.177998   Singapore -2.108274  1.806009      Singapore   SG  SGP  \n",
       "3   -0.071767      Berlin  1.132480  0.673189        Germany   DE  DEU  \n",
       "4    1.006397       Paris  0.901257  0.534574         France   FR  FRA  "
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.columns = [col.replace(\"\",\"_\") for col in dfnew.columns]\n",
    "for col in x_cols:\n",
    "    dfnew[col] = (dfnew[col] - dfnew[col].mean())/dfnew[col].std()\n",
    "dfnew.head()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "train , test = train_test_split(dfnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>PASSENGERS</td>    <th>  R-squared:         </th> <td>   0.469</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.468</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   547.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 05 Mar 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:12:45</td>     <th>  Log-Likelihood:    </th> <td> -76311.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4968</td>      <th>  AIC:               </th> <td>1.526e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4959</td>      <th>  BIC:               </th> <td>1.527e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td> 2.168e+06</td> <td> 1.61e+04</td> <td>  134.566</td> <td> 0.000</td> <td> 2.14e+06</td> <td>  2.2e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>temperature_mean</th>     <td> 3.298e+05</td> <td> 1.98e+04</td> <td>   16.629</td> <td> 0.000</td> <td> 2.91e+05</td> <td> 3.69e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>precipitation</th>        <td>-7.753e+04</td> <td> 2.51e+04</td> <td>   -3.090</td> <td> 0.002</td> <td>-1.27e+05</td> <td>-2.83e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rain_days</th>            <td>-6.564e+04</td> <td> 2.58e+04</td> <td>   -2.543</td> <td> 0.011</td> <td>-1.16e+05</td> <td> -1.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pressure</th>             <td> 2.695e+05</td> <td> 1.85e+04</td> <td>   14.600</td> <td> 0.000</td> <td> 2.33e+05</td> <td> 3.06e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cost_of_living_index</th> <td> 4.353e+05</td> <td> 1.73e+04</td> <td>   25.224</td> <td> 0.000</td> <td> 4.01e+05</td> <td> 4.69e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>population</th>           <td> 9.801e+05</td> <td> 2.02e+04</td> <td>   48.546</td> <td> 0.000</td> <td> 9.41e+05</td> <td> 1.02e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lat</th>                  <td>  753.4067</td> <td> 1.81e+04</td> <td>    0.042</td> <td> 0.967</td> <td>-3.48e+04</td> <td> 3.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lng</th>                  <td> -8.87e+04</td> <td> 2.16e+04</td> <td>   -4.108</td> <td> 0.000</td> <td>-1.31e+05</td> <td>-4.64e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>310.589</td> <th>  Durbin-Watson:     </th> <td>   1.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 381.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.614</td>  <th>  Prob(JB):          </th> <td>1.80e-83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.577</td>  <th>  Cond. No.          </th> <td>    3.25</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             PASSENGERS   R-squared:                       0.469\n",
       "Model:                            OLS   Adj. R-squared:                  0.468\n",
       "Method:                 Least Squares   F-statistic:                     547.0\n",
       "Date:                Thu, 05 Mar 2020   Prob (F-statistic):               0.00\n",
       "Time:                        11:12:45   Log-Likelihood:                -76311.\n",
       "No. Observations:                4968   AIC:                         1.526e+05\n",
       "Df Residuals:                    4959   BIC:                         1.527e+05\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept             2.168e+06   1.61e+04    134.566      0.000    2.14e+06     2.2e+06\n",
       "temperature_mean      3.298e+05   1.98e+04     16.629      0.000    2.91e+05    3.69e+05\n",
       "precipitation        -7.753e+04   2.51e+04     -3.090      0.002   -1.27e+05   -2.83e+04\n",
       "rain_days            -6.564e+04   2.58e+04     -2.543      0.011   -1.16e+05    -1.5e+04\n",
       "pressure              2.695e+05   1.85e+04     14.600      0.000    2.33e+05    3.06e+05\n",
       "cost_of_living_index  4.353e+05   1.73e+04     25.224      0.000    4.01e+05    4.69e+05\n",
       "population            9.801e+05   2.02e+04     48.546      0.000    9.41e+05    1.02e+06\n",
       "lat                    753.4067   1.81e+04      0.042      0.967   -3.48e+04    3.63e+04\n",
       "lng                   -8.87e+04   2.16e+04     -4.108      0.000   -1.31e+05   -4.64e+04\n",
       "==============================================================================\n",
       "Omnibus:                      310.589   Durbin-Watson:                   1.399\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              381.050\n",
       "Skew:                           0.614   Prob(JB):                     1.80e-83\n",
       "Kurtosis:                       3.577   Cond. No.                         3.25\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "#fitting the model\n",
    "predictors = '+'.join(x_cols)\n",
    "formula = outcome + '~' + predictors\n",
    "model = ols(formula = formula , data = dfnew).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('temperature_mean', 1.515671742835012),\n",
       " ('precipitation', 2.425308728168441),\n",
       " ('rain_days', 2.5673963950267993),\n",
       " ('pressure', 1.312619413941749),\n",
       " ('cost_of_living_index', 1.1474078245030446),\n",
       " ('population', 1.5701465271952202),\n",
       " ('lat', 1.2651850240781812),\n",
       " ('lng', 1.7957968898545817)]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = dfnew[x_cols]\n",
    "vif = [variance_inflation_factor(X2.values , i) for i in range(X2.shape[1])]\n",
    "list(zip(x_cols , vif))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: While the p-values indicate that all of the current features are impactful, the variance inflation factor indicates that there is moderate multicollinearity between our variables. With that, it makes sense to briefly update the features once again and recheck for multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>PASSENGERS</td>    <th>  R-squared:         </th> <td>   0.469</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.468</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   547.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 05 Mar 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:31:45</td>     <th>  Log-Likelihood:    </th> <td> -76311.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4968</td>      <th>  AIC:               </th> <td>1.526e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4959</td>      <th>  BIC:               </th> <td>1.527e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td> 2.168e+06</td> <td> 1.61e+04</td> <td>  134.566</td> <td> 0.000</td> <td> 2.14e+06</td> <td>  2.2e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>temperature_mean</th>     <td> 3.298e+05</td> <td> 1.98e+04</td> <td>   16.629</td> <td> 0.000</td> <td> 2.91e+05</td> <td> 3.69e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>precipitation</th>        <td>-7.753e+04</td> <td> 2.51e+04</td> <td>   -3.090</td> <td> 0.002</td> <td>-1.27e+05</td> <td>-2.83e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rain_days</th>            <td>-6.564e+04</td> <td> 2.58e+04</td> <td>   -2.543</td> <td> 0.011</td> <td>-1.16e+05</td> <td> -1.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pressure</th>             <td> 2.695e+05</td> <td> 1.85e+04</td> <td>   14.600</td> <td> 0.000</td> <td> 2.33e+05</td> <td> 3.06e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cost_of_living_index</th> <td> 4.353e+05</td> <td> 1.73e+04</td> <td>   25.224</td> <td> 0.000</td> <td> 4.01e+05</td> <td> 4.69e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>population</th>           <td> 9.801e+05</td> <td> 2.02e+04</td> <td>   48.546</td> <td> 0.000</td> <td> 9.41e+05</td> <td> 1.02e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lat</th>                  <td>  753.4067</td> <td> 1.81e+04</td> <td>    0.042</td> <td> 0.967</td> <td>-3.48e+04</td> <td> 3.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lng</th>                  <td> -8.87e+04</td> <td> 2.16e+04</td> <td>   -4.108</td> <td> 0.000</td> <td>-1.31e+05</td> <td>-4.64e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>310.589</td> <th>  Durbin-Watson:     </th> <td>   1.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 381.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.614</td>  <th>  Prob(JB):          </th> <td>1.80e-83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.577</td>  <th>  Cond. No.          </th> <td>    3.25</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             PASSENGERS   R-squared:                       0.469\n",
       "Model:                            OLS   Adj. R-squared:                  0.468\n",
       "Method:                 Least Squares   F-statistic:                     547.0\n",
       "Date:                Thu, 05 Mar 2020   Prob (F-statistic):               0.00\n",
       "Time:                        11:31:45   Log-Likelihood:                -76311.\n",
       "No. Observations:                4968   AIC:                         1.526e+05\n",
       "Df Residuals:                    4959   BIC:                         1.527e+05\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Intercept             2.168e+06   1.61e+04    134.566      0.000    2.14e+06     2.2e+06\n",
       "temperature_mean      3.298e+05   1.98e+04     16.629      0.000    2.91e+05    3.69e+05\n",
       "precipitation        -7.753e+04   2.51e+04     -3.090      0.002   -1.27e+05   -2.83e+04\n",
       "rain_days            -6.564e+04   2.58e+04     -2.543      0.011   -1.16e+05    -1.5e+04\n",
       "pressure              2.695e+05   1.85e+04     14.600      0.000    2.33e+05    3.06e+05\n",
       "cost_of_living_index  4.353e+05   1.73e+04     25.224      0.000    4.01e+05    4.69e+05\n",
       "population            9.801e+05   2.02e+04     48.546      0.000    9.41e+05    1.02e+06\n",
       "lat                    753.4067   1.81e+04      0.042      0.967   -3.48e+04    3.63e+04\n",
       "lng                   -8.87e+04   2.16e+04     -4.108      0.000   -1.31e+05   -4.64e+04\n",
       "==============================================================================\n",
       "Omnibus:                      310.589   Durbin-Watson:                   1.399\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              381.050\n",
       "Skew:                           0.614   Prob(JB):                     1.80e-83\n",
       "Kurtosis:                       3.577   Cond. No.                         3.25\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome = 'PASSENGERS'\n",
    "x_cols = dfnew[['temperature_mean','precipitation', 'rain_days', 'pressure','cost_of_living_index','population','lat','lng']]\n",
    "predictors = '+'.join(x_cols)\n",
    "formula = outcome + '~' + predictors\n",
    "model = ols(formula=formula, data=dfnew).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "fig = sm.graphics.qqplot(model.resid, dist=stats.norm, line='45', fit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "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>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-1.465700</td>\n",
       "      <td>0.675845</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.898949</td>\n",
       "      <td>1.077087</td>\n",
       "      <td>-0.536458</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>-0.054228</td>\n",
       "      <td>-0.551928</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-1.817862</td>\n",
       "      <td>-0.267331</td>\n",
       "      <td>0.245274</td>\n",
       "      <td>0.312224</td>\n",
       "      <td>0.902155</td>\n",
       "      <td>-0.509404</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.271057</td>\n",
       "      <td>-0.459175</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.164511</td>\n",
       "      <td>-0.114065</td>\n",
       "      <td>0.245274</td>\n",
       "      <td>-0.756452</td>\n",
       "      <td>-1.112693</td>\n",
       "      <td>0.177998</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>-2.108274</td>\n",
       "      <td>1.806009</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-2.401130</td>\n",
       "      <td>-0.691761</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>-0.090101</td>\n",
       "      <td>1.967369</td>\n",
       "      <td>-0.071767</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>1.132480</td>\n",
       "      <td>0.673189</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>-1.795852</td>\n",
       "      <td>-0.514915</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.081725</td>\n",
       "      <td>2.908256</td>\n",
       "      <td>1.006397</td>\n",
       "      <td>Paris</td>\n",
       "      <td>0.901257</td>\n",
       "      <td>0.534574</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>New York City</td>\n",
       "      <td>3794306</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>-1.344644</td>\n",
       "      <td>1.123853</td>\n",
       "      <td>0.892970</td>\n",
       "      <td>0.815131</td>\n",
       "      <td>1.015236</td>\n",
       "      <td>0.689338</td>\n",
       "      <td>New York City</td>\n",
       "      <td>0.384268</td>\n",
       "      <td>-0.420459</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>1295947</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>-2.225049</td>\n",
       "      <td>-0.479546</td>\n",
       "      <td>-0.618321</td>\n",
       "      <td>0.689404</td>\n",
       "      <td>-0.143067</td>\n",
       "      <td>-0.537041</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>0.654339</td>\n",
       "      <td>-0.662704</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3491734</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>-0.233132</td>\n",
       "      <td>-0.503126</td>\n",
       "      <td>-1.050118</td>\n",
       "      <td>0.563678</td>\n",
       "      <td>-0.090587</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>-0.032011</td>\n",
       "      <td>-0.977536</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4966</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>3491734</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>-0.233132</td>\n",
       "      <td>-0.503126</td>\n",
       "      <td>-1.050118</td>\n",
       "      <td>0.563678</td>\n",
       "      <td>-0.090587</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>-4.559805</td>\n",
       "      <td>-0.400861</td>\n",
       "      <td>Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>CHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4967</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Miami</td>\n",
       "      <td>2267294</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.581243</td>\n",
       "      <td>-0.467756</td>\n",
       "      <td>-1.050118</td>\n",
       "      <td>0.836085</td>\n",
       "      <td>0.141199</td>\n",
       "      <td>-0.530041</td>\n",
       "      <td>Miami</td>\n",
       "      <td>-0.558970</td>\n",
       "      <td>-0.499174</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4968 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ds           city  PASSENGERS           Type  Month  Year  \\\n",
       "0     2010-01-01        Atlanta     2735491       Domestic      1  2010   \n",
       "1     2010-01-01     Washington     1929494       Domestic      1  2010   \n",
       "2     2010-01-01      Singapore     1681005  International      1  2010   \n",
       "3     2010-01-01         Berlin      489487  International      1  2010   \n",
       "4     2010-01-01          Paris     4154249  International      1  2010   \n",
       "...          ...            ...         ...            ...    ...   ...   \n",
       "4963  2018-12-01  New York City     3794306       Domestic     12  2018   \n",
       "4964  2018-12-01    Minneapolis     1295947       Domestic     12  2018   \n",
       "4965  2018-12-01    Los Angeles     3491734       Domestic     12  2018   \n",
       "4966  2018-12-01    Los Angeles     3491734       Domestic     12  2018   \n",
       "4967  2018-12-01          Miami     2267294       Domestic     12  2018   \n",
       "\n",
       "      temperature_mean  precipitation  rain_days  pressure  \\\n",
       "0            -1.465700       0.675845   0.029375  0.898949   \n",
       "1            -1.817862      -0.267331   0.245274  0.312224   \n",
       "2             1.164511      -0.114065   0.245274 -0.756452   \n",
       "3            -2.401130      -0.691761   0.029375 -0.090101   \n",
       "4            -1.795852      -0.514915   0.029375  0.081725   \n",
       "...                ...            ...        ...       ...   \n",
       "4963         -1.344644       1.123853   0.892970  0.815131   \n",
       "4964         -2.225049      -0.479546  -0.618321  0.689404   \n",
       "4965         -0.233132      -0.503126  -1.050118  0.563678   \n",
       "4966         -0.233132      -0.503126  -1.050118  0.563678   \n",
       "4967          0.581243      -0.467756  -1.050118  0.836085   \n",
       "\n",
       "      cost_of_living_index  population     city_ascii       lat       lng  \\\n",
       "0                 1.077087   -0.536458        Atlanta -0.054228 -0.551928   \n",
       "1                 0.902155   -0.509404     Washington  0.271057 -0.459175   \n",
       "2                -1.112693    0.177998      Singapore -2.108274  1.806009   \n",
       "3                 1.967369   -0.071767         Berlin  1.132480  0.673189   \n",
       "4                 2.908256    1.006397          Paris  0.901257  0.534574   \n",
       "...                    ...         ...            ...       ...       ...   \n",
       "4963              1.015236    0.689338  New York City  0.384268 -0.420459   \n",
       "4964             -0.143067   -0.537041    Minneapolis  0.654339 -0.662704   \n",
       "4965             -0.090587    0.011299    Los Angeles -0.032011 -0.977536   \n",
       "4966             -0.090587    0.011299    Los Angeles -4.559805 -0.400861   \n",
       "4967              0.141199   -0.530041          Miami -0.558970 -0.499174   \n",
       "\n",
       "            country iso2 iso3  \n",
       "0     United States   US  USA  \n",
       "1     United States   US  USA  \n",
       "2         Singapore   SG  SGP  \n",
       "3           Germany   DE  DEU  \n",
       "4            France   FR  FRA  \n",
       "...             ...  ...  ...  \n",
       "4963  United States   US  USA  \n",
       "4964  United States   US  USA  \n",
       "4965  United States   US  USA  \n",
       "4966          Chile   CL  CHL  \n",
       "4967  United States   US  USA  \n",
       "\n",
       "[4968 rows x 18 columns]"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for Homoscedasticity Assumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2639d390>]"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(model.predict(dfnew[x_cols]), model.resid)\n",
    "plt.plot(model.predict(dfnew[x_cols]), [0 for i in range(len(dfnew))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pylab \n",
    "import scipy.stats as stats1\n",
    "\n",
    "Y = df1.PASSENGERS\n",
    "stats1.probplot(Y, dist=\"norm\", plot=pylab)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regr = linear_model.LinearRegression()\n",
    "# x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "\n",
    "#    #Fitting the model to the training dataset\n",
    "# regr.fit(x_train,y_train)\n",
    "# regr.intercept_\n",
    "# print('Coefficients: \\n', regr.coef_)\n",
    "# print(\"Mean Squared Error(MSE): %.2f\"\n",
    "#          % np.mean((regr.predict(x_test) - y_test) ** 2))\n",
    "# print('Variance Score: %.2f' % regr.score(x_test, y_test))\n",
    "#    regr.coef_[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df0.drop('PASSENGERS' , 1)\n",
    "y = df0['PASSENGERS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn cross_validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train ,X_test , y_train , y_test = train_test_split(X , y , test_size = 0.2 , random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3974, 10)\n",
      "(994, 10)\n",
      "(3974,)\n",
      "(994,)\n"
     ]
    }
   ],
   "source": [
    "# Print the shapes of the training and testing datasets\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>ds</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>Type_International</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>734747</td>\n",
       "      <td>16.6</td>\n",
       "      <td>99.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>118.52</td>\n",
       "      <td>8044433.0</td>\n",
       "      <td>42.9700</td>\n",
       "      <td>-81.2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4054</th>\n",
       "      <td>736450</td>\n",
       "      <td>27.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1007.5</td>\n",
       "      <td>69.08</td>\n",
       "      <td>1634984.0</td>\n",
       "      <td>33.5722</td>\n",
       "      <td>-112.0891</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>735234</td>\n",
       "      <td>22.6</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>106.27</td>\n",
       "      <td>349627.0</td>\n",
       "      <td>21.3294</td>\n",
       "      <td>-157.8460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4102</th>\n",
       "      <td>736481</td>\n",
       "      <td>26.7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>99.54</td>\n",
       "      <td>349651.0</td>\n",
       "      <td>21.3294</td>\n",
       "      <td>-157.8460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>733804</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>80.35</td>\n",
       "      <td>319318.0</td>\n",
       "      <td>38.6358</td>\n",
       "      <td>-90.2451</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds  temperature_mean  precipitation  rain_days  pressure  \\\n",
       "1477  734747              16.6           99.0        9.0    1019.4   \n",
       "4054  736450              27.7            0.0        0.0    1007.5   \n",
       "2218  735234              22.6           63.0        9.0    1013.5   \n",
       "4102  736481              26.7           14.0        4.0    1017.0   \n",
       "80    733804              -0.8           53.0        7.0    1019.2   \n",
       "\n",
       "      cost_of_living_index  population      lat       lng  Type_International  \n",
       "1477                118.52   8044433.0  42.9700  -81.2500                   1  \n",
       "4054                 69.08   1634984.0  33.5722 -112.0891                   0  \n",
       "2218                106.27    349627.0  21.3294 -157.8460                   0  \n",
       "4102                 99.54    349651.0  21.3294 -157.8460                   0  \n",
       "80                   80.35    319318.0  38.6358  -90.2451                   0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import Linear Regression Model\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create an instance for the linear Regression Model\n",
    "lm = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train your model on X_train and y_train\n",
    "lm.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check intercepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-309913186.06459874"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Estimated Intercept -309913186.06\n"
     ]
    }
   ],
   "source": [
    "print(\"The Estimated Intercept %.2f\"%lm.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.50446599e+02,  2.69876600e+04,  9.18740433e+02, -3.53494667e+04,\n",
       "        4.94474927e+04,  3.03404942e+04,  1.52994006e-01, -7.80762035e+03,\n",
       "       -9.24708546e+03,  1.55396734e+06])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Estimated Intercept 10\n",
      "----------------------------------------\n",
      "[ 3.50446599e+02  2.69876600e+04  9.18740433e+02 -3.53494667e+04\n",
      "  4.94474927e+04  3.03404942e+04  1.52994006e-01 -7.80762035e+03\n",
      " -9.24708546e+03  1.55396734e+06]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"The Estimated Intercept %d\"%len(lm.coef_))\n",
    "print(\"----------------------------------------\")\n",
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets create a new dataframe for our coefficients\n",
    "X.columns\n",
    "coeff = pd.DataFrame(data = lm.coef_ , index = X.columns , columns = ['Coefficients'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <td>3.504466e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature_mean</th>\n",
       "      <td>2.698766e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipitation</th>\n",
       "      <td>9.187404e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain_days</th>\n",
       "      <td>-3.534947e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pressure</th>\n",
       "      <td>4.944749e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <td>3.034049e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>1.529940e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-7.807620e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lng</th>\n",
       "      <td>-9.247085e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_International</th>\n",
       "      <td>1.553967e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Coefficients\n",
       "ds                    3.504466e+02\n",
       "temperature_mean      2.698766e+04\n",
       "precipitation         9.187404e+02\n",
       "rain_days            -3.534947e+04\n",
       "pressure              4.944749e+04\n",
       "cost_of_living_index  3.034049e+04\n",
       "population            1.529940e-01\n",
       "lat                  -7.807620e+03\n",
       "lng                  -9.247085e+03\n",
       "Type_International    1.553967e+06"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explaining The Results of the Coefficients\n",
    "- An increase or decrese in the coefficients has an effect on the target ie Passengers in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1320921.14248013, 2374059.14730787, 1229825.92609286,\n",
       "       1849451.83347869, 1431825.02114642, 2054121.20455945,\n",
       "       1732204.79847938, 2029658.64130849, 4962783.73819178,\n",
       "       1182701.73650056, 2259560.18525881, 1831130.22010583,\n",
       "       2553141.24199849, 3828908.0048151 , 1928018.53266674,\n",
       "       1498574.38514775, 1669321.08165509, 2153527.24033713,\n",
       "       2141809.25137913, 2982655.43046528, 2017511.7564857 ,\n",
       "       2436207.9011113 , 2340270.0874151 ,  999577.66082084,\n",
       "       4092098.84913605,  961694.41937417, 4173824.87291098,\n",
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       "       2902283.40945947, 1427268.45690757, 1607795.06377745,\n",
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       "       1513894.2184121 , 2254921.92809951, 1520747.1790874 ,\n",
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       "       1979670.95227706, 3197161.62683213, 3074039.2238363 ,\n",
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       "       2244046.46819216,  761310.27588779, 1502637.0160712 ,\n",
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       "       1858482.4378382 , 2618121.86815786, 1854715.41177881,\n",
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       "        990766.81432712, 2372784.23769569, 2031397.49801624,\n",
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       "       1816164.81384236, 1858288.71105713, 1373178.09305155,\n",
       "       2911718.23969668, 6340020.7890653 , 3343546.95046335,\n",
       "       1109357.10180837, 2316261.88994962, 2077553.81251913,\n",
       "       1542446.89282745, 3258212.05279899, 1234771.64727974,\n",
       "       2422645.47244751, 1881309.36975688, 3908455.02264124,\n",
       "       1888800.2215597 , 1160889.265733  ,  474863.54048574,\n",
       "       2480962.20340532, 2021487.08998692, 2918676.09599817,\n",
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       "       1696666.30934525, 4203224.02114284,  616246.44738716,\n",
       "       2075247.32380539])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c26e1e128>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test , pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tuFaWMQzDaBH1mt7SCJSkMZp144/S6gAeXno6zy8/i4eXnj7OV1TUMO48yaIpvQ9Y557/OfAneNrK0sgzxqPAj0RkvYgscW3vUNXtAO7v2117H7A1cO421xbXvi2kvZYxxiEiS0RknYis27FjR8q3ahhGVvIwvSUJlDRjNOvGX4tWV9Qw7jzJEn03WVX3ikgfMFVVHwYQkXekPH++qr4kIm8H1ojI0zF9JaRNa2iPI9U5qnoTcBPA3Llzk65pGEaNpAmHTuLKM44ZF50G4wVKmjHSlHbPIzqvVjNhEcO48ySLUNogIlcBRwH3AjgB9Zs0J6vqS+7vKyLyXTwt62UROVxVtzvT2Suu+zbgyMDp04GXXPt7K9ofcu3TQ/pTwxiGYbSAPHw5SQIl7RhxN/68wrKP6O1mMGQ+E8k/VAtZzHeXAScA3cDVru09wG1JJ4rIISLyW/5zYAHwC2AV4EfQLQbucc9XAZe6CLl5wKvO9LYaWCAiU1yAwwJgtTv2mojMc1F3l1ZcK8sYhmG0gLx8OYvm9EX6ZPIYI6/ovE7wD9VCak1JVZ8FPlTRdhdwV4rT3wF810VpTwL+WVV/KCKPAXe6YIktwIWu/33AB4DNwG7gY268nSLyeeAx1+9zqrrTPf848E94QvMH7gGwPMsYhmEk04jNpUmmtzzGTBojDXlF56UxE3Yi4gWrpewssgCYDbwl2K6qn815XoVk7ty5um7duuSOhjGBqTRfgXdjz8PhHiV48hyzXuE2f/kDoWa3vt5uHl56elPm0G6IyPrAVqD4vmmFkoh8Ffgg8CCeZjGGqnaElmFCySg6zbjZzfncj9i1e6SqPe1NuZY55iEI8qIeAdk/MMi139tY9fnlJdSLShahlCXQ4RJgtqpuTexpGEbTaUZetP6BwVCB5I8X7JdG40k7xyJlMqjV7BYmzHyyRhlOZLIIpV8DQ42aiGEY9ZFHSHWaMaIoeT7jWMGTNMcoYVa0SLVawrLD3nuQsPfXiWSJvvsycJuIvMelGBp7NGpyhmGkpxnaRNy1Rp0rIE7wxM0xbmNrKyPV8iq6l/Q9iBur08miKfm54f60ol2BEoZhtARfu4jyDmfRJpL8PVEaC3j+HYgXjnEaT5ww8/1GzQ4OyNMkGvfZgXcjNRNeBk1JVbsiHiaQDKNFBLWLMLJoE2lT8JS7qpOhlEsyNk7cXqA4jSdJ04vbf9Qo8swYHvbeK5lI2b5rJXOWcBGZ4Ux4Ryb3NgyjkcT5KbLmRUtzA140p48VF55Ib3d5rG1KT5kVF5w4Nk6c4InL3VbEDNh5mkSD7z2KI3q7czMXtiupzXcuRc8deFkcfg0cJiJrgYv9FEKGYTSXqJujQOZQ6TxS8PjHIdrUFnV+Hhtb8ybvAAv/vUeFlZ927LQJX1k2iaw+pceBD6jqGy5d0P8A/g44pxGTMwwjnjxvmnleq5botCJmOEgrKLPuvYp6r7VGJ04ksmye/RVwuKqOBNoOAgZV9W0Nml+hsM2zRtHIO9NBozI1tDNJgiDPz23m0ntDA1YEuP6i2VXjCPDheTO4btEJGd9Vc2nU5tldwHF42pLPMdjeJcNoGbVoF1E32VZpKpXzOe3YaTz49I6WagNZNJI894dljU5U4La1W5h71NQJs3DIIpT+J/B/ROQbwIt4JSw+Bvz3RkzMMPIkq9mjncwklcLED0wIm29SiHOl2c13utf6OWTVMgaHhvnW2i1jx1vhU8kaBp5nMEScufCKlRtCz5looeRZsoT/g4j4mcJ/D6/20CWq+kCjJmcYeZB0kwlbqd+9fjC0PxTL5wHZbqJRq/prVm2sel9AXU73NPNKynLgz6+ZN92smk/evjh/DpW/sRWrN0WG/k+kUPJMWcI7HfMptSdxyTzDVqZCeNniKT1l9ozsL5zPJUuy0iifRSXd5RIHl7vqSryaZl5p5wPwlYtm5/o5R2lxcX6d55efFXqdyt9QuSQcMnkSrw6PcGh3GREY2j1S10Kmf2CQK1ZuCJ1bKxLTZiE3n5KI/LWqfsE9/1xUv04pXWG0J3HmlSg7fRhhN+giJNLMYj5KyirgMzwyGqnBpF2Vp1nVp50PkKilZTG5xmlxWTWfSu2mt6fM63v2MTTs/V78v5Xj1BKduO7Fndy2dsu432irw+bzJmnzbLDE+JExD8MoLHGbMvMwe7TadJJl02marAK1jhekf2CQ6rwP1ednmU9cJoU02SiCxJnokvLshW1uDWab6Jk8iZH90fpfrRkhAK5bdALXXzQ7dPPxRCFWU1LVjwee110zSURKwDq8MPI/FZGZeBtypwI/Az6qqntdqPmtwMl4G3UvUtUX3DWuwivNPgr8V1Vd7doXAn+Ll4fv66q63LVnHsNoD9KujOOcx1F2+koTXne5xEGTusaten16e8pVbc0ky6bTMJ/F7r37QrXA3u4yb+6rNlemWZVH5eITN9+4+Zx27LRxwQ5BohYAWf1AcdplnF8njZ8szSKlnoVMLXvA2oksGR12qurUkPZXVPXtKS/zaeAp4K3u9ZeA61X1DhH5Ozxhc6P7u0tVf0dELnb9LhKR44CLgeOBI/CiAf+du9bXgPcD24DHRGSVqj6ZdYy0n4fRWrI495NCncNu6Oef3FcVlgxw5V2PMzI6/nb7+p59Y6vlVpA1lDsswi7sM7jmnOMzXTdI1E1XCf9+KtsefHpHJhNa1gi4KBNdlwgzl94b+V7TCL80JslWpk4qOllCwquWgyJSJmWGcBGZDpwFfAH4cxER4HS8aD6AW4Br8ATGue45wF3AV13/c4E7VPVN4HkR2Qyc4vptVtXn3Fh3AOeKyFNZx1CL/GgLsq6Mo1aXWW/o16zaWKUtjezXqnH7BwbH9Z3SU2bZ2cc3THDVs3pO8xmkCTcPEnVjjsv7FiRryqGsfqCw68OB8htRi5w0wi/q2mneh5FCKInIT/AWOAeLyI8rDk8H/jXlWF8B/hL4Lff6MGBIVfe519sA/9vvA7YCqOo+EXnV9e8D1gauGTxna0X7qTWO8avgpEVkCbAEYMaMGSnfqtFosq6M89p39GqI+a5y3P6BQa789uPj/Aq7do9w5V3evvMiml6ihFqtpRtqzWMX/J4O7S5zcLkrVdRa1vH864SVJvcJW+SkEX6VQj4s+g6oa//XRCaNpvR1PFPw7wPfCLQr8DKQuE9JRP4UeEVV14vIe/3mkK6acCyqPSxgI65/0vgHGlRvAm4CLyQ85ByjBWRZGcfdWCHbXpw0465YvSnU0T0yWq1RFZ0sGmmlQAlWuOjtLnPNOQc0xbBFAoz/LoaGR+gul7g+RSh4Ldko/L0/UUIJqhc5ScKv8n1Vzr1Sg4bOTLoaR6JQUtVbAERkrao+XeM484FzROQDwMF4PqWvAL0iMslpMtPxNuSCp9EcCWwTkUnAocDOQLtP8Jyw9l/VMIbRBmRZGSeVZAg79pk7w7WasHHLJeGNN/eN+SLi/AmtjtTLqjGm1UgrBX+lifPNffvHnl/d/8S4sObBoWEuX7kBEag0nmcJua/FhJn0fVQucuoJggjz3fkUYWtBUcjiU/qEiNyhqmPmOhH5A+CDqnp53ImqehVwlTvnvcBfqOqHReTbwAV40XGLgXvcKavc65+64w+oqorIKuCfReRv8AIdZgGP4mk9s1yk3SBeMMSH3DkPZhkjw+dhtJAsK+Na0sCMqoauXpP2pAwODUduvoXWOrhrMcWl1UiTMjMEFwFRkXVR/32NFORxi4i4CMawzytJq0z6jFq9YCkKWYTSJcBfVLStB/qBWKEUw18Bd4jIdcAAB8yD3wC+6QIZduIJGVR1o4jcCTwJ7AM+qaqjACLyKWA1XuDFzaq6sZYxjPYh7co46cYadVOKWr0Gx52//IEq80+UQApWZ20FtSQOPe3YaaGbNU87dto4n0iaDbAvDQ1z7fc2JvarpJGCPCooodLcmIakxU9WraxTySKUwnw3pZC2+IuoPgQ85J4/x4HouWCfPcCFEed/AS+Cr7L9PuC+kPbMYxgTiyRTX1ykVNKNJO54b3e5adF3UQTNdVHCMi445O71g+POE+CkGYdW5QaM0w59smRv8Gl0pFqemdGTFj+1aGWdSBah9BPgOhH5S1XdLyJdeCHVP2nIzAwjJ9LceD5z5+Nj4cBBklavcaHPrcxFFuZQjyLqPUalYFr73K6qz8qPKooSTP5N9/KITNdBSiLsV80lm3saH1pem1GTFj9RWlmrFixFJYtQ+jTwfWC7iLwIzAC2A2c3YmKGkSdxN564TbRJq9cilvCOc6hXEjfXKA0qTHiDJ5D6XOqmqCSkSUIpS4LbrIEFjY5yS1r85KmVTWSylK7YJiIn4e3/mY63x+dRVd0ff6ZhFJ9abxhZzmtWjaY05SAEEucQpQWWREIFUxrtMGjSDDs/y2dSS2BBo6PckrSuiZ4iKA+yaEo4AfTTBs3FMFpKrTeMNOc1c9We5AdLa1qM0gLPP7lvnE/Jb0+jHV5zzvFVG4vLXcKKC0/MLZw7KbDAotyKTVLpiqdU9d3u+VYiTMaqaqkOjKbQPzA4bhd+VJTU1f1PcPsjWxlVpSTCJaceyXWLTmjqPCuTntazas+iZeXlUI/TAuceNbUmra8IgQUW5VZsYov8icgfqur/dc//OKqfqv5LA+ZWOKzIX2vpHxgMTYhaudK+uv+J0L0wH5k3oy7BlFYwZPHpRBWOS7penO8lavyJ5lAPe58CfNh9z1k/N6Nx5FbkzxdI7nlHCJ52oVn+iSKxYvWmKoEE1QlRb39ka1Ufv71WoVRvyfEoolbtwe+3K8SHk5R81p/HRP59LJpTXfROgbvXDzL3qKkd8zlMNJLMd5HVZoNY5dnmEnaDvGLlBta9uLOpJqpmE+cL8I/1DwxGRodFtachi9M8rc8ibBNqWImMqHnHjdMpDvUHn95R5VMIfi+d8jlMJJICHYL55A4GzgceA/yQ8FOAuxszNSOKa1ZtDN0/ctvaLeNWiBONOF9Jb095TFhHUZKoWqjJRAmAwaHhqlpKaTaJdpe7qgIGfO2rSwjVCCsx34gFM0xEYrMxqOrH/AeeufYSVZ2vqh9S1T/EUvM0nav7n4gMqVWoucxyswkrKZ3ElWccQ7kULlhe37OPa79XLayDXHLqkZHHkogTAJVlt9OU+B4e2c/d67eFal9v7K1vf1EnkaUUvNEeZEkRdCZenrsg9wAfyG86Rhz9A4ORySx9sqZxaQW+RjPoUt/4GkKSYFo0p48VF5xImMIzsl9jSxDUG+QQJ2iCyUb9eX7xvBPo7Y4vkz48UtsWvy6B809uL7NULYuQNIR9Lyaw25ssQmkz8MmKtk8Az+Y3HSOOa1alS2Z5dX+0CasIJJWSiMJ3/md1DfX1dtfta/MFTRSV5qJFc/rYsGwBX7loduaxkoyM+9Vz5ud1Y280tS5CgudHCTT/e+nr7UbwvmuLrmtvsmye/Y/Ad0XkL/HKQ/ThZeo+rxETM6pJk8cMiu9bSuMHqIwuPO3YaVUbNtNy2rHTap5rED9LQJa9L4vm9EXm1QvD35y68tGtoYUCfdqp/k49mRXSRD1aMMPEIrWmpKoDePWLLgH+BvgQMEtVf9aguRkBsqyKi+5bSvIDhK2sb1u7pSaBBF6EVl6ECbikkhRZfFkHl7u8jakXnpgYmNEOplqoLxihVq3aaF8ylZ0Ioqo/BiaLyCE5zseIIOs/YZGjj5L8AFHZqWulUgObv/wBjl56L++66j6OzuDj6B8YZOWj1XugRmM0GoC5R00dVxo8jl27R8Y0gSRhFiW0GuW/qZV6ghEsuq7zSC2UROQE4JfAP3CgUN4fAzc3YF5GBVn/CYscfZTkB8j7hhOmgcGB/T9pfRwrVm8KNant1/hFw4rVm0iQW+PwNYEkDS/MJFiv/6YR1BOMYNF1nUcWTelG4LOqeizgOzf+BfjDpBNF5GAReVREHheRjSJyrWufKSKPiMgzIrJSRCa79oPc683u+NGBa13l2jeJyBmB9oWubbOILA20Zx6jiGT5J2yH6KNFc/p4eOnpPL/8LB5eenrVPp9aKHdJVci4/1n0DwzymTsfjzQBxpmEfM0jzlw2ODQcqZnUYmYbHBpOPK8kUjVWEc1d9QSeGcnyAAAgAElEQVQjWHRd55FFKB0PfMs9VwBVfQNIcwd5EzhdVU8EZgMLRWQe8CXgelWdBewCLnP9LwN2qervANe7fojIcXh7o44HFgL/W0RKIlICvoYXtn4ccInrS9YxikqavS8+J804tK0dv2HvNcr6VRIZu9GtuPBEVlxwYtXND7y9REnBBmEaWv/AIFd++/FUgiVMM2mkhjKqWqUFFcXcVWlCBCIXIXFYdF3nkSX67gXgZGAsI6mInIIXKh6LellfX3cvy+6hwOl4ARMAt+BVsr0RONc9B7gL+KqIiGu/Q1XfBJ4Xkc0cKHW+2ZU+R0TuAM4VkaeyjqFxGWpbzEGTulI5+//12Z1VWQaaRR45+cJylkUJhf2qVQlNK8ebv/yBVJ/bod3lqpQ/16zaGBsFF0YwsixtGH+tVEaxJWXGbkbOxLzLdFh0XWeRRVP678C9zvQ2WUSuAr4NXJ3mZKfRbABeAdbg7W8aUtV9rss2vDBz3N+tAO74q8BhwfaKc6LaD6thjMp5LxGRdSKybseO/KK4suD/k6cNCW9V9F2e/oxK815fhEmvK8SEVUlaLeGNvfvGzf3ylRtSf+aV+IKh1vMriYuTCL6/OHNXLd9PLUETRTQhGu1DlpDw7+OZx6bh+ZKOAs5T1R+lPH9UVWfjVa09BXh3WDf3N+x/UHNsjxtjfIPqTao6V1XnTpuWz36XrGTJOu3TiuikRt6MosyXYSasSnp74jMrABwyuZQq31xahPxMd3293Ty//KxIwRz0wcWZu9J+P8EIxStWbsi8yCiKCdFoT1KZ75zP5mZgiap+op4BVXVIRB4C5gG9IjLJaSrTgZdct214yWC3icgk4FBgZ6DdJ3hOWPuvahijcNTiKG9FdFLam1GWukR+P0+whAuN4ZFRPnPn41yxckPV9foHBnl9z77Q83wOmVxKlW8uiAixmSV8bXVKTzk2/VEa/L1RUZVgK53+QXOX/xlesXJDZFh9Zch8cIy4DNxRWHE9ox5SaUqqOgosAGpK1iUi00Sk1z3vBv4EeAp4ELjAdVuMl0sPYJV7jTv+gPP1rAIudpFzM/E28z6Kl7l8lou0m4wXDLHKnZN1jELRPzCYmHYmjEZHJ4WZdaJuOr6m0j8wyOxrf8TlKVbfV/c/MW6Vvmv3SGyuuFHV0OtFhXEHySqQAD586ozEwJPBoeHMKZHCWPno1jEfYRanf6W5Lorg95ZGK0/SeCxizqiHLIEO1wPXisgyVc269DscuMVpXF3Anar6fRF5ErhDRK4DBjiw/+kbwDddIMNOXDZyVd0oIncCT+KlOPqkE5iIyKeA1UAJuFlVfQ/zX2UZo2isWL0p88bRnnJXQx3DUfWc/uBdU3nltT1VZrDX9+zj6v4nItMEBU1I16zaWLcfZnhklGu/t5FFc/oaZjK6btEJPL/jdR5+Nl65zsOnNLJfuWbVxsz1gdIImEphkebzStJ4rLieUQ+x5dDHdRTZCvw2MArs4IDPRlV1RsNmWCCaUQ690rRVi+nukMklNn5uYQNm5xG1Z0fw0uSEaTSlkOqplZS7JHOkWxwfmTeDB5/ekXs6nj53k40ziTWCFxLKpvv4v6G49y0QKiyS9mNZOXGjFnIrh17BR2qcj5GSMA1EyJ5i5429o5lCf7OGCUetppXocgxJAqkk+QokILHMRy2Uu7w8d7VosM2g8jcURl9vNw8vPT30WJjfyv8N9pnGYzSBLELpp3jh35cAR+AFDNwBfKEB8+pI8sz5lmafSP/AINd+b+M4R3yaPSW1aHBxmlK5JLlGvjUU5+CrxSzY19s9dl7WdzslRQQhJJvsknw7ZnozWk0W8903gGPwhNCLeCHhV+FtWv2zhs2wQDTafDdz6b25rL6jIsNKIuxXTVUKIm413T8wGGm6mtJTZs/I/qoIsZNmHJrof5novLD8LC9DxF2PZxLC5ZKw4oITqwRDmIYbZ1I0TcdoFVnMd1mE0q+Bd6nqUKBtKp5QmlrTTNuMPIVSpZbiVykNc4yn8cdkJcksKFCVKSHI1f1PcNvaLeOu4fsbYPxK+7Rjp7Hysa3tow01gN7uMhuWLUj02fhM6SkztHskUlMJM9N1l0scXO4KDUH3BZJpQEYraJRP6d+AHmAo0NYNbM9wDQNCV8tDwyN0SbWzv7tcyrxxNo0fKul4UoTVdYtO8Or+RNzkKp3nnSyQyl3CNeccD6Tfczbw2QWxx6M2wh40qavqN9NdLnHasdNyTf1jGI0ii1D6JvBDEflfHNh4+kngVhEZs/Oo6gP5TnHisWL1ptCb9H6FQ7sn0TN50jgt4/ZHtmbSlOq9/QveTWv+8gdiV9Npw5M7eSd/SYQVFx4wvSVtuoV0/qOoz/TV4RGuv2h21WKhnuqvhtFMsgil/+z+/reK9v/iHuDdD99Z76QmOnE36aHdI2OrZN9Ek7fpLgl/NH//0boXd3LdohNqvl6toe1xpLm5t5qw8OmkOZe6hGVnH5947bisCWGLhStWbgi9TicvGIxikiX33cwUDxNIMfhZEPLcXV8vwdIPYSt0BW5buyV1Zdaw5J1Zd/KLHPCxRdFMgSRC6sqx9ZRYmNJT5ssXVgc0hJE1a4IVyzPahSyaklEHfl2euL04/h4Yn2asYverjpl7onK0+XncKvOpDQ4NR2oslVrWuhd3pt435F+v1CWJpcabgsKH5s2oCuyopLe7HBmx6BOVC29KT3mchpwUkJA1dDtt3jzDaDUmlJpEUl2e3u4y15xz/LibSm8OyTyT6O0pJwpLOCAgqxJ2JiQlvW3tFuYeNXVcYESaTcF5lXzIg96eMisf3ZroqxsZTU4Nuezs46uCXMqlAya7LLWIsqQcsv1HRrtgQqlJxN1kv+I0lStWbmDF6k1jN4tGm6jKJWHPyGiqTAq+mSerSTGoZQVvolf3P9GQjAuNIO3CwM+kEXejTxIOUQEJn7nz8XHn14IVyzPaARNKBSC4cvaLy617cSevNlhbGBnVVKHaQTNPLSbFoEM+LIvERMJPLBunkcQJh6jP168b5Z9vGBMVE0pNIsqXIBAqGL61dgvdEclNm0Vl0s7+gUG6atzIe/TSe5nSU+b1Pftyz3FXJHxzW637geIiFS2E2+gEUmd0MOrL6BC2YTYp51uXeHuXWkHQ8Q7pEn0a0cSlbQqSNqGq+YWMdiJLRofUIeFGfSya08eKC04cF+r8loPiFdU8BJKQPpzZJ+h492lGePpEJq3Z0y/kV5LwL83f2JylPLlhtBMmlJqE70sJBjwk+VWibkxZULIJt5IIF/3+kVWrb9tkeYC+Gvb2ZNkPtGhOH1/+4IlV+5DCIhaDBRINYyLQFKEkIkeKyIMi8pSIbBSRT7v2qSKyRkSecX+nuHYRkRtEZLOI/FxETgpca7Hr/4yILA60nywiT7hzbhDx7ui1jJE3vkkmi3O/XBIuOfVIyqX6BVMWRlX51totHP/ZH6Yqdd5plETomZzt36ZyP1DUJuMgYaXPo9YWtmAwJhLN0pT2AZ9R1XcD84BPishxwFLgflWdBdzvXgOcCcxyjyXAjTCWlXwZcCpwCrDMFzKuz5LAeX7p1UxjNIKspq8pPWVWXHAi1y06gXJW21tOvLF3dJyJ6LRjp1Wt3DuRUVWeeeWN1P0rMzv4C5Q0JrhFc/p4eOnpPL/8LB5eenpklgtbMBgTiaYIJVXdrqo/c89fA54C+oBzgVtct1uARe75ucCt6rEW6BWRw4EzgDWqulNVdwFrgIXu2FtV9afqRW7cWnGtLGPkTpaV7AvLz2LgswvGot12tzD6zmd4ZJQHn94xtnIvGuUCGqG7yyW+ctFsHl56+jhTaFxi1Dj6BwZ5Y+++0GO79+4zv5IxYWh6SLiIHA3MAR4B3qGq28ETXCLydtetD9gaOG2ba4tr3xbSTg1jjCvFISJL8DQpZsyYke3NOmrNzFAkX0FRTURRofatJK6YXtTnWNlemWpo9959kZGau3aP5JI41zCKQFOFkoi8BbgbuFxVfyPRjvywA1pDe+x00pyjqjcBN4EXEp5wzSr6BwZ5fU/4CreS7nIX85c/MHYjyjuzdj30TC7FVjVtBa0ybcYhEBv6HZfd2ycs1VASwZROFiJutDNNM3yISBlPIN2mqt9xzS/7JjP39xXX7tdr8pkOvJTQPj2kvZYxcmXF6k2pN4sOj+wf52so0i3X9zEVhe5yF/tJnwKoWST5d8KyewO88eYBE1yt4fd+SifDaGeaFX0nwDeAp1T1bwKHVgF+BN1i4J5A+6UuQm4e8Kozwa0GFojIFBfgsABY7Y69JiLz3FiXVlwryxi5Uo/Zq0hCoGgMj+xvSAbx3u4ygif0alHE/Ci7sAg73yQ3PDJateAYGh4ZC3io5zdTVDOrYaSlWea7+cBHgSdExK829t+A5cCdInIZsAW40B27D/gAsBnYDXwMQFV3isjngcdcv8+p6k73/OPAP+GVaP+Be5B1jLwpmhmuHejr7ebow7r512d3Nl0wH3LQpHHZ2ucvfyD199dT7hoLUKk0v1151+OgjGnNYe/LD3iI+s2kKWzYm6JqrWEUGUszlIFa0gz1DwxyeUTVTyOcPndTbmWaJb+USFo/WrnrQNnzLIKsEgGuv2h2aO2jNCa94DwMoyhYmiGjrfFv6K3M2+qb07pTxJuXZLwgqMeE5pczr9w4mzYcf2S/ml/JaGssS3iDqfUGUcRQ505jeGSUpExP3eVSVdnzWk22wgGfVFh5i3Uv7kysfgvmVzLaG9OUGkzWG8RH5s3gheVn0TPZ1gtFIM66XRIZ8wMFN69GRdgl8eF5MyLNbv0Dg9y9fjCVKdEyPBjtjN35Gsyh3eXYqrOHTC6xe+9oVRmCTguOELy9UG/sLVYm8lJE/SiBsfbKmknBKrJpv8f575o6tvG1cuPslWcckzpMvDLPnmG0G6YpNZC41DA++9VzbFemo8kjQ3g7cMjk0pivpFzqanoC2ji6yyUuOfXI1Nm6L1+5YSz8289b95F58VlASiJ8ZN4MbvtP7wHCc+NdvnJDrHDzfyuVefYMox0xTamBrFi9KbHceFQ10Vqqu7Yje/ftH7vhDg2PUO6ShkfdzX/XVNY+tytSA4LxFXfnHjV1nOYSJyAqtSZf+7n9ka2MqlISL/t7VDqgrBtn0xYPNIx2wYRSA0nrTwrr19cB+5vCyr03ulT6/HdN5bb/9J7QCq9hQQtQHXSQFPIdTLBaaYZL0mKy+CDNVGdMRMx810DSOpzD+p127LS8p1MoekIEUjP42ZZXx8xrYWHXaUxfaQIZfI0pqkRFVE2lNL+ZrPM1jHbCNs9mIOvm2bDVeCWVq3PfyT3RtaRWkofJK2lTdFSAhJ9BPEpLA7jy249HaoxmrjPaEds8WxCCq3GoTksuwPkn940TSP7q2mgceezjWTSnL3IzazAyL2zsxJpKEbEeZq4zOgHTlDJQS5ohnyQ/xJSeMqrEho8b+dDbXWbDsgVAePh1WpNYmCYsePuNHnx6R+j33dfbzUvOpBc1t7DfQEmEL3/Q0gcZ7UkWTckCHZpEkvZj2RvqJ23Unh9tH5Y4NRg5FyRMeAEcNKlr7PwpPWWWnX18aFJWOKDpxJlnoxYl+1VNIBkdgZnvmkD/wGChaiNNVNIG7g25BUDa0uRhe4euvOtxrvz24+OEyNDukbG9SkBkIEUtGR8sS4PRKZim1ARWrN5ktZEKhH+DT1uaPEx4he0/81t8jeuL550QGpTgazxps8ebL8noJExTagJZHOuHTM6eM60dKZdkrKDelJ5yLqXNveJ8yZ+fH24fpX1UttcSGBGmcQWJC5QIYqHfRqdhQqkJpDW9lEvCvz+p+TefVpgWV1xwIhuWLeD6i2bTM3lSLptmlfEmsykRBe9WPraV/oHBUDNad7nEacdOG7eH6NDu2grnDQ4Nj0vUWkmSGc8P/zaBZHQSzSqHfrOIvCIivwi0TRWRNSLyjPs7xbWLiNwgIptF5OciclLgnMWu/zMisjjQfrKIPOHOucGVRK9pjEaQxvRy0KQuVlxwIg8+vaORUwmlFabFYDBAXiHwfa4W0cNLT+f5mEzrI6M6ltqp0u9z/sl93L1+cJz/6I29+6o0ubQ5+oIbZn38jbNXrNzAQZO6QrVjM9kZnUqzNKV/AhZWtC0F7lfVWcD97jXAmcAs91gC3AiegAGWAacCpwDLfCHj+iwJnLewljEaxaI5fZGrdp839+3nmlUbO2qPUtY8b3GE3cTjzG5Rx77/+PZQ/9E+Vab0lMeE14oLTkz8TqHajFcZNDE0PMJ+9UqW1JJdwjAmGk0JdFDVH4vI0RXN5wLvdc9vAR4C/sq136reBqq1ItIrIoe7vmtUdSeAiKwBForIQ8BbVfWnrv1WYBHwg6xjqOr2PN93kGVnHx+7Ux86Z49SX0KgQS3XC9tfFJc89Yje7tCQ8ChUYc/Ifq6/aPa4cZIydsD49xkV8ffg0zssU4Nh0Fqf0jt8IeD+vt219wFbA/22uba49m0h7bWMUYWILBGRdSKybseOOk1rFhc+TqPJEuZ8yORSqP/nKyFlP3yuPOOY0ACKckky1SjyqdR6Ks1/UbEawfeZNuLPMDqVIgY6hP1raw3ttYxR3ah6k6rOVdW506bVniQ1TRmLic6UnjIHTeriCreX57Rjp6Xer7N77yhfPO8EegNBBweX43++i+b0seLCE8edM6WnzIoLvMwItQiCynN8H9b1F80OrYHlC0CftBF/htGptHKf0su+ycyZ515x7duAIwP9pgMvufb3VrQ/5Nqnh/SvZYyGUYSV8JSeMntGRluSnRu8zaXBvTx3rx/kpBmHRtY2CuLftN/cd2Duu3aPRGZg8KksO1F5zaw+vCjhsWL1plDT7CGTJ40bPyoZqwU1GIZHKzWlVYAfQbcYuCfQfqmLkJsHvOpMb6uBBSIyxQU4LABWu2Ovicg8F3V3acW1sozRMFKHhXdJw6qv7to90jKBBOHVWv/12Z2JAinO3Ja0HyiOrJkV4oRH1KLj1Qo/YT0lMwyjE2iKpiQit+NpOW8TkW14UXTLgTtF5DJgC3Ch634f8AFgM7Ab+BiAqu4Ukc8Dj7l+n/ODHoCP40X4deMFOPzAtWcao5GErZArKYmw4sITWffiTm5bu6UjskAkvcdDJpf4wr/3btpRGRAGh4aZv/yBTMlU4YB2FZeLriTCftXEZK1RWlfYYiROezOMTqdZ0XeXRBx6X0hfBT4ZcZ2bgZtD2tcBvxvS/uusYzSKyhugMP6GHKyrZGmJDhC0iEXVKIL4ZKpx+AIiSyXaMMwsZxj5UMRAhwmL7xR/YflZXH/R7EgTThH8T3kSFgCQlqB5LsnMV48pr16zmpnlDCMfLCFri8jbAV8EustdgFRpC36WhErT5ay3H8K2XXtS7/PpS/G51CPQ6zWrmVnOMOrHNKUCUktpg1ZT7hK+eN7vhWoL1y06gfNP7quKw9+2aw/nn9w31j9Ko/L9Mmk+FwutNoz2xjSlAhEsJHdod5mDy12FL/4nUBUEEKYtPPj0jtDou2Amg7jCeMHrxvnlzIdjGO2NCaWCUHlDHhoeobtc4iPzZoSavrJS7hImT+rijb355JqDA1ms05Amk8GiOX2se3Entz+ylVFVSiKcf/J4k1jQRFZPKXPDMIqJCaUmkObmGZcT7YvnnTDu/LT+pu5yF3tG9nNod5k39u7LVSBl1UrShEz3Dwxy9/rBsYCGUVXuXj/I3KOmhgob8+EYxsTDfEoNJqyUdlg5gzhNIpjKJhvC9RfN5pCDJuWa4qhLyBxZFlW7KCjY8t4caxhG+2FCqcGkvdEm5UTrHxjkyrsezxSVNzwyymfuTD4na8S2ara9QJAuZNqSlRqGYea7BpP2Rpu0+fLa722sSdtJ2tvTXS5l9lfVGuEWZm4Lmja7IjbHWkSdYXQOJpQaTNr0M8HIsjDfU6Oi8LIKpLwi3PoHBrlm1cZxNaTCBJJF1BlGZ2FCqcFkST9TdMd9SSSXLAVhod+V46TJN2cYxsTDhFKDSdKA0tLbXW55Zdr9qrkIiKTievtVeX75WXWPYxhG+2FCqQnkoQFdc05yOfUkyiUBZdw1KjegxpGXbycpcMF8SIbRuZhQagJ5bPL0+3/mzscTgxfC6HPjwnit7bRjp6XenJuXbydur5X5kAyjszGh1GAq/Se1llgI9k+qyxQkrPxC5bhzj5qaGAE3paecm28nqrbUlJ4yy84+3nxIhtHBmFBqMHH7lGq5+fqpeL61dkti376UWlll6p6wwIxlZx+fea5x40H9fjbDMCYeHS2URGQh8LdACfi6qi7Pe4xGbAh98OkdscezFKerpFkCo+iRhoZhtIaOFUoiUgK+Brwf2AY8JiKrVPXJPMfJUiY7LXECrbe7zDXn1GcCM4FhGEar6FihBJwCbFbV5wBE5A7gXCBXoZRHmezKQInennLoZtre7jIbli3IZd6GYRitoJOFUh+wNfB6G3BqZScRWQIsAZgxY0bmQeo1h4UFSpS7hHJJxqUd6i6XuOac/Pw+hmEYraCThVJYGtKqsDNVvQm4CWDu3Lk1bRKqxxwWFigxsl/p7S5zyEGTLFDAMIwJRScLpW3AkYHX04GXWjSXSKL8R68Oj5ipzjCMCUcnl654DJglIjNFZDJwMbCqxXOqIqmkhWEYxkSiY4WSqu4DPgWsBp4C7lTVja2dVTVpiuMZhmFMFDrZfIeq3gfc1+p5xGEbTQ3D6CQ6Wii1C7ZvyDCMTqFjzXeGYRhG8TChZBiGYRQGE0qGYRhGYTChZBiGYRQGE0qGYRhGYRCtoYpppyIiO4AXazj1bcCvcp5OHti8smHzyobNKxsTeV5Hqeq0NB1NKDUBEVmnqnNbPY9KbF7ZsHllw+aVDZuXh5nvDMMwjMJgQskwDMMoDCaUmsNNrZ5ABDavbNi8smHzyobNC/MpGYZhGAXCNCXDMAyjOKiqPRr0ABYCm4DNwNI6r3Uz8Arwi0DbVGAN8Iz7O8W1C3CDG/fnwEmBcxa7/s8AiwPtJwNPuHNu4IAWnTTGC8DrwPPARuDTBZnb14A9wDDwLHCtOzYTeMSdsxKY7NoPcq83u+NHB8a/yrVvAs5I+n5TjvEGcH/B5vQm8DSwAVhXkO/xBuA5YAjvN/YU8J4CzOtW93kNu8/7N8DlBZjXj9289gA/BA5O+d034/c1bozIe12rb9wT9QGU8G6G7wQmA48Dx9VxvT8CTmK8UPqf/g8DWAp8yT3/APAD9yOdBzwS+DE/5/5Occ/9H/SjeP/s4s49M+UYh7t/qkeA3wJ+CRxXkLm9xY3xqJvfPOBO4GLX7++Aj7vnnwD+zj2/GFjpnh/nvruD3D/es+67jfx+k8YA/hz4v8BLafo3Y07u+Q7guxW/uyJ8j7cA/8N9h5OB3oLMa2wM4N+Ao1o8r48Cu4FuN8avgf+Q8rtvxu9rbIzYe12rb94T9eF+TKsDr68CrqrzmkczXihtAg53zw8HNrnnfw9cUtkPuAT4+0D737u2w4GnA+1j/WoY4x7g/QWb2y/xVqen4m0CnFT5HeEVe3yPez7J9ZPK783vF/X9unPixjgHuB/4E2Bviv7NmJM/xgt4NzIp0G/sz/A0JKno1+p5BcfYCjxagHl9031/U93v5XV3XlF+X2NjxN3nzKfUOPrwfqw+21xbnrxDVbcDuL9vTxg7rn1bxFyzjDEXmIO3cmz53ESkJCIb8FZ2A3irvCH1qg5XXmtsfHf8VeCwGuZ7WMIY/xn4S2AfMALMKsCc/HMUT7scEJElrq3V3+N+PA3uH4EjgBtF5JACzCt4rRLwUMZzGjGvKXiCaQuwHU8ovUJBfl8VY0RiQqlxSEibtnjsrO1ZxijhmRUuV9XfFGFuqjqqqrOBh/FMEu+OuVZe84qb728Bv1LV9dFTb/qcgsfm491QLgI+KSJ/FDPHZn2PJTyz9Y145qxhPJNVq+flPRGZjJeGZ03acxo4rzLw/+CZ3I7A++z+IOY6zf59VR4LxYRS49gGHBl4PR14KecxXhaRwwHc31cSxo5rnx4x18QxRKQM/D5wh6p+p0hzc/w23kp2HtArIn7F5eC1gu9nEnAosLOG+f4qZoxR4EwReQG4A+gBlrV4TsFzXnFj/BL4LnAKrf8ey8A2VX3EnbMST0i1el7+tc7E03h/keGcRs2rC/iNqu5Q1RE8bfx3KMjvq2KMSEwoNY7HgFkiMtOtpi4GVuU8xiq8IAPc33sC7ZeKxzzgVafmrwYWiMgUEZkCLMCz/W4HXhOReSIiwKUV14odw7W9pqrXFmhul4lIrxvjN3gC6SngQeCCiHP8a10APKCeIXwVcLGIHCQiM/HMbY8S8f26c6LG+DLwHVU9Gm9D4nZV/XCL57QKWOxMYh8BHsATlgvwbrSt/h7PxTPFfhDP9DMbeLIA8/J/+58CXvbNaS2e133AXBHpcWNMAtZRgN9XyBjRxDmc7FHfAy/i5pd4voy/rvNat+PZiUfwVh+X4dlm78cLw7wfmOr6+iHRz+KFlM4NXOfP8MIzNwMfC7TPxbsJPQt8lQPhp0ljDOKp48/ghRJvcO+71XO7gwMhu5uBz7pj78T7B9sMfBs4yLUf7F5vdsffGRj/r93Ym3ARUHHfb8oxnsL7By3KnF7Ai9x6Ci+0/69Tfv/N+I1tdXN7BujH850UYV7P4Wkjfxy4Vqvn9SheAM0e4F68CLoi/L6qxoh6WEYHwzAMozCY+c4wDMMoDCaUDMMwjMJgQskwDMMoDCaUDMMwjMJgQskwDMMoDCaUDMMwjMJgQskw2hAReUhE/mMO13mviGxL7mkYzcGEkmEYhlEYTCgZRosRkStF5O6Ktv8lIl+J6P8FvMSbXxWR10Xkq679WBFZIyI7RWSTS83jn/MBEXlSRF4TkUER+QuXWugHwBHuOq+LyBGNe6eGkYxldDCMFuOSaqFphGgAAAHbSURBVG4G+lR1yCWufAkvvUtoRnEReQj4lqp+3b0+BK9q7Gfxyhf8HvAjvBQ4G0VkO/BBVf2Jy7E2U1V/JiLvddeZHjaOYTQb05QMo8Wol3zzx8CFrmkh6UpcBPlT4AVV/UdV3aeqPwPu5kCSzBHgOBF5q6rucscNo3CYUDKMYnALXpZu3N9vZjz/KOBUERnyH8CH8cp2AJyPl0zzRRH5FxF5Tx6TNoy8MaFkGMWgH/g9EfldPK3ntoT+lXb3rcC/qGpv4PEWVf04gKo+pqrn4lUp7QfujLiOYbQUE0qGUQBUdQ9wF/DPwKOquiXhlJfxygX4fB/4dyLyUREpu8fvi8i7RWSyiHxYRA5Vr/jbb/AKDvrXOUxEDs35LRlGTZhQMozicAtwAulMd38LXCAiu0TkBlV9Da9A3MV4QRL/BnwJr54OwEeBF0TkN8B/wZkKVfVpvFpdzzmzn0XfGS3Fou8MoyCIyAy8CLrfVtXftHo+htEKTFMyjAIgIl3AnwN3mEAyOplJrZ6AYXQ6bo/Ry8CLeOHgfvvrEaecqao/acbcDKPZmPnOMAzDKAxmvjMMwzAKgwklwzAMozCYUDIMwzAKgwklwzAMozCYUDIMwzAKgwklwzAMozD8/9YhVKK/8+EkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test , pred)\n",
    "plt.xlabel('y_test' , fontsize = 12)\n",
    "plt.ylabel('predictions' , fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recreate a histogram for the residuals : y_test - prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test - pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE : 798341.30176276\n",
      "MSE : 1084186725245.7777\n",
      "RMSE : 1041242.875243705\n"
     ]
    }
   ],
   "source": [
    "print('MAE :',metrics.mean_absolute_error(y_test , pred))\n",
    "print('MSE :',metrics.mean_squared_error(y_test , pred))\n",
    "print('RMSE :',np.sqrt(metrics.mean_squared_error(y_test , pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean Square Error (MSE) or the residuals sum of squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error(MSE): 1084186725245.78\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Error(MSE): %.2f\"\n",
    "         % np.mean((lm.predict(X_test) - y_test) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking r2\n",
    "\n",
    "### Rules of r2\n",
    "\n",
    "- r2 < 0.19   - very weak relationship/correlation between variables\n",
    "- 0.20 - 0.39 - weak relationship/correlation\n",
    "- 0.4 - 0.59 - moderate relationship\n",
    "- 0.6 - 0.79 - strong relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To be sure that our predictions we need to check for our r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5633232867125878\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error , r2_score\n",
    "print(r2_score(y_test , pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the varaince"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The closer the number is to 1 the better the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance score is 0.56\n"
     ]
    }
   ],
   "source": [
    "print(\"variance score is %.2f\"%lm.score(X_test , y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FINAL PREDICTION Into A Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1010405.2542907607\n",
      "1041242.875243705\n"
     ]
    },
    {
     "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",
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       "    }\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>2690353</td>\n",
       "      <td>1.776616e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>3360871</td>\n",
       "      <td>3.491671e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>1787661</td>\n",
       "      <td>1.972372e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>1038264</td>\n",
       "      <td>1.805660e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>1163942</td>\n",
       "      <td>1.076569e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>2389606</td>\n",
       "      <td>2.024098e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4238</th>\n",
       "      <td>2465729</td>\n",
       "      <td>2.367479e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2634</th>\n",
       "      <td>2947033</td>\n",
       "      <td>2.188882e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>654910</td>\n",
       "      <td>2.740087e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4049</th>\n",
       "      <td>835387</td>\n",
       "      <td>1.906075e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual     Predicted\n",
       "856   2690353  1.776616e+06\n",
       "3656  3360871  3.491671e+06\n",
       "4900  1787661  1.972372e+06\n",
       "4177  1038264  1.805660e+06\n",
       "3867  1163942  1.076569e+06\n",
       "2295  2389606  2.024098e+06\n",
       "4238  2465729  2.367479e+06\n",
       "2634  2947033  2.188882e+06\n",
       "350    654910  2.740087e+06\n",
       "4049   835387  1.906075e+06"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "plt.scatter(y_train, lm.predict(X_train))\n",
    "print(np.sqrt(mean_squared_error(y_train, lm.predict(X_train))))\n",
    "plt.scatter(y_test, lm.predict(X_test))\n",
    "print(np.sqrt(mean_squared_error(y_test, lm.predict(X_test))))\n",
    "pd.DataFrame({'Actual':y_test, 'Predicted': lm.predict(X_test)}).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization\n",
    "\n",
    "### Regularisation is used to solve overfitting to the training data by restricting the value or parameters that are estimated in the model\n",
    "### Note : It doesnt improve the model performance of the algorithm on the dataset that has been defined as per the model parameters. It can improve the generalization performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge Regression L2 \n",
    "`- It is used when there is the problem of multicollinearity . By adding a degree of bias to the regression estimates\n",
    "  ,Ridge regression reduces the standard errors`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1010405.3146769239\n",
      "1041239.3443338225\n",
      "R2 Value/Coefficient of Determination: 0.563326248295413\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridgeReg = Ridge(alpha=0.0001, normalize=True)\n",
    "ridgeReg.fit(X_train,y_train)\n",
    "print(np.sqrt(mean_squared_error(y_train, ridgeReg.predict(X_train))))\n",
    "print(np.sqrt(mean_squared_error(y_test, ridgeReg.predict(X_test))))\n",
    "print('R2 Value/Coefficient of Determination: {}'.format(ridgeReg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso Regression (L1)\n",
    "`- Similar to Ridge , but it also performs feature selection . It uses shrinkage , where data values are shrunk toward\n",
    "  a central point like the mean`.\n",
    "    \n",
    "    `- It tends to exclude variables that are not required from the equation ,whereas Ridge tends to do better when   all the variables are present`.\n",
    "\n",
    "`- Lasso is well suited for models showing high levels of multicollinearity or when you want to automate certain parts\n",
    "  of the model selection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1003444.4480152754\n",
      "1049451.899432467\n",
      "R2 Value/Coefficient of Determination: 0.5577427251129935\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lassoReg = Lasso(alpha=0.00001, normalize=True)\n",
    "lassoReg.fit(xtrain,ytrain)\n",
    "print(np.sqrt(mean_squared_error(ytrain, lassoReg.predict(xtrain))))\n",
    "print(np.sqrt(mean_squared_error(ytest, lassoReg.predict(xtest))))\n",
    "print('R2 Value/Coefficient of Determination: {}'.format(lassoReg.score(xtest, ytest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1004878.0025986015\n",
      "1049781.0300515688\n",
      "R2 Value/Coefficient of Determination: 0.5574652788837808\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "Elastic = ElasticNet(alpha=0.00001, normalize=True)\n",
    "Elastic.fit(xtrain,ytrain)\n",
    "print(np.sqrt(mean_squared_error(ytrain, Elastic.predict(xtrain))))\n",
    "print(np.sqrt(mean_squared_error(ytest, Elastic.predict(xtest))))\n",
    "print('R2 Value/Coefficient of Determination: {}'.format(Elastic.score(xtest, ytest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Note :Since i got an r2 of 0.56 as the highest r2 this means that the model is moderately strong as per the r2 rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5737020644457895"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = lm.score(X,y)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3457772524747176"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for the variance\n",
    "1 / (1 - r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Note since this value is not above 4 then we are sure that there is no Multicollinearity so no further investigationsneed to be done. If it around 1 then we no that there is no correlation between the varialbes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improving the model - As Per CRISDM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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",
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       "    }\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>field1</th>\n",
       "      <th>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>733773</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>3.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.50</td>\n",
       "      <td>100.99</td>\n",
       "      <td>429193.0</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.7627</td>\n",
       "      <td>-84.4225</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>733773</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>0.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>98.19</td>\n",
       "      <td>605085.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>38.9047</td>\n",
       "      <td>-77.0163</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>733773</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>27.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1011.60</td>\n",
       "      <td>65.94</td>\n",
       "      <td>5074252.0</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>103.8558</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>733773</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1014.78</td>\n",
       "      <td>115.24</td>\n",
       "      <td>3450395.0</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5218</td>\n",
       "      <td>13.4015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>733773</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>0.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1015.60</td>\n",
       "      <td>130.30</td>\n",
       "      <td>10460118.0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8667</td>\n",
       "      <td>2.3333</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   field1      ds        city  PASSENGERS           Type  temperature_mean  \\\n",
       "0       0  733773     Atlanta     2735491       Domestic               3.6   \n",
       "1       1  733773  Washington     1929494       Domestic               0.4   \n",
       "2       2  733773   Singapore     1681005  International              27.5   \n",
       "3       3  733773      Berlin      489487  International              -4.9   \n",
       "4       4  733773       Paris     4154249  International               0.6   \n",
       "\n",
       "   precipitation  rain_days  pressure  cost_of_living_index  population  \\\n",
       "0          137.0        7.0   1019.50                100.99    429193.0   \n",
       "1           57.0        8.0   1016.70                 98.19    605085.0   \n",
       "2           70.0        8.0   1011.60                 65.94   5074252.0   \n",
       "3           21.0        7.0   1014.78                115.24   3450395.0   \n",
       "4           36.0        7.0   1015.60                130.30  10460118.0   \n",
       "\n",
       "   city_ascii      lat       lng        country iso2 iso3  \n",
       "0     Atlanta  33.7627  -84.4225  United States   US  USA  \n",
       "1  Washington  38.9047  -77.0163  United States   US  USA  \n",
       "2   Singapore   1.2930  103.8558      Singapore   SG  SGP  \n",
       "3      Berlin  52.5218   13.4015        Germany   DE  DEU  \n",
       "4       Paris  48.8667    2.3333         France   FR  FRA  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(\"field1\" , 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ds</th>\n",
       "      <th>city</th>\n",
       "      <th>PASSENGERS</th>\n",
       "      <th>Type</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>city_ascii</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>country</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>733773</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2735491</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>3.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.50</td>\n",
       "      <td>100.99</td>\n",
       "      <td>429193.0</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.7627</td>\n",
       "      <td>-84.4225</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>733773</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1929494</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>0.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1016.70</td>\n",
       "      <td>98.19</td>\n",
       "      <td>605085.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>38.9047</td>\n",
       "      <td>-77.0163</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733773</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1681005</td>\n",
       "      <td>International</td>\n",
       "      <td>27.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1011.60</td>\n",
       "      <td>65.94</td>\n",
       "      <td>5074252.0</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>103.8558</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SG</td>\n",
       "      <td>SGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733773</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>489487</td>\n",
       "      <td>International</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1014.78</td>\n",
       "      <td>115.24</td>\n",
       "      <td>3450395.0</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5218</td>\n",
       "      <td>13.4015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>733773</td>\n",
       "      <td>Paris</td>\n",
       "      <td>4154249</td>\n",
       "      <td>International</td>\n",
       "      <td>0.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1015.60</td>\n",
       "      <td>130.30</td>\n",
       "      <td>10460118.0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8667</td>\n",
       "      <td>2.3333</td>\n",
       "      <td>France</td>\n",
       "      <td>FR</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ds        city  PASSENGERS           Type  temperature_mean  \\\n",
       "0  733773     Atlanta     2735491       Domestic               3.6   \n",
       "1  733773  Washington     1929494       Domestic               0.4   \n",
       "2  733773   Singapore     1681005  International              27.5   \n",
       "3  733773      Berlin      489487  International              -4.9   \n",
       "4  733773       Paris     4154249  International               0.6   \n",
       "\n",
       "   precipitation  rain_days  pressure  cost_of_living_index  population  \\\n",
       "0          137.0        7.0   1019.50                100.99    429193.0   \n",
       "1           57.0        8.0   1016.70                 98.19    605085.0   \n",
       "2           70.0        8.0   1011.60                 65.94   5074252.0   \n",
       "3           21.0        7.0   1014.78                115.24   3450395.0   \n",
       "4           36.0        7.0   1015.60                130.30  10460118.0   \n",
       "\n",
       "   city_ascii      lat       lng        country iso2 iso3  \n",
       "0     Atlanta  33.7627  -84.4225  United States   US  USA  \n",
       "1  Washington  38.9047  -77.0163  United States   US  USA  \n",
       "2   Singapore   1.2930  103.8558      Singapore   SG  SGP  \n",
       "3      Berlin  52.5218   13.4015        Germany   DE  DEU  \n",
       "4       Paris  48.8667    2.3333         France   FR  FRA  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ds                      0\n",
       "city                    0\n",
       "PASSENGERS              0\n",
       "Type                    0\n",
       "temperature_mean        0\n",
       "precipitation           0\n",
       "rain_days               0\n",
       "pressure                0\n",
       "cost_of_living_index    0\n",
       "population              0\n",
       "city_ascii              0\n",
       "lat                     0\n",
       "lng                     0\n",
       "country                 0\n",
       "iso2                    0\n",
       "iso3                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for nulls\n",
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop columns\n",
    "df2.drop(columns=['country','iso2','iso3'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Coverting the categorical data to numerical data using onehot encoder\n",
    "df2 = pd.get_dummies(df2, columns=[\"city\",\"Type\"], )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ds</th>\n",
       "      <th>PASSENGERS</th>\n",
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       "      <td>65.94</td>\n",
       "      <td>5074252.0</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>...</td>\n",
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       "      <td>-4.9</td>\n",
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       "      <td>1014.78</td>\n",
       "      <td>115.24</td>\n",
       "      <td>3450395.0</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>52.5218</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>130.30</td>\n",
       "      <td>10460118.0</td>\n",
       "      <td>Paris</td>\n",
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       "      <td>...</td>\n",
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       "<p>5 rows × 55 columns</p>\n",
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      ],
      "text/plain": [
       "       ds  PASSENGERS  temperature_mean  precipitation  rain_days  pressure  \\\n",
       "0  733773     2735491               3.6          137.0        7.0   1019.50   \n",
       "1  733773     1929494               0.4           57.0        8.0   1016.70   \n",
       "2  733773     1681005              27.5           70.0        8.0   1011.60   \n",
       "3  733773      489487              -4.9           21.0        7.0   1014.78   \n",
       "4  733773     4154249               0.6           36.0        7.0   1015.60   \n",
       "\n",
       "   cost_of_living_index  population  city_ascii      lat  ...  city_Seattle  \\\n",
       "0                100.99    429193.0     Atlanta  33.7627  ...             0   \n",
       "1                 98.19    605085.0  Washington  38.9047  ...             0   \n",
       "2                 65.94   5074252.0   Singapore   1.2930  ...             0   \n",
       "3                115.24   3450395.0      Berlin  52.5218  ...             0   \n",
       "4                130.30  10460118.0       Paris  48.8667  ...             0   \n",
       "\n",
       "   city_Seoul  city_Singapore  city_St. Louis  city_Taipei  city_Tampa  \\\n",
       "0           0               0               0            0           0   \n",
       "1           0               0               0            0           0   \n",
       "2           0               1               0            0           0   \n",
       "3           0               0               0            0           0   \n",
       "4           0               0               0            0           0   \n",
       "\n",
       "   city_Tokyo  city_Washington  Type_Domestic  Type_International  \n",
       "0           0                0              1                   0  \n",
       "1           0                1              1                   0  \n",
       "2           0                0              0                   1  \n",
       "3           0                0              0                   1  \n",
       "4           0                0              0                   1  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check results\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy variables\n",
    "df3=pd.get_dummies(df3, columns=[\"city\",\"Type\",\"city_ascii\",\"iso3\"], drop_first=True )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.drop(\"country\" , 1 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.drop('iso2' , 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3.drop(\"city_ascii\" , 1 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          ds  PASSENGERS  temperature_mean  precipitation  rain_days  \\\n",
       "0     733773     2735491               3.6          137.0        7.0   \n",
       "1     733773     1929494               0.4           57.0        8.0   \n",
       "2     733773     1681005              27.5           70.0        8.0   \n",
       "3     733773      489487              -4.9           21.0        7.0   \n",
       "4     733773     4154249               0.6           36.0        7.0   \n",
       "...      ...         ...               ...            ...        ...   \n",
       "4963  737029     3794306               4.7          175.0       11.0   \n",
       "4964  737029     1295947              -3.3           39.0        4.0   \n",
       "4965  737029     3491734              14.8           37.0        2.0   \n",
       "4966  737029     3491734              14.8           37.0        2.0   \n",
       "4967  737029     2267294              22.2           40.0        2.0   \n",
       "\n",
       "      pressure  cost_of_living_index  population      lat       lng  ...  \\\n",
       "0      1019.50                100.99    429193.0  33.7627  -84.4225  ...   \n",
       "1      1016.70                 98.19    605085.0  38.9047  -77.0163  ...   \n",
       "2      1011.60                 65.94   5074252.0   1.2930  103.8558  ...   \n",
       "3      1014.78                115.24   3450395.0  52.5218   13.4015  ...   \n",
       "4      1015.60                130.30  10460118.0  48.8667    2.3333  ...   \n",
       "...        ...                   ...         ...      ...       ...  ...   \n",
       "4963   1019.10                100.00   8398748.0  40.6943  -73.9249  ...   \n",
       "4964   1018.50                 81.46    425403.0  44.9635  -93.2678  ...   \n",
       "4965   1017.90                 82.30   3990456.0  34.1139 -118.4068  ...   \n",
       "4966   1017.90                 82.30   3990456.0 -37.4600  -72.3600  ...   \n",
       "4967   1019.20                 86.01    470914.0  25.7839  -80.2102  ...   \n",
       "\n",
       "     iso3_ISL iso3_ITA  iso3_JPN  iso3_KOR  iso3_NLD  iso3_SGP  iso3_THA  \\\n",
       "0           0        0         0         0         0         0         0   \n",
       "1           0        0         0         0         0         0         0   \n",
       "2           0        0         0         0         0         1         0   \n",
       "3           0        0         0         0         0         0         0   \n",
       "4           0        0         0         0         0         0         0   \n",
       "...       ...      ...       ...       ...       ...       ...       ...   \n",
       "4963        0        0         0         0         0         0         0   \n",
       "4964        0        0         0         0         0         0         0   \n",
       "4965        0        0         0         0         0         0         0   \n",
       "4966        0        0         0         0         0         0         0   \n",
       "4967        0        0         0         0         0         0         0   \n",
       "\n",
       "      iso3_TWN  iso3_USA  iso3_VEN  \n",
       "0            0         1         0  \n",
       "1            0         1         0  \n",
       "2            0         0         0  \n",
       "3            0         0         0  \n",
       "4            0         0         0  \n",
       "...        ...       ...       ...  \n",
       "4963         0         1         0  \n",
       "4964         0         1         0  \n",
       "4965         0         1         0  \n",
       "4966         0         0         0  \n",
       "4967         0         1         0  \n",
       "\n",
       "[4968 rows x 115 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df3.drop('PASSENGERS' ,1 )\n",
    "y1 = df3['PASSENGERS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data\n",
    "Xtrain , Xtest , ytrain, ytest = train_test_split(X1 , y1 , test_size = 0.2 , random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3974, 112)\n",
      "(994, 112)\n",
      "(3974,)\n",
      "(994,)\n"
     ]
    }
   ],
   "source": [
    "#Check shape of DF\n",
    "print(Xtrain.shape)\n",
    "print(Xtest.shape)\n",
    "print(ytrain.shape)\n",
    "print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>ds</th>\n",
       "      <th>temperature_mean</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>rain_days</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cost_of_living_index</th>\n",
       "      <th>population</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>city_Athens</th>\n",
       "      <th>...</th>\n",
       "      <th>iso3_ISL</th>\n",
       "      <th>iso3_ITA</th>\n",
       "      <th>iso3_JPN</th>\n",
       "      <th>iso3_KOR</th>\n",
       "      <th>iso3_NLD</th>\n",
       "      <th>iso3_SGP</th>\n",
       "      <th>iso3_THA</th>\n",
       "      <th>iso3_TWN</th>\n",
       "      <th>iso3_USA</th>\n",
       "      <th>iso3_VEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>734747</td>\n",
       "      <td>16.6</td>\n",
       "      <td>99.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>118.52</td>\n",
       "      <td>8044433.0</td>\n",
       "      <td>42.9700</td>\n",
       "      <td>-81.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4054</th>\n",
       "      <td>736450</td>\n",
       "      <td>27.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1007.5</td>\n",
       "      <td>69.08</td>\n",
       "      <td>1634984.0</td>\n",
       "      <td>33.5722</td>\n",
       "      <td>-112.0891</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>735234</td>\n",
       "      <td>22.6</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>106.27</td>\n",
       "      <td>349627.0</td>\n",
       "      <td>21.3294</td>\n",
       "      <td>-157.8460</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4102</th>\n",
       "      <td>736481</td>\n",
       "      <td>26.7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>99.54</td>\n",
       "      <td>349651.0</td>\n",
       "      <td>21.3294</td>\n",
       "      <td>-157.8460</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>733804</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>80.35</td>\n",
       "      <td>319318.0</td>\n",
       "      <td>38.6358</td>\n",
       "      <td>-90.2451</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds  temperature_mean  precipitation  rain_days  pressure  \\\n",
       "1477  734747              16.6           99.0        9.0    1019.4   \n",
       "4054  736450              27.7            0.0        0.0    1007.5   \n",
       "2218  735234              22.6           63.0        9.0    1013.5   \n",
       "4102  736481              26.7           14.0        4.0    1017.0   \n",
       "80    733804              -0.8           53.0        7.0    1019.2   \n",
       "\n",
       "      cost_of_living_index  population      lat       lng  city_Athens  ...  \\\n",
       "1477                118.52   8044433.0  42.9700  -81.2500            0  ...   \n",
       "4054                 69.08   1634984.0  33.5722 -112.0891            0  ...   \n",
       "2218                106.27    349627.0  21.3294 -157.8460            0  ...   \n",
       "4102                 99.54    349651.0  21.3294 -157.8460            0  ...   \n",
       "80                   80.35    319318.0  38.6358  -90.2451            0  ...   \n",
       "\n",
       "      iso3_ISL  iso3_ITA  iso3_JPN  iso3_KOR  iso3_NLD  iso3_SGP  iso3_THA  \\\n",
       "1477         0         0         0         0         0         0         0   \n",
       "4054         0         0         0         0         0         0         0   \n",
       "2218         0         0         0         0         0         0         0   \n",
       "4102         0         0         0         0         0         0         0   \n",
       "80           0         0         0         0         0         0         0   \n",
       "\n",
       "      iso3_TWN  iso3_USA  iso3_VEN  \n",
       "1477         0         0         0  \n",
       "4054         0         1         0  \n",
       "2218         0         1         0  \n",
       "4102         0         1         0  \n",
       "80           0         1         0  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ds', 'temperature_mean', 'precipitation', 'rain_days', 'pressure',\n",
       "       'cost_of_living_index', 'population', 'lat', 'lng', 'city_Athens',\n",
       "       ...\n",
       "       'iso3_ISL', 'iso3_ITA', 'iso3_JPN', 'iso3_KOR', 'iso3_NLD', 'iso3_SGP',\n",
       "       'iso3_THA', 'iso3_TWN', 'iso3_USA', 'iso3_VEN'],\n",
       "      dtype='object', length=112)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear model\n",
    "# Create an instance for the linear model\n",
    "model = linear_model.LinearRegression(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain , ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.3955560079681726e+19\n"
     ]
    }
   ],
   "source": [
    "#print intercept\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.97775303e+02  1.96347846e+04 -1.50624757e+02 -1.09883407e+04\n",
      "  7.51521412e+03 -8.48818456e+02  3.85207794e-01  3.74404059e+17\n",
      "  1.32939406e+16  1.96784207e+19  2.26363435e+19 -3.70323429e+19\n",
      "  2.12691749e+19  1.38466949e+18 -3.01260329e+18 -1.09926992e+19\n",
      "  1.05971588e+19  2.48246571e+19 -6.41342279e+17  9.20509203e+17\n",
      " -1.05416888e+19 -4.46232139e+18  1.29353094e+19  2.22890738e+19\n",
      " -3.20276960e+18 -1.35010961e+19  2.62995217e+18 -2.27215868e+18\n",
      "  1.74510126e+18  1.96873108e+19 -8.90581428e+18  7.79305615e+18\n",
      "  4.12733641e+18  2.48297731e+17  6.80885785e+18  2.34076261e+19\n",
      "  3.56423940e+18  2.33644875e+19 -1.62934038e+19 -3.43372684e+18\n",
      " -2.42107240e+17 -2.30964556e+18 -1.25474891e+19 -5.96040061e+17\n",
      "  1.70681697e+19  1.08793267e+19 -2.24803997e+19 -1.50439336e+19\n",
      "  1.12887519e+19 -5.15083810e+18 -5.28411115e+18 -1.29337014e+19\n",
      " -1.79677151e+19  4.77537969e+19 -1.63648468e+19 -9.67928854e+16\n",
      "  7.09238981e+18  1.26815520e+19 -2.23629671e+18 -1.96290973e+19\n",
      "  4.73649172e+18  1.77382580e+18  1.19651428e+19  1.35208892e+19\n",
      "  1.72545587e+18 -1.19892798e+19  9.50576163e+18  1.76547456e+19\n",
      " -8.50326220e+18  7.26108167e+18  5.85482421e+18 -1.90947185e+19\n",
      "  1.08396691e+19 -1.18653485e+18 -1.60728739e+18  1.95869525e+18\n",
      " -1.70110731e+18 -4.21241312e+18 -2.79580001e+18 -1.39255173e+19\n",
      "  1.21912566e+19  5.84170618e+18  5.69420491e+18  5.98853364e+18\n",
      "  1.25313419e+19  2.04120480e+18 -3.23154809e+17 -7.95780136e+18\n",
      "  1.53652220e+19  2.18460244e+19 -3.63376818e+18  7.79582327e+18\n",
      "  1.01049715e+19  2.39537627e+19 -5.50719076e+18 -7.00488425e+18\n",
      " -1.60728739e+18  5.83272362e+18 -2.04165884e+18 -5.29014518e+18\n",
      " -6.48159984e+18  3.38366637e+18 -1.39255173e+19  7.48983366e+18\n",
      " -3.63376818e+18  2.04120480e+18 -4.25742260e+17  6.29899088e+17\n",
      " -4.21241312e+18  1.53652220e+19 -2.23165258e+18  5.58573820e+18]\n"
     ]
    }
   ],
   "source": [
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R2_SCORE:0.9569924830827083\n",
      "Intercept:  -1.3955560079681726e+19\n",
      "Slope:  [ 1.97775303e+02  1.96347846e+04 -1.50624757e+02 -1.09883407e+04\n",
      "  7.51521412e+03 -8.48818456e+02  3.85207794e-01  3.74404059e+17\n",
      "  1.32939406e+16  1.96784207e+19  2.26363435e+19 -3.70323429e+19\n",
      "  2.12691749e+19  1.38466949e+18 -3.01260329e+18 -1.09926992e+19\n",
      "  1.05971588e+19  2.48246571e+19 -6.41342279e+17  9.20509203e+17\n",
      " -1.05416888e+19 -4.46232139e+18  1.29353094e+19  2.22890738e+19\n",
      " -3.20276960e+18 -1.35010961e+19  2.62995217e+18 -2.27215868e+18\n",
      "  1.74510126e+18  1.96873108e+19 -8.90581428e+18  7.79305615e+18\n",
      "  4.12733641e+18  2.48297731e+17  6.80885785e+18  2.34076261e+19\n",
      "  3.56423940e+18  2.33644875e+19 -1.62934038e+19 -3.43372684e+18\n",
      " -2.42107240e+17 -2.30964556e+18 -1.25474891e+19 -5.96040061e+17\n",
      "  1.70681697e+19  1.08793267e+19 -2.24803997e+19 -1.50439336e+19\n",
      "  1.12887519e+19 -5.15083810e+18 -5.28411115e+18 -1.29337014e+19\n",
      " -1.79677151e+19  4.77537969e+19 -1.63648468e+19 -9.67928854e+16\n",
      "  7.09238981e+18  1.26815520e+19 -2.23629671e+18 -1.96290973e+19\n",
      "  4.73649172e+18  1.77382580e+18  1.19651428e+19  1.35208892e+19\n",
      "  1.72545587e+18 -1.19892798e+19  9.50576163e+18  1.76547456e+19\n",
      " -8.50326220e+18  7.26108167e+18  5.85482421e+18 -1.90947185e+19\n",
      "  1.08396691e+19 -1.18653485e+18 -1.60728739e+18  1.95869525e+18\n",
      " -1.70110731e+18 -4.21241312e+18 -2.79580001e+18 -1.39255173e+19\n",
      "  1.21912566e+19  5.84170618e+18  5.69420491e+18  5.98853364e+18\n",
      "  1.25313419e+19  2.04120480e+18 -3.23154809e+17 -7.95780136e+18\n",
      "  1.53652220e+19  2.18460244e+19 -3.63376818e+18  7.79582327e+18\n",
      "  1.01049715e+19  2.39537627e+19 -5.50719076e+18 -7.00488425e+18\n",
      " -1.60728739e+18  5.83272362e+18 -2.04165884e+18 -5.29014518e+18\n",
      " -6.48159984e+18  3.38366637e+18 -1.39255173e+19  7.48983366e+18\n",
      " -3.63376818e+18  2.04120480e+18 -4.25742260e+17  6.29899088e+17\n",
      " -4.21241312e+18  1.53652220e+19 -2.23165258e+18  5.58573820e+18]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "#Getting the Mean squared error\n",
    "print(f' R2_SCORE:{metrics.r2_score(ytest, pred1)}')\n",
    "\n",
    "#Getting the Intercept\n",
    "print('Intercept: ',model.intercept_)\n",
    "\n",
    "#Getting the Slope\n",
    "print('Slope: ',model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9569924830827083\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(ytest , pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted with normal regrssion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>2690353</td>\n",
       "      <td>2.698870e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>3360871</td>\n",
       "      <td>3.633384e+06</td>\n",
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       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>1787661</td>\n",
       "      <td>1.881748e+06</td>\n",
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       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>1038264</td>\n",
       "      <td>1.410725e+06</td>\n",
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       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>1163942</td>\n",
       "      <td>1.235290e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>1182762</td>\n",
       "      <td>1.291871e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>3439150</td>\n",
       "      <td>3.261787e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4654929</td>\n",
       "      <td>4.956171e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>690312</td>\n",
       "      <td>9.700358e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>196000</td>\n",
       "      <td>5.372574e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>994 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual  Predicted with normal regrssion\n",
       "856   2690353                     2.698870e+06\n",
       "3656  3360871                     3.633384e+06\n",
       "4900  1787661                     1.881748e+06\n",
       "4177  1038264                     1.410725e+06\n",
       "3867  1163942                     1.235290e+06\n",
       "...       ...                              ...\n",
       "2890  1182762                     1.291871e+06\n",
       "422   3439150                     3.261787e+06\n",
       "95    4654929                     4.956171e+06\n",
       "2457   690312                     9.700358e+05\n",
       "2172   196000                     5.372574e+05\n",
       "\n",
       "[994 rows x 2 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Newdf = pd.DataFrame({'Actual': ytest, 'Predicted with normal regrssion': pred1})\n",
    "Newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression using Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning:\n",
      "\n",
      "You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0, copy_X=True, fit_intercept=True, max_iter=None, normalize=True,\n",
       "   random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying linear model\n",
    "#Ridge is linear regression with L2 regularization and gridsearch for alpha to prevent overfitting\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'alpha':[0,0.0001,0.001]}\n",
    "\n",
    "ridge = Ridge(normalize=True)\n",
    "clf_ridge= GridSearchCV(ridge, param_grid=parameters)\n",
    "clf_ridge.fit(Xtrain, ytrain).best_estimator_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting using xtest dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf_ridge.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R2_SCORE:0.9569924830827083\n"
     ]
    }
   ],
   "source": [
    "#Getting the Mean squared error\n",
    "print(f' R2_SCORE:{metrics.r2_score(ytest, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Actual</th>\n",
       "      <th>Predicted with Ridge regrssion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>2690353</td>\n",
       "      <td>2699648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>3360871</td>\n",
       "      <td>3626368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>1787661</td>\n",
       "      <td>1880704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>1038264</td>\n",
       "      <td>1410432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>1163942</td>\n",
       "      <td>1238144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>1182762</td>\n",
       "      <td>1295488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>3439150</td>\n",
       "      <td>3255680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4654929</td>\n",
       "      <td>4953728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>690312</td>\n",
       "      <td>970880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>196000</td>\n",
       "      <td>539264.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>994 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual  Predicted with Ridge regrssion\n",
       "856   2690353                       2699648.0\n",
       "3656  3360871                       3626368.0\n",
       "4900  1787661                       1880704.0\n",
       "4177  1038264                       1410432.0\n",
       "3867  1163942                       1238144.0\n",
       "...       ...                             ...\n",
       "2890  1182762                       1295488.0\n",
       "422   3439150                       3255680.0\n",
       "95    4654929                       4953728.0\n",
       "2457   690312                        970880.0\n",
       "2172   196000                        539264.0\n",
       "\n",
       "[994 rows x 2 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ridgedf = pd.DataFrame({'Actual': y_test, 'Predicted with Ridge regrssion': y_pred})\n",
    "Ridgedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression using Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning:\n",
      "\n",
      "You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "\n",
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/Users/flatironschool/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=10, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=True, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.001, warm_start=False)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying linear model\n",
    "#Ridge is linear regression with L1 regularization and gridsearch for alpha to prevent overfitting\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'alpha':[10,100,150,200],'tol':[100,10,0.001,0.0001]}\n",
    "\n",
    "lasso = Lasso(normalize=True)\n",
    "clf= GridSearchCV(lasso, param_grid=parameters)\n",
    "clf.fit(Xtrain, ytrain).best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting using x_test dataset\n",
    "y_pred = clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R2_SCORE:0.9569573464893558\n"
     ]
    }
   ],
   "source": [
    "#Getting the Mean squared error\n",
    "print(f' R2_SCORE:{metrics.r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted with Ridge regrssion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>2690353</td>\n",
       "      <td>2.688163e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3656</th>\n",
       "      <td>3360871</td>\n",
       "      <td>3.397848e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>1787661</td>\n",
       "      <td>1.891469e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>1038264</td>\n",
       "      <td>1.423875e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>1163942</td>\n",
       "      <td>1.244207e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>1182762</td>\n",
       "      <td>1.295211e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>3439150</td>\n",
       "      <td>3.247392e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4654929</td>\n",
       "      <td>4.951213e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>690312</td>\n",
       "      <td>9.750555e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>196000</td>\n",
       "      <td>5.385234e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>994 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual  Predicted with Ridge regrssion\n",
       "856   2690353                    2.688163e+06\n",
       "3656  3360871                    3.397848e+06\n",
       "4900  1787661                    1.891469e+06\n",
       "4177  1038264                    1.423875e+06\n",
       "3867  1163942                    1.244207e+06\n",
       "...       ...                             ...\n",
       "2890  1182762                    1.295211e+06\n",
       "422   3439150                    3.247392e+06\n",
       "95    4654929                    4.951213e+06\n",
       "2457   690312                    9.750555e+05\n",
       "2172   196000                    5.385234e+05\n",
       "\n",
       "[994 rows x 2 columns]"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lassodf = pd.DataFrame({'Actual': y_test, 'Predicted with Ridge regrssion': y_pred})\n",
    "Lassodf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9553040846718461"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_squared = model.score(X1 , y1)\n",
    "r_squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.373409128285644"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#chcking for multicollinearity\n",
    "1 / (1 - r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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