Conversation rate otimization-sreenath.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import base\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Data=pd.read_csv(\"CRO.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(316200, 6)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'age', 'new_user', 'source', 'total_pages_visited',\n",
       "       'converted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 316200 entries, 0 to 316199\n",
      "Data columns (total 6 columns):\n",
      "country                316200 non-null object\n",
      "age                    316200 non-null int64\n",
      "new_user               316200 non-null int64\n",
      "source                 316200 non-null object\n",
      "total_pages_visited    316200 non-null int64\n",
      "converted              316200 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "#to check null columns and data type of columns\n",
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  age  new_user source  total_pages_visited  converted\n",
       "0      UK   25         1    Ads                    1          0\n",
       "1      US   23         1    Seo                    5          0\n",
       "2      US   28         1    Seo                    4          0\n",
       "3   China   39         1    Seo                    5          0\n",
       "4      US   30         1    Seo                    6          0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['UK', 'US', 'China', 'Germany'], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data[\"country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ads', 'Seo', 'Direct'], dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data[\"source\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data[\"new_user\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data[\"converted\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#new_user=1\n",
    "#converted=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.569858</td>\n",
       "      <td>0.685465</td>\n",
       "      <td>4.872966</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.271802</td>\n",
       "      <td>0.464331</td>\n",
       "      <td>3.341104</td>\n",
       "      <td>0.176685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age       new_user  total_pages_visited      converted\n",
       "count  316200.000000  316200.000000        316200.000000  316200.000000\n",
       "mean       30.569858       0.685465             4.872966       0.032258\n",
       "std         8.271802       0.464331             3.341104       0.176685\n",
       "min        17.000000       0.000000             1.000000       0.000000\n",
       "25%        24.000000       0.000000             2.000000       0.000000\n",
       "50%        30.000000       1.000000             4.000000       0.000000\n",
       "75%        36.000000       1.000000             7.000000       0.000000\n",
       "max       123.000000       1.000000            29.000000       1.000000"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90928</th>\n",
       "      <td>Germany</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295581</th>\n",
       "      <td>UK</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265167</th>\n",
       "      <td>US</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192644</th>\n",
       "      <td>US</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>Direct</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154217</th>\n",
       "      <td>US</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  age  new_user  source  total_pages_visited  converted\n",
       "90928   Germany  123         0     Seo                   15          1\n",
       "295581       UK  111         0     Ads                   10          1\n",
       "265167       US   79         1  Direct                    1          0\n",
       "192644       US   77         0  Direct                    4          0\n",
       "154217       US   73         1     Seo                    5          0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.sort_values(by=\"age\",ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=Data.loc[Data['age']<100].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.drop(\"index\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  age  new_user source  total_pages_visited  converted\n",
       "0      UK   25         1    Ads                    1          0\n",
       "1      US   23         1    Seo                    5          0\n",
       "2      US   28         1    Seo                    4          0\n",
       "3   China   39         1    Seo                    5          0\n",
       "4      US   30         1    Seo                    6          0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ba99825a58>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ZUuq1CwoKyqwzPT2dqVOnFo2yLEmfPn3KrGf06NGMHj26xGMZGRlkZGSU2QaA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba82c45588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(1,2,figsize=(16, 5))\n",
    "data.loc[data['converted']==1 ,'age'].plot.hist( bins= 30, ax=axes[0], alpha=.3, title= 'Age', label='Converted')\n",
    "data.loc[data['converted']==0 ,'age'].plot.hist( bins= 30, ax=axes[0], alpha=.3, label='Not converted')\n",
    "\n",
    "data.loc[data['converted']==1 ,'total_pages_visited'].plot.hist( bins= 30, ax=axes[1], alpha=.3, \n",
    "                                                                title= 'Total pages visited', label='Converted')\n",
    "data.loc[data['converted']==0 ,'total_pages_visited'].plot.hist( bins= 30, ax=axes[1], alpha=.3,  label='Not converted')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     total_pages_visited       age\n",
      "total_pages_visited             1.000000 -0.000465\n",
      "age                            -0.000465  1.000000\n",
      "                     total_pages_visited       age\n",
      "total_pages_visited             1.000000  0.001367\n",
      "age                             0.001367  1.000000\n"
     ]
    }
   ],
   "source": [
    "# compute correlation between age and total_pages_visted:\n",
    "print(data.loc[data['converted']==1][['total_pages_visited','age']].corr())\n",
    "print(data.loc[data['converted']==0][['total_pages_visited','age']].corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012445</td>\n",
       "      <td>-0.046093</td>\n",
       "      <td>-0.089199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <td>0.012445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.082522</td>\n",
       "      <td>-0.152338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pages_visited</th>\n",
       "      <td>-0.046093</td>\n",
       "      <td>-0.082522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.528975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>converted</th>\n",
       "      <td>-0.089199</td>\n",
       "      <td>-0.152338</td>\n",
       "      <td>0.528975</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          age  new_user  total_pages_visited  converted\n",
       "age                  1.000000  0.012445            -0.046093  -0.089199\n",
       "new_user             0.012445  1.000000            -0.082522  -0.152338\n",
       "total_pages_visited -0.046093 -0.082522             1.000000   0.528975\n",
       "converted           -0.089199 -0.152338             0.528975   1.000000"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ba82c45128>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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20ERERESkgH4VLRKAtRbreYQcB8fJ/q5geAmb53nYUEhL2kREREQqhGZ2RIKy\ndlRDY4zJLmkTERERkYqhZkckKGOKwgkgF1agGR0RERGRiqJmRyQAkwsj8Hwf3/ex1uL7Pp7vY7SE\nTURERKSiaM+OSEDhcBiX7B4dfD+bxhaJKI1NREREpMLo6kykBOFwGBsK5R9rRkdERESk8qjZESmR\nGhwRERGRyqY9OyIiIiIiMiWp2RERERERkSlJzY6IiIiIiExJanZERERERGRKUrMjIiIiIiJTkpod\nERERERGZktTsiIiIiIjIlKRmR0REREREpiQ1OyIiIiIiMiWp2RERERERkSlJzY6IiIiIiExJanZE\nRERERGRKUrMjIiIiIiJTUniyByAiYK3N/3djzCSORERERGTqULMjMslc18V6HlgLxmBCIcJh/U9T\nREREZKK0jE1kErmui81kCAFhxyEE2EwG13Une2giIiIiJz39+lhkklhrsZ5HyHFwnOzvHYaXsHme\nhw2FtKRNREREZAI0syMymawd1dAYY7JL2kRERERkQtTsiEwmY4rCCSAXVqAZHREREZEJ0zI2kRJN\nNEHN5MIIvEwm/9hai+f7mEhES9hEREREJkjNjkgJypWgFg6Hccnu0cH3s7UiEaWxiYiIiJSBrqhE\nAsonqDkOxnGyszGZDC6U3PDYUCj/WDM6IiIiIuWhZkckgOOVoKYGR0RERKT8FFAgEpQS1ERERERO\nCmp2RIJSgpqIiIjISUHNjkgA+QQ138f3fay1+L6fTVDTTUBFREREKor27IgEpAQ1ERERkZODrs5E\nSqAENREREZHKp2ZHpERqcEREREQqW6A9O8aYncYYf4w/f1vwmr8yxuw3xiSMMU8bYxaWf9giIiIi\nIiJHFzSg4CygveDPbwMW+A6AMebzwGeAPwLeAwwCTxpjouUasIiIiIiIyHgEWsZmrT1U+NgYcwnw\nurX2p7lD/x241Vr7w9zzVwAHgN8j1xCJyGiFUdZaHjd++txERETkaEqOnjbGRIBPAvflHs8jO9vz\n4+HXWGv7gS3Aeyc2TJGpy3Vd3HQaN5XK/qfrTvaQTgr63ERERORYJnKfnd8HGoH7c4/byS5pOzDi\ndQdyz4nICK7rYjMZQkDYcQgBNpPRhfsx6HMTERGR8ZhIGts64AlrbWe5BiNyKrHWYj2PkOPgONnf\nOwwvxfI8D6ublI5Jn5uIiIiMV0nNjjFmNvBbZPfiDOsEDDCd4tmd6cCLx6q5fv16Ghsbi46tXbuW\ntWvXljIUrqm/AAAgAElEQVREkZODtRineILVGJO9WakcmT43ERGRk95DDz3EQw89VHSsr6+vrO9h\nCjf4jvuHjPkicB3wDmutX3B8P/C/rbVfyT1uINv4XGGt/e4Raq0EOjo6Oli5cmXwMxA5SVlrcdNp\nQpCfoQDwfR8PCEejmqEYgz43ERGRqWvr1q2sWrUKYJW1dutE6wWe2THZq4irgG8WNjo5dwEbjDGv\nAbuAW4G9wPcnNkyRyuMXzCI4TvDtb8YYTCiEl8nkH1tr8XwfE4mUfME+1RPKjtfnJiIiIlNPKcvY\nfgt4B/CNkU9Ya+80xtQA9wBNwE+BD1lr0xMapUiFSSaT+Ol0dtmU4+BEo1RXVweuEw6HccnuNcH3\nwRhMJEI4XNp2Otd1sZ4H1mZrhUIl16pk5f7cREREZGoKfGVgrX0aCB3l+S8CXyx9SCKVLZlM4icS\nRMNhnHAY3/dJJxIkoeSGx4be/p9UqTMT+YQyx8E4Tna2I5PBzb3HVFOuz01ERESmrql3BSRyHPm+\nj59OEw2H8w3E8BK2dDqNH42WvKRtIk7VhLKpeE4iIiJSPhO5z47Iqcn3RzU0juNMfhKYtaMu/o0x\n2SVtIiIiIqcgzexIRavIzfaOg+/7RePxc3t3SjXRsAMAchv1C8dlc3t3JqIivwMRERGRcVCzIxWr\nEjfbO7kwgkR/PxFjcIzBt5aMtUQbGkpqVMoRdnC8Esoq8TsQERERGS8tY5OKlN9sD4QdhxBgMxlc\n153soREOhzGRCB7g5u7tUmoSWD7sAKgOh4kCfiJBMpmc1HFBZX8HIiIiIuOhX9FKxankzfbDY6ut\nqRn1XNCxHY+wg3IllFXydyAiIiIyXmp2pDJZixlxoW+MmfwQAMiPbdTemFLG5vs4I2ZeHMeBCcye\nlK0JqeTvQERERGQctIxNKlNuz0mhcmy2L4tyji0XdlBoomEHZVPJ34GIiIjIOGhmRypO4Wb74XQx\nay2+tRPabF+OxLNyjm047CCdSOQf+75P2nVxampKT2Urg+MVeKBkNxERETmR1OxIRQqHwyRdl8zI\nlLISN9uXI/HseIyturqaJNk9OrhutlZNTcljK6dwOIxLdo8Ovp9NY5to4IGS3UREROQE0pWGVCTX\ndQlZSyQaLZo9cV038AVyPvEsHMYJh7OzJ4kESSipqSjn2MiNwY9G848nc0ZnpHIFHuST3RwH4zjZ\nWaJMBjf3HiIiIiLHg64ypOKMlQQGYHx/0hPPyjm2QpXU4Iw00eVmSnYTERGRyVK5V1hyasvthylk\njMkugQrK90c1E47jlJ4qVs6xnSr0mYmIiMgkULMjlamSE8+UUhacPjMRERGZBFrGJmU30dSzwiQw\n3/fz+2IsTHri2fFKKStHUtywSks8O17pelB55yoiIiKVRc2OlFW5Us/C4TDxZBJvaAg8D0IhQrEY\ndbFY4FrlTjwrd0pZOZPiKjXxrNzpelC55yoiIiKVQ1cGUjblTD1LJpM46TRVuQAB3/fJpNMkk8mS\nGoFyJ56VK6WsnJ9ZJSeelTvBrpLPVURERCqHrgqkLMqZejZWrVAusauUBLVh5U48m+iyqXJ+ZpWc\neFbuBLtKPlcRERGpLAookPIpZ+pZuRPUKlU5z7OSE8/KPbZKPlcRERGpGGp2pHzKmXpW7gS1SlXO\n86zkxLNyj62Sz1VEREQqhpaxSVkUpp5Za9/eZ+N5gVPPCmu5rpuv5UNJCWrDPM/L//dQwX6byVJ4\nnp7n5c/TszbweVZy4lm5E+xOhkQ8ERERqQxqdqRsqquribsuiREJajUlBgp09vaS7umBdBqiUaIt\nLbS3tJQ0tng8PjrZra6upFrlVF1dTXc8TqavDzIZiESINDbSWmKCXaUmnpU7wa6SE/FERESkcqjZ\nkbJxXZeqUAhTV1d0b5xSErd6e3sJx+M01NVlL2xdl0Q8Tm9vL01NTYFqxeNxvL4+YtEooWgUz/MY\n6usjDpPe8MTjcSKpFHV1dYRCITzPI5VKEY/HA4+t0hPPypVgV+565UzEExERkcqiZkfK4kiJW34J\niVuu6+LG49RVV+cvNqO5ZLJ4PI6ba4DGw/M8vKEhYtEo0Vz09PAStqGhIbxYbNKWtI01tkgkgpNO\nBx7byZJ4Vu6UtEpKxBMREZHKo3/FpXzKmZA1xmxEOBzO3hQ0KM8b1TSEQqHskrbJVs6xKfGsNKdK\n8p+IiMgpSM2OlE85E7JyS9cKua4LpezJyC0PK+Tl9u5MunKOTYlnpTlVkv9EREROQVrGJmVRmJDl\n+37Rnp2gCVnhcJhwXR3x7m48z8vv2RnKZAi3tgbaLxLKhREM9fVhrc3vi0lmMoQaGye0hG2i6W6F\nY0ulUvmx+cYEHlvh5+95Xv7zH964P5HEs3LUG1buxLOJ1itMxBt+7Ps+adedUPLfRBPsREREpDzU\n7EjZhMNh4snk6NSzWCxwraamJvbG4/R1deXT2Kra2pgVMJwAsiEEva7LQDyeXQaXa6YmEk5QrnS3\nuro6Xtm7l8S+fTA0BLEYNTNnsnjmzMC1wuEwvfE47ojzbCrh8z8e9cqdeFauetXV1STJ7tHBdbO1\nampKHlu5EuxERERk4vQvsJRNMpnESaepym3q9n2fTDpNMpkMfOEYj8epN4aW9vb8jEfG90tOKaut\nqoKClDKMKTmlrJzpbnv37iVy8CDzGhupmjaNVCpFz8GD7K2rY9asWYHHZQYHqY9GCcVi2RmswUHi\n4XBJjVg565U78azc9aqrq/FzIRFQ+qxTuRPsREREZGL0r6+UxVipVqFcYlfQVKuxUsog+5v3cqWU\nlZISd6SxlZrulkqlSHV10VZfT0NDAwA1NTWEQiG6urpItbVRVVVV8rjC4TDGmJJS58pZr9yJZ8cr\nQW2iy+qOV4KdiIiIlE47cKV8yplqVckpZeUcWzo9qqGpqqrKLt2bzHGVu165E88qNUHtVEmwExER\nOUloZkfKJ7d0bWSyVUmpVrmlayOTyiaSUpbJZPKHwuFw6aliBTf/LKpXytiiUVKpFJFIJH8olUpB\nwYxW0HEVXmxPKHWunElxub8bhWObUOJZueuVS+7vWuG4pmSCnYiIyElCzY6UxXCq1cH9+3GSSRzf\nx3cc/OpqWk4/PdASoeGUsp2vvkookSDkeXihEF5NDTMXLSoppWzfm2/i9fZiMhlsJEKoqYkZ73hH\n4GVFw2N78ec/h0OHcHLLpjjtNN75nvcEGltVVRVVbW28uX07pw0MUBWJkMpkOJRK0bhkybiXsBWO\n6/CBA1QBIWPwrCUF1E6fHjgtbrhe1759RD2PEOAB6VCIxpkzA9Ub/ruR6O8nYgyOMfjWkrGWaEND\n4OVj5a5XLoUJdsOPrbV4vj+hBDsREREpnZaxSdnkN40D1eEwUcBPJEgmk4FrxeNxvIEBYtZSF4kQ\nsxZvYIB4PB64Vnd3N5nOThqB1poaGoFMZyfd3d2Ba0E2VCC9bx+t1jKrro5Wa0nv28fevXsD12pt\nbSUyfTp9xtA9NESfMUSmT6e1tTVwrXA4jIlE8ADX9/HIxn5Xwsb4co+tUs+1UsclIiJyqtK/wFIW\nruvixuO0NjUVhQrUpNPE43HcurpxX/Cl02nSPT3MnjaNWEHMcePQED09PaRbWoreYzy1pjU2Ultb\nmx0TEBkcDFwLIJFIkNi3jyUzZtDc2Pj22Pr62LNvH4nZs6mpqRlXreGN9vPnzh11z56gG+2Ha7U0\nNY1aQlXKpv3hgIK2lpaiWRzP80oOiagd43MpZeN+ueuVWzgcxhZ8NprRERERmTxqdqR8XJdwdXXR\nRXU4HIYSZnZIp4nW1RVdUEejUShhZme4VuFFZ8m1AIaGqGluLhpbTSwGhw8Hr+X7OAWpYtlDfvZ+\nLyXWGpk6V1ItyAYURKOjm5pSwhOsxTjO6L0spQYKlLtemanBERERqQxaxiblEw7jjriwdnM3owws\nGs3e5LFAOndz0UmtBRCLkRgaKjqUyN0QNLDcRvtCJW+0L2ctKG9AwfD9jQpMaON+ueuJiIjIlKSZ\nHSmLcDhMuK6OeHc36XSacK7xSfs+4dbWQHsWotEo0ZYWevbsIZFIEM01K0nfJzp7dqBlZ+WsBdn7\n4NTMnMmeX/2KeCJBTa7x6UmnqVm2bNxL2ODtjfbpRIJUKpW/EasJhXBqagItOyusZa19+6aunhe4\nFrwdUDDU10cqlcrf2NU3hlBjY0khEV4mk0+LG25MStm4X1hvOPnMWotvbcUEARQ2nZMVmCAiIiJq\ndqSMmpqaeKWzk8S+fZCb6aiZOZPFTU2Ba7W3t7P5lVcYfPXVfK3aRYs4r719UmsBLF68mEdefpkX\nf/5zQokEXk0NzStX8rHFiwPXqq6u5rW9exl6663scr/qamIzZrCwpaWkWnHXzc4y5WZgQrEYNdXV\ngWsB1NXV8crevaO/z5kzA9cKh8P0xuO48Xh2WV2uOW4qZTYsVy/pumTS6ezStVyzV10BQQDJZBJ/\n5LhK/A5ERERkYib/ykCmjM7OTmr6+2lva8vPoPT399PZ2Ul7wMbilVdeoaG7m9+YPTs/e9LZ3c0r\nr7zC4oBNRTlrAfziF79gem8vK975Tupqa4kPDvJWby+/+MUvWLFiRaBae/fuxezfzztqa6lqbiaV\nSnFo/372Vlcza9asQLVc16UqFMLk9idZa7G546Wkge3du5fIwYPMa2ykato0UqkUPQcPsreuLvDY\n4vE4ZnCQ+miUUCyG53kkBweJh8PU1dUFHpvruoSsJRKNFs3slHqu5ZJPJAyHccJhfN8nnUiQBDU8\nIiIik0DNjpTFcOpZS11d/uK1pqaGcDweOPVsOPFsdlMTrbkZjqaGBqI9PYETz8pZC7IX7YO7d7Og\npYX2tjYApp12GjVdXby+ezfxhQvHffGeSqVIdXXRVl9PQ0MDALFYDMdx6OrqItXWNu577QwnlIUc\nZ1RAQSkJZWONraamhlAoFHhsw8lusWg0/3cgHA5jjAmc7Ha0czUlnmu5DCfiRQsCJ4bHV0oinoiI\niEyc/uWV8kmnRzU00Wi0tPSuoaFswlmBmlgsu5xqMmsBDA5Sl4uxHlZXWwuDg8FrpdOjmoaqqqrS\nE89GXOQbY2DERv5JGZvnjWpoQqFQdrldKcp9ruXi+6MaGsdxKiYlTkRE5FSjZkfKp5ypZ+VMPCtn\nLYDc0rVC8cFBGNEAjUs0SiqVKjqUSqVK+8zKnVBWzrGVM9kNKjeNrdyJeCIiIjIhWsYmZVGYejY0\nNJTfszPkeYFTzwoTz1LpdH6fzYFEInDiWWGtZCqVr3VwaChwLchu2q+dM4fXt24lmUrl9+zsGxyk\nduXKQPtPqqqqqGpro+uNN0gkElRVVZFKpRj0PKrmzx/3MjEoTigbHBzM72OpjsVKSigrHFsqlcqP\nrT+dDjy2wmQ3LzfD43keac8LnOw28lwzmUz+XJ1QaEJpbIXNUyk1ChPxhh/7vk/adUtKxCukdDcR\nEZHSqNmRsmlvb+e5115jcMcOSCSgpobaM87g7BJSzxYvXsyP9+3jze3b80lgdUuWsLKEQIHFixfz\n1J497Ny2LV+r/p3v5MISagGsWLGCH+7dy66XXsIMDWFjMRrPPJP3BwwnAJg1axY/fvVV4r/8ZdF5\nXhAwAACy+2B+8atfMbh7d3ZJXW0ttXPmBA5NKBzbz/buZdfrr79db8ECfrOEsdXV1dEZj9PX05Nd\nBpdrjttLCCeA7Ll2dneT7OnFpjOYaITqlqbAQRjDXNfFel52GVyumSol6KC6upokuRlN182msdXU\nTCicQOluIiIipVOzI2Xz2muv0Xj4MAtnzyZWXc1QMknX4cO89tprLFy4MFCtvXv3MjcSoXblSqqi\nUVLpNIOuy969ewMnge3atYvZQMO73011VRXJ3AzFrl27mDt3bqBaw/UWRqPUvfe9+Xpx1y2p3iuv\nvELz4cMsnj//7Rmsw4dLSor71a9+RWrbNhY2NVE7YwaDiQS7tm3jV+Ewy5YtC1QLst9BWzrNggUL\nimZ2SvkO4vE4Mc+jsaWlaGYnHo+XlMbW3d1NpuswTbEaIrUNZDIZBroO0x0O09raGqiW67rYTIaQ\n42AcB2stXiaDCyU3PH7BTOZEZmKU7iYiIjIxanakLIaGhhh66y1mNjbS0twMQGNDA9HDh9n31lsM\nzZxJbJx7ZMZKAgPo7+8PnASWTCZJHjhAe0MDTQX3+6np7aXzwAGS7e2BLhqPVK+3hHpjJcU1NzZS\nVUJS3ODgIINvvsm85mamnXYaAHU1NRhj2PnmmwzOm0dtgD1FR/oOqkr4DsZKYwMIpdMlpbFlMhlS\nfQM01NTmP59oLoI63jdAprGRSCQyrlpjJbsNL2GbSLJbOZaaKd1NRERk4gL/S2mMOd0Y84AxptsY\nkzDGvGSMWTniNX9ljNmfe/5pY0ywX+vLySmZJDbiQj9WXZ29WWZQ5UwCS6VGNSDV1dUwYvP9pNQr\nZ1JcIkHtiOaotqYmu6SwFBWcxmYz7qiGJhKJYDNuCcUqNNkNlO4mIiIyQYGaHWNME/A8kAIuApYA\nnwMOF7zm88BngD8C3gMMAk8aY0qIcJKTSm7pWqGhZBJKWW5TziSwqiqSI8aVTCYhwCb741avnElx\nNTUMjmhsBnN7p0pSwWlsJhImk8kUHctkMphICZPVlZrsBkp3ExERmaCgVwZ/Aeyx1l5bcGz3iNf8\nd+BWa+0PAYwxVwAHgN8DvlPqQKWyxWIxYjNmsG/7dvoGBvJ7duK+T2zJknEvYYPiJLCe3t78nh0v\nFAqcBFZdXU319Ol0/vrXJJLJoj071b/xG4H3PRTW6xsYyNcbsjZwvcKkuHQmk9+z0zk4GDgprra2\nltp3vIOdL71EKp2mNhZjcGiI/YOD1J55ZqAlbFD8HaRzMzypVIq+VGpCaWzpdDq/Z8eDktLYIpEI\nVY319L/VRSaTIRKJkMlkGMqkic1oG/cSNihOdvM8L5/shjETSnYrR3ra8Ux3EzkRJppwKCJSDkGb\nnUuAfzXGfAc4F9gH/L219v8CGGPmAe3Aj4d/wFrbb4zZArwXNTtT2sKFC/n2Cy/Q88ILhONx3Lo6\nWs46i8sChhNANgnsn559lp7//M+3a/23/8YnP/CBwLXmzp3LU7/+NQMvvlicxlZCOMFwve/+53/S\n09FBeHAQt7aWllWr+EQJ9RYvXszmzk7eevXV/NhqFy0qKXVu2bJlPLV/P3tHpM6tLiGcALLfwQud\nnezdufPtNLZ58zirxDS21zo7GXrrreyyxupqYjNmsHDmzJLG1trayivd3by1by92KIWJVdEwczrv\nCBhOANkQgt54HDcezyaohcOE6+poKvE+TOVMTzse6W4iJ4LrunieHQ44JBQyJQV+iIhMVND/55kP\n/DHwZeCvyS5T22iMSVlrHyDb6FiyMzmFDuSekynsueeeo+XNN3n3/Pk0NjTQ19/P7jff5LnnnuPs\ns88OVOupp56ibts2VsyYQUtDAz39/by2bRtPPfUUF154YaBar7zyCq39/Sz9jd94+z47/f0lJZ5B\n9jwbd+3izDlz8ue5a9euks6zs7OT+TU1VK9alb83UdL36ezsDByjvHfvXhZUV1PznvfkZ8MSnldS\netrw2Kb5PrULF+bHNpjJlDS2zs5Ooj09tDU15Wv19fSUVAuyaWy1GUvrzFlFMzvd3d2B09ji8Thm\ncJD6aJRQLIbneSQHB4mHw4GT4o5Helo5091ETgTXdclkLI4TwnGys6WZjAe4anhE5IQL+v86DvBz\na+3/yD1+yRizDPg08EBZRyYnlb6+PgZ37GDJaacxO/fb+va2NmL79rF9xw763vUuGhsbx1Wrp6eH\ngW3bOLO1lYVz5gAwY9o0qnbv5qVt2+g56yxacullxzJW4llLUxPVJSSeHek8p7e2Ul3CeabTadI9\nPbTU1RVdVMfjcXp6eki3tIz7ZqzlTLA72tiqShjbWLViuQS2oLVg7DQ2gEgiETiNbaykuHA4jDEm\ncFLc8UxPU4MjJwtrLZ433OiMTjgMhUYHgoiIHE9Bm523gO0jjm0HPpb7752AAaZTPLszHXjxaIXX\nr18/6iJx7dq1rF27NuAQZdIkEjROn150qLGhAQ6MnOgbh6EhWkbUamlsLLlWTS4Oe1hNLAaHDx/h\nB46hnOeZThMdMXsQjUYhHi+pVlV9fdGhqqoqGBgIXus4jK1stcilsdUUz5REIhFsooREPM8jNKLZ\nCoVCpaXO+T7OiN9cO46TXYImcoqwFhxndMKhQgRFZKSHHnqIhx56qOhYX19fWd8jaLPzPLBoxLFF\n5EIKrLU7jTGdwAXALwGMMQ3AauDvjlb4K1/5CitXrjzaS6TS1dTQ199PY8EFd19/f2lpYLEYPX19\ntBTcy6anr6+0lLLc0rXCiOeSE8+gvOeZW9JVOLORTqdLSzzLpacVzuCUnJ52HMZWtlq8ncZWOINT\nchpbLjChcAan5KS4XIhA4UyM0tPkVJNNbi+ewck+nsRBiUhFGmtiY+vWraxataps7xH0yuArwPPG\nmL8kGzawGrgWuK7gNXcBG4wxrwG7gFuBvcD3JzxaqViNjY3UnnEGr/zHf9Afj9NYX0/fwAD7Uylq\n3/vecS/tAmhpaaH+ne9k2+bNHOrtpbmhgcP9/XS6LvXnnTfuJWxQnHgWTyTye3Z60unAiWflPs9o\nNEq0pYWePXvo6+vL72XxQiGis2cHWtpVmJ7WdehQfs8O0Wjg9LSRY+vt7c2PzQ+HA4+tsFYqlSra\n/xO0FhSnsVlr83t2BoYSgdPYCpPihhsez/NIe17gpLjC9DTP8/LpaZ61E05PU6qVnCyMMYRCJrdH\nh3zCoe97RCJGf39F5IQL1OxYa18wxvw+cAfwP4CdwH+31n674DV3GmNqgHuAJuCnwIestSWsCZGT\nydlnn80927bRsWUL1f39JBsaaFm9musDbtoHuPDCC7n5ueeIb9lCbX8/gw0N1K1ezV8FDCeAbOLZ\nIy+/zIs//zmhRAKvpobmlSv5WAnhBFDe82xvb+f7W7bQ++KLOEND+LEYTe9+N7/7nvcErjVr1iz+\n77/+Kz1btlA1MECqvp6W1au5toQEu+GxPfLv/87hrVuLP7cSxtbe3s4Le/cy+NprxcluJYQTQDaN\nrdN16e3pxcb7MdEI1W3NgcMJIJsU1xmP09fTk126lmvO2gOGE0A2TKA7HifT1weZDEQiRBobaZ1A\neprruljPYzjWyoRC2uQtFS3799PF8zx8PzvTE4kojU1EJocZeTO9Ez4AY1YCHR0dHVrGdpL72c9+\nxuCWLbRHIjTU19M/MEBnJkPt6tX85m/+ZqBaP/zhDxn60Y94R1UVLU1N9PT28mYqRey3fouLL744\nUK1f/OIXDG7dyoxYjLraWuKDg7w1NETtypWsWLEiUC0oOM9o9O3zTKdLOs/nnnuOxH/8B/Pr6/PJ\nbm8MDFDz3vcGTnb7/ve/z9BTT7GosZG2pia6ent5ta+P2IUX8ru/+7uBagFs3ryZoeefZ0FjI031\n9fQODPB6Xx+x97+f8847L1CtvXv3knrjDRqrqkbds6eUpDjXdbGZDH7BvXGcUAgTiQS+oIrH43h9\nfURDoVEzO0HT2IZrVYXD+Vop1y2pFrx9niHHyZ+n5/slnafIiaYZSREpRcEytlXW2q0Trad/LaUs\n+vv7GXz9dRaddhqzZszIH2986y1eff11+pcuLUoJO5ru7m76XnqJVa2tLJ43L3tw9myadu6k46WX\n6P7N3xz3b/Dj8TiDu3ezoKWF9rY2AKaddho1XV28vns38YULA12EjnmeM2aUdJ5jJbu1nXYa0RKS\n3bq6uuh98UX+W2srS+fPB2DW9OlUvfEG//nii3S973205c5/PHp7exl89VXeedppzH3HOwCY1tpK\n9M032fbqq/SuWEFTwX6qoxkrKa6+vp5oiUlx1lqs5xFyHCIFF/y+7+N5HjYUGveF1VhpbAChdDpw\nGttYtSKRCE4JtUae51ipVkHOU2Qy6O+niFQC7ZqV8hkcpGFEGlhDfX122VJAZmiI1hEX061NTZih\noZLGVVdbW3Sorra2pHEN1yvXeZJIZJPcCjQ2NEAiEbiUMzRE24jUubbmZpxSPjOAoSGaRjRbTY2N\n2RuWBpVOj2poqqqqSks8A7Cj42tNdld08Fojwgkgl8bmeZNbC8p7niIiIqcgzexI+dTW0j8wQDqT\nyR9KJpMwotEYDxuL0d3by2DBhfVQKoUtJUEtt3RtKJnMH/I8r6RxDdcr13kOJ7sVbl4vNdnNj8Xo\nOnyY6oIZiq7Dh/FLTZ2Lxejt6yNaMHvSW2oiXi4pLlPwmfm+X3pSXG5JV7LgO62qqqKkuKfhpWsF\njVcoFCotjS1Xq1DJyW6QP89CdviW9CUq59IivyBLWPcCEhGRSqRmR8qioaGB2gULePDrX6fh8GHq\nMxkGIhH6m5s577rrxr20C7KbzxvPPJO7/tf/orWnhxbfp8dx6G5p4eINGwJtQq+rq6N2zhwevOce\nqg8dojadZjAaJXnaafzW9dcH3kcxfJ4P3Xsv9b291GYyDEYiDDQ1cc4f/VGg8xxOdnt80yamJ5PU\nAwPAgepqlq1ZEyjZra2tjaZ3v5vvf+tbzPmv/6IR6AN2G8PSK64ItIQNoKmpidpFi3jy4YeZ5fvU\nR6MMpNPsdRwWffzj417CBm8nxf3kiSeI9fQQBdLAUEsLKz70ocBJcSa3Sf/V7dtJHziASaex0SjR\n6dM5Y8mSQBfww2ls/7V1Kxw+TCidxotGobmZRStXBlp2Nlyra98+op5HCPCAdChE48yZgWoVnmdq\naAjHWgxgAd8YIrFYSY2K67p4nh3OOiAUKn3TeDKZxE27WN9iHEM4GqZ6AkEMIiIix4N+FSdl093d\nDf391ANNkQj1AP392eMB7dy5k2g8TgNwmuPQAETjcXbu3Bm41u7du7Hd3Uy3lplVVUy3Ftvdze7d\nuyJ6K/gAACAASURBVAPXgux5pvv7qQWao1FqgXSJ51ldXY0Nh0lay6Dvk7QWGy7tonHmzJmEWlo4\nDHRlMhwGQi0tzMztBwqqvb2dWEsLCWMYSCRIGEOspYX2EhLUuru78Xp6qDGGhqoqaozB6+kp6TMD\n2LVrF8nXX6fdGObW19NuDMnXX2fXrl2Ba3V2dpI5cIAWa2mvraXFWjIHDtDZ2VnS2CqV67pkMhYI\n4ThhIEQmY3FLuOFpMpkknUgTIkQ0HCVEiHQiXTTTJiIiUgk0syNlMRwq8HtnnsnpBbMI+7u6AocK\n7N69m54tW1h75pmcMX16/viOAwd4assWdn/0o8yZM2dctYY37n9k6VJmF1yk7+nsLGnj/vB5fnzF\nCk4vOJ/93d2Bz3NgYIDBnTv53fe+l9qCZWuDiQQ7du5kYNEi6kfsDTpWrct/+7dH3WgzaC2ARCJB\nYt8+PnzOOUW/+Xddlz379pGYPXvc9ygaDolYvWgRzQWzVYf7+koKiRgaGmLorbeY09JCc26GqZ5s\nGMC+t95iaOZMYuNcajdc6zemTy/aO9XY3x+41nBAQVtLy6gblE4koKAqGh11c8agAQXZn7E4TmjM\nsINQaPTeoCPxfR837RIJv50IN1zTTbv4UV9L2kREpGKo2ZGyMUNDtLa301Bw4ZrOZDAl/Ia8pr+f\n008/ndaCi+PBRIKa/fsD13KGhmibMYO6govztuZmnLfeClwLCs6zoHlIu25J58ngIPXTplFfsN/H\ncRz4/9l78+C6qjvf97PPPGoWsuV5ngcMiQxGjhsIIW6CEl+SEG46L93PBf36puh0B0hlqNzu3FRS\nIWR4qbxUO8W7yeumEgi00yJuTAgQx7IxAmwwHiTL8izLMtZw5nEP748zaOtImLP32bQP9vpUqYSW\nOR//9pYt73XW+n3XO+9Y4orG4+ZcAMkkvvp6fLqH/UQyCWNjpmoLNDfj1vXoBPx+uHTJXG2pFN7a\n2gkP6F6PB8Jh0y79RMS0S1Gwu1yTJzWVBDHkY6cnoOuVMaDCZpscdmBChaZq2BwTJzQ2m83UKpFA\nIBAIBO8n4u03gWUUQgX0DIdCpkIFEjU1DEajE8YGo1ESBnpiChQa9/VU0rhv5XXi9xONxSYMRWMx\nc2EHVroAvN7c5EZHIpk0F1CQD4nQE8sfLmoKj2dC4ASQ+9pMz4iVrioOKMiFuE12mckokGzShHAC\nyK34SDYRNSwQCASC6kKs7AgsoRAqcOjFFzl+6hT1NTWMRSKkgkFqb7/dUKjAnDlzaGhr49Xf/Y7h\nkRGuCwZ5JxqlP5Oh4VOfKnsLG4w37r/9wgtcHB4uHtx5Eai74w7Djfv66zx19iy1wSDhaJSYz2f4\nOoPBIP558zj++usMnD9fPPA06fHg/9CHDG0707suXrpEMBAgGosRsdkMuwB8Ph++GTM4e/gwI2Nj\n+PITn7gk4Vu5suwtbDAeEnHiwAHGwuHidY4qCv516wyHRHi9XrzTp3O+pwdZUfC63STTaS7GYniX\nLSt721mpK53J4M1PfIaTScOuQkBBMhxGluXioaJZVcVesnJUDoWAAiWbzU9KchMfVdOQnE5DAQWS\nJGG3S2SzygSXpqk4nZIhl81mw+FykElkcoe52my5rW2KjMvnumq3sIkDMgUCgeCDiVT6Tt9/eQGS\ntA7Yv3//ftatW3dFaxFUzl133YW7qwtnIkHW5yPd3s6OHTtMue655x7sOpfS3s4zzzxjynX//feT\n0blc7e384he/MOUC2LJlCw6dT25vZ/v27aZcjzzyCLG9e/HHYsQDAQIbNvDoo4+acn3nO98h0t2N\nPxIhXlNDTVsb3/zmN025ALZt28ZodzeeSIRUTQ0NbW088MADV9wF8MYbbxA/dSp3vpHfj3/ePG68\n8UZTrl27dhE/dix3hpDXi3/JEjZt2mTKNTAwQPrSpdzWNZcLd3MzM2fONOWCXBiAmsnktq7ZbNhc\nLtOpZ1YmqMViMbLJNJqiItltOL1uwxPXDwqyLKMpCoUYO8luN51iJxAIBILLc+DAAW644QaAGzRN\nO1CpT0x2BJbx0EMP4e7sZJ3bzUyvl4FkkgPpNOmODh577DFDrscff5z0s88yx2ajye9nOB7njKri\nvvtutm7dasi1bds2Mp2dLPH5iq5jiQSujg5TD9tf//rXcXR2cr3bzYxAgPOxGG+m08gdHXz3u981\nVduaujqaAgGGYzEOhkKmanv66adJ7tzJokCA5ro6LoVCHI/F8H7843z605825ALYuXMnyZdfZr7f\nT2NdHSOhECfjcby33srHP/5xQ66XXnqJ5O7dzPb7qQ8GGYtGORuP4924kdtuu81wbaFQCHl4GLum\n4XA4cpHKkoSjqclQLDbkJycnTxJwOHC73aTTaWKyjHv+fMOTlEJdXqezWFcymzVVF+QfsrNZbJI0\naWXH6MP2++GSYHyVCEy5qp3CtdrzvVOapqGo6lV5rQKBQFANWD3ZET+pBZZw5MgRYnv28LGGBj66\nbBkAbUBNTw//vmcPR44cYcWKFWW5BgYGGO3uZtN11/HhxYuL46/19bGru5uBO+8s+yH03LlzjHZ3\nc2tLC206V21fHy93d3Nu82ZmzZpV9nX29vYS27ePTzY3c2uJ7z/27aO3t5elS5dWVJvbRG0XL15k\ndP9+bmpuZvX8+QAsnDED/8mT7Nu/n4sbN9KiS7Z7L0ZGRogcOsT1TU0snjsXgFnTp+M7fZo3Dx1i\n5MMfprGxsSzX2NgYsZ4eVjU1MX/27JwLCJw9y6GeHsbWraO+vr7s2mRZRo7FCHg8E1YlUqkUsVgM\nORAo+yE0nU6TvnSJ5mBwwhlJ3kiES5cukW5uLvscoKnqcrvd2E3UBeNpbHabbcLWMElVTaWxvd8u\n1YSr2pnqWvUpdlfTtQoEAsHVytW5uVpwRfDH47SWbGNpDQTwlzSml4M7GqW15J3w1ro63CWhBeXg\niURoLTmgs7W2Fk8kYtgF4I9Epr5OEz4ra3PE47SUTBpa6utxmLj/ACSTNJbU1lhbm9vqZcJVX+Kq\nN+sCkOVJEweHwwFm0sAymUkTGrfbbS5Bzcq6IJfGJk1OUMPMiny1uqqda+laBQKB4CpETHYElhH3\n+xksSQMbjMWIm0jcSgeDDJYkng2GQqQNNtoDpGpqGCyJER4Mh0mZSHYDiNfUTH2dJnxW1ib7/Vws\nSZ27ODaGXEEa20hJbSPhsLk0Nq+XsRLXmFkXQH6LmB5ZlsHMtiKXi3Q6PWEonU6DLib7itQFlqax\nVa2r2rmWrlUgEAiuQsQ2NoElrFixgsAtt7Crs5OhPXuY5fNxLpGgV5IIdHSUvYUNYObMmTS0tfH6\ns88yePEizYEAl2IxzksSDXffbaiPYtasWTS0tfFaZycDg4PFvphBu52Gjg5DW9gAli5dSuCmm9jV\n2Un/mTPM8no5l0wy4PMR6OgoewtbaW0XR0a4zufjnUSCM7JsuLaWlhYabriBt3buZOidd6gLBglF\nowxJEg0f/7ihLWwAjY2N1KxaxeGXX2Y0HKa+tpaxcJjBbJaaW28tewsbQH19PYFlyzi6ezcjoVAx\nqe+iLBPYuNHQFjbIrZQ4AgFiw8Mkk8lJPTtGtoq53W7czc1cOnmSdDpd7NmJZDK4588vewub1XWB\n9Wls74dLVdVJPTtX07Yu/bUWvtb37FxN1yoQCARXKyKgQGAp7e3tBPfswQ/Egegtt9DV1WXKdccd\nd+Dr6sKZSpH1eEi0t/PCCy+Yct1+++34u7pwZTJkXC7i7e28+OKLplwAa9euJXjwINOAISC6Zg1v\nvfWWKdfmzZvx6JLdUu3tPPfcc6Zc9913H+hctLfz61//2pQL4Jvf/CaxV18tprsF1q/nO9/5jimX\n1UlxVqaxvfrqq8RPnBh3LVjA+vXrr3hdYH0am1WuWCyGkkxC/hwhu9cr0tgEAoFAUDEijU1QtRSS\nxaYpCtMDAS7EYgzZ7aaSxb71rW9h7+xkmc1WTDzrUVWUjg6+/e1vm3IttduZEwhwJhajV1FMuQC+\n/OUv4+3sZK4ss8Tr5VgyyWmHg2RHBz/5yU8Mub73ve/B9u0stNmK96xfVWHLFr72ta8Zcv3whz9E\n3b6dpV4vLYEAF2MxepNJbFu28JWvfMWQC2DHjh0kX3yRmS4XDbW1jIbDDGQyeG+/nbvuusuQq5Ds\nNtPjGXelUqaS3QD6+/tJ9vTQEggUz8YpnLOzcOFCQ65CGluNyzVpZcdoGpuVdUH1prGlUinURAKn\n3V48ZyerKNh8PtOTp2pHnLMjEAgE/zWINDZBVfJuyWLdJpLF+vr6iHV3c3dLC5t0rl19fTzb3U1f\nXx+LdeNGXetNugAOHTpEvKuLj9fX87HlywHYBPzh6FGe6eri0KFDrFq1qizXiRMnCHd385etrbTr\naujq6+M/u7s5ceIECxYsKMt16tQpxrq7+XhrKxt0roa+PnZ2d3Pq1CnmzZtX9nUODw8TPniQG5qa\nWKp7Xe+pU+w/eJDh9evLPkB1qmQ3Zs2izkSyG0AymSR54QIzamtpyG+BCwYCOOx2zl+4QHLGjLIP\nA323NDa3iTQ2K+uC6k1jU1UVNZPB5XAUJ0n2/OszmQyq6+o8WFRMcAQCgeCDydX3L5LgimFlspiV\niWdWugB8iQStJU3/rX4/vkTiitZm9XVKySRNJYl4TXV1SCbT2CxLdgNIpfCWrCB4PR5IpYy7rExj\ns7IuqN4ENVWdNKGx2Wy57XECgUAgEFQRYrIjsAwrk8WsTDyz0gWQ8PkYLIlzHozHSfh8V7Q2q69T\n83oZLknEGw6F0EymsVmW7AaQ3yKmJ5lKgZktVFamsVlZF1Rvglp+65oeNd8HJBAIBAJBNSG2sQks\nQZ8sdqi3l9ZAgMFYjGR9veFkscWLFxNoa+PPnZ0c6utjjsfDmVSK0dpaAh0dhrad6V2Hjx0rpsSN\n1NUZdgGsWrUKf3s7uzo7OfXiiyzw+TiRSHDG68Xf0VH2FjaABQsWUNvWxp7t2xkYHCz27JwGards\nKXsLG8C8efOob2vjle3bGTh/nha/n4vxOGclifotWwxtYQNoamqids0aDr34Iu8MD1NTU0MkEuEi\nUHv77WVvYYOJyW7nL1wousacTsPJbgBerxfv9Omc7+khlkgUe2PC2SzeZcsMbRXTp7FFYzHcLhfp\nTIaUphlOY9PXlUgmi3WNZTKG64KJSWCKohT7bJCkihLUKnXZ8sEGmUQCWZaLPTsqYPP5KtrCZmVf\njKIoxf+22+0VuQQCgUDwwUUEFAgsZfXq1QQPHaIVGASiq1bx9ttvm3ItXryY4PHjzAdOAtFFi+jr\n6zPlWrJkCcG+PuYBp4Do4sUcO3bMlAusvc5NmzYR7OrCo6qkbDai7e3s2rXLlGvjxo0Eu7rwAQkg\n2t7O7t27TbkA/u7v/o5UVxfuRIK0z4envZ2f//znplxbt24lq0uKc7a38/jjj5uurbOzk9Cbb2JL\nJlG9Xuquv56Ojg5Trh07djB28BAkU+D1UL9mleEQhgI7d+4kcuhQboue10vNqlWmQhgKhEIh5Fgs\ndzBpPt66rmR74ZVwDQ0NkRkdzW33c7lwNTQwbdo0Uy6wNvEsFouRTmZRFQ2bXcLtdV61SXECgUBw\ntSHS2ARVy2c+8xmu6+xkhaqy1OulN5nkiM3GOx0d/Pa3vzXkuvfee2nu7GRZOs0ij4fjqRQ9bjeX\nOjp48sknDbnuu+8+mjo7WZLJsNTjoTeV4pjLxXBHh6lY5kceeQRXZyez0mnmud2cSqc553aT6ejg\n0UcfNeR69NFH0bZvZ77DwUy/n4F4nJOyjLRlC4888oghVyF1bjEwOxDgbCxGH5hOnXv88cdJP/ss\nS7ze4vlEx5JJ3HffzdatWw25fvKTn6Bs387Kmpqi63Akgn3LFr785S8bru2tt94ifuAArT4fAb+f\nWDzOYCKBf9061q5da8j1xhtvEH79TWYFawgGAkRjMc5FI9R+6HrDkdG9vb0kDh+m2ePB5/WSSCa5\nlErhW7nS0BlMBWKxGEo4jMfpxG63oygKqWwWe22t4Yd3K12hUAh5eBify1U8TyiRyeBoajI1eSok\nxdlttkln2Rid8MRiMeLhNG6Xp3id6UwKf61bTHgEAoHgA4BIYxNUJa+88gq2ri421ddzTz6l7C+A\nZ/IpZa+88go333xzWa59+/Yh5V3/Le/6KPDvede+ffu46aabynK9+uqrkHdtybtuA7bnXa+++qqh\n81SOHj1KbO9etjQ2crvu4fXF3l62793L0aNHWZ7/fd6LkydPEuruZnNrK7fottPt6evjue5uTp48\nyfz588tyTZU6twHzqXMDAwOMdnez6brr+LDudTV9fezq7mbgzjvLjmU+ffo0o93d3Nnays06V7Cv\nj+e7uzl9+jRzCyltZRCLxYifOcOChgamNTcD0NzQgPfSJU6cOUNs4cKyH2qj0SjRU2dY2NhEa/7g\n1ab6etwXL3Ly1BmiS5YQDAbLciUSCRLnzzO7ro6mhgYAGuvr8Y6Ocvb8eRKzZ+Mz0NelKApKMonX\n5cKV7x9yOBxIkkQymUTxesvenmWlS5Zl5FiMgMdTjJl25RPYYrEYciBgaIIyVVJcYQub0aQ4RVFI\nJ7O4XZ7idRauK53M4PUqYkubQCAQXGOIblKBZTgTCeaWNGLP9XhwmkgpcyYSzClxzanANbvENduk\nC8Afi02delYSDlCWq4rT2NzRKK0l79K31tXhjkaveG3E4wRKEvECfn/uIE+DaPEEwZLagoEAWtzE\nn49kEl9Jb47P6zWfOqdMfji32+25gzyvpEuWJ01oHA5HbnucGSxMilMVbcrrVJUru4tBIBAIBFcG\nMdkRWEbW5+N0SRLV6VSKrImUsqzPx5kS15kKXGdLXGdNugDi+fAFPYOxGHETW2SqOY0tHQwyWJLG\nNhgKkS5zpeP9rI381jU9sXgcSiZA5SD5fURLaovGYkh+E38+8lvX9CTyvTumyG/D0qMoCphZnbDS\nld+6pkfO9wGZwsKkOJtdmvI6bXZxTo5AIBBci4htbAJLuPnmm/lJPqXs5J/+xCLgOHDW5ULt6Ch7\nCxvATTfdxP+dd/W+9BILgBPAkMeD1tFR9hY2gPXr1/PTguuPf2QOcAYY8vmgo8PQFjaA5cuXE9iw\ngV2dnbz+m98wzetlKJkkPns2gY6OsrewAcyfP5+6tjb2bt/Omz09zPJ6OZdMkqitpW7LlrK3sMHE\n1LmjJ08yz+vlVDLJJb/fVOrczJkzaWhr4/Vnn+Xk2bM0+/1ciscZ8XhouPvusrewAcydO5eGtjb2\nbd/OxeHhYlLcyUyGhi1bDG1hAwgEAvjnzOHEgQOMhcPFnp1RRcG/bp2hvoxgMEhw3hz6X3+TUDhc\n7NkZlrPUfuj6srewAfh8PnwzZnD28GESySQ+n49EIsFwOo1v5UpDW9ggtxph93pJhsMo+VUZRVHI\nKAr22lpD27GsdDnywQax4WE0TSv27MTTaRxNTYZ7bPRJcYWv9T07RlLZ7HY7bq+TeDiFpmnj15lN\n4691X5Vb2KxMsBMIBIKrERFQILCUuro6FoTDxQS1E7W1hEpWB8olEAiwJB4vuo75/cRMbBWD3APa\nGkUpug7a7ZPemTaCz+djWTJZ9PV4vSRMbotbtGgRNf39RVdk4UKOHz9uyrVq1SqChw8zAzgPRFeu\n5NChQ6ZcABs2bCD4yisEgSgQvflm9u7da8p155134tWlsSXb23n++edN17Zt2zZGu7vxRCKkampo\naGvjgQceMO0a7n4ddyRKuiZIU9uHTLt27NhB+OBBpGQSzeulds0a08luYG3qmZWu4eFhsuEwZLPg\ndOKsrTUUSV6KlWlsoVCIVCyFKqvYHDY8AY/p1LlqRpZlFEUr3DLsdsn0PRMIBIJqQaSxCaqWe+65\nh5bOTmbLcnEF5azDwcWODp555hlDrs2bNzNz504WAXOB0+RWigY+/nGee+45Q65bbrmFZXv3Mh1Y\nCvQCF4CeDRvYs2ePIRfAjTfeyPX79zMPWAkcJhdn/eYNN/DGG28Yct199920/v73rNTVdhgY/MQn\nePbZZw257r//fmo6O1mUybDQ46E/leK4y0Wko4Nf/OIXhlwADz74IL7OTpYBC30++hMJeoBERwc/\n/elPDbl++MMfom7fzjybjenBIBeiUU6pKrYtW/jKV75iuLaXXnqJ5O7dzPb5qK+pYSwS4WwigXfj\nRm677TZDrl27dpHcu5eZXi91wSChaJSBZBLvhg1s2rTJkKuQxtbkdhfT2AorO5Wksbns9kmrMWbT\n2KxwFdLTNFUtrsRINpup9DQ9VqxSFGor9AHpzxO6miYCsiyTzWrYbPbidaqqgtMpJjwCgeCDjUhj\nE1Qle/fuxZ5PPfu0bivX0/nUs71797Jhw4ayXLt27cLT1cUmv5/7dK5f5127du0q+yF0586dBPfu\nZT3wf65YURz/f48cYWDvXnbu3GnoDJTf/e53BPfv50bggfyhn3cD206coH//fn73u9/xqU99qizX\n7t27cXV1sam2lnvz13k78GT+Onfv3s3GjRvLch04cIBM/v7fpUud25F3HThwwNCbCW+//TaJri42\n19dzZ963AXg+73v77bdZvXp1Wa5Tp04x1t3Nx1tb2aDbTre3r4+d3d2cOnXK0KGnY2NjxHp6WNXU\nxPzZswGY1dpK4OxZDvX0MLZuHfX19WW5QqEQ8WPHWNHYyNz8wbezgOC5cxw5dozQ2rVlrwhMlcYG\n4LMwjQ3AnslYksZm1jUhPU33UK2qquH0tFIq3YY1VbKbVbVVE5qmoSi5ic5UCXZ2++TAB4FAILhW\nEQEFAstwJhLMK0k9m1dBgtriEtdik64AsKbkH/41koTZEzeagBtKxm7IjxvFmUiwsOQ6F1Zwz1pL\nXK0VpM55EwlaSx7OW30+vCZ8lqexJZPU19ZOGKqvrTWXepZMUlfiqqvAdU2ksVmYnmY51VybheQW\nrCZf51V2mQKBQFAxYrIjsIysz8epktSzUxUkqPWVuPpMumLAwZIngIOahrnuHxgG9peM7c+PGyXr\n89Ffcp39FdyzwRLXYAWpc0mfj8GSic1gIkHShM/yNDavl7FweMLQWDhsLvXM6yVU4gpV4Lom0tgs\nTE+znGquzUJy87fJ13mVXaZAIBBUjOjZEVhGoWenTpaZCQwAoQp7dlqg2Gx/kcp6duqgGAIQovKe\nnSYo9iYNU1nPzgxgNnCW3LVW0rMzbWSE+W43J9NphhobK+7ZmZNMFnt2zni9FfXsTJdlZgaDDESj\nXHA4Ku7ZaZIk6mpqCEUiDGtaRT07LU5nsWfnYjZbUc9Os8dT7Nm5lEpV3LODLkENu72inh2nzVZ0\nZVW1op4dmySN94toWsV9MaqqFv9bvw3NTG12m21SstuV7ieyEtGzIxAIrlZEQIGgqpEkiXWMTyoO\nMPndxw+6q5pra2pqYs7ISNF1prGR4WEza045rEx3u+mmmwi++iq1QBiIrl/Pvn37TNf29a9/ndi+\nffgjEeI1NQRuuonvfve7plyPPvoooe7uoquurY1HHnnElGvnzp1EDh3KbV3zeqlZtcpQX1gpb731\nFvEzZ3IHpvr9+OfMYe3ataZcAwMDJC+NoGUySC4X3uZGQzHielKpFGomA6oKNhs2lwtPyTbKK+Wz\nMtmt4KvG1LNqrUsgEAgqQQQUCKqW66+/nq3kVmIWA33Auvz4m2++aci1evVqtgLTS1yrV6/m7bff\nNuRatmwZW4EWxldP1uXHe3p6DLkgd3bPVqCV8VWndfnxV1991ZCrvb296NJfZ3t7O11dXYZcd911\nF/8tP9FZCPQDJ0dGuOuuu9ixY4chF8AjjzxCRybDzNmzi+f2DGQyPPLIIzz66KOGXA899BC3Dg+z\neN485vh8nEkk6Bse5qGHHuKxxx4zXNvTTz/N0qEhFixfTmNtLSPhMCeGhnj66af59Kc/bcj1wgsv\nsCQUYsbq1TTU1jIaDnM+FOKFF17gjjvuMOTq7+9npixTv3YtXo+HZCrFWDpNf38/CxcuNOQq+Ozn\nzrG4thZvSwvJVIqhc+foDwQM+0KhEI6UTHNDc/FsnFQqQygUMhzLLMsydk3D6XJNWNmRZdnUw3Yq\nlUJNJHA5HNgcDlRVJZNIkAJTEx6Hw4Gm255XyUqMfgXFZstdazarAOau1UocDgd2e3WtOAkEAkG1\nISY7Akt45plnCL71Vi6lTHdI5LbTpzn21ls888wz3HPPPWW5nnrqKYKHDuVc+YQsgG3nznH00CGe\neuopPvvZz5bleuKJJwj29uZcuvNEtg0NcbC3lyeeeILPf/7zZbkAfv/73xPs7s75liwZ9x07xunu\nbn7/+9/ziU98oizX888/T3DPHm4C/mblyuL4/z58mPN79vD8889z5513luX685//jLuri03BIJ/T\nJdj9Jp+e9uc//5mPfOQjZbkAjh49SmzvXrY0NnK7bvvVi729bN+7l6NHj5Z9gOqRI0eI7dnDxxoa\n+OiyZcXxP/b08O979nDkyBFW6JLy3ouLFy8yun8/NzU3s1p38Grw5En27d/PxY0baWlpKcs1OjpK\n9MgR1jQ1sXDOHADmz5pF3ZkzHDxyhNEbb6RBl6x2OZLJJMkLF5hRW0uDLg3ONzbG+QsXSM6YgddA\n785UvoDfj91mM+yTZZlMLIHf4y1OHtxuN/aUnWQsgRwIlP3g/m6JZ5LJxDNVVVEzGVwOR7GGgjeT\nyaC6XKa2tFnx4P9BSD270r+/QCAQVDsioEBgGS1MnVJW3mPnRFrfxdVqwjXnXVxzTLgA6t7FZ+bI\nQj+wuqRBfLXdjt+Ey5lIsKjkXfBFFaSx+WOxqRPUTBzs6o/Hp3bF46Zqc8TjtJTES7fU1+Mw40sm\naShJY2swm8aWSuEt+R54PR4oCY64Ej5NViZNaBwOB5pcBWlsqjppQmOz2XJb2q4wIvVMIBAI1SRg\nNQAAIABJREFUPtiIlR2BZVwkl0o2cPr0hLGLJlyDedexc+eKY7H8uFHO5F2XhoaKY2fz42YI5X09\nx44VxxL5caPEgbcVhROHD08YMzMFyPp8HE+lSOu2+Z11OEynscUDAQZjMS69/vr47xEMEjfYzA4Q\n9/sZjMWQz54tjr0TjxP3m5nWgez3c3FsDEX3MDwcDiOb8Xm9jIbDpNLp4lAilTKXoJbfuqbqnoTT\n6TSY7WXJ+/Q9XCmTPslhz/Wy6FyKoiA5zKex6dPdbDab+cQzmw01f0BpATXfu3OleT9Sz6ot7EAg\nEAiuZkRAgcAyrr/+emz5rWwF3gDUtWtN9exw6NCEFZT9AKtWmerZCfT2om/pfguILV1qumcn1t3N\nCnLvFsjAESDQ1maqZ+fSnj2sJbfMquZra77lFlM9Oxf/8z9Zoxs7CLT85V+a7tl5+Qc/YKVu7DBw\n68MPm+rZOfjDH7KU3OrcINALrPnKV0z37HT94Ac0xGJcp2m8I0mMBgK0P/ywqZ6dP/zwh9SOjFCv\nqozZbIQbG/nYV75iqmfn0HPPEYxG8dhspFSVaDDIqs2bTffs9PzhDwRiCVx2BxlFJhbwsexjHzPV\nszPQ248jo2DXQJFAdtmZuXShqZ6dsUuXUBIpUFSw27D7PNQ3N5vu2clEIjglCZskoWoaWU3DVVNT\nUeiBFciyTDKZRdNsgARoSJKK12su3U2ECggEAsHlsTqg4Mq/bSa4ajhx4gR+QAMa8p/9+XGjHDp0\niMb866fnPzfmx43S19eHC7ADNfnPrvy4Gbq7u2kktw1udf5zY37cKD09PbQAs4Dr859b8uNGGRsb\nww04yd0zJ+DOj5uhq6uLILkQhg/lPwfz40ZJpVJkAQUmfE6Z3N6VTCaRFAU3YNc03ICkKCRNbD0b\nGhrCGQrRKEk0Ao2ShDMUYki3ElguqVQK0mmcmoZTknBqGqTTpq9TlmUyioQqOUCyo0oOMoqELMum\nXJosY5fAYbdhl0CTZVOuWCxGOhzHhR2fy4MLO+lwnJiJLY6Q204nOZ0ogKyqKFBxVHQ1Ugg7ADs2\nmwOwk81qpr4HAoFAICiPq+tfEsEV4/vf/z6LolH+O7k0sQIHgHg0yve//32++tWvluX627/9W9YB\n98KkVaJI/tf/5V/+pSzXt771LdaqKl+ASSseKVXlW9/6Ft/+9rfLcgE8/PDDrAPuY2LfzgJy2+we\nfvhhfvCDH5Tl+vGPf8yckZFJroXAL0ZG+PGPf8w//MM/lOV67rnnCL7yCvcAq3RbuQ7F4zzzyis8\n99xzbN68uSwXwI4dOwi++ir3ALfoemPmjI3xzKuvsmPHDu66666yXAcPHiTZ1cVXV61itq62s/E4\nz3R1cfDgQdasWXMZw0QGBwcZfe01/mr9emY1NRXHzw0Ps+e11xi8/XZaW8vr7iq47rnxRubrAixO\nDg0ZdsXjceLnzrFhxQqCuq1+0ViMU+fOEZ83D7+BbXaJRILY+QvcvHwFft1WxHgiwfnzF0jMno2v\nzC2K2WyWdDjK7OmtuFyu4ngmkyEWjpKtrcXpdJblKoQd1AaCuN3u4rgznTYcdgDjgQf+Ka7FTOCB\nlRQCClwu94QaClv4jAQUfBDCDgQCgeBqREx2BJYxn9xDu36CIuXHzbr0kwCtQpe+LodJ17v5CuOm\nXfqQAkUx5QoCa4Eb54xHL9iPHuUPJlwAteQmrst1yWaJsTH+aMLlSSRobWhgha42+dQpPKOjpmpz\nxmK0zpw5IaRAURScAwOmXQ3BYHEslU6bcpFI4G9sxKubBKiqCiMjxl2Alkzhq2+cEFKgaRramHGf\nlpVx+jwTJiKapqEl0pd51bu4ZAWHZ+IEwOFwoKUyhl35QpDyh4Dqa6uWgAKbTZo0ETFTWsGlR5Kk\narhMgUAguGoR29gElnGSfF+Njv358avFZbXPSleUXL+Pnrfy42YIk1uZ03MgP26UlM/HYMkWs8Fk\nkpTJ8IRsIMBgyfa8wbExsibCE6x04fMRL0m/iycSYPI6Ja+HRMl9SySTSF4TAQVOB9lsdsJYNptF\nchp/z6sQdqBHlmVzYQdQDDzQoxWaWq4wVgYUvB9hBwKBQCC4PCKgQGAZNTU1fDYaxcn4YZtZ4Klg\nkEgkYsglSRJbGe8/uZB3Pc7kh4X3wm638zeqSkBXVwz43zbbhDQpo7W16HwXTdbW1NTEp0ZGCJLr\n/TlDbnLyu8ZGhoeHDbk2bNjA8ldeoUlX1zBw9Oab2bt3ryEXwE033cTKV1/lOsYPY30HOLx+Pfv2\n7TPk+tKXvoS/s5Ppo6PM8Xg4k0pxoaGBeEcHP/vZzwzX9q//+q8kd+zAMzrKdX4/78TjpBoa8N51\nF1/4whdMueY6HDT4fIwmEpyWZVOuw4cPEz94kDqbDX9+4hNSVfxr1rBy5cr3FpTQ29tL5HAv1/n8\n+LxeEskk7yTi1KxcylLd+UflMDw8TPLCJTwOJ06nk2w2S0rO4p3eTJNuO2A5hEIhUsMhvC538YDS\nZCaNp6nOcNgB5PuJslkkCrHOGhqV9e3oJ2OV9P7oDxUtHqCqKjidxoMFrHQVqOZkt2quTSAQVC9W\nBxSIyY7AUiRJYh257Vknya0EmP0zVq2uaq6tmq9z7ty5NJ45U3SNzJnDaV1MuVHWrVtH8M03uY7c\nJCx6/fUcOGDuZ+LnP/95lK69OBJJZJ8Xe/sGnnjiCVOuxx9/nNHubtzRKOlgkIa2NrZu3WrKBfDS\nSy8R6TmGlkwheT3ULFvCbbfdZsp1+PBhYufOoyWSSD4vgVkzTE3CIBfskBoNoWWySC4nnoY6pun6\nnowSi8XIJtNoiopkt+H0ugmYWV0jNxlLxDIosobdIeELuExNwgpYmaBWrS6rqebaBAJBdWP1ZEf8\n5BFYRmHFYxq5lYWl5Ho+pCm2qJTrKqyeLLHANZ3xFQ+zLr2vsILVbEFtzXnXogpc06dPn/L+T58+\nnQsXLhhyAdxyyy1sJRcVXVh1Wpcf37NnjyHXF77wBT45NsZCu52Fbjf96TT9Y2N84Qtf4F//9V8N\n13bvvfeyoaeHlR4Py7xeepJJDvf0cO+99/Lkk08acm3bto0bRiPMX3sDTTU1DEcinByNsG3bNh54\n4AFDrpdeeolp587RtmgR9TU1jEUinDl3jpdeesnUBGVgYIC5Tif+dWtxu1ykMxnisszAwAAzZ840\n5BoaGqImkWBG6zRcLheZTIZoIsHQ0JDhSUoqlaLG5aJu2nXY8mfkqPlxM1HRqVQKNaPidnmLPjkj\nm/KFQiFCwym8Hj9uT27VKTQcB0KmJzwOhwO73ZpVCqtc+lUimy338yKbVQD5ik8qqrk2gUBw7SF6\ndgSWcP/997OOXNP+/wL+Nv/5RnIPyPfff78p13eA/yv/2YzrG9/4RtH1beCB/OeC6xvf+EbZLoC/\n//u/L/p+Dnwj/7ng+/u///uyXQ8++GDR9V3gf+Q/F1wPPvhg2a6f/exntA4N5e6/x8Pfejz8L4+H\nG4HWoSHDW8V27txJcO9e1gP/vGIFf7NiBf+8YgXrgeDevezcubNs12uvvYba1cWm+nq+dMcd3PmR\nj/ClO+5gU309alcXr732mqHa9u3bh5T3PbBhAxvXreOBDRvYVF+P1NVlaIvduXPnGO5+nQ+1TGPz\n+vV8ePlyNq9fz4dapjHc/TrndIfavhdjY2PEenpY3tTEqiVLmDl9OquWLGF5UxOxnh7DEeDpdJr0\npUs0B4NMa2mhvr6eaS0tNAeDpC9dyh1WWiaZTIbM6CgNgQD19fX4/X7q6+tpCATIjI6SyZQfLKCq\nKmomg8vhwOPx4HK5cp8dDtRMJhfIYIDCxMaZ315nt9txOp04HU7kjGzIJ8syiVgGr8ePx5MLY/B4\nPHg9fhKxTEURz5IkFT8qpVJXabKbJEnYbDZsNnt+NeXK7dio5toEAsG1iaHJjiRJ/1OSJLXk42jJ\n//NtSZIGJUlKSJL0R0mSjJ+kJ/hAUkgW03MDlaWUVZvLal+1ugACwJqSh7E1koSZjUXORIKZJe/Q\nz/R4cJY08xvxzSnxzTHpc0eitNbWThhrra3FHTER7ZBMUl/iqq+tBRPn/wCQyUyIdwZyXxuYnOhd\n+thpIPe1GZeqFuOTC9hsNtPpaZqqTenTVOMPxoqsTVo9cDgcKPLV9ZCd2x42OdmtGuYS1VybQCC4\n9jCzsnOY3O6iafmPWwq/IEnSV4EvAfcDHwbiwB8kSXJN4RFcZVRrStm1ksZm9XXGgIMlTycHNQ0z\nx0ZmfT4GSg7WHEilyJpNY/P5OFPiO2PSl64JMhiemDE3GA6Trgm+yysug9fLWIlrLBwGr9e4C8Dl\nmrSCk06nwWXiR2p+65qeTCZjzpXfaqZHVVWwmdssINmkKX2SzfjKh90x+dBVWZaxO66uBvlqTnar\n5toEAsG1h6GAAkmS/ifQoWnalEkCkiQNAj/QNO3H+a9ryAVV/R+apv32XV4jAgquEgr9J/WM98aM\nYS6lrODykGssk4FUhS4HUEPuYFLZpEvvs5Pr28kCSoW16fuJLph0TZ8+nbuGhrADM4GBfF07pk0z\n3bOzbO9eWnW1DQI9GzaY6tlp6OykORJhHnAKuFRTw2hHh+menebOTqanUsXAgwseD5c6Okz17CQ6\n/5NZko3GQICRWIxzmoqv4y9N9ewkd++mTlGoCQaJRKOE7Ha8Gzea7tlJnzyJS9OKPTsZScI9f76p\nnp3M2bN47fZiz05SUXDNnm2qZ0dNJHDa7cUem6yiYPP5TPfsZBIZnA7nuE/O4vK5KurZKSbFpeLU\nNXkqCimwEitSyt6PZDerqObaBAJB9XNF09jyk52HyD0vpoB9wNc0TTsnSdI84ASwVtO0t3Wv2QW8\nqWnalEfBi8nO1UW1JotVc0pZtbqs9gWDQRbHYkVXXyBANGr2FCCoq6tjQThc9J2orSUUCplybd68\nGVtXN45EGtnnRm1v47nnnjPlevDBB4l07cWdyJD2uahp38BPf/pTUy6A7du3M3LgLWyJJKrPS+O6\ntWzZssWUa8+ePYSPn0JNJLH5vNQumsctt9zy3i+cglgshpJMgqKA3Y7d6zWdnga5CY+ckdFUDckm\n4XA5TE2cwPo0NisRaWwCgUBwea50GturwBeBY+TejP4nYLckSSvJbWnTyK3k6LmY/zXBVY4+WWwm\nlSWLFVyN5FYVFljgsiIlzmqffgVrVt5ZLalzq1evZitwXb62xXnf6tWrefvtty/72lLuuOMO7o3F\nWEzue3kC6IvFuOOOO3jhhRcM17Zlyxbui0aZr/OdjEbZsmUL27dvN+T63ve+x4cuRVmwYg0zg0EG\nolFOXIryve99j6997WuGXNu2bWNW/wDLF6+mMeBnJBbnaP+AqWQ3yE1OPCfOsGHmHGpraghHIpw+\ncYY9e/YYnqT09/fjHkuybPZCvB43yVSakbEY/f39LFxorLVSlmXcdjtSIDDhXBxZNp+25fF4UF3j\nW9lKe3iMUFdXRyBgzTk7VmJ1SpmVKXFWU821CQSCawtDP101TfuD7svDkiS9Ri6R9jNAr5WFCT5Y\n3HfffcVkMf0j3TZyqwH33Xcfv/71r8tyLVmy5LKuJUuWcOzYsbJcN99882VdN998M6+88kpZLoC2\ntrbL+tra2uju7i7L9c1vfvOyrm9+85t85zvfKcv18MMPX9b18MMP84Mf/KAsF8BTTz1F8NChnG/W\nrHHfuXMcPXSIp556is9+9rNluV5++WV8XV18xO3m8ytWFMefOHKEZ7q6ePnll7n11lvLrm3Pnj04\n8mlsn1m+vDj+26NHeaary9BE4MSJE4x07+ejrTNpX7y4ON7V18cfu/dz4sQJFixYUJbr3LlzvNP9\nBre0tNC2ePzAT19fL3u63+Dc5s3M0t3L9yIcDhM9foIljU3MnjEDgOsaG3GfP0/f8ROEV62itiQM\n4d1IJpNEL7zD9No6GuobAKitAdeYg0sX3iE5YwbeMvuKNE1DUxTsNtuECYmqqiiKgma3m36wrWSC\nU0q1THAKlKaUwfgEQFEU7HbN1H2r5klENdcmEAiuHSr610DTtLAkSX3AQmAXIJELL9Cv7rQAb76X\n6x/+4R8m/cP9uc99js997nOVlCj4L+S/KlnM2JrC5V3nLa7trIUuK+syQ+u7+F404XKmUiwt+fu9\n1OPBWdLMX7YvkWB+ff2EsfkeD06D8c4A3kiM1ukTJyGtgQDeC+XHThdwReK0zpw70VVbj2vgtGEX\ngJZIUNvSOmGstqYG7eKgYZeayuKtrZkw5vV4UcMmEvE0DalkYiJJkuk0tmsFTQObbXJKmbhtAoHg\nWuU3v/kNv/nNbyaMhU0+G7wbFU12JEkKkJvo/H+app2SJGkIuI3882g+oKAN+H/ey/XjH/9Y9Ox8\nwCmkgd2oGyukgS2x0GVlXe4pX2HeZ/Qv1H/VPZtr0AW5MIKpfMYfsyHr8dCbSk1w9aZSZE32ZGR9\nPk6W+E6aTGNL1gQYjMXQr98MxmIka4z3n2Rq/AyGx5jV0jLuCo+RqfEbdgFIPh/hSITa4HgyXDgS\nQTJxnTaPk2QqOWEFJ5lKYvM4TRQm5dO1xh/ctUJzhuBdKaSUld43cdsEAsG1ylQLG7qeHUswGlDw\nA+D35LauzQD+GVgNLNc0bUSSpEeAr5Lr6zlN7lzJFcAKTdOmPMxBBBRcPRR6RgBcQOEbXklKmZ/x\n/pN4hS7I9cYUAosrTWObxnjq2VCFtdkYT4pTK3S1MH7PLpp0Qa43p+3QIbyMX2cS6F61ylTPzpw/\n/pFGnWsEOPPRj5ru2ZnW2cksVS327Jyz2Rjq6DDVs5Pa/hzTsllm+gIMJGIMOZ14tmw21bMT6fwD\nCzw+mvx+huNxTqQS1HR8zHTPTmTf60xzuakNBglHowxl0tTc9CFTPTtjPado9AXGe3YSMeqXzTPV\ns6Nls2iqWuzZkWw2JKezou1j+sjoSrehWZF4ZjWFnh2QdL10WlWllFXjfRMIBNcWVzqN7TdAO7m+\n8UvAHuAbmqad0v0//0TunJ06oAv4H5qm9V/GKSY7VxHVmixWzSll1eqq9toCgQCL4nHmIXEKjeN+\nP7GYmVOAcr1W7tdeowY7ERTSH/5w2b1XpXzxi18k2dWNLZFG9bnxtrfxq1/9ypQL4Oc//zlD3Qdw\nReJkavxMa1vH3/3d35lyWZnGNjw8TDocRcvKSE4H7togTU1NplxgbYJaNSeBxWIx0sksqqJhs0u4\nvc6KUuyspJrvm0AguHa4omlsmqa9ZwONpmn/RC6lTXCNUVhZ0J/LUg3JYlbWpfd58j4rEtSsTHZr\nyrsWVuACqK+vn/J7UF9fz5jB3phly5YVvwdzyC0Nr8uP9/T0GK7tk5/8JJ+Lp5mNvXhuz9l4mk9+\n8pP8x3/8hyHXQw89xKbRCAvnL2Ge18upZJL+0QgPPfQQjz32mCHXk08+yQbVxsz2W6gLBglFowyo\nCk8++ST33nuvIRfkJicz4wo3rPswtcEawtEIF+NpU2lsQ0NDzHa58K5ePuGcnaGhIcPn7IRCIeRQ\njKDbi8OXO8smEYoRcjhMTVD0Z+O4PTlfaDgOhAz7rE48s5JUKoWaUfG63MXzhOSMTCqVMh2zbRXV\nfN8EAoGgEsRPMIElzJs377JpYPPmzePUqVNTvraUlpaWy7paWlq4eLE04Xxq5s6de1nX3LlzOX36\ndFkuq3233nrrZV233norL7/8clkuK9PwAB577DHmh0JT+0IhHnvsMR566KGyXE888QTB3t6cS/dQ\nvW1oiIO9vTzxxBN8/vOfL7u2rq4u7F17+Uh9A5/VpbE9dfQov+vaS1dXF+3t7WW5jhw5QmTPK3yy\noZGPLlsGwEeAP/b08B97XuHIkSOs0CXIXY6hoSGG3zhAW/N1rNUluL114gTdbxxgaNMmQ5OKcDhM\n+PgpFjc2F9PYYAa1589z4vgpQ2lsmUyGzOgoDYHAhFWEWCzG6OgomYYGXC5XWS5ZlsnEEvg93uID\nutPpRJIkkrEEciBg6OFYlmUSsQxej7/oK7w+EUsSCJT/sP1+JZ5ZQWFi43SMb/Wz55Pr5IyM6lIt\nTaMzQjXfN4FAIKiUK/OTVXBVUq3JYlanlFVrbdV8nXPexTXHhAvAkUiyoOSd8AUeD45E0rDLF0/Q\nWrKNqDUQwBc3nlJmj8WZVpISN62+HnssbtgFoCaS1NZMTFCrralBNXGdZDKTJjQulwsyU7ZTXhZN\nViZNQBwOB5qsGK8LUGRtSp8iG1+RzG3Bmpx4VsGOScvQVG3ShMZms6GpV764ar5vAoFAUAlisiOw\njEIamJ5KE9SqzWW1r1pdVvvOvIvrjAkXgOzzciKVmjB2IpVC9pV3VoyehN/HYEmvz2AsRsJvPPFM\nCfgZKtniNzQ2hhIwl8Zm83kJRyITxsKRCDYT10l+65qeTCYDZa7o6JEc9glhApBboZEcduN1AXaH\nNKXP7jBz7szkXrBqSTyTbBJqSc60qqpItitfXDXfN4FAIKgEQwEF70sBIqDgqqHQM+JlPPUsSWXJ\nYnbACWQBpUKXPmA3a9Kl96V1Y+4Ka/OTS/0YwZrUuWnkEuIw6YJcb849oRBOxnt2ssAzdXWmenZu\n6e2lAZhN7jyiUWDP0qWme3aaO/+TIFrRF0XiUsdfmurZsXf+Jw2Xhpnr83E6kWC0uQml4y9N9exE\nn3uB2Q5nMT3trJwluPkO0z07o/vepNnhLPbsXJKzNNx0vamenczZs9gVpdizo9jtuGbPNtWzkxoO\n4XG6cDhyPTapbAZPU11FPTsO+7hPVjLUNXlM9+wUVioKD+yVJp5ZkVKWSqXIJDLY89vFVFVFURVc\nPpfpnh395KmSbXD6np3CfVNVpaL7ZlVtAoHg2uKKprG9H4jJztVFtaZ3XSspZe/Hda6FYuLZW1dp\nbTNnzqT+/HnmYOcMCmMzZjAwMGDKtXXrVpJd+3AkUsg+D972m3j88cdNuQB+8pOfMNj9No5IHLnG\nT2vbar785S+bcnV2djL65kGkZArN66Hh+jV0dHSYcg0MDBC9FEbJyNhdDoLNtcycOdOUC3LR2CMX\nxpBTCg6Pncbp9YYjsQuEQiFSsRSqrGJz2PAEjE+a9FiZUmZl6lwqlSKTUVFVsNnA5bJVFHRg5XVa\nXZtAILh2uKJpbALB5SisLDSSWwlYQHWlsRXOn1lSgUvvszJ1rj7vmmeBy4p7VvB9ERvXITEDWASs\nRTPla2xsnPJ70NjYyMjIiOHa1q5dy1ZgFrAIjePkwhnWrl3LW2+9Zch1991389Hzl1iMi4VAP3b6\nzl/i7rvv5tlnnzXk+uUvf8ma0Qjz1l5PUzDAcDTGqdEIv/zlL/nrv/5rQy6Al156iWljWa5ffSN1\nNUFCkSgXxuK89NJL3HbbbYZce/bswdl3kvXTZ1JbU0M4EuFU30lTyW6xWAyn5KV5WhC73Y6iKCiq\nTCwWMxWjPDw8jD2pMee6aTidTrLZLMlkiuHhYcNx1rFYDCWeJuByY/fmakvH08Qc5mqzMqUslUqh\nqi58AV9xZUdVzaWxpVIpEgkVh8OFw5FzJRIZwHyym8PhwG63ZgXL6toEAoHALGKyI7CEwrv275YG\nZuQBuVpdVvtaW1sv62ptbWVwcLAs1/Llyy/rWr58OUePHi3LBfBXf/VXrAVuQOIBxh94tgFv5X/9\n3/7t38py/ehHP2Lu6GiuNt1Wlm2qyoHRUX70ox/xj//4j2XX9vTTTxM8eDDnmzMecbDtzBl6Dx7k\n6aef5tOf/nRZrt27d2Pr2kd7bSP36pLdnjx6lGe79rF79242btxYluv8+fMMd7/Ohubr+NDixcXx\n1/v62Nv9OufvuIMZxVS192ZsbIzRnn6WNF3H/Nnj11lz9gz9Pf2MrVtHfUkYwrsRDoeJHj/Bksam\nYrJbc0MDrvPn6Tt+wlCym6IopJNZ3C7PhMCDTCZDOpnB61Ww28vv3clmsyTDcYK+AD5frk/K6/Xi\nSDhIhuNka2txOp3vYRmvLZtM43G5i7U5HA4kSSKTTKN4vYZqszKlTFVVMpnCBGD8n15ZtpHJZHAZ\nSGObylV4rVFXKZWmrr2ftQkEAoEZxE8cgWVUa7JYNaeUVasLctvDpvLNw/jDkNW1TXsXn7HOkxz2\nRIqFJe82L/R4sCdS7/KKd8cZi9NaMgFpra/HaTKNTUlmqK+duMWpvrYOJWkiQS2RmDLZTUsYT51T\nFW3SpMFut6Mq5rYRqlll0oTG6XSiZo2nu2mKOmVtmqK+yyvew2dhSlluS9fkNDbVRGlWuqymmmsT\nCATXHmJlR2AZhfSuQ7qxDJUli5UmeFXisqIuva9fNxausLZu3ZijQpd+c6tm0gVwCo39TAxiOJwf\nX2mytpDuaedEfvwmE7UN5X3qmfE8tzcZD2UwguLz0J9KwfHjxbH+bBbFZ3y7TTbgZ3BsjOHR0eJY\nRpLImkxjs3tdjIVD6COxxsIh7F4TCWo+H+FIBIduIhCORJB8xlPnbHYJRVEmJKhJkoTNbm5VwOa0\nk81mJzwgZ7NZbE7j6W6S3YaiKBNciqIg2c2udJBv1ld1Y5KplDKbjfzWNXXSuFmXfiKmqqopl9VU\nc20CgeDaQwQUCCyj0DCuf8d9P5hqHC+49H8iDlTguoHcFq8Cb+RrM9vLcguwXDd2FNhjsralTL7O\nXpMuq+5/wfchbKxEAiRA4zAar6Oa6tlZPDrKWt3YW0BfQ4Ppnp3gwYOs0o0dAqJr1pjq2bnw++dY\nih0NCQmNXhSmf2KzqZ6dJ77+LaYPXaAGGxFULkybzue/+23TPTu7f/kU9QmFGlUlYrMx5rOz8a8/\na6pn5/D2Z6lPg8fhJiWnGXPDyi13m+rZOXbkBHIsjSRraA4JR8DNkhULTPfsXDh+DpfNjV1yoGgy\nGTXN9EWzTPXsxEfCuO0O7DYbiqqSVmT8jbWme3bGxqKk06CqEjabhtsN9fVBUz07g4NVcW3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VmzhgPYcl4K49jYlVvXu0m5//77+UA4Q7urgbVeLxcSPsbCGe6//36eeeYZXV4Ajz/+OFtmJVZv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HKAaqXyEkRGNzAT0E+CrFC51/ZVqzl7qk5mpkt9bWVkZHR6/y6Cv11a9+FYBmFnF/CVrgbS4x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\nIk6e9BT8enq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VGR6x3vSUngQ1g8Zm6LMuY7BjSDfpST3T02uQ3Ixeg4h9U+vppyeNbYjM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\nkNDW3NyQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba82c45080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10,6))\n",
    "plt.scatter(data.loc[data['converted']==0, 'total_pages_visited'], data.loc[data['converted']==0, 'age'], \n",
    "            label = 'Converted', alpha=0.02,c='r')\n",
    "plt.scatter(data.loc[data['converted']==1, 'total_pages_visited'], data.loc[data['converted']==1, 'age'], \n",
    "            label =  'Not converted', alpha=0.02)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba80346048>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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duxfw7YbjDAJuBDYC9gAOBz4BnN4QszlwE/AIMB44CZiW5Kg1OO8NnvmoJEnr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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba8033c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp1 = data.groupby(['country', 'converted']).size().unstack()\n",
    "tmp2 = data.groupby(['source', 'converted']).size().unstack()\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))\n",
    "tmp2.plot.bar(stacked= True, ax=axes[0], title = 'Conversion by source')\n",
    "tmp1.plot.bar(stacked= True, ax=axes[1], title = 'Conversion by country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country  converted\n",
       "China    0             76500\n",
       "         1               102\n",
       "Germany  0             12240\n",
       "         1               815\n",
       "UK       0             45900\n",
       "         1              2549\n",
       "US       0            171360\n",
       "         1              6732\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['country', 'converted']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>converted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ads</th>\n",
       "      <td>85680</td>\n",
       "      <td>3059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <td>70380</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seo</th>\n",
       "      <td>149940</td>\n",
       "      <td>5099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "converted       0     1\n",
       "source                 \n",
       "Ads         85680  3059\n",
       "Direct      70380  2040\n",
       "Seo        149940  5099"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " data.groupby(['source', 'converted']).size().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ads</th>\n",
       "      <td>0.034472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <td>0.028169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seo</th>\n",
       "      <td>0.032888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        converted\n",
       "source           \n",
       "Ads      0.034472\n",
       "Direct   0.028169\n",
       "Seo      0.032888"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['converted','source']].groupby(['source']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>0.062428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>0.052612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>0.037801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         converted\n",
       "country           \n",
       "China     0.001332\n",
       "Germany   0.062428\n",
       "UK        0.052612\n",
       "US        0.037801"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['converted','country']].groupby(['country']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 316198 entries, 0 to 316197\n",
      "Data columns (total 6 columns):\n",
      "country                316198 non-null object\n",
      "age                    316198 non-null int64\n",
      "new_user               316198 non-null int64\n",
      "source                 316198 non-null object\n",
      "total_pages_visited    316198 non-null int64\n",
      "converted              316198 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 316198 entries, 0 to 316197\n",
      "Data columns (total 6 columns):\n",
      "country                316198 non-null object\n",
      "age                    316198 non-null int64\n",
      "new_user               316198 non-null int64\n",
      "source                 316198 non-null object\n",
      "total_pages_visited    316198 non-null int64\n",
      "converted              316198 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 14.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 316198 entries, 0 to 316197\n",
      "Data columns (total 6 columns):\n",
      "country                316198 non-null object\n",
      "age                    316198 non-null int64\n",
      "new_user               316198 non-null int64\n",
      "source                 316198 non-null object\n",
      "total_pages_visited    316198 non-null int64\n",
      "converted              316198 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dt=pd.DataFrame(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(list(data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# seperate the variables into numeric and categorical\n",
    "\n",
    "categorical_variables=[]\n",
    "numeric_variables=[]\n",
    "for i in list(data.columns):\n",
    "    if data[i].dtype == \"object\":\n",
    "        categorical_variables.append(i)\n",
    "    else:\n",
    "        numeric_variables.append(i)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'new_user', 'total_pages_visited', 'converted']"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['country', 'source']"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  total_pages_visited  converted  country_Germany  country_UK  \\\n",
       "0   25         1                    1          0              0.0         1.0   \n",
       "1   23         1                    5          0              0.0         0.0   \n",
       "2   28         1                    4          0              0.0         0.0   \n",
       "3   39         1                    5          0              0.0         0.0   \n",
       "4   30         1                    6          0              0.0         0.0   \n",
       "\n",
       "   country_US  source_Direct  source_Seo  \n",
       "0         0.0            0.0         0.0  \n",
       "1         1.0            0.0         1.0  \n",
       "2         1.0            0.0         1.0  \n",
       "3         0.0            0.0         1.0  \n",
       "4         1.0            0.0         1.0  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data=pd.get_dummies(data,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  total_pages_visited  converted  country_Germany  country_UK  \\\n",
       "0   25         1                    1          0              0.0         1.0   \n",
       "1   23         1                    5          0              0.0         0.0   \n",
       "2   28         1                    4          0              0.0         0.0   \n",
       "3   39         1                    5          0              0.0         0.0   \n",
       "4   30         1                    6          0              0.0         0.0   \n",
       "\n",
       "   country_US  source_Direct  source_Seo  \n",
       "0         0.0            0.0         0.0  \n",
       "1         1.0            0.0         1.0  \n",
       "2         1.0            0.0         1.0  \n",
       "3         0.0            0.0         1.0  \n",
       "4         1.0            0.0         1.0  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(316198, 9)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc, f1_score,confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data into training set and test set\n",
    "Split data into traing set(75%) and test set(25%): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X =  data.drop('converted', axis=1)\n",
    "y = data['converted']\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size= 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "models={\"LogisticReg\":LogisticRegression(),\n",
    "        \"DecisionTreeClassifier\":DecisionTreeClassifier(),\n",
    "        \"RandomForestClassifier\":RandomForestClassifier(n_estimators=100),\n",
    "        \"GradientBoostingClassifier\":GradientBoostingClassifier(n_estimators=100),\n",
    "        \"ExtraTreesClassifier\":ExtraTreesClassifier(n_estimators=100),\n",
    "        \"GaussianN\":GaussianNB()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianN : 97.8747628083\n",
      "RandomForestClassifier : 98.4996837445\n",
      "DecisionTreeClassifier : 98.4655281467\n",
      "GradientBoostingClassifier : 98.6476913346\n",
      "LogisticReg : 98.6818469323\n",
      "ExtraTreesClassifier : 98.5022137887\n"
     ]
    }
   ],
   "source": [
    "for key in models.keys():\n",
    "    model=models[key]\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    acc=accuracy_score(y_pred,y_test)*100\n",
    "    print(key, \":\",acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['GaussianN', 'RandomForestClassifier', 'DecisionTreeClassifier', 'GradientBoostingClassifier', 'LogisticReg', 'ExtraTreesClassifier'])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Building the optimal model using Backward Elimination\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regressor_OLS = sm.OLS(endog = y, exog = X).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>converted</td>    <th>  R-squared:         </th>  <td>   0.321</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.321</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>1.866e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Dec 2017</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:24:03</td>     <th>  Log-Likelihood:    </th> <td>1.5542e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>316198</td>      <th>  AIC:               </th> <td>-3.108e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>316190</td>      <th>  BIC:               </th> <td>-3.107e+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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>                 <td>   -0.0023</td> <td> 2.12e-05</td> <td> -106.705</td> <td> 0.000</td> <td>   -0.002    -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>new_user</th>            <td>   -0.0479</td> <td>    0.001</td> <td>  -88.819</td> <td> 0.000</td> <td>   -0.049    -0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_pages_visited</th> <td>    0.0261</td> <td> 7.48e-05</td> <td>  349.239</td> <td> 0.000</td> <td>    0.026     0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>country_Germany</th>     <td>    0.0347</td> <td>    0.001</td> <td>   24.998</td> <td> 0.000</td> <td>    0.032     0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>country_UK</th>          <td>    0.0279</td> <td>    0.001</td> <td>   33.457</td> <td> 0.000</td> <td>    0.026     0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>country_US</th>          <td>    0.0174</td> <td>    0.001</td> <td>   28.823</td> <td> 0.000</td> <td>    0.016     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_Direct</th>       <td>   -0.0111</td> <td>    0.001</td> <td>  -15.456</td> <td> 0.000</td> <td>   -0.012    -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>source_Seo</th>          <td>   -0.0082</td> <td>    0.001</td> <td>  -13.750</td> <td> 0.000</td> <td>   -0.009    -0.007</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>214553.374</td> <th>  Durbin-Watson:     </th>  <td>   2.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>3106180.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 3.152</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>17.001</td>   <th>  Cond. No.          </th>  <td>    173.</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              converted   R-squared:                       0.321\n",
       "Model:                            OLS   Adj. R-squared:                  0.321\n",
       "Method:                 Least Squares   F-statistic:                 1.866e+04\n",
       "Date:                Thu, 07 Dec 2017   Prob (F-statistic):               0.00\n",
       "Time:                        14:24:03   Log-Likelihood:             1.5542e+05\n",
       "No. Observations:              316198   AIC:                        -3.108e+05\n",
       "Df Residuals:                  316190   BIC:                        -3.107e+05\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------------\n",
       "age                    -0.0023   2.12e-05   -106.705      0.000        -0.002    -0.002\n",
       "new_user               -0.0479      0.001    -88.819      0.000        -0.049    -0.047\n",
       "total_pages_visited     0.0261   7.48e-05    349.239      0.000         0.026     0.026\n",
       "country_Germany         0.0347      0.001     24.998      0.000         0.032     0.037\n",
       "country_UK              0.0279      0.001     33.457      0.000         0.026     0.030\n",
       "country_US              0.0174      0.001     28.823      0.000         0.016     0.019\n",
       "source_Direct          -0.0111      0.001    -15.456      0.000        -0.012    -0.010\n",
       "source_Seo             -0.0082      0.001    -13.750      0.000        -0.009    -0.007\n",
       "==============================================================================\n",
       "Omnibus:                   214553.374   Durbin-Watson:                   2.000\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          3106180.888\n",
       "Skew:                           3.152   Prob(JB):                         0.00\n",
       "Kurtosis:                      17.001   Cond. No.                         173.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:All columns are important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = regressor_OLS.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   -0.002262\n",
       "new_user              -0.047938\n",
       "total_pages_visited    0.026117\n",
       "country_Germany        0.034664\n",
       "country_UK             0.027908\n",
       "country_US             0.017421\n",
       "source_Direct         -0.011085\n",
       "source_Seo            -0.008179\n",
       "dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conf = regressor_OLS.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.002303</td>\n",
       "      <td>-0.002220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <td>-0.048996</td>\n",
       "      <td>-0.046880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pages_visited</th>\n",
       "      <td>0.025970</td>\n",
       "      <td>0.026263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_Germany</th>\n",
       "      <td>0.031946</td>\n",
       "      <td>0.037381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_UK</th>\n",
       "      <td>0.026273</td>\n",
       "      <td>0.029543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_US</th>\n",
       "      <td>0.016236</td>\n",
       "      <td>0.018606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Direct</th>\n",
       "      <td>-0.012491</td>\n",
       "      <td>-0.009680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Seo</th>\n",
       "      <td>-0.009344</td>\n",
       "      <td>-0.007013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0         1\n",
       "age                 -0.002303 -0.002220\n",
       "new_user            -0.048996 -0.046880\n",
       "total_pages_visited  0.025970  0.026263\n",
       "country_Germany      0.031946  0.037381\n",
       "country_UK           0.026273  0.029543\n",
       "country_US           0.016236  0.018606\n",
       "source_Direct       -0.012491 -0.009680\n",
       "source_Seo          -0.009344 -0.007013"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conf['OR'] = params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>OR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.002303</td>\n",
       "      <td>-0.002220</td>\n",
       "      <td>-0.002262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <td>-0.048996</td>\n",
       "      <td>-0.046880</td>\n",
       "      <td>-0.047938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pages_visited</th>\n",
       "      <td>0.025970</td>\n",
       "      <td>0.026263</td>\n",
       "      <td>0.026117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_Germany</th>\n",
       "      <td>0.031946</td>\n",
       "      <td>0.037381</td>\n",
       "      <td>0.034664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_UK</th>\n",
       "      <td>0.026273</td>\n",
       "      <td>0.029543</td>\n",
       "      <td>0.027908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_US</th>\n",
       "      <td>0.016236</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>0.017421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Direct</th>\n",
       "      <td>-0.012491</td>\n",
       "      <td>-0.009680</td>\n",
       "      <td>-0.011085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Seo</th>\n",
       "      <td>-0.009344</td>\n",
       "      <td>-0.007013</td>\n",
       "      <td>-0.008179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0         1        OR\n",
       "age                 -0.002303 -0.002220 -0.002262\n",
       "new_user            -0.048996 -0.046880 -0.047938\n",
       "total_pages_visited  0.025970  0.026263  0.026117\n",
       "country_Germany      0.031946  0.037381  0.034664\n",
       "country_UK           0.026273  0.029543  0.027908\n",
       "country_US           0.016236  0.018606  0.017421\n",
       "source_Direct       -0.012491 -0.009680 -0.011085\n",
       "source_Seo          -0.009344 -0.007013 -0.008179"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conf.columns = ['2.5%', '97.5%', 'OR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2.5%</th>\n",
       "      <th>97.5%</th>\n",
       "      <th>OR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.002303</td>\n",
       "      <td>-0.002220</td>\n",
       "      <td>-0.002262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <td>-0.048996</td>\n",
       "      <td>-0.046880</td>\n",
       "      <td>-0.047938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pages_visited</th>\n",
       "      <td>0.025970</td>\n",
       "      <td>0.026263</td>\n",
       "      <td>0.026117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_Germany</th>\n",
       "      <td>0.031946</td>\n",
       "      <td>0.037381</td>\n",
       "      <td>0.034664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_UK</th>\n",
       "      <td>0.026273</td>\n",
       "      <td>0.029543</td>\n",
       "      <td>0.027908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_US</th>\n",
       "      <td>0.016236</td>\n",
       "      <td>0.018606</td>\n",
       "      <td>0.017421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Direct</th>\n",
       "      <td>-0.012491</td>\n",
       "      <td>-0.009680</td>\n",
       "      <td>-0.011085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Seo</th>\n",
       "      <td>-0.009344</td>\n",
       "      <td>-0.007013</td>\n",
       "      <td>-0.008179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         2.5%     97.5%        OR\n",
       "age                 -0.002303 -0.002220 -0.002262\n",
       "new_user            -0.048996 -0.046880 -0.047938\n",
       "total_pages_visited  0.025970  0.026263  0.026117\n",
       "country_Germany      0.031946  0.037381  0.034664\n",
       "country_UK           0.026273  0.029543  0.027908\n",
       "country_US           0.016236  0.018606  0.017421\n",
       "source_Direct       -0.012491 -0.009680 -0.011085\n",
       "source_Seo          -0.009344 -0.007013 -0.008179"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         2.5%     97.5%        OR\n",
      "age                  0.997699  0.997782  0.997741\n",
      "new_user             0.952185  0.954201  0.953193\n",
      "total_pages_visited  1.026311  1.026611  1.026461\n",
      "country_Germany      1.032462  1.038089  1.035271\n",
      "country_UK           1.026621  1.029984  1.028301\n",
      "country_US           1.016369  1.018780  1.017574\n",
      "source_Direct        0.987587  0.990367  0.988976\n",
      "source_Seo           0.990699  0.993012  0.991855\n"
     ]
    }
   ],
   "source": [
    "print (np.exp(conf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "traincorr=data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012445</td>\n",
       "      <td>-0.046093</td>\n",
       "      <td>-0.089199</td>\n",
       "      <td>-0.003173</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>-0.000088</td>\n",
       "      <td>0.002482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_user</th>\n",
       "      <td>0.012445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.082522</td>\n",
       "      <td>-0.152338</td>\n",
       "      <td>-0.003656</td>\n",
       "      <td>-0.005149</td>\n",
       "      <td>-0.008522</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.001655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pages_visited</th>\n",
       "      <td>-0.046093</td>\n",
       "      <td>-0.082522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.528975</td>\n",
       "      <td>0.019693</td>\n",
       "      <td>0.026629</td>\n",
       "      <td>0.019456</td>\n",
       "      <td>-0.009138</td>\n",
       "      <td>0.003218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>converted</th>\n",
       "      <td>-0.089199</td>\n",
       "      <td>-0.152338</td>\n",
       "      <td>0.528975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035446</td>\n",
       "      <td>0.049023</td>\n",
       "      <td>0.035666</td>\n",
       "      <td>-0.012596</td>\n",
       "      <td>0.003534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_Germany</th>\n",
       "      <td>-0.003173</td>\n",
       "      <td>-0.003656</td>\n",
       "      <td>0.019693</td>\n",
       "      <td>0.035446</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088276</td>\n",
       "      <td>-0.235657</td>\n",
       "      <td>-0.004768</td>\n",
       "      <td>0.000949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_UK</th>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.005149</td>\n",
       "      <td>0.026629</td>\n",
       "      <td>0.049023</td>\n",
       "      <td>-0.088276</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.483053</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.000797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_US</th>\n",
       "      <td>-0.000388</td>\n",
       "      <td>-0.008522</td>\n",
       "      <td>0.019456</td>\n",
       "      <td>0.035666</td>\n",
       "      <td>-0.235657</td>\n",
       "      <td>-0.483053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002624</td>\n",
       "      <td>-0.001193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Direct</th>\n",
       "      <td>-0.000088</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.009138</td>\n",
       "      <td>-0.012596</td>\n",
       "      <td>-0.004768</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.002624</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.534595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_Seo</th>\n",
       "      <td>0.002482</td>\n",
       "      <td>-0.001655</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.003534</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>-0.001193</td>\n",
       "      <td>-0.534595</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          age  new_user  total_pages_visited  converted  \\\n",
       "age                  1.000000  0.012445            -0.046093  -0.089199   \n",
       "new_user             0.012445  1.000000            -0.082522  -0.152338   \n",
       "total_pages_visited -0.046093 -0.082522             1.000000   0.528975   \n",
       "converted           -0.089199 -0.152338             0.528975   1.000000   \n",
       "country_Germany     -0.003173 -0.003656             0.019693   0.035446   \n",
       "country_UK          -0.006144 -0.005149             0.026629   0.049023   \n",
       "country_US          -0.000388 -0.008522             0.019456   0.035666   \n",
       "source_Direct       -0.000088  0.001626            -0.009138  -0.012596   \n",
       "source_Seo           0.002482 -0.001655             0.003218   0.003534   \n",
       "\n",
       "                     country_Germany  country_UK  country_US  source_Direct  \\\n",
       "age                        -0.003173   -0.006144   -0.000388      -0.000088   \n",
       "new_user                   -0.003656   -0.005149   -0.008522       0.001626   \n",
       "total_pages_visited         0.019693    0.026629    0.019456      -0.009138   \n",
       "converted                   0.035446    0.049023    0.035666      -0.012596   \n",
       "country_Germany             1.000000   -0.088276   -0.235657      -0.004768   \n",
       "country_UK                 -0.088276    1.000000   -0.483053       0.000722   \n",
       "country_US                 -0.235657   -0.483053    1.000000       0.002624   \n",
       "source_Direct              -0.004768    0.000722    0.002624       1.000000   \n",
       "source_Seo                  0.000949    0.000797   -0.001193      -0.534595   \n",
       "\n",
       "                     source_Seo  \n",
       "age                    0.002482  \n",
       "new_user              -0.001655  \n",
       "total_pages_visited    0.003218  \n",
       "converted              0.003534  \n",
       "country_Germany        0.000949  \n",
       "country_UK             0.000797  \n",
       "country_US            -0.001193  \n",
       "source_Direct         -0.534595  \n",
       "source_Seo             1.000000  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traincorr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c/2VmZmalcs+ImZlZCXw2TQ8nI2ZmZiXwME0PD9OYmZlZqdwzYmZmVoIOWnBv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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba86eb1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(traincorr, cmap='RdYlGn', interpolation='none', aspect='auto')\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(traincorr)), traincorr.columns, rotation='vertical')\n",
    "plt.yticks(range(len(traincorr)), traincorr.columns);\n",
    "plt.suptitle(' Correlations Heat Map before label encoding', fontsize=10, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(316198, 9)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "converted              1.000000\n",
       "total_pages_visited    0.528975\n",
       "new_user               0.152338\n",
       "age                    0.089199\n",
       "country_UK             0.049023\n",
       "country_US             0.035666\n",
       "country_Germany        0.035446\n",
       "source_Direct          0.012596\n",
       "source_Seo             0.003534\n",
       "Name: converted, dtype: float64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(traincorr['converted']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}