Mercedes-Benz Greener Manufacturing project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mercedes-Benz Greener Manufacturing ML Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(r\"C:\\D-Drive\\Data Science\\Machine Learning\\Projects\\Mercedes-Benz Greener Manufacturing\\train.csv\")\n",
    "test = pd.read_csv(r\"C:\\D-Drive\\Data Science\\Machine Learning\\Projects\\Mercedes-Benz Greener Manufacturing\\test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   ID       y  X0 X1  X2 X3 X4 X5 X6 X8  ...  X375  X376  X377  X378  X379  \\\n",
       "0   0  130.81   k  v  at  a  d  u  j  o  ...     0     0     1     0     0   \n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   ID  X0 X1  X2 X3 X4 X5 X6 X8  X10  ...  X375  X376  X377  X378  X379  X380  \\\n",
       "0   1  az  v   n  f  d  t  a  w    0  ...     0     0     0     1     0     0   \n",
       "1   2   t  b  ai  a  d  b  g  y    0  ...     0     0     1     0     0     0   \n",
       "2   3  az  v  as  f  d  a  j  j    0  ...     0     0     0     1     0     0   \n",
       "3   4  az  l   n  f  d  z  l  n    0  ...     0     0     0     1     0     0   \n",
       "4   5   w  s  as  c  d  y  i  m    0  ...     1     0     0     0     0     0   \n",
       "\n",
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       "0     0     0     0     0  \n",
       "1     0     0     0     0  \n",
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       "[5 rows x 377 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of Test data <class 'pandas.core.frame.DataFrame'>\n",
      "Type of Train data <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of Test data\",type(test))\n",
    "print(\"Type of Train data\",type(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8', 'X10',\n",
       "       ...\n",
       "       'X375', 'X376', 'X377', 'X378', 'X379', 'X380', 'X382', 'X383', 'X384',\n",
       "       'X385'],\n",
       "      dtype='object', length=377)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'y', 'X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8',\n",
       "       ...\n",
       "       'X375', 'X376', 'X377', 'X378', 'X379', 'X380', 'X382', 'X383', 'X384',\n",
       "       'X385'],\n",
       "      dtype='object', length=378)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 377)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It means the data set has 4209 rows and 377 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 378)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of NULL values present in train dataset: 0\n",
      "Sum of NULL values present in test dataset: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Sum of NULL values present in train dataset:\",train.isna().any().sum())\n",
    "print(\"Sum of NULL values present in test dataset:\",test.isna().any().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankita\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visulizing the Y distribution of the dataset, As Y is the output variable\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(train.y.values, bins=50, kde=False)\n",
    "plt.xlabel('y value', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>4209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>2545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X0</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X380</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X382</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X383</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X384</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X385</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>378 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unique\n",
       "ID      4209\n",
       "y       2545\n",
       "X0        47\n",
       "X1        27\n",
       "X2        44\n",
       "...      ...\n",
       "X380       2\n",
       "X382       2\n",
       "X383       2\n",
       "X384       2\n",
       "X385       2\n",
       "\n",
       "[378 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the unique values against every columns\n",
    "train_UV=pd.DataFrame(train.nunique(),columns=['Unique'])\n",
    "train_UV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Features with unique values greater than 2\n",
      " Unique  ID    4209\n",
      "        y     2545\n",
      "        X0      47\n",
      "        X1      27\n",
      "        X2      44\n",
      "        X3       7\n",
      "        X4       4\n",
      "        X5      29\n",
      "        X6      12\n",
      "        X8      25\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#columns who have more than 2 unique values\n",
    "print('Train Features with unique values greater than 2\\n',train_UV[train_UV.Unique >2].unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>4209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X0</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X380</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X382</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X383</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X384</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X385</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>377 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unique\n",
       "ID      4209\n",
       "X0        49\n",
       "X1        27\n",
       "X2        45\n",
       "X3         7\n",
       "...      ...\n",
       "X380       2\n",
       "X382       2\n",
       "X383       2\n",
       "X384       2\n",
       "X385       2\n",
       "\n",
       "[377 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the same in test dataset\n",
    "test_UV=pd.DataFrame(test.nunique(),columns=['Unique'])\n",
    "test_UV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Features with unique values greater than 2\n",
      " Unique  ID    4209\n",
      "        X0      49\n",
      "        X1      27\n",
      "        X2      45\n",
      "        X3       7\n",
      "        X4       4\n",
      "        X5      32\n",
      "        X6      12\n",
      "        X8      25\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Test Features with unique values greater than 2\\n',test_UV[test_UV.Unique >2].unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>5.941936e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>1.607667e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X10</th>\n",
       "      <td>1.313092e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X11</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X12</th>\n",
       "      <td>6.945713e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X380</th>\n",
       "      <td>8.014579e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X382</th>\n",
       "      <td>7.546747e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X383</th>\n",
       "      <td>1.660732e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X384</th>\n",
       "      <td>4.750593e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X385</th>\n",
       "      <td>1.423823e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>370 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Variance\n",
       "ID    5.941936e+06\n",
       "y     1.607667e+02\n",
       "X10   1.313092e-02\n",
       "X11   0.000000e+00\n",
       "X12   6.945713e-02\n",
       "...            ...\n",
       "X380  8.014579e-03\n",
       "X382  7.546747e-03\n",
       "X383  1.660732e-03\n",
       "X384  4.750593e-04\n",
       "X385  1.423823e-03\n",
       "\n",
       "[370 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the variance of the dataset, \n",
    "train_var=pd.DataFrame(train.var(axis=0),columns=['Variance'])\n",
    "train_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to call zero variance columns\n",
    "def features_zero_var(df):\n",
    "    df_var=pd.DataFrame(df.var(axis=0),columns=['Variance']) \n",
    "    return((df_var[df_var.Variance==0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X11</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X93</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X107</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X233</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X235</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X268</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X289</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X290</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X293</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X297</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X330</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X347</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Variance\n",
       "X11        0.0\n",
       "X93        0.0\n",
       "X107       0.0\n",
       "X233       0.0\n",
       "X235       0.0\n",
       "X268       0.0\n",
       "X289       0.0\n",
       "X290       0.0\n",
       "X293       0.0\n",
       "X297       0.0\n",
       "X330       0.0\n",
       "X347       0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_zero_var(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new= train.drop(columns=train_var[train_var.Variance==0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new.drop('ID', axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As so many column have zero variance we will delete it, as it produces noise in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
       "      <th>X382</th>\n",
       "      <th>X383</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130.81</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>at</td>\n",
       "      <td>a</td>\n",
       "      <td>d</td>\n",
       "      <td>u</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.53</td>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>av</td>\n",
       "      <td>e</td>\n",
       "      <td>d</td>\n",
       "      <td>y</td>\n",
       "      <td>l</td>\n",
       "      <td>o</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.26</td>\n",
       "      <td>az</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
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       "      <td>x</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.62</td>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>x</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.02</td>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>h</td>\n",
       "      <td>d</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4204</th>\n",
       "      <td>107.39</td>\n",
       "      <td>ak</td>\n",
       "      <td>s</td>\n",
       "      <td>as</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>aa</td>\n",
       "      <td>d</td>\n",
       "      <td>q</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4205</th>\n",
       "      <td>108.77</td>\n",
       "      <td>j</td>\n",
       "      <td>o</td>\n",
       "      <td>t</td>\n",
       "      <td>d</td>\n",
       "      <td>d</td>\n",
       "      <td>aa</td>\n",
       "      <td>h</td>\n",
       "      <td>h</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>109.22</td>\n",
       "      <td>ak</td>\n",
       "      <td>v</td>\n",
       "      <td>r</td>\n",
       "      <td>a</td>\n",
       "      <td>d</td>\n",
       "      <td>aa</td>\n",
       "      <td>g</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>87.48</td>\n",
       "      <td>al</td>\n",
       "      <td>r</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>aa</td>\n",
       "      <td>l</td>\n",
       "      <td>u</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>110.85</td>\n",
       "      <td>z</td>\n",
       "      <td>r</td>\n",
       "      <td>ae</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>aa</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4209 rows × 365 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           y  X0 X1  X2 X3 X4  X5 X6 X8  X10  ...  X375  X376  X377  X378  \\\n",
       "0     130.81   k  v  at  a  d   u  j  o    0  ...     0     0     1     0   \n",
       "1      88.53   k  t  av  e  d   y  l  o    0  ...     1     0     0     0   \n",
       "2      76.26  az  w   n  c  d   x  j  x    0  ...     0     0     0     0   \n",
       "3      80.62  az  t   n  f  d   x  l  e    0  ...     0     0     0     0   \n",
       "4      78.02  az  v   n  f  d   h  d  n    0  ...     0     0     0     0   \n",
       "...      ...  .. ..  .. .. ..  .. .. ..  ...  ...   ...   ...   ...   ...   \n",
       "4204  107.39  ak  s  as  c  d  aa  d  q    0  ...     1     0     0     0   \n",
       "4205  108.77   j  o   t  d  d  aa  h  h    0  ...     0     1     0     0   \n",
       "4206  109.22  ak  v   r  a  d  aa  g  e    0  ...     0     0     1     0   \n",
       "4207   87.48  al  r   e  f  d  aa  l  u    0  ...     0     0     0     0   \n",
       "4208  110.85   z  r  ae  c  d  aa  g  w    0  ...     1     0     0     0   \n",
       "\n",
       "      X379  X380  X382  X383  X384  X385  \n",
       "0        0     0     0     0     0     0  \n",
       "1        0     0     0     0     0     0  \n",
       "2        0     0     1     0     0     0  \n",
       "3        0     0     0     0     0     0  \n",
       "4        0     0     0     0     0     0  \n",
       "...    ...   ...   ...   ...   ...   ...  \n",
       "4204     0     0     0     0     0     0  \n",
       "4205     0     0     0     0     0     0  \n",
       "4206     0     0     0     0     0     0  \n",
       "4207     0     0     0     0     0     0  \n",
       "4208     0     0     0     0     0     0  \n",
       "\n",
       "[4209 rows x 365 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 365)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>5.871311e+06</td>\n",
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       "    <tr>\n",
       "      <th>X10</th>\n",
       "      <td>1.865006e-02</td>\n",
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       "    <tr>\n",
       "      <th>X11</th>\n",
       "      <td>2.375861e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X12</th>\n",
       "      <td>6.885074e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X13</th>\n",
       "      <td>5.734498e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X380</th>\n",
       "      <td>8.014579e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X382</th>\n",
       "      <td>8.715481e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X383</th>\n",
       "      <td>4.750593e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X384</th>\n",
       "      <td>7.124196e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X385</th>\n",
       "      <td>1.660732e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>369 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Variance\n",
       "ID    5.871311e+06\n",
       "X10   1.865006e-02\n",
       "X11   2.375861e-04\n",
       "X12   6.885074e-02\n",
       "X13   5.734498e-02\n",
       "...            ...\n",
       "X380  8.014579e-03\n",
       "X382  8.715481e-03\n",
       "X383  4.750593e-04\n",
       "X384  7.124196e-04\n",
       "X385  1.660732e-03\n",
       "\n",
       "[369 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the variance in test dataset\n",
    "test_var=pd.DataFrame(test.var(axis=0),columns=['Variance'])\n",
    "test_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X257</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>X258</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X295</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X296</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>X369</th>\n",
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      ],
      "text/plain": [
       "      Variance\n",
       "X257       0.0\n",
       "X258       0.0\n",
       "X295       0.0\n",
       "X296       0.0\n",
       "X369       0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_zero_var(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_new= test.drop(columns=['ID','X11', 'X93', 'X107','X233', 'X235', 'X268', 'X289', 'X290', 'X293','X297','X330','X347'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 364)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>47</td>\n",
       "      <td>27</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>z</td>\n",
       "      <td>aa</td>\n",
       "      <td>as</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>w</td>\n",
       "      <td>g</td>\n",
       "      <td>j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>360</td>\n",
       "      <td>833</td>\n",
       "      <td>1659</td>\n",
       "      <td>1942</td>\n",
       "      <td>4205</td>\n",
       "      <td>231</td>\n",
       "      <td>1042</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X0    X1    X2    X3    X4    X5    X6    X8\n",
       "count   4209  4209  4209  4209  4209  4209  4209  4209\n",
       "unique    47    27    44     7     4    29    12    25\n",
       "top        z    aa    as     c     d     w     g     j\n",
       "freq     360   833  1659  1942  4205   231  1042   277"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Checking Categorical Data in DataFrame for train Data\n",
    "train_new.describe(include=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As the categorical data is not numerical so we need to do label encoding\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(df):\n",
    "    features=df.select_dtypes(include='object').columns \n",
    "    le=LabelEncoder() \n",
    "    for i in features:\n",
    "        df[i]=le.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder(train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>88.53</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.26</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>80.62</td>\n",
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       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.02</td>\n",
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       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 365 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        y  X0  X1  X2  X3  X4  X5  X6  X8  X10  ...  X375  X376  X377  X378  \\\n",
       "0  130.81  32  23  17   0   3  24   9  14    0  ...     0     0     1     0   \n",
       "1   88.53  32  21  19   4   3  28  11  14    0  ...     1     0     0     0   \n",
       "2   76.26  20  24  34   2   3  27   9  23    0  ...     0     0     0     0   \n",
       "3   80.62  20  21  34   5   3  27  11   4    0  ...     0     0     0     0   \n",
       "4   78.02  20  23  34   5   3  12   3  13    0  ...     0     0     0     0   \n",
       "\n",
       "   X379  X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0     0  \n",
       "1     0     0     0     0     0     0  \n",
       "2     0     0     1     0     0     0  \n",
       "3     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 365 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "      <td>4209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>49</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ak</td>\n",
       "      <td>aa</td>\n",
       "      <td>as</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>v</td>\n",
       "      <td>g</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>432</td>\n",
       "      <td>826</td>\n",
       "      <td>1658</td>\n",
       "      <td>1900</td>\n",
       "      <td>4203</td>\n",
       "      <td>246</td>\n",
       "      <td>1073</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X0    X1    X2    X3    X4    X5    X6    X8\n",
       "count   4209  4209  4209  4209  4209  4209  4209  4209\n",
       "unique    49    27    45     7     4    32    12    25\n",
       "top       ak    aa    as     c     d     v     g     e\n",
       "freq     432   826  1658  1900  4203   246  1073   274"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Checking Categorical Data in DataFrame for train Data\n",
    "test_new.describe(include=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization the Categorical variable w.r.t y(outcome variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X0\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.stripplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X1\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJkpbgWik1TSn1pFJqp1Jqu1Lq44PLv6yUOqaU2jL43/UjtvmcUmqfUmq3UuqadLRTCCGEEEKIU+FJ037CwKe01puUUoXARqXU44Prvqe1/p+RT1ZKLQbeAiwBaoG/K6Xma62tNLVXCCGEEEKIcUtLz7XW+oTWetPgv7uBnUCdyyY3AXdqrQNa64PAPuCc1LdUCCGEEEKIiUt7zrVSaiawElg3uOhjSqmtSqnblVKlg8vqgKMjNqvHIRhXSn1QKbVBKbWhubk5lc0WQgghhBAirrQG10qpAuAe4BNa6y7gJ8AcYAVwAvjO0FMdNtdRC7S+TWu9Wmu9urKyMjWNFkIIIYQQIkFpC66VUl4igfUftNb3AmitG7XWltbaBn7OydSPemDaiM2nAsfT1VYhhBBCCCEmIl3VQhTwS2Cn1vq7I5bXjHjazcCrg/9+AHiLUsqvlJoFzAPWp6OtQgghhBBCTFS6qoVcALwD2KaU2jK47N+BW5VSK4ikfBwCPgSgtd6ulLoL2EGk0shHpVKIEEIIIYTIdmkJrrXWz+GcR/2wyzZfB76eskYJIYQQQgiRZDJDoxBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJkpbgWik1TSn1pFJqp1Jqu1Lq44PLv62U2qWU2qqUuk8pVTK4fKZSql8ptWXwv5+mo51CCCGEEEKcinT1XIeBT2mtFwHnAR9VSi0GHgeWaq2XA3uAz43YZr/WesXgfx9OUzuFEEIIIYSYsLQE11rrE1rrTYP/7gZ2AnVa68e01uHBp70ETE1He4QQQgghhEiFtOdcK6VmAiuBdWNWvRf424jHs5RSm5VSTyulLorxWh9USm1QSm1obm5OTYOFEEIIIYRIUFqDa6VUAXAP8AmtddeI5Z8nkjryh8FFJ4DpWuuVwCeBPyqlisa+ntb6Nq31aq316srKytT/AUIIIYQQQrhIW3CtlPISCaz/oLW+d8TydwE3Am/TWmsArXVAa906+O+NwH5gfrraKoQQQgghxESkq1qIAn4J7NRaf3fE8muBzwCv1Vr3jVheqZQyB/89G5gHHEhHW4UQQgghhJgoT5r2cwHwDmCbUmrL4LJ/B34A+IHHI/E3Lw1WBrkY+KpSKgxYwIe11m1paqsQQgghhBATkpbgWmv9HKAcVj0c4/n3EEkhEUIIIYQQ4rQhMzQKIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSQSXAshhBBCCJEkElwLIYQQQgiRJBJcCyGEEEIIkSRpCa6VUtOUUk8qpXYqpbYrpT4+uLxMKfW4Umrv4P9LR2zzOaXUPqXUbqXUNelopxBCCCGEEKciXT3XYeBTWutFwHnAR5VSi4HPAv/QWs8D/jH4mMF1bwGWANcCP1ZKmWlqqxBCCCGEEBOSluBaa31Ca71p8N/dwE6gDrgJ+M3g034DvG7w3zcBd2qtA1rrg8A+4Jx0tFUIIYQQQoiJSnvOtVJqJrASWAdUa61PQCQAB6oGn1YHHB2xWf3gMiGEEEIIIbJWWoNrpVQBcA/wCa11l9tTHZZph9f7oFJqg1JqQ3Nzc7KaKYQQQgghxISkLbhWSnmJBNZ/0FrfO7i4USlVM7i+BmgaXF4PTBux+VTg+NjX1FrfprVerbVeXVlZmbrGCyGEEEIIkYB0VQtRwC+BnVrr745Y9QDwrsF/vwu4f8Tytyil/EqpWcA8YH062iqEEEIIIcREedK0nwuAdwDblFJbBpf9O/BN4C6l1PuAI8AbAbTW25VSdwE7iFQa+ajW2kpTW4UQQgghhJiQtATXWuvncM6jBrgixjZfB76eskYJIYQQQgiRZDJDoxBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSSLBtRBCCCGEEEkiwbUQQgghhBBJIsG1EEIIIYQQSZKW4FopdbtSqkkp9eqIZX9SSm0Z/O+QUmrL4PKZSqn+Eet+mo42CiGEEEIIcao8adrPr4H/A347tEBr/eahfyulvgN0jnj+fq31ijS1TQghhBBCiKRIS3CttX5GKTXTaZ1SSgFvAi5PR1uEEEIIIYRIlWzIub4IaNRa7x2xbJZSarNS6mml1EWxNlRKfVAptUEptaG5uTn1LRVCCCGEEMJFNgTXtwJ3jHh8ApiutV4JfBL4o1KqyGlDrfVtWuvVWuvVlZWVaWiqEEIIIYQQsWU0uFZKeYDXA38aWqa1DmitWwf/vRHYD8zPTAuFEEIIIYRIXKZ7rq8Edmmt64cWKKUqlVLm4L9nA/OAAxlqnxBCCCGEEAlLVym+O4AXgQVKqXql1PsGV72F0SkhABcDW5VSrwB3Ax/WWrelo51CCCGEEEKcinRVC7k1xvJ3Oyy7B7gn1W0Sk09/1xEaDvwFpUymzLmZnILaTDdJCCGEEGeYdNW5FiKlBnpP8Mrj78YK9QLQuP9+Vl5/J76csgy3TAghhBBnkkznXAuRFC2HHx0OrAHCwU5ajz6RwRYJIYQQ4kyUcHCtlPquUmpFCtsixISZ3vyElgkhhBBCpNJ4eq69wKNKqVeVUp9RSk1NVaPOdLqvi9CjPyf4288RfuK36OBAppuU9SpnXEdu0czhx/mlCyifelnmGiSEEEKIM5LSWif+5EiJvOuAtwE3AuuA3wL3aq17UtLCBK1evVpv2LAhk01ImuA930If3jb82FhyEd5rPpTBFp0ebCtI+4kXUcqkpOY8DEOGFAghhBAi+ZRSG7XWq53WjSvnWmttaa0fHKz+cR5QCfwaaFBK/UIpVXfKrT3D6XBoVGANYO/fnKHWnF4M00f51Esoq7tQAmshhBBCZMS4gmulVJFS6n1KqSeBZ4j0XF8ELAJ6gL8lv4lnFuXxQnHV6GVlUlJOCCGEEONjN4cJ/L6Dge+1EvpbNzqceLaCmLjxDGi8GzhGZLrynwK1WusPaq2f11ofBT4JzEpNM88s3qveB3lFkQdFFXguf2dmGySEEEKI04q2NcHfd2LvCqJbLcLP9xN+ojf+huKUjefe+UvAx7TWDU4rtda2Uqo6Oc06sxnTl+D7wA+gqwWKq1CGVEwUQgghROJ0m4VutUYts/YE8V6doQadQRIOrrXW/5PAc/pOrTliiDI9UDol080QQgghxGlIFZuQo2DgZCqIUW1msEVnDukSFUIIIYSYZJRX4Xt9EeSpyOM6D96rCzLcqjODlFQQQgghhJiEzMV+cuZXQL+NKpRe63SR4FoIIYQQYpJSHgUSWKeVBNdCiKwXssP8sf4RNnXuYlHBLN41/UZyTX+mmyWEEEJEkeBaCJH1/vfAndx9/B8ArGvfztGBRr6+6J8y3CohhBAimgxoFEJkvceb1o16/FTzRsJ2OEOtEUIIIWKT4FoIkfWq/WWjHhd7CzHk9CWEECILya+TECLrfXzOW8g3c4cft4U6uXXj5zncdyKDrRLZpC9s8av9x/jiK3t5+FhzppsjhDiDSXAthMh6q0oWcd+536bIkz+87HB/A/974M4Mtkpkky++so9f7T/GU43tfHP7Qe44JBdeQojMkOBaCHFasGyLrnDvqGWHpOdaAG2BEOtbO0cte+RYS4ZaI4Q400lwLYQ4LZT6ilhcOHvUsgvKzspQa0Q2yTUNcs3RP2elfm+GWiOEONNJcC0mjfYTL7HnxS9xcPP3CfQ2Zro5IgW+ufijXF15LrPz6ri17ho+OuuNmW6SyAK5HpP3z506/IOW7zF5/9y6jLZJCHHmUlrrTLchKVavXq03bNiQ6WYkje5px9r5PBgm5uILUbmFmW5SVutoWMf2pz4ORI5nf14NZ99wF4bpy2zDhBBpc6I/wKGefpaXFpLvkRnphBCpo5TaqLVe7bROJpHJQrq7jeDvPw/93QBYmx7B947/QuXkx9nyzNV08GGGAmuAQN8JOps2UVpzXuYaJVJiW9c+dnUf4uyShczJn5rp5ogsUpPrpyZXZu4UYojdFCb0WA+6zcJc4sdzWT7KUJlu1qQnwXUWsnY+NxxYA9Ddir13PeayyzLXqCznzSlzWFaagZaIVPruvj9w1/G/A6BQfGnBB7i2+vwMt0oIIbKPtjTB33SgO20Awk194FF4L5GOulSTnOtsZDjcznRaJobVLngL/vya4cdVs26goHRBBlskku3xpnXDgTWARvOrI3/NYIuEECJ76abwcGA9xN4dzFBrzizSc52FzMUXYW16FHraAFClNRjzzslwq7KbP6+as6//M51NG/HmlFFQOj/TTRJJ9tujD0UtC9ihDLRECJEpbY0W9fst/LmKWYs8+HIkxSEWVWKCFxhxmlRV0lGXDhJcZyGVV4Tvnd/A3rMeTBNj3jkoX06mm5X1DNMrOdaTWEhbUcveUHtp+hsihMiIlhMWL/wtyFAdhmMHLC672S85xDGoXAPvawsJPdQDAxo11YP3CkkJSQcJrrOUysnHXC451kIMeUvdVXxz72+GH68tXc47pt2QwRYJIdLp8G6LkQXOujs0DUctamZIKBOLZ2Uu5tIcdK+NUSK91umSlpxrpdTtSqkmpdSrI5Z9WSl1TCm1ZfC/60es+5xSap9SardS6pp0tFEIkd1eV3MpX1v4IRYUTGdRwUyur74g000SQqSRx2FeoM3PhujrsaNXiGHKqySwTrN0DWj8NXCtw/Lvaa1XDP73MIBSajHwFmDJ4DY/VkrJUSHEGW53z2G+v/8OdvccYWfPIb6w6yc80fxyppslhEiTOcs8eMZMXRAKwKGd0SljInm01lh7egk924Z9fCDTzTktpCW41lo/A7Ql+PSbgDu11gGt9UFgHyCj+YQ4g31v/x9516Yv0xrqGrX80aaXMtQiIUS6FRQZnLU2uvvasibHZHjZKvRIC8E/HCf891YCtx0lvK07/kZnuEyX4vuYUmrrYNrIUFHiOuDoiOfUDy6LopT6oFJqg1JqQ3Nzc6rbKoTIgGP9Tdx17O+O67xKci2FOJPUzDDJLzo5gNE0YcZ8OQ+kig7YWC93jFgA4efbM9ae00Umg+ufAHOAFcAJ4DuDy52G/Tpelmqtb9Nar9Zar66srExJIzNF9/cQ3vAQ4RfuwW5vyHRzhMiYjlAP2vkU4Hy2EEJMWqZHcfFr/Cxe42HeWR4ueZ2forJM9xNOYlpHR2ByoyCujF3uaa0bh/6tlPo58ODgw3pg2oinTgWOp7FpGadDQUJ3fAndEXmLrI0P433b1zDKajPcMiHSb1HhTObk1bG/71jUOtuWgUxCnGl8OYp5yx1GN4qkUzkm5upirPWdgwvAc0FJRtt0OsjY5Z5SqmbEw5uBoUoiDwBvUUr5lVKzgHnA+nS3L5Psg1uGA2sAQgHsbU9lqjlCZJShDL6w4P0UeaLrs7aHJfdPCCFSyXt9Jb5ba/BcXo7//dPwLC/KdJOyXlp6rpVSdwCXAhVKqXrgS8ClSqkVRG4wHAI+BKC13q6UugvYAYSBj2rtMHtEGuiODkIP/w19/Diqthbv9dehSkpSv2OnekNOy4Q4Q/z52ON0hXujlm/t3IutbQwlt4WFECIVlFKYCwswF2a6JaePtATXWutbHRb/0uX5Xwe+nroWJSb08N/Q9fUA6Pp6Qg//Dd9bnf6U5DJmLkdNmYNu2B9ZkFeMufzylO9XiGx1dKDJcXlNToUE1kIIIbKKDLF1oY8fd32cKsow8b7pC9gHNkFwAGPOKlRuQVr2LUQ2OrdkCdu69o1alm/m8q9z35GhFgkhhBDOJLh2oWprh3uuhx6nbd8eL+b8c9O2PyGy2Yvt26KW2dqm2CsXnUIIIbKL3E914b3+OtTUqWAYqKlT8V5/XaabJMQZ52h/I9u7D0Qt77cD/O7owxlokRBCCBGb9Fy7UCUlacmxHkt3NhN++o/olqMYs5ZjXvhmlNef9nYIkQ2KPPl4MAkTPa653w5koEVCCICOHpunt4Ro6dTMqjG4aLkXr0eKzwshwXUWCj3wfXTzYQCszQ2gwXP5OzPcKiEyo9hbwIy8KVF1rhWKm2suy1CrhBD3PxekuSMyo0h7d+Ti9/KzfZlskhBZQdJCsozu7RgOrIdYBzZlqDVCZN6R/oaowLraX8ZPzvosF5WvyEyjhDjD9fTr4cB6yMETqZ3UybY0OzaEePLeAV5+Ikhvl0wiJbKTBNfZJqcA8opHL+vtQHc6lyITYtJzmGq3M9TDsqK56W+LEFlIa83GJot794c43J2egDPXD3ljshUrilObErJrc5i9r4TpatccP2ix7vHghF6nv8um+YBFoFfm8Y5HB23spgDalvdqPCS4zjLK9GCuuXH0QiuMtenRzDToNGFbAUKBjkw3Q6TA9LwpFHtGVwUZsIPscBjkKMSZ6LbtIf5jXZBf7Ajz0acDvNiQ+nnXTENx7bm+4QC7skRxyYrUTnbWeGT039XdoXn6/gG62hK/oDixK8y6OwJsfyzIS38YoOVgRuaoOy1YO3oY+J+DBH50hMD/HsJulDEuiZLgOgsZ5VOjlulgfwZacnpoPPAA6/9yPevvu4ZXn/xnwkGZEnuy8RvRP9qlXpmCV4iekObBQycDRFvDn/eF0rLv2bUmH7ophw+9Nod3XZtDSUFqQ4qCkujX72jRrP9HEK3j96xqrTnwUmj4bpi24cD69LxXpxttaYIPNkEgcuGiO8KEHmvJcKtOHxJcZyE1bRGqtObkAsPEXHZpxtqTzYIDbezf8N9YoR4AOhvXU7/jNxlulUi2jlD0BVNzoD0DLREiu9gaxsaVVhpTkU1DUZiXngohi9d4KCiJXt7bpRlIIMVDawiPySQJDUi6g6MBG3pH9+rr1oml4ZyJJLjOQsr04H3LFzHX3oK54mq8b/kiRu38TDcrK/V3HUbbo3seejv3xXi2OB11hLoJ6nDU8v/e+zssLQOaxJmtyKe4Ypo5atnrZk/OQmD5hQYLV0bfxfL4wJ9AgG8Yiur5o9+rmoWT8706VSrfxJieM2qZuVAm7UqUHFXZKtCHbm9Ad7egGmthypxMtygrFZQtwuMrJhzsHF5WOuW8DLZIJFvn4F2JsQ72H+Pu43/nzXVXp7lFQmSXfznLy9mVBoe7NaurDBaXmfE3Ok0N9EUvq51pYhiJ9Z7Pv8hLQblBd5NNca1BzcLJ+16Npfts7BYLo8aD8sZ/v3xvriH0j1as4wMYfhOtwW4IYEyReTfikeA6C2nbInjPN6GzGYBw/S5QBubyyzPcsuxjenJYfMn3OfzKjwj0N1E5/Wpq5r8p080SSTQjrwav8hBy6L1e375DgmtxWgvbmm3tIYq9itlFExsQaCrFJXVnxs95zQyDnRvBGjwdGCbMXZb4326YiqnjeP5kEd7cT+j+bggD+Qr/O0sw6tyPN1XgwXt9JdYPDmM3BOFwP9aGTvzvn4pRk+O67ZnuzDvCTgO68eBwYD3E2rtegusYCssXs/TyH2W6GSJF+qwBx8AaoC88kObWCJE8bQGLj7/UztHB3NZr6nL43FnFcbY6s+UVGlx4g58D28NoDbMWeyh0GOgoTtJhTejhnkhgDdCrCT3ag/+9pXG3DT/TBl0jzr9hTXhTF74bJLh2I0dkFlKF5ZHL8ZHLiqsy1Jr0sEJ99DZvwwr1ZropZ4yu/kbqO7Zh286Ba7bINfzU+Csc1+3vrU9za4RInnsP9Q8H1gCPHhtgV4dUr4inpMLg7Et8rLrUR1mVhDFxBTX0jx64qTsTG69i7XRIyzNlivt45KjMQqqgFPOCNw4H2KqsFs+5N2W4VanTdfxFXv3ztex99AO8+udr6Tjy5IRexwoP0N99NKGSTGe65/b/kh8/8zp+t+79/Oy5N9LRfzzTTYpJKcXn578Hp9N5l9XL8f5mhzVCZL/WQHSNZadlQpwKlWdgzB6dAmIuTSxvWjsE0p4VUgY1HkkLyVKeNTdiLrkI3duBqpiOUpP3SvHwc19C25ESP9oOceSFr1Iy/bJxvcaBTd+lYe89aB0mp3AGSy75HjkFdalo7mktZA3w8qE7eXbfzxkq9trRf5wX9v+K65d+3nVbrTWPHPwlLzc8TJGvnBvn/BNzS1emodUwK7/OaaJGANa1v8rNueM7XoTIBlfV5vJI/cDwsV3uN1hVIYPFRPJ5rsjHyuvD7rMx5+fgWZub0Hbe80oI/eXkDNHGkgIZ0JgACa6zlB7oxd69Dh0OYuYUQmFZppuUMlawa9RjO9yPHR7A8CSW03V0x284sedPw48Hug9z6JUfsfCC/0pqOyeDuzb+P460b4pa3jXQGHfb54/dxyMHfw5Aa/8xbnvlk3zlwr+S60l9eaZCTx4+w0vQjr5lPitfLqJEcnQFw/zuYDOHegY4r6KQm6eXY6SwY+PsCh//vaaEh+v7KfYavGl2HjkTvOVuaU13EEr8k7cjRkxM8J4urM2R8Smq0sSzIgeVYHUVz8piVLEXe28vqsqPubwwlU2dNCS4zkI6NEDoj19Ed0QCHuulv+B95zcwiisz3LLUMP0lWCOmLleGD2X6Et6+cf99Ucv6Og8mo2mTSlP3PsfAGmBxzVVxt9/dtm7U44DVx6HObSwqPz8p7XPjM7xMz6lmX9/oHGsFVPsn74WnSK8vbDnClvbIuI91LT30WTbvmJ3a8S5rKv2sqTy1nsAtzRbf2RKkdQBmFyk+v9pHTf7kzPq0LE1TvY1hQlWtkXCQeKay60PDgTWAbrYIv9SH98rEO0XM2XmYs/NS0bxJa3J++05z9v5Nw4E1AKEBQnd/Az1JJ8yYceFXUcZgMK1Mpp33OZRK/NB0el/Kai9IVvMmDY8RfcGS7yvnhqVfZHnda+JuX1cwb9RjQ5nU5M9OWvvcHOg9xqH+E1HLNfBi27a0tEFMbi0DoeHAesjfT3RkpjHjYGk9HFgDHOjS/Hz75BwUGQxonrovwPq/B3np0SDPPRTEtmSMjZvw9uiKSnaz5PWnmvRcZyOnwLKzCX14O2rmsvS3J8WKas5h6Zsepb9tNznFs/D4Ey9FpbUmPCatxDD8TF/+4WQ387RXlj+dxVOuZkfDYwB4zVxuOft/qC1enND2l814G/Xdu3m15VlyPAW8du5HKcmpnlBbTvQf4zt7vsaOrq0sLlrOp+b/BzW5sdM7fnPkQcLa+QehwlcyoTYIMVKB1yTXNOgfMXd4Vc7E6k6nU0+Q4cB6yIGuyRlwHtkTpqfz5N/W1mTTcMSmdtaZMxHMeFk7A9ELJfJLOXmLs5Ax52zIKYCB0SVwdGDylqkzPbl4cyvpb99HfuUyjETTQrQd+W/ka/kKMQw5tJ28dvlXWFZ3A539x5lbeSGFOYnf8vabubz/rG/TH+7Ba/jxGBMPPL6z52ts69wMwLbOzXxnz9f4n7N+GvP5z7W+EnOdOY67HM+17uLFtt3MzKvippo1+OQ4EYNyTIOPLqjh+zuPE9aaEp/JB+ZNyXSz4ir2K+YUKfaPCKhXTdLydGGHDvlQcHJeSCSLQkUNBjfOkAmHMkne4SykPD48r/sk4T/958nAMb8UY9ZZmW1YCp145ec0brsd0HjzKplz5Y/IKZoedztlmEyZdwvHd/1heFmtzNAYk1IGsysmPj18f7iHXa3rKM2pZmbx0gm/zo6ura6Px+q1+x2XK1TCAxrvP/Ey39zzl+HHmzoO8o0lb01oW3FmeO20Mi6oKqS+L8jColz85ukRpH5+jY/bXg1xsEtzdpXB+xdnf4/7REydY7JvW3h4dkZfDtTMkF5rN+YFuYTvH9FRl6vwnCv506kmwXWWMmvno279Mtb2p1HeXMyVV6F8iZXOOd2E+pppfPVXDJWGizz+NTPWfjGh7Wee9c8Uli2mq/kVcotnUzP35hS29sx1oucAP9j4IfrCkTSc82tv4i2L/n1Cr7W4aPlwz/XQYzdTcyqpH4iuZ31R2Qpqc5wnmBnr3uOjB2Q+3bKDtmAPZb7UVzs5HR3s6ebEQB8rSsrJ85w5PxXlfi/l/tMrOJ2SZ/DFcyZ/ebSCYoOLX+vn8O4whqmYtdDElyMDGt141+Rh5JuEN/Wjik28V+Wj0jAJjLY04WdOYO/tRFXk4L2iDlWceKGC092Zc8Y8DRlTZmNMSc+AsUwKDbRFpXaE+hKfGEQpRVfLVk7s/TOgOfzK/7H8qtvJK5qR5JZmj/qWDTR2vEpt2QpqylakZZ//OPzb4cAa4MXj93PFjHdQmTdt3K/1qfn/EZVz7ea6qrX8/Mj9UcsbAq0J7zPPHB18eJQhaSEx/GTvTv5weB8AJV4fP1i1ltkFp0cJrgHL4unGdgYsm0urSyn2JR4ob27r4d4jrXiV4k0zK1hYLD182aao1GDZeWdOkJYM5mI/5uKJXXxZh/ux9/dh1PgxFuYnPOdG+OkTWM81AKAb+gk2D+D/0KIJteF0JL8sWUoP9BK679vopsOQW4Dnmg9hzpj4bfh0scMBWjffRu+xF/GXzqFi1UfxFrjnLQZ7GqKW+QoTr10c6GscVefaCvWw+/nPsvK6OxJv+Glk477bWb/nZ8OPL1z8aZbNfGPK99sfjp4G12lZImpy61xzrEfa033YMbAG2NN7hL09R5lXED/Af++My/n0tt8S1JF7ym+bdhEFCdZSP5M0D/Rzx2BgDdARCvLbg3v58rKzM9iqxARtm4+s28H+nkga0a/2H+Pn5y2hMid+MLa/u59PbjjIUPGJ55q7+N0F86nOlUAu21hhzY6NIZqO2hQUK5Zf4CU37/RI4ckEbWl0h4UqMcfVax3e2EnogZMTyJjnleC7LrGSwPaejtFtaOzH7ghglEz+OywgpfiyVujeb6FP7AMrBD3thO/7NjoYXVIn27Rs+gnt2/9AsOMA3Qcf5/iTn4m7Tdv+B6KWdR15OuF9nthzd9Sy/u6jCW9/OtFas+XA70ct23zgt2nZ9/m1N6FGTEI+tXAB0woXpny/v6//m+v67V37E3qdNaVz+PO5n+Q/FtzC7Wd/hA/Nil/b+0zUFQ4xtrhle8ih4kAWeqGpYziwBmgLhnjwWGJ3wZ5s6GRkVbcBS/N8c3eymyiS4NkHAxx41aKnU9NwxOaJewKEQzKw0YldHyLwnVYC32tj4H9asQ4FE942/EL7qMfWyx3oUGIlgVX5mI6LHBOVf3qlW50KCa6zjO5sIvT3X6EbDoxeYVvY9bsy06hx6D363KjHgdbdhPtaXLcxfdG3m61QD1ondrLs7dgTtUzbYWwr8ZPI6URHjf1Oz4/K0sqL+KeV/8fa2tdxzcz3Mad4JT/Z8i88dujXhB1mTkyWmXm1rutNlfiApip/MddPWcmiwqmn2qxJa05BEQsKR5fDvL5m/Kk/mWA5nDOcljmpcujdPh1K8Z1pWhstOltHf6bhIDQeldrNToIPdKO7BgPibpvQ/eO4YBybAmKBdcR5cPlYnivqUOWDvdR+A+8N01DeMyfkPHP+0tOADgUJ/ulr2Fv/4bjeKHMPMrKBr2TWqMdmTilmTonrNlVL3nFyEplBZbNvSDi3SzmWhNPYKQz4MkUpxYpZbxu1bMXsd6Rt//PLVvPmRZ+jofcAT9ffye629Ty0/yfcv/cHKdvne2e8llwj9q3Eq6rOSdm+s8GAFWZ/TzshO33Bw3dXnsc7Z87jqil1/Nfy1VxTc3pcjKytLKEu7+SxUuAxub42sQGv19SWsKI0f/jxRVVFnF95euSZn0kObA87Ljc9MrDRiW4Zfd7QzVbCHVfmyujjP/yIe2fZEKPMj++fFuP76GL8/28Z5pIzaybdtORcK6VuB24EmrTWSweXfRt4DRAE9gPv0Vp3KKVmAjuB3YObv6S1PiNmBLHrd0JPu/PKwnJUSWqn4U2GyjX/TKjrCMHOwxj+YqrXfg4VZ9BYbskcFr/+AU5s/hHB3gaK6i6gcsEtCe+zbsGttJ94ftSgyMoZ1+Lx5rtsdfpaPe/9VJcspbFjO7VlK6ktT28ubNgOsrV5dNrOpsbHecOCT6Vsn7fUXMHvjj3suG5Xz2FWFM9P2b4z6aXWY3x5+7N0h4OU+3L55vLLWFyUWLB4Kop9Pj44N/UpP8mW6zG57dwlPHK8hQHL5uqacqpzE8vx9JsGPzhnNnu6+vEailkFp0c+vtaa+w9aPHfcojpP8fYFnkk79TlAr8MEOb4cqJo6ef/mU6HKDHTD6AA7+MdOfLcUofzu75kqiP7t1m0hdMhOqBdaKRWdHnKGSNeAxl8D/weMTA59HPic1jqslPoW8DlgKEF3v9Z6RZraljVUQUnsladJzqOvaDozXncn4Z4TmHkVCU8G480pYfr5n5/QPkPBTgrKlhDsa8SXU07lrOuYMuf1E3qt08W0yvOYVjnxetWnwlReivzldAZO5rKWTnCmxkT9PkZgDdAciHFBeprTWvPt3S/RHY6kN7UG+/nenvX8fPX1GW5Zdiv0enjjjIlP/jK/6PQqefrAQYvbBqc739EOuztsbrvMj5Hgnb/TTXG5QWfr6GBx7XVeDGNy/r2nQoc1ui06R9reGST8XB/eK9zLkBq1DoFxWBP6e2vCAxvPVGm51NNaPwO0jVn2mNZ66P7OS8Dpcd8xhYzKGagYFUGMugVpbs3EKaXwFtYmPsviKWg7/gK7n/8cPa3bCPY30dd9iMrpV2OYkiuZKkop3jD/U3gHUzVyPYW8bt4nUra/3nC/a1Z5bc7kPMmHtE3jwOhZWY/1ywA7MdoLY3olj/dqDk7S6c8B5p/lIWdEhcRZi02Ky6TwmSMNxBjoaTc4p9eMZG3pclxu75m8s0UnS7Ycke8F/jTi8Syl1GagC/iC1vrZzDQr/YxZK7EOvzpqmSqdgufK92SoRdnt4ObvjXpsh/tpb3iJqpnXZahFZ4azqi5jXukqTvQeYGrhAvxm6nr79vXWu66PHuA5OfgMk/PL63ih9djwsksq489aejr77YEj3HWkHo8yeNfs6dw8LfvHmWRaTZ5i24hy7x4DKibxxCr5RQZXvjGH1gabnDxFUZmkg8SivApjpR97U/Sdb3Nu/M4vu9H5jrmqkvKU8WT8qFRKfR4IA0PzV58ApmutVwKfBP6olCqKse0HlVIblFIbmptjl1uyjzcRuvcxgnc+hLXrQMznZQNz7irwjMgRND14XvMJVH5JxtqUrbS2GXAouefPS22KgojI8xYxp2RFSgNrgJfat7mun5d/elSymIj/WHwhr69bwOKiCt42fQmfmLcm001KmReaW/n5/kN0hsK0BoN8d9c+dnVKT308b53vYVpBJJj2GvCBxV6K/ZM3uIbI4MWqqaYE1gnw3VSEmjm6H9WY6cVcE/+8bc52nkTJ4zDQUYyW0Z5rpdS7iAx0vEIPDl/VWgeAwOC/Nyql9gPzgQ1jt9da3wbcBrB69WrH7ivd20/oTw9DMJKTFj58HJXrx5iR+CQl6aSKKjBmr8Te81Lk8dRFqLKaDLcqPWwrQOfRZ9DaonjqxZhe99nRlDLw5lYQ6h99YbXruc8y6+z/J73XKdTYe5gNDX8j11PAubWvId9bHH+jCTKIHSiYGHgn8SyLRV4/n1pwbqabkRZbO6JvQW/t6GRhcXp/yDuCYR473gHAVTUllPrjH1/rmvt4uL4LS2tumVHCivL05W1X5Rn89FI/h7s15TmKQl96AmvL1gRCkDfJA/nTnW4InyzFl6vwXJKH98LEBvsbq4rg0ejqIKH1nZgLJcB2k7FfJaXUtUQGMF6ite4bsbwSaNNaW0qp2cA8YMLdzfbB+uHAeoi151DWBtf24W3DgTWAPryN0APfx/uaj6PMyRtE2OEB9vztvQx0Rj5qX0Ed86+7HY/fPWibc/an2PPSl7Ctk7evwsFO9q3/OqVTzsObU5rSdgP09h5j08av0da2lbKy5Zy96j/Iz8/O4ysZTvQc4Lsvv4egHZnU6IVjf+Ez5/4Br5mambfOLlnEL49ETzQEYGFjKOm9mgyWlUTfoFzisCyVOoNh3vfiPpoHIr8Zdx5q5va18yjxxT73bmjp4zMbTgwnJz3b2MctM4r558Wpr+oyRCnFzKL0Bbl7jlo8sj5IMARTyhQ3X+wnP42pKOGQ5thBC9uC2lkm/kmcBnOqAn/uhKFBjf2a8HN9mAv8GJXx4wnd7ZyXrQ/2o7VOuFzumSgtv0pKqTuAF4EFSql6pdT7iFQPKQQeV0ptUUoNzYV8MbBVKfUKcDfwYa11m+MLJ7Jvh5Oz07JsYZ2Inm1OH9iMtd45uJgsGrbdPhxYAwR7jtF2wH1mPoDyaZdRUnN+1HJth+jrOpTMJsa0aePXaG3djNYWra2b2bTxa2nZb6a8ePz+4cAaoLn/KDvbXnLZ4tSEbfeBN0f6GlK2b5E+F1SW857Z0ynwmBR7vfzLgjksKU7vufqJhs7hwBqgJRDm8RMdrts8frwnKuv/7sOdHOudfHX2AYIhzYMvBIf7rBraNH99LpBw7eRTZYU1zzwQYMuzIba+EOKJuwfo60ls1sAhoYDm+I4wx3eECQcm55gNiEx7TsuY96ZHE/jfNkJ/74m7vd0aYyI2TbrmLjttpaUrVGt9q8PiX8Z47j3APcnatzG1GuPsxdibdgCgptdgnpW99Vt1g/NUzvaBLXD+5C0v17b/oahl2opffrB+1x9pq3/KcZ0dTmwmKa01XW3bMUwfhSXjr5fc1rbV9fFk4zWiB7M4LUuWPmvglNaL08d758zkvXNmZmz/TtXc4vVAlfmdZwhtCYSpm4TTPTe02dhjAqv6Fs2P7hvgdRf5mFqZ+IypE3HisEV3x8kGBAPw5L0BLrrRn1AOdmhAs+HuAIGeyGsc2Rxm1S1+vJMwvcU+GvsCL/xMH57z8lAFzu+Z7rMI39fovLHfQEnpQ1dnxP1U71UX4PvIrXjf/0Z8t96I8mXvCU+PqAwwkqqYvGkGAOGB6FrFpbOujbtd29EnYq5rPvJ4/P2G+3j5yfex/sn38dLf38GWF/4NrcfXC1JWttz18WRzwdQ3UOg7OdvWrOLlLChL3SyJy4vnua4vMN1z889ULYF+jvXH752aTB493sKnN+7iExt28kB9EyF7fN/ly6eUUJN78vehOsfLlTUlrtvcMrOY8jEBdk2uhyUlk3PyjOJ856BqIAh3PxUkEExtl6ZTB3k4BNvXJ3anoHGfNRxYAwx0a5r2Tc6p063tLh1UNui+2N8Pa2cP9Mf4LAM2Oixd124mbxLvGKqowGVYVPYwpi7E7myKXpGf+tzhTCqefgmdR54cflxYdxG+/PhVPwrKl9Dd6lxNwuOLP+Di+MG/0tm2ffhx8/GnaWl4kcqaCxJodcTZq/4jKud6MivLmcK/n/cntjY/TY4nn2UVF2Oo1PVWdYbcA0RTcq6jfG/3Zu6t34cNnFNWzTeWryVnEo/ZAPj9gePctu9k2cZNbd1saO3kq2e5X5yNVOg1+cX583iyoRON5vIpJRR63Y/tcr+Huy6dwW/3tbO1vZ/pBT7eNrsEzyTt2SvMUxgGOF23hC3Ye8xi6azUHWuVtQbKGDUhLwCdDpOlOHJ62iSNE1VR7HOjmurBqHL5nHwu51UNdnsQszI142wmg8l9tgXsxlasFzejA0HMsxZiLpyd6Sa5MlZdh739majl9vq/oi+4BTVJA4m61Z8k1NfCQMdelOHH6y8i2NeEL899yvdZK/6Z1vqnCfadGLVcGV5qFzhlI40WGIgeCe20zE1+fh0XXfzT+E8co7P7IF5PHnm5EysduP/EPzjY+AyVxQtZPvPNaT028rxFnFf7mrTs61Dfcfe2eCZnD+FEvdLRwt31+4Yfr29r5FNbnuWHZ1867ln7tne2E9Y2y4rLsn7GvwePRZdjfaqxnZaBIBU57mlLttY8eryDbe29LCnJ48appeP6ez2G4r3zy+I/cRIIWc6B9RCfJ7XHScMROyqwBuce7ejnaHpaR2/sy4equalNZckUz+pcws/3wtj+iTID/ztLXLfVHvc3VB8PgEtwrbXG3t6OfawXY3oB5qLJ3UE41qQOrvVAgNAdD0IgkpQfPnQM5fNizM7eurjWi/fGWKOxD23DnHVWWtuTLkdf+i/6WoZ6oAdoO/AQfW27WHDD7+OMSFZoHX07UNshcvLjlzCsnnolh/f8Aa0jtwU9nnwqay6cwF+QuFC4lydf+iTNbVtQGMyfdQurl31qXK+xfs/P2LjvdgD2Hn+EXUcf4M0X35GK5mZcKM6AxpfaX+WaqsxMBZ+N6vuie/q3dLTwSMNhrq+ZmdBrhG2bf9uynvVtkYB1UVEJP1h1PrlZ3Ptd5PVwvD/6NngiPcg/3dPAnYciF9UPHmvnYE+Ajy1MvATqEyd6uG13Kz1hmxunFvHBBdl/MTJRfq+iIBd6HIa05PhgTm1qL/J7u5wje483/vvduMeiYffoFJBFl/vwTtZqIyFNJPt39HumcgyIdz3R7X4nwKhx77UO//0Y1ouRu/DWumb0pQN4Lk5vWWEdttCd/aiy/LRXNpmc3aCD7EPHhgPrIdbugxlqTWJ0S/SkKMPrju5IY0vSJ9jXRPfxF6OWD3TsZ6DT/fMKBToI9Tv1NCv64mwLUFS6gFUX/4jqqVdSO+MGVl92G/6c8kSbPiF7Dt5Lc9sWADQ2uw/eRUv7dveNxth68M5Rj9t6DlDfsj5ZTYxLa409ztz0iTraH2NQzaCOoEw0MtK55dV4HH5IdnQmXnTp+ZbG4cAaYGdXB4+ccJ8pM9PeP3dq1DJDQUkCY2z+Wt/m+thNQ3+I/3ylkRP9YbpDNncc7OBv9ZP7mJxW5Rw6XLzCg2mmLoixLc2Rvc750TMXxg9nupujz1n9k3iq+PC6fnCopKKPhwn8OHqc00jG8oLYK/MMjCr3Xmtrw+jf5fDLsSf6c2O39RPeeBz7cMe4trP2NhH4wRMEf/oMwR8/jd2c3u/kpA6ulcPkA9lchk93NkF77LJi2p9Y4ffTjeHJxfky2sCb6x7o+nLL8eY43Y7VHN9zp8PyaKWVK1l+3tdZsuaLFBbPTWibU9HTFz1o1WlZLK1de7Hs6BJJXWNSY1Ll0YO385mnL+ezT1/B3w78IuX76w73ua73T+JJZCaiwp/LeeVTopYXehMfyN0aiO4BbnNYlk3OqShmVsHoyVsWFiV2zsz3jD7/FHgS/2nc3j6ANSY+29o+uSvYrFnoxevwtXtyU5iBFA5obG20CTq8tSWVinnL41csKqkb8zujoKRm8oZBeiD2Z6FbLXRv7A4S+xWXYDSs0WNLxoyglIIx6UHKO/732drXRvCnGwj/bR/B320l9PfEpjzRtib08Dboj9zV1h19hB/fOe79n4rJe1QBRk0l5qolw49VbRXm2Ysz2CJ3dqNLT6tSeBatTV9j0ujAE58AHHojlIo7iQxA2dTLHJf3tO9OaP8D/c00HX+agf6JXVmP1/Sa0e31evKpqUxsFr5guJf71/0Tto5OlfB5Ul81Y0/bBh4+8DMCVh8Bq49HDv6cna2pq3ENkVkY3fRbMWqxnsFqc6N7nRYXJZ4TfHHVFPJGpID4DIPLq2uT0rZU+velsykcEShPzctJqP7yB+dVDx9lBvCBedEXJ7EsKsmJOkKXlKRnoNerrRb/+twAH3pygD/vS19d7apSg3df66eieHQAFQrDsbF1lZPIKdfb9MLaaxN7vytnmcw+14O/QJFXolh8hZe8kskbBnlWxhmP4pZK45ZOFdToBveLbc8lo1NAzAmkhISfP8LIuo/W+mPoAfc0QQACYegZ3T67Nb2VkyZ9l4/nyrWY5yxHB4IYlekdcKLDIexXN6LbWzDmLcGYOsv1+UaNS6+p1u6jSE5TLXvuo6/FOSUiXq/1ECvU67jcSGDGwMb6f7Bt3RfROoxSHpae8xWmTLsyof1OVE3VuVy46uvsO/wXvN58lsx7N35fYtOHH2vdSCAUPU00wKtH7mFu7VXJbGqUw12vRi070rWdReWpy3lu7He/RZ9vyoDGsV5TO4u/HNtPcPCcUeDxYqCwtU4oF7gzFGRlaTkHerqYmpfPB+YsZFZB9k933BOy6A6fvFB/7EQrF1eVcnG1+7n/6tpSlpbk82pHZEBjXV7iwXFtnpfPLKvkZ3va6A3ZXD+tkBumpf4OafuAzedeDA73mv9qZ5hSv+LKaen5WS8uMJg31aClc3THSJyxo6eksDj62DUUeMcx5fv0lV5ql3joabUpSKAu9unMmOoFHxCr/yGkIcZ7p8rd73TpOG+555wqjOkFkQGN0wowqnLdN3ASHhPz2Bqs+HGQyvWippWij55MfTHnT6xwwERN+uAaMleGL/zAH7AP7ALA2vg8nte8FXPBspjPV4Xl4PVDyPmKMLzzebzn35yStmZKb7NzGT0A5Unsy2gYzj+EZbUXxd12z9Yfogd7gbUOs3fb/6U8uAaYUXclM+rGv5+ivNj1zkNx0ieSwVDRp4xZxakdZLuvJ/Y4BAAzhWUAT1dew8Aa0WPbEw7xb1uf5/KqqXxtWfSMpiN1BAN8dMPz9IQj34vGgX7ePyelzU2avd3RF9p7u/viBtcAtXk+qnO9/P5AM881dTE1z8cH5k2hNi9+tHjt1CKunVqU1imhf7c7HJWO8vwJK23BNUBpkcHYu46HTtjUVaTmO5lXaJCbD/0jPuZQELY8H2TFBYlF9W1HLbY/FsQKgeGBJVf5KJ8xic8hpiJWrUHdYaHynS8wjJI4aWS98WuDq6pc9CutBJ86gSry4bmyDnNW4hfpnjV1hB44eQfaWFKJyk/sc/a9/mxCT+5CN3RhzK7Ec3FiJTl1KIy1cR/WiTZUvh9VmIc5txajMrEOsOG2juvZpyG7sQX78HF0mnt97faW4cA6QmNtfsF9m8aDMQNrANthavRk0tqmZ+sfaH3gw3Q8819YvQ71tpOsfN5rY64zEwiuD2z6Hk0H73dc190af5BgKNAx6nEw4D7II9PKC+dQW7bKcd3SGbekdN9hO8jD+38WtfwPO75KZyB1KTUh5X4bsDHQ6rre0jYPnNjAt/b8hb83Te7ZM4e82NIwKrge8kRTPQd7nO98nNy2aTiwhkidgSca3cshZouzy4qiOlJWlSXei/yHA838cl8ju7v6+UdDJ/+26dC4pvVOZ0WCdodpu3PT2F3W3m2z61D0d9MlFfeUtTVaowLrIYd3JT4JzL4XQliDGTR2GPY+PzmnqR9iLI4RjBqgpsQ+YFRxnOA6gZAqdPcBrHXN0BtGn+gjdOf+xNI6BpnLq/G+fTnmeVPx3Dgf72sXJLytKvDje81Z+D9wEd4rFqLi1KsfbvOD6wk/uRW9qx57436sp7YR/OVjWPvGdw6c1MF16P5/EPr1fYTufIjgL+9G96VvkIn17KPRCz1xDlZPnCuyvs6JNygBva/8nu6XfkiwYQv9ux6g7ZFPp3R/AAVVK6hY8JYY61a6bhscaOPEnrtiru9q2hR3/7Uzbxz1uG5memo3n4q+QHR1lIKcKeTnuNcEP1WbG/5OWEffX+wINPK77V9O2X67YqT9DPEr9+/Nd/b+lW/suY+/nHiZ/9j5J24/HHtWz8liWl7skf4B2z0QqfBHp9lUOizLRvOL8vnckllMzc1haq6fTy+eyYpxBNfPNY2+8DjSG+BIb3YO5FxdNTpYUMDb56dn9uHmDpvfPBLgwInRkbTPA8tmp64XuH5/7GO3uyOxDrSB7tFtHjlb42Rj99rYm2IcvyYol8ou9gH3O6Gqxv28q7tD2LvGxCwhG/vY+O6wmjNL8F45G8+KKSgztSGrDoSwdzsUF9Ca8LrExnANmbTBtX30BPauESNL2zoJPxFd7i0VdF8P9t7o3FTPuZe6bmeU17kH2GWJD7KZiIEDo4OOcOsewp3ut+SToWqx82Qvzbv/THigI+Z22grhdvmcSM71/LM+wdTZb6CgaC7T593K/LM+EXebTApbATp7oz+TnoEG/rbhk7T3pK7U5DP1f4657kDHKynbb592vyj+a+OzMdeFbYsHGzaOWnbf8fSVLBypNxxkX08rVhpKGJ5XPoXX1M6K6sVdWlzOwiL3yRxWl1VwxYjBi4uKSnhN3fQUtDL5trZ38/N9x6jvH0ApxbISl3JiI3SHLH6250RUIJ1rGlTmpCdgHa9rZ5i8fraHAi+U58C8YvjOliAvHE+8Z3Cith0IE3aIc9cu9VBSkLqwouFo7OC60WXdSNVjJoyZrBPIAISf7I09+2S8myzx1ve7n8fCO5zvAhvVE8i9ThePEblCdDKOO1gwiXOudW90hXt710H0dZek/OoHpYgcmSc/DFU9FWNa/Nkhjcvfjf3Ybc7r5iVWUWKizMJaQi0nU1mUJwcjN/WDQDsOPe68QofpadpMyXTnaiD+/GpKpqylo8E53aZy5g1x931g5y+pP3APAD1d+8gvnMHU2dmb124oE68nl2A4ujfX1haHmp6jtMB94OxEneiNXQYplXnPBgrbZX7ijnDsklFKKXJMH6HwyfNBfgZmdHyscS/f2v00/VaYKTkFfGf59czKT913SynFZxet5v2zl7C7u50t7S1U5eRyQ038Y0MpxVeWreJds+YzYIVZVFSS9gkYJuorW/fRHIjc5j/aN8D3dh7mB2sWxd3u85sPs6V99HdKAR9fWEOeZ3zHdl/YxmeolE9/birF+5d4ecNcD+9+fIDWAQDNf24M8cmw5srpqbsoMGL8bXkpnozFcrlusBPMR5l3kRdPDjTutUCDxw9WWGOmeGbJTLD7Y19wuKWEABiz3atP2V733ll7T0f0ay4uQRVk58UqgDJNPJcuI/zY5tHBtFJ4zkk8JQUmcc+1qnW4RW5Z0Jv6QV8qNx9jxYhAWBmYa69IbFuXdWZRaic3KVzzYczCwXI5po+i8z+B4Ut9be3+9j2xVzoMoBtp1tn/EnNd3ULndJMhth3m8J4/jlp2YMcvXbfJNFuHXS+gS/Li9zDubXqGv7zyBR7c9jWOtieeg+xxqSddkZu6nk0/7rcfp/tjl3gylcEHZp787pmMfpyI51r285FNd/KRTXfyTPO++BuMEbDCfGfPs/QPRgYNAz38eP+6cb/OeO3t7uBDG57gs6+8wOONR1hcWE6eJ/H+lNkFhbQHg/ziwG5ebk1PmcpTsbe7dziwHrKvO/75vqE/GBVYQ6RrZGFx4uUte0M2n375ONc/fpCbnzjE48fTM2nFSw3hyER8I/xmV2p7r1fMNRl7zVGQC3PrxhdSdHfbPPxogF//vp+HHw3QHWdWwDqXlJOpsxM7trUNLQdtgr0Q7INj2yz2vTA5866N4tjvl4ozo6Vudi9xGn7MfawLPQ7HYE3ivdZ6IEx4SwPW9ib02KohKeQ5ey6+D1+H5/Vr8Vy/Gs9FS/C9+0rM+bGLCTi+Toral3kxBjDaew9hrlqa8t17Ln8tevYi7LYmjJnzMcoTy4e1dj3vvCK/DKM6NT2SQzwl06l8892E2/ZhFkzByBnf6NiJKqg5h/ZDjzm3yec+sriv61DMdd2tr1JWe8G42hIYaGbb+i+x7JyvjGu7dDnc9AIhyzkHeX7d9cysdq+QsqvhH9z3yr8PP952/EEWVl/B6876T5Ry/2H0GXn041wrVJO6H/OBmHWkIhpD7if5N9adz+qSOezsPsaK4hnUjuNuzI6uBj6z7b7hfvNXOuv5wVlvZHXZjIRfoys8QHd49N9Q35/a8RNaaz6++Wk6Q5H9Ngf6+fiWp3nk4pvwGPEDoLuPHOBXB/fQGYoEHb9hLx+dt5hbZ2Rn2RBLa77ySvSA7wKPiaU1pkvPe4HHxGcogmN6PnNNg+rcxHrZwrbmgy8cpb4v8j3oCtl8c2sTayryKPGlNu3gWHf0d68zxaXf65vtqLSQnn7449+DvOkyf8I92M++EKKxKfJb3dhk8+wLIa6/JnY6X1WdwcEdzr2xxw6G404k09dpsf6O6Den5aDFgosTavJpxZzrx3rGYZ56wG51T6Ox4uRc43M/j+giLzSPTumzn2yAC+LXu9bdAQK3b4buyGelphTge8+K1GcdDFKmCX0BVEEuxrKZqATOmWNN2p5ra4dzZY3w31/E2rLLcV0yKaUwZs3Hs+rChANrrW04HqNnrDfx6XhPhTJMvBUL0hZYA5TNvoHi6Zc7rsstd7+l29m0Oea6hn33um5rGB5mzIvO92448ghd7emdzSlRphH7x6O8cE7cAHnr8Yeilu1q/Aebj7q/VwCdwdjVY0yVult9Vpxh6T4j/r5n5Vdx/ZSV4wqsAf537xNRCSn/tu0vNA0k3itZ6S9gSdHoc8CllfFTxIZorTnQ00GXSyWhsZoC/cOB9ZB+K8y+no642/724F6+v2f7cGA95M7Dqa1WNCRk27QGxhcdvtjcwRGHAesNA0G+v/Ow67YFXpP3zh1dAzfXNPjk4tqomRtjuW1P63BgPSSs4XBPYn/HI/X9fPSFNv51fTvb2hL/28O25iGHP28cE0xOyCv7nC+mWzo1m/YkfqHdNGY68rGPx9ryXOwe5p0b4+dc73vOuW2+3MmXEgJgzIx9blRFcY7tfPf1nnPcYwTHknu2TqgX2trSMBxYA+iGHsK7ogfyp4Ld2EHgtr8RfmQjobufI3Sve5W3WCZtcG2viz3Ayt57KH0NGQfdeBDCLqX4WhOfIjvdtNZ0H36Kls0/p79xfIPblFIUVDtXBgn1Nbpum5MXuzB8sD/ObStg7tIPU1q5OnrbMSX6ssX0yvPI8Tqf1DYf+H3c7fNibPviwd+cUrvOr7vplLY/FQErddUcjvZHD8oZsEP89UTs+uxOvrH0Gl5Ts5ClRdV8YNYa3jcz+phz8kLzUW587s+8Y/0D3PT83dx3LLER6+W+HLwOF1qHe+NfFNxXf8hxuVvvb7I82djM6555idc98xIfXr854SC7NRA76HrwWBOBOBNPvHVWJXdetIBvnz2TX6+dy/2XLeKaWveBn0MOdAf400HnOxELiuMPql7XFOCbW7vY3hHi5ZYg//pyO22BxAbnvdJiE3D40+aXpO6zsm1Nc0fs3LT2cVTfqKo0XB+PFXbJ3khknHCg17lt+eWTM7i2j7m8YdPcg2ej1L3TQsUZ0Og5L8ZvcwKHh1MAbt23i9Bj40vL010DhJ7eQ+iJXQnP0BhevxuCI0qR7j2O3dQxrv3CJA2utWVBMPZBpUpTP3vWRKhc93bZbdlba7Z53Xc48eRnaXvllxz924fo3PvguLb3FUTnMxm+QvwOy0cyfbHfM5XgILvZi97DyGz3nLwayiqda0lnmmF4qC0723Gd1vF/kMO28/eia6AJ23bvcfK4lLybXrQ47r5TpTecuhKb8wuc7zrpRH4hRqjw5/O5hZdy26qbec/MVQmlZvziwBb+dduTdIQif1/QtvjB3g0J9WB7DIOLK6KnK28KON8iHqkgRsnQd85KbBKGieoLW3xz+x66QpHjcHtnNz/fdyihbS+oLImZ42hp6AvH702tzfNxbmUhswtzyRnH7edHjjlfsNTmehJ6necaR3+eAxZsbEnsoiLWnfl/Xp66O0lhC8dKIUPGkabORWu9VFcZKAXVVQYXrXVvt98lZTc/gZ/1yrnOb1j1vPRVDLH6NHYwPeX/rL2xjyP9/AA6HLsdym0SGQOMae750/qoQzDrV+je+Pnt5llTHA9ua/1x7Hr3Ov3D++8PEbj9eazn9mG9eIDg7c9jO4ytiN6Jw0WD2wEfw6QMrjEMyI1REcBjYp6/Ii3N0FpjtzSi+xL4QAHdHaenNV4d7AyxQ3107PnLqGWtm50rnsRy7OXvRS3z5U1BGe4nvZZDj8RcZyU4Y2FZ1WpWXfx/1My4gRnz386ay27DMLPzvQZo7nLuvQxbA/QMxE7dsLXFnsanHNfVlSzDcBmwCOA3Y/9qbmxwzplPh4BO3WCkf5l3GTU5o3+1i7253FCT2nEbDQM9/PpQ9GDToG3RHEjsuLbHdMYpYG1F/HzH/7dg6agfhhzD4Hsrz+V1U2cmtN+JahgYoM8a/SN2oCexc2exz4PHJZDdm8DAxonKi7HfW2Ymllo3vSD6HNcVTGwAV1+M4OihQ+MPBhLl8yrqKmL39M6fnvhQrsJCg+uv8fPut+dy/TV+CgvdQ5IBl49xyTnxLyhaDka/r6YXCitTH1zbIU3TPQGO/mCAoz8YoOPF1A+itPa6X4jrntjHmVtfvufyMpQ/9melgxbBPzqkkQU0wZ/vQse5k2SU5eJ9s/M51m5IrAfa2t0AI0trBi2sbfHv/nvOngsjquGounKM2vEXk5iUwbVSCs8V5zmvDFvY+1Nfu1n3dBH6zf8S+vX3CP7svwi//Ezcbez6OLng/ekZfT5+Q6UHTwr3NdGf4Gx4LXvuJegwxfVAx156mra4btvbEfs2USJ1rodobWGF+wgG2gkHs/V9jhgIdjgut+wgB07EniBFYeD3jq37q5hRtorXLPty/P3GGEgJsP5EdC53uoRJXSAxO7+Cu857P79Y9TY+PvcyPjbnEn675l3UpHhMwgst9Y594x5lMCMv/r5trXm+ZfSdrlzTw5yC+NueXVbBH9dextKiEqbl5XHrjLmsKK1ItOkTNiM/j5qc0d/Z8yoSS814qaWTAZcf7Lq81JVfvGGa86DrG6Yldof0NdPzGBun3H8k/h0GgF0x0jOeOpa67wTAdefG7nzIS/y0m1Sdre4BW1+HTU9z9PtlhWDLA6mfYK57U5i+vZE26jB0PB0mGCfH/JT1ufeQq+LYIaAq9MQseWEuiXOX/UA3hGL8bf0W9pH4F83mjBIoiT6YjJklcbcFUE71qhOYpdGYXonv3Vdhrl2E5+qV+N5ySUL7i3qdCW11GtDtsW8d2PvcB7gkQ/ilJ9AtDZEHloX1zCPoONMOEyenUXdlZzksw5tLbvVZUct7jj4Xd1ttWxzb+IOY6/uaoyfjGcm2Y9/2cqskMlJH6zY2PfsJmo49yYnDD/HyUx8iGEhtNYeJClsBQlbs7hu/a5qM4tJ5/4Qa/NobyuT1K77BW9f8mNK8qXH3bbn0EIfszM1iZ6b4NGYqg8VFNbx52ireOn0Nlf7EJiY5FVNynPcR1jZ7e+IPbjaUikrvKPMlHmD+eO9OXu3q4GhfH786uIcf7tme8LYTZSrFt1Yu5byKMqbm5fLWmVN556zESjzGnw8jdTm1+7ujz0FzC7wJp5bkmAprTAzUPJBY0HUsRs9jXorrgB1qjN2+7v7UpTxMd5nwpfm4+3vm1tfS1w69bakNdEMt0e9LqCWxfWpbE9ppEXwpjN2ZeDuNRXFmUeyK855d5tBja4BRFicfO14t6wRrkvvfuQJVWwimgjwvntctxKhIMO/IG/39U/mJXfkZ1SV4L1mGZ9U85yA9kdeY0FanAXvPoZjrVGnqK2HojjE/gNpGdzrPWDTEmOZeGcNuPnKqzUqZsmXviFrmK45frswKdqOt2L0GeZXLXLf3+GJ/ltol8B6p8ejjjJzpMRzqpqUhRknEDDvaErs+clHeVOZMca/hfNbU1/KmVd9jWd2NVBXO56FXv8aPnr6JzUfvO6V2FflSP9lQLFWezO07Vc4rr6PKH/0jooByX2K1Yi+tGn3B9I4ZiU2CELQtnmtuGLXsicb0jPeYVZDPt1cu5Y4L1vCRebMTyk0HOLeimNrc2D+cr3bGvxt1pDfArs4+9DhnYnu2MboX7so69xKiY11RO/rC58ra+BdC21stnjvhHBy9d1FqJ+qIVUSlIBdqylIXVsxfGfvvamty/9y6YrxXQ7wprhiSO2fMdPUeyJmeWDpK/x1B+v8YZOChED0/CGAdSyzAtuPM1qnizKbpu7As+so1ga+HMTXf9YpXH0ss3UsV+fG/dyU5n7uInE+ej2dpYpXXAHSrQ+369sTSw3TvAOF1uwm/tAvdk9hdpLEmbZ1r3R6j59HjwTw3upc12cz5SwkfGjE5SmEJakqc3sEK916aeGXWMim3+mwMfxF2YKh3XuErmhZ3O09OCYavBDtGqkN/x0EKqpw/L601oYHYeeq+3Mq4+wfwOzwvJzfxL3E6+T2xe02XTH89njipME/t+QkvHvz1qGWBcC+P7Pgmud4iFroE5x7lJ6yde6jnl61x3W8q9dvuJ78TA+18ffe9vNp1lGVF0/n3BTdTk5NYukGmGEoxp6CUpjH51TfVzqcqJ/7ETp2hAA8ePzhq2Xf3bGFuYWnc6c+VBp9hMmCfTC2Ykjv+KYuP9fXxje3b2dbZwbLiEj63ZAl1eeMY7TYOA5ZNl8sgdqfKKSP917ajPHK8A4BKv4fvrZnF9PzEevrr8qIDvj2dQZr6w1TlJvYT+8mlRdTkmezoCLGs1MubZ8X/jI/GqMrhUVCTwinIAeZPM9mwK0xLZ6QNhoLZtQaXrvTGnL0xGQbc0hw0BAc0vhg1tkMumR8ef+rL8eUvNLGu9NL9ShgjR1FyoQczP/4+rQab8K4RwXQQAi+EyXtjAuOCjrkE1wYoM4G/udgDHSNepyjBsHH0JNWjJXBrxdrXhvVSPQDm+VMx54yvE8WYGZ3KZs6On96m+wIEfvU4dEd+V8Lr9+B/39WoBM8Hw/sf17NPJznOQYa5egkqhfl3w/tZfg6eK25C1c3EWLQC7xvfFylM7sLeGjtfFkBncXDd37h5RGANoOnc+9e427UffCxmYA3Q27gh5jrbGnDtnZ57zufj7h+gbvbNFJWevGswZdrVlGZptZCaspUU5ESXODINP3Nq3Hut+4KdriX39jS5jwu4aua7HJcX+yu5aua7XbdNpR7bPV/yP3fdw8aOAwTsEBs69vP13fFremeDN0xdiDGi+2dZcSX/ujDGWJIx9vd0EhwzkVbAtvjZ/vglBO+uPzQqsAZ498zxVwr5xvbtbOlox9KaLR3tfGN76lJLXmzuoMcl59ptKvJX2nuHA2uA5kCYdz2/l8M9ieXhvm56MSvLRv+mPNHQw7+sO0Y40dvfpuLd8wr47zWlvGNuAb4Egp6VlYZjPeuwht/sSu1gOZ9H8far/dy41sv153n52BtyeN1FfkpSHNSXVCjcbtwYZuz3u3K2GbM3tcol3WSsQKfN3rsDbPlhP3vvDhAYR5pG0WoPde/LoeZtfnJnJLhPp/T5RFPqXecnT+wl1JjvjvIleBESazp5n4GxuMR1U7uxh9CfXsU+1IF9qIPQn7ZjNyfW2z3EmFKE93UrUFWFqMoCPDcuw5gWP0C3dh0dDqwB6B0gtHn8M/Nmb7R2iozZzr2m2qV3I9nMlefju/XDeG94C0ZZ/F5Uu7fDdb2RhjqzE2V4oi9YnJaN1brvftf1RdNiDyYwPbm4HcKlU86Ju38Ar7eAcy7/FedcfjvnX30Hy879GipL32ulFFMrzo1aftHiT1OYO8V120C4B7d7emX57ndOpuTPjFq2uvo6vrT2L5TnRpd9S5cywz0H+pXO0WMstnQeSmFrkuf88jp+tvo63j59Cf+64Dz+d8VVCW+7oLAUv0Mksb2zFStO2sOmtujJGl52WBbPts4O18fJVOJzT4NYXRZ7LEJTf/RvgqXhi68kloaX5zH4/rl1rKkYHfWd6A+zrT11A+Wq8wy+vMbHAoea1k1xBrIlg8dULJzuYfFMD75YgVSSaQ2Wa6ZD7HZ4cxRLr/E5Buf+gsTbf+TxEL3HbLCh95jNkcdTG1OYdQbmzBG/cyb4zk0wMK9x6SFO8CV0e2jM48QmCTIWx7hDFrSxN7tXRrP2tI7+qbI11lb3OS9G0t0DhDcdAZ8H3/svxP/Bi/GcFf9OOjCqUsjw7l/YhXappe/4MuN69mnEXOg8A5p2mMUrW6gpcWZtq0ltndlTkVu1nLzak71qhr+YkkVvirtdTrHLlO7KS+n0y1y3Vy4zFja5lOmLeh2lKC5bQkFR4jPnnar9Rx7gyZc+wfqt/01ff+wSemMtnXELHuPknZmq4sUsnHZj3O1K8+ooznEuxVaRP5s1M97sur1TtZDyvFrMOCX8Us3rdb89unRMetLSwgRPsllgcVEFH55zNt2hAO95+SE+tulRtnbEP1b2dncQdLiQ6rXCPNPsXo7qoMNEMy+0JP7DNmRZcYnr42RaXV7EBZWxX9/tguLcigL8Dj+oR3rHN0h3an50gF/mT22Jt7OrTL53UQ6LSkf/lF9Sl766zel0cGcYK0aMU1at8Hjdg+SKmSZnv8EfFYMf3RLGTvAuQ++Y3O2xj1Mh750+cl7rxXeph/wP+/HMSuzz9V196oOvjVm5ro9j8d4wA3KcQ0z7RJzc52B017xuTyz32W7sIvDTZwj/7VVCd20gdM+mhLYbYi5y6GSybMJbDozrdSZtcK1m1jmnhlipLVF0KgyHAUyjhCaWWJ8udVd+h9orv0v1BV9g1uvvSijnumKhS0CnwxBnIhi3Dua2Y8/G3X+m7D10Hy9t+TrHm15k76F7eOKlf0l4IFVl8QLeeNHvOWf+h7lk6Wd57bk/Tjgf/13n3U510QIUBgqTQn8Vl83/GB+48A7XfG6AZZWXUDhi4KJCcbxnHx0utbWTwYhT6aHE414W6vMLXs/SosgJc1nRdD6/8A1Ja1s6PHRiHz89sJnDfZ1s7mjkU6/8g56w+2DdH+zdEvMexfH+2LdX+8JhGgaizzM9CUzCMtbnlixhRUkpplKsKCnlc0uWjPs1EmUoxTdWzqcixsj+gz2xf8yLfB6+t3pW1FE2s2B86YO3ziphyogc69fPKGZGQXrq5X9xjY8bZpgsKTP44BIvb5w7OYdTNccYyGeYsPKixAZx1m+zom7ghQOQwBxcAOTXGK6PU0F5Fb41HnKu8GJOGcf+3K4PLbBb4n+vfTdPwViYDwUmxuICfDfFnhV5JGUqzLOcc5wNp6nRR25b67C+JLHvY3jdwdEzLO5uxG5KbOIZAOX3Os6EpPvHd7E9Ob+BRHoiVXEhemD0G6L3HsbashNzhXtljkzQcWZg1K3ZO0MjgDJMCqauHdc2nUf+4bJWExpox5frnCeltcZ2qTRSPvXScbUlnQ4fGz3pSmf3Qdq79lJWPD+h7Uvyp7Nq7nvGvd98fxnvPf+3494OIN9bzCfX/IrfbPs8h7peRaPZ1vw0HQNNfPqcX0/oNRNR6SujMRj7NmIwThnAaXkV/Hzlh9BaZ226j5uXxnzv+6wQWzuaWFsRe4B0o0OADOBRigsdZm4ckmOalPv8tAZHv6elvvEHiXV5efzfmvQOdH399Gpu2xfdM18VYwzOkKWl+fzvmln8x5YjdIYspuR6+eyS+OUpR6rO9fKHi6eztX2Acr+ZtsAaYGOzxVPHLHrDkV76i2tNylI/tCjtcmJ0mtoWbHo6xMWvjd+jGw5EX3YW1xiYcXq9h0y/yhtJDTlhk19jMP2q1FZmORXK7/43qfz4gboq8uC/dWJpf8acQqx1oztfjFXlmGe7Dyw055VjleeiWwfPYz4Tz8r4E2AB6JDDVVKsmtsxeK9ZReiuEZ1zhsKzJH71s5Embc81gDHHuefU2ukwc1AWUGXuB7AqS+zgsjuP0X/3R+j9vwvov/sj2J3xZyXKFPfeWhUzsAZoPPBAzHW+3GoqZySeo5puebmjr/6VMsn1j38WqHQry5lCX3h02sDR7p0p7b2udim3CNARTrCs02kYWAM0BUb/fQqYlV/ius08h8liqv25fG/lxczIj91rZCjFF5asxBzzXr1+qkv6VhZxmnRGARU58QPdFWUF3H/ZIu69ZCF3XrSABcXjr5DiMRRnl+emNbA+1GXz3S0hegc763a1a/6wO31ji9IpJy/2d7i9WRNKYFrxKQtGD2w0TFhyTeL9jP5ig3m3+Fnxz7nMu8WP32UilkwzpnrBJfaPV+f6VOnmMZ1fuSbe66bHPRcrj4HvPSvxXDMHz6Uz8X1wFUZ5YpWGjNwxf7ChoGp86THmnBrMy8+CHC/keDHXLsaoHF8J5+w9KpLAXLPMMTldFaV+EoiJMGYsBZfUEM+q6xN6ncDj/4l9fDPYFvbxzQQe/89kNTHpckvdau8qtO122yr2idSXl52l9IZMr7tqeDIXgLrqC8nNyf7gGqB0TLUSr+GnwFeSsv292nPQdf3SgvTlyafbq53NbO8aPZjw4srp1OS6n8P+bdFqfGNShRoD/TzTHP/u15rySu654EpeUzudCyqq+eyis7hlevYH10d7+/nYyzuill9dUx51sRCLoRQVOd6sHjw+kmVrvrguEDUnx+Hu1A9ozIR+l1Td3AKFJ4FO5MIKA3NELG1b0HJgcr5futtyrSyiXC5WksHaM6Ykcr+Fbohfa1oHLcIvHsXe3Yq2bNQ4LlajalnbGuvl8U0caDd3Yj25FQZCMBDCem47dv34BnWnJbhWSt2ulGpSSr06YlmZUupxpdTewf+Xjlj3OaXUPqXUbqXUNRPdb3jzDseZgDxrz57oS6aUUgb4YgfXKpEzB2A3bHV9nE0Kqle4rLXpadwYc21pzUUx1/W0biPQO/5BWOmy79B96BG1kBqaXyYczu6c+qA1wM+2fJLdbetHLQ/ZAfa0xS6ZeKrsOLMWXFKenWUTk+FoX3SuYCKTyGzpaKbY64s6wd9zdC/NgfjHWUVODp9ZfBbfWnEON9YlNktipj14rIW+MeX4Lq0q5dOLx3dhcLQ3wF2HWnixuQt7nBPKpNueDpsWh8y4NdWTs9+sszV2pJiXn9jdqa5GO2pQZOuR9IzFsvo0PdvC9B+yxj1ZEURmatQDiW8Xeq4vdsk9P6jC1A58NSrG5CaZCuUwpflYoYf2YD1/FPtQB9azRwg/kngpPFUdPQbHenI34c2JT8JnH2yIlKYZ+RoHGmI821m6voG/Bq4ds+yzwD+01vOAfww+Rim1GHgLsGRwmx8rFWdUmwP7WCP2M9E/+Gr+TFSp+wCoTLFbj0F37KsjqzGx0arGlOWuj91oK0jXuh/RfPfbaX/iS1g9qR2sZsWZZlx5Yl9shF3qYwMc3v6LiTQpLXr7T4x6HLb6CASzc8r1IS8df4Adrc4zV75w7C/pbcwIfzyWeFWY082ashr8xujT38WV7gOFD/R08l87XqY5OBD1u2oDA+61zE5bXoe7lOtbO3m1oyfh11jf3MU7ntvD/+0+wWc2Hea/t2dvSh1AWU70xO5zihS3zJm0w6liam1MLOjMLTGiqoXkl6Y+FAo22dT/bICWh0I03hmk5cHxpe6E91r0fCdA99cH6P1FADuBuxP2PpeBz4FIsJ5KnktqULWDv+E+A89101BxJpDRWmPvHB0HWTuaE9/nBXNR06PTSa3NRxN+DVURnQKiHAY5uklLcK21fgYYMx84NwFDM1r8BnjdiOV3aq0DWuuDwD4gsYLFI9gH6x2Xm6uXjvel0sY+sMV1ffgl95rQQ/xXfQGjdiUYJkbtSvxXfSHhNnSv/wm9r/yOcNs+BvY9Svvjn01424kI9bvXu8SOfQLKK56NW13T9mPPTbBVqTejdnQ+eEXpUvLz3OtUZ1pTX+xba8X+xGbDTIWmYHvG9p1qFf48vrfiSs4rr2N5cRVfWnwha+KMzdja0RKzr392fjHT8sY3NffpYkFh9KyGfZbNd3a6pxWN9I3tx0ZdkDx8rJ2WgezNX67OM3jzPM/wWXBKnuILa3yYKZwlMbNO/e/KKVDMPsfDUNZU0RSDaStSfzHSuS7MyAlue7dbhFoTy3nWYU3/3UF0V+SbbR22CTwa/7jUVuzgWVWbURPEJJsq8OJ//0J8/7wE/yeX4YkzkBEGi1EUje7dVsXxe7uHn5vrxTwrejCyykn8MzbqyqNSinVjR8LbQ2arhVRrrU8AaK1PKKWGkmTrgJdGPK9+cFkUpdQHgQ8CTJ8++talqnQYCJfjR9UlVkYmE3ScHinqdyb0OkZxHbm3/GRCbRg4PLp8Xah5B1ZfC2Ze/C/FROSVLyJyjed8kgn2xb5ijczO6JJ37TIYMtOWzHsnHk8Oxxqep7hwJkvnvzfTTYpracVFPFt/t+O66UWL09yakxYVjG8U9+nmrJJqvlOS+HlrcXHs3P2jfV10hgIUexP/sUq3oG3zl6PH2dTWwbKSIm6eVkeeJ/7Ny11dzj3Ux/sC2FrHzaM+1hegNRDdqx9v0p1Me/sCD/1hzXMnLEr90NyvqU7NLPMZ1xuntzYc1njiTGjTVm9x9JUwevAnp7/Txg4DKf5KaIfBli59R6O37dLoManEVgL1tVWViW5xfp7v9em7g2+Uju/N9Vwzh9B9uyL1rv0mnqvnJLyttaeR8F/HpMJ6DDwXJj5PiG7viUoptscZXGdjYpbTN8PxG6W1vk1rvVprvbqycnTPmZo2BYwxf95AAHtT9ICXbKGMOD8gntT/IHpKRgcqRk4phn98o2THo79jH27zsFqh2IMfDNOPW0/G3DWJTX+eCUoZLJz9Fq5Y+0NWL/sUOf4Ys1llkYXl5/GaOR91XJfnzdwg4ddMiT2L55lofmEJRR7nAUAhrdnXnb3pR7bWfGjdZn645wDPt7Tx032H+OD6TQlNIz41z7n2XLk/sQGKh3qiSzqW+zxU56av8sdE/O2wxf0HLVoHYGe75svrg/SFsvuCYMLixJOBfve/+9DGEFsfDBIakace6oeG3alPlSpc6Rn1c+WrVfgTrFkd3BbdPs+c+Nuas2KM01KgqrI3dcicV47/X87F966z8H/8PMxZif8+WtujB22rwhx0v/vcAKOeX1kE+aPPJ+bM8XXMZjK4blRK1QAM/n8oubceGNmFOxUYd4Fnvfcw2NHfRHvrrvG3NE10S5yE++mpm4RhSNF5/4JZHHn7lb+I4os/izJTV8ez6/hLrutzimP3SkYC79gn0+7W7RNtlojhypnv5KMrfoSpTh4T0woXsrj8gpTts8zjnsZw1/HHU7bv09VFlbFTR+YXlqSvIeO0qa2DfT2jSw8e7u1nY1v81J/FxdFpIQAtgRAByz0q+9OhZj63OTrt6dNLHG+aZpXNzaMH4/WFYWd76mcNzIR4k8HmF8YOaWxLc2Rz5sYb5M42KbnYg1kMvmpF+ZWJ/66Gnh8z4NID/gS2D78YYx4IDdbW7J2tGiJpHMa0YpRvfEPuVGH0RbZu7yN0z2bs5ujZZx1fwzTxvfFC1LQKKMzFXDMPc+3CcbUjk8H1A8C7Bv/9LuD+EcvfopTyK6VmAfOA9Q7bu4sxaYBubsc+esJxXabpjjhJ+13jKwUz7v1rTbj9IP6Zl1F8yReoftsD5MxMba9gKE5FDx2KPUGIMt17lFqOuk1QIyZqd/t6LH3yfub80jUpnQJ9Xr57tYoj/eMbxX0m+Mjc5czKdb4oKYwzXXwmxUrBSKTn+ViMGdR8hoHHJbc0YNn8Yq/zeeh0yFyeVTT6Z9xQMMMlyJzM3CpwaM1wKshI3lyYsiD1vbj9Byw6ng5jdUKwUdP45yC2w4Q2iVB+UL4Ejk6XawndMzkvwMzVM2LkP2jsg4nHUEZNGf63X07Ox16D98qVKHN8QX66SvHdAbwILFBK1Sul3gd8E7hKKbUXuGrwMVrr7cBdwA7gEeCjWic6MelJxtwZUF7iuM7annhZl3TSwThXkl2Jj5idiO71P6b98c/S98pv6Xz6P+ndcW9K9wdQPO1i1/WGS26oYXhQRuxAwQ5n95X56erZ+j+PevzC8b+kdH9H+90vwKb4sje3PlMsbeP3RAcM1S519LPBqrJSSn2je+QUMKsgfruDMXqn3zyz2rXOddDWBGKknXwry6uFPHgozIYmi7LB02SBFz62zEtF7ulwWTB+vniZkS6xqulR1C4eHSBVzDJY86Yc/Pmpf796d40OY+x+6D+UWIDru8jj+jgWY02MN8wEc9kknMITUKYR8zhQlekbzJ2WpBut9a0xVl0R4/lfB75+KvtUpoExdQp2a0f0uvz0/cDYxw+juzowZsxD5brvV/nz3Sv6hlI3al3bFr3b/jRqWc/WOyhY/taU7ROgcMpql7UG+ZXuZQQNTz5W0DmXqmKa4+ElTkHA6mds8S+fmdqTdEucaiBXV58X9zW01ljYeMZf1fO0dPuBHezqjn7fFhRld26/x1AsLi7i+eaTVYQ08HRjC2+Y7p6icedh5zsYb57hPrNtoddkap6P+r7o80h7MEzY1q4935ny2JEwP9528jeh1A8/v8xPnnfy9lpbcbrZ4lW/mHuBl6IpBj3NmtKpBmXT0nc+8BRFt81TnNhx5b/Ii1lnYB21MacbeGYl1m6z1ItNdF178/xcjLLsPxfqkIW9swUdtDAXVaDy4991U4U5qKml6PoR5z8F5jmzMGelpjCDk8n7LQRUmcNAvPxczLPTU9kg9Ni9hP74E8IP3kHwF/+N3eyejqLynHMGh5mp+7i0bUUNXdaB7gkVuh+PnqZXXNbahF0GNAL4XAZbNh99YoKtSr+e3uOEQonX482EoDXAd19+LwPWyJxYxXWzPpDS/cY7Ajd17nZd/7fGzdz44je57Nmv8J+77yHkOuvn5LAlRorZM83HONwbPTFNNnFKDXn4ePzUH//YAezA7PxcCr3x+5A+MM95RlcVoz3Z4MWG0ZFmewD2d2ZnW5NlIM7kfl0d7tG3UorquR7qlpl4c1TKf99GKlzlwVd9MpguPNtMeEAjgGe2if8Sb8KBNYAqc359+1Ao5TWuT5W2bIK/2kLogd2EH9lH4EcvY3ckNtGa75azMVfPwJhRhnnRXHyfuBLvlYvG3Qa7sR27vmVCx0n2DhdNAnPFIqwXNkPgZI+EMXcGKj/+DGenSne0Ym8dkSoeGMBa9xTGjbE68UFNjRP0j6Mc13iF2/YRFcZYA/Rsup3CVe9L2X5Nn3slkrb9D1K92KX33GWwZX9X4vVtM2Ug0MZT6z5Na8d2TDOHlYs/xoJZb8x0sxxta36aht6xExlpnq2/m0Xl51GSk5rjc07eNHb1HYq5fm3ZspjrmgKdfH3XvViDZQYeatjE7Lxq3jrtwmQ3M2tYWnNioDfm+p5w9tZtbuwfYF3L2CkR4Ghf/BSvseX6PAq+vyaxQUiLipzvKmrgyYZOrq3Lvh7/mjGpDAYwJQ3pDZnklDM9ki+Bm2gH1oU4siUMGvJKFWe9xo8/xdOAA5i5ipp3+wk2aowc8Jakvm9T5TnvQ9eHsQ+GMOdk7/gLe18bumnEeSxoEXpwD/63nxV3W5Xvx3vNxAtAaK0J3fsC9p5IWpiaUorvrZei/IkPQp3UPddoPSqwBrD3Jz4F5intOugwuCbg/gOh2uMMtExhKb5gs3OJwr7tf3Zcniyt+//quj7eJDP9HXtjrlNm9tbyHfLqnl/R2hGpamJZA2x69fv0D8SZWCdD7Bi/bMd69vDQgZ+lbr/KvTfqSH/sWUR3dx8fDqyH7Ox2nmBqsghYFkGHSklDwi7rMu14/4DjnYrzK9zz6o/3DfB8c8eoZZaGnZ2J3Q360pbYvwuGys4evjfN9TJ3MK3AY8C7FnmozJ3cP+nx5OS49+r2ddiRiiGDH2lfu+boK+m7k6VUpPxeOgJrAKPCAwXOFw66Kz1Tvk+U7olO09JNcW5djGC39BB+Zi/hjYfRwfF9xvaBhuHAGkA3tBPevH9crzFpv4laa0JPrYteMc4RnxNlVNWiakZWFFQYZ7lPNGl3xQmq8ktOuV0x993jfNvVDseu1pEMPQ2bXNe7Bde9He7TwdfMvWVCbUqnrp7R5b9sHaanLzsHUS2vvIQ8j/PEAw29qbtL0B10P6Fubd8Tc92Somn41OgbdCtLZiWlXdkqz+NhWm7suuO7uzvS15hxWlJSRKl3dO/Q7II8Pr1orut2Jxxq2GrgM5v3sqktfhrMzu7YHR9Glv5MlvgVP7g4h59e6ud3V+bwxrmpK5k6WQz0RF8oBbqz92IzKWJUJLGbsju4NhZG50er8sSyDuzjHQR/8RzhZ/cSfmQ7wd+vG1dqh+6JTj+xXxnfb1x2njWSwNq0A73FoaZ1MH23RL23vA/zomsxzjoX75vejzk3zm2KwtgzqwFwLLEZGifCP22t8wptoVOYo6rj3OcLdMWectsw3H9MisozN2tgouqmXDTqcW5OJWUl488NSwe/J4/3LPsvx3VLUljnule759kVukxgU+Yr4OtLbmVWXhUFZg6z8qo43NdMw0BHklsZm9aadW1H+evxnbQGEu95mahj/T0c7XfusVXAytLMTVUfj88wWFA0+vPsDoXJNd0zGJeXFsQsm/dgfew7G0DcCWq2d8ZOsckG0wsNiv2TOx0kUZbLdN8AxVMMvGPis8o56c2ODXfatDwcpOGPAbo2hhMO+vSAJrg+TOC5EHZXgtv02RAr5BnI7osKI9+HednMkwtyPHgTnKnR2ngERlQP0ic60Ufj18of3ndFdCeSbutG9yXe2Thpc66tjTEmEAkEsJvbMJymR08y5c/Bc+6liW/QH6eHpTexAugT4ZuyHEw/WGMOHitIoH49OdNjBN+nyA65/03Bvtg/jLlF04iEC84nmmN776KsLnVBXzLMn3kLljXA4WOPk5c7hbMWfhgzzkVDJs0vW8PbFn2JRw7+gp5QOzmefM6reQ1XzXx3yvbZHXYPSE3l3kdwYflC8kwf//zK7Rzsa+JgXxNPtezgT2v+HzkpnCBpyJd2/IO/N0XKf+aZXn688ibmF6Zu1PqGttjfmeXFFcwpSN2Mq6dqW0cnm9tHzyDZHAjSHgxSGWPuAgCvYfCpxTP4nx3RF+P5DiUJR+qIc8s4TrwmsomtwYx9oWEYYHoVoREzOabhFDBMa03jXUFCrZH9DxyxQUPRavdjVIc0vbcFsJsj2wWeCVPwET9Gqfu5z9oTe1ZC3Z7dPdcA3gum41lWjd3ahzG1COVNMPPAqWpMghV/rMNNhO54ynmlJ/H+6Enbc81AjNt8tib8hPusgMlgHz1A6J5fEfzTbVh7Xk1oG1XsPGL95BNSd5YPHHkxOrAe2u3YS/0kCgfdg2vD65yGMMwlsOpq3DCRJqVVd+9R2rv24fUWUFd9ISVFszPdJFf7O7bw6KFf0jpwjDxvMWumXM8FU1+f0klk4h31baH403k/3LgZe8QrNQU62dAxvhy6iTjU2z4cWAP0WSH+eNStQs6pcwueX+ls4fmW7JxEqyMY4lObthEYkxNel5tDhT/+wKsrplTgFCctL4l9ZwNgd5f7nZGVZe7bZ4OXGy0+/swAH3higPv2T/5qOLHoONP+dDbaDIzp9W3Ylb4gM9SshwPrIWPrXzsJ77aHA2sA+iG4IYF2uwzUtE+cHseJKvJjzipNPLAGzDUzwXfyN8mYWY4xNbFByda63bF/dBKoPDRk0vZcq9pqdIzBi7o9/o/xqdCd7YTuvh2syMEbPnoQdeuHMOpmum/ojzPUOa8kKe1z0v3yTx2XG3nl+KasSNl+4zE9sd+TcKgX3OYXije0PMMsK8jfX/gn+gciZdMaWzZiGl5mTbsuwy1zZmub32//Mm0DkeCsfeAEfz/8G148fj//ds5vU1YtJJ7SONOjA5Q5pI6UeuOUvkyCgENKVcBK7Y/a0uJyKv25NAecg8Yjfam7A3YqNrV10O8wEcyqshJUnBkaLa155/NbHe+Af3vnIS6sKiXX4/zjPC0vduBuApdWx7nAz7DmfpuvvRwkPBgQ/HxHiOo8xdqa7K9jnGzhEHhceqK9Dukz3jTOpWIWqMhBNeJny1OSQI+q01MS6Yh1uyZNZIbH05RRVYj/wxdj7W5A5fsx5o/jt8llwLc+0Y6qTSzrYdL2XHuuiz3znzl3Zkr3bR/cPRxYR2jsfc7VOEaytj/v/oQUltAKd0VXUPBULKTi9b+N+8N2SuLkmwW7j8Zc19W82XVbX26cOwEZ1tL+6nBgPeTIieydsr0v1DkcWI/UG+rg5Ya/pWy/8Y6+Tit+RYg31Z1PXc7Jk+LVVWexpGiayxbJsaCwkjl5o3tMYlVdSaab6mLfAVlb7j6pSqZMj1EiNZhAPd47Dp6gOeB8fhywbHZ3xc6bfrg+di6mBak9/yXB1hZ7OLAesrE5+2/5p4LpdT9WTC+jcq5NH0w7K319jGaeovRS73Dk5SlRlFwYf/+eBQbGiBrZKg98q+NfPOnDsS/kPWtTX5I4k1RhDkZlIboviHYZsDyWuXp+7JV5iVcgm7Q910Z+LpQUQUd0HrN56ZrU7rwguqdDlSaQY3kidlk5AHo7JtaeBOTMupSBfY+OWKIoOucjmHlxBlmeKsMLtsuB74ndu5hT4B4cFU9xr86Safl5UxibM56fV5ux9sRT4CulpmAuJ3r2Ra0zUjjzYbzQqi+Bae4r/EX86Kz385+77+FwXzMeZdAZ6qPYm9rZWvf1tNI1puLO861HaA70UulPXc95h1MpUCDXMJnlcH4a61BvN9/a8Qq7ujqYkV/Il5eezcyC1E4dPLewAL+KLm6wtb2D7lDYdTKYeCX3ZhXEDiTq4wxS2tjazary9E2bPF6zix0mzymatP1mrrxxZqfc9kiQ0IgbOlYw/qyPyVa8xkPBYpNwt8ZXpeLOKgmgPIr8D/oJbQxjNWq8K0yMRMr5ueSfm3Ozv1TtqQg9vgNr/aHIA0PhffNqzNnxB3Obc2sIz6lB7x/dkWSumYdRkvg5e9J+A3XfgGNgDWB3p3bEvjVy8hiA4lKMxWfH3zBez7TDDGTJ4ikeG6hqerffk7L9DVNxcinDsX8084pmuG4a7Is/q1smFeTVsnzBB1CDgWlx4RyWzH1Xhlvl7r3LvsG80lWoEaeOEn8V59TckLJ9euKcpvLNxHpgvrX3L2zo2E9zsIuHGzfzzT1/SULrYmsN9PGRTffTPKaUoI1mwErdXSitNffUR18AAZQnMssG8MWtG9jW2U5Ia/b1dPGedc/Q0J/6SifaoZf4+ECAPx12r01+49TYd6lyDEWxL3auwFU1Ja6v/bdjHa7rM21WkcF7FnnIMSM/6JfVmVwz/cxLCYknNKDpbY2+VG/cnf7cYzM/Uu86kcB6iNVgM/CPMKGNFn2/DBJ4Kv45xFzhj33rr3DShn/oviDWhhGDm22N9cI4xtg4TRaTYDrIkEnbc61bY9/qUym8LauDQfSBMSUAe7pRcUasA5BbBH0dsdfnpK6XrX9P9G19baThBB1nMJq2XEY7x0kp6WpJbCBpJi1b8D7mzngdA4FWSormZf0t6Kq86Xzs7B8TCPexpekJLB1iRdUV5MUbeHoKvHgJE7t3MceT2CxjL7WNvjP0Ylvs+tjJ8HzrYXodjt81pXVMS+H4iUO93TF7++sHeum33EvbdYdCHOgdfVEb0jb3HzvMh+amrkykrXXMFJAt7R2u255fWRJznd90DyJy4lQAqPBl/8/kG+d6uWmWh7ANed7xnUNae2zu2BjiUKvNzHKDW1d5KS+YfIGXxxeZh23s1A2N+yxmn6ez/twbeDLMyNNg4OkwvvM8qJzY7VYe5Xzrz2Bcgf1px9aR/0bQocTjPn04uuKS/dJuWOzeoTfS5PsGDVJTKp1HdlZXYJSVpG7HHk/0iAorjN10PP62LhM/AOBLXY6UHYju5Q8efIqO5/4nbi3qUxLnhGbkxE5L6WmLUW5xkLYSz7PKpNycckqL52f9yX0kvyePc2tvZG3dzSkNrAF8cfoA8szEemNn54/u3ZyTn9oBmE5pH5dUzOJby65N6X4bB9x7mPfFmUSm0Oulwhd9y9gaxyQME2EohT/GD35HyL2XrisUu/exJtf99veyUvdbvcX+7A+uAXymGndgDXDHxhAHWmxsDQdaIoH2ZKQMxZzzoj/LYC90N2V/vUXdN6aNYdCx+54AUDmGY5RnrsxBTeIBjarAj7Fo9NgSz5rEA2PlkP6hSseXGjZ5g2uvB++br4ey4kg6hd+PWrMM362pu30NoAwDVeWQNxsjB3IkY9ZZ7k8IxfkmnQLD7xQgafp33E3/rgdStl/T535BkVsce2CWESeoMk6D6c9FfLbh/sN3pK8xodf53PybqcmJDC6syynjM/NvOuW2uTm3bBqXVp6cDXJJURX/sejylNfWXlVWhdelRGVLIP5F538uXz3qNbxKcWV1XVLa58aOEcAXupWAAI72xv6b9nf3M+CSWJvnMXG7R+dUwWQyOdRquz7OKqcYD8Y6DDy+7A+ufWePvjAw5xoYRQnka1eMOboLwXtT9o4hSBbvTWfhuWEZ5nmz8L39XMyliZ+/PG9YOzoN1zTwXLViXPuftME1EPkmtXVGSqsEAugd++L2lCaD58KrGXkWUBVTUHUJXDXVzHNfH28Gx1Pgd5kkJnB8Y8r2m1e5zHV98dTzY67LL5mLNyf2AIXS2uyeQEYkZlHhTNf1RoJjEZYUTePucz7JA+d9hj+f80nmF6R28KihFP+19Bp+t+aN/GLVzdx29s3kxQkSk8FrGHx7xYXO65RiVVn8KjpLS8r46rJVw2exkNZ8b/e2JLbSWVWO8wXzm2a4/zDOK8ojJ0b6R0hrXm5xTz+7qCp2sHFJlpfiO1Uzyw3Xx9nE65IBlkhJvVhjnxv3ZfEFxSDfuR5y3+TFe5aJ/0oPeW+Jnw6nB2x02+grCqPGN7lTQgYp08CzYhreKxZhzBhf7KT6Q5iXLcM4ew6eK1bg/+TNGC6Dop1k77coCaztYwb19PZjHz6W8v0a0+fgfetHMFeuxbzoGrxv+SAqzixyAOx5wXW155wbk9TCaIWr3gce54PHW7kwZft165kGKK6LHVwDFFevirlu9qpPTahNIrt8Ys6trus/MuuWhF/LUAaV/qK0puDMKShncVF1Wve5pqyaby6/gNn5RXgAn1IsLirjR6suo8gtQhnhL/WHRqVrbutsZ1dXRyqaO+yDc2cOFzgwgEurKvjpOSu4rNp9lL/PMPjGinkxiyNUx0kNKc9xvuj5+MIa5hRO7pJlt67yMrvCwFAwuyKSc52t3CYXXZBISb0Yx0fj3tNjQhXvMg+5t/jwX+JFJTDlve6xYcyfpnuz/0Iik6zd9QR/8SjWP17B3rQf3TeAilEj383pkUw2QcqhbqrKT23prSFG7XSM2unj2kbNWA5b/h77NesWnGqzYr92TgmGNw87PGbiCV8h+UvelLL9+oumxlxXWHcB/sLY6wG0wyQdw+tcBkOK00e/y+eogAvL46RTnaEuqqzlosqJ9c63BgbY1N4Stdyf4kHOfzveODzduA1cPqWSJcWJ9RwXeD2OU5UvKMpjfpF7XvV+hzq4XqV4w4zUTVOfLcoLDD52yemRQldaYdDe5Bwclk+Jf2zGOnztSVoW3KjwoKZ40A0nfyfNpafHZ50p4Rd2jpp/w1q/B8/axahxDmye1D3X5qqlqBGDF42l8zBqs3diEc8cl3J9OfmQk9ppeHNmXzZmiaL0iq+iUpgjWjLtMjx5TgPLTKaujt/zXFS1Oua6tuPPnULLRLbY03s45roiM/unpj4dPdV0gtCY/OdZ+YXMSmGt6+P9/bw0psrTvUcTGAg+6HBv9IyUy0sKuO3cJXG3vaQ6ukt0TqEEIdlm6jznACe3QFFSET+cqd/q3BkT7Itffep05X9nMebqHIxZXrzXF+C5MD0djKetsWMstI472Z2TSR1cq/xcvO97A95bb8T73jfgveHSTDcpvliJYwO90NOW0l0XnfcvFJz9XrwVC/FPv4CKN/2JnGnuaRmnyvTlUz73NQ5rLPY+8j7CAfdcyaqZo/PbR/LnZ+dMdGJ8lhbNjbnu6urz0tiSM4fTAMLXJTJu5BT4DCPqBynHTLynfFVZEf4x+fdvm1WbUDrO2sroi4Zp45iNTaRHaYVJxZjTelEZXPa6xFKdQrHGvWpIw8SpGaGKTHyvK8L/vlI8a/NOq6pUmWCuHj32zVw2E+VU9zqOSZ0WAoPVO6afPkGW54p3E37kp9ErCsogvzR6eRIp00fh6g9SuPqDKd3PWE3bf++4PBxop/PIU5TPi13VwePNZ/aqf+XAxv8evdxfSmlNai8MRHrMK5iGF5MQo+/devGwtsx9QKyYmEuqalh09CA7B3OsZ+cXcm2te4rWqarw+7lpag331UdmRvMbBu+YmfgU9eV+H98+ez6/OXCc3rDFa6dWuda/HqklEN2j2TPJq4Scrs6/NofjBy36ezU1M00KkjQbpeEym6E4c3hWzEaVFmAfOIFRVYKxKPFz0KjXSXK7xCkyF1+Iqp6Ftf1Z9KFX0C1HUaVT8Fz9AVQKZ2jMlGDPCdd61IYv/nSjVbOu5/DWH2OFTk58UT37NYkNIhWnhW8u+Wc+tf37o5aFCPP1Pb/iL+f+D54UTr9+JvKbJj9dcyEvtzZjac255ZV40nD++eSieVw+pZL6vn7OLS+jMmd8vccryopYXlrIrq5eChOZuGvQouI86nJ9HOs/md9/5ZSSce1bpIdhKKbOGX/o0nwwPGl7p0VymTOqMGecWgqxBNdZyCivw7j4LXDxW9BWGOUym9rpzvDmgTJBR48oyatYSvHUS+K+hunJZcHar3Ng0/8Q6D1B+bQrmLbkvalorsiQC8rP4h/n/4g3vPxZOsLdw8tbgh20BjqodplsSEzctLx8qnNy0xJYD1lRWsKK0pJxbzdgWTxyvIXfHThBcyASJL92aiWfXjwrzpbgMRTfXzOLPxxspmkgxJU1JVwZZ1p0cXrpbIwdWadjMmJxZpm8UdskMZkDawCPv5gpy95Dw9ZfRBYYPmrO+gC5JXMorDkXleBZr7TmPFbdcHcKWypGsuwwe9s3YCoPc0rPxkjDXYJ8bx6XV67m3hNPDi+bkTuFKn9Zyvd9ptnZ2cHnt75MU2CAcp+fry5fxVkl2XsBM2BZfGjdDg72jB7U+EB9MzfWVbKwOP7A1+pcH59cnPqJckRmFJYbgHNZkPzsPbTFaWpyR27itDBl+fspnn45ga7DFFSvwuM4W6TIFgPh/9/emYfJUdZ5/POb6Z7JTDJhckxIIIkxhEQQQwyRBbmE4AHucniw6qKurLewouKKj7uCul54rrriqqA+oCiisnhhQFZBIUgCBJKQQI4JkAk5gByTSebqd//oCnQydbzV6Z4+5vt5nn6mumq+/ftV9bff+lXVW/Xu5htL38PG7scAmHnIsXxg/n+TaSjv83FzLsfm3udv6m2yDJfNulA36JSBL696iC3BSI5P9/Xy+ZXL+OnLz6hwVtHcufnZIYX1Prb29lO+J/WLWmHXtugz120dOnUtSos6pYqqoKV9Ju3TT1dhXQPct+l3zxXWAOt2LOPhrX8ua8yl21dx1j0f5K/PLHtuXp8b4O5nyj9q4Ehkw+7u/d4/2bObb65eXqFskjnwsYH7aG1s4LjxalMEdD8d/Ti1lvbhy0OMDFRcCyG86R3o4e6um4fM37anfCOf5lyOj634BjsGuocsC5snDp6TOoY+e/5nT6xnXffOCmSTzGmTxjFp1NDHsfUM5rj/merMWRw8e3Y7Nq4fZPfO5DsVW9qir3BtfKhOR5ERFUPFdRlwe/fQv/gO+n75IwbuuhXXs7vSKdU9zjm2dN7Kmvs+z+Z1v8bptvCy8ItHv0pX95oh88u5vbv2bqN7MPyS/5Gj042CKvx4xwtnMybkaRtP7Q3/HirNmGyG/zx2FuOyQ3P+4op1DNbpACEjmU2dg9x2416W3NHH7Tf1smF1/BDmre3RxfXeXdC3Rx4RpUN9rkuMczn6b/webkt+ZLHBdY8w+Lc7yb75vamHQxd+7NhyP12rf8IzG+8CYPPam9m9/VFmzk8e4VGkY+XTfw2d39ZUmZsKc/U6bnEF2dHXx7vuu4s9g0O3baZK+7fvHhjk8gce49n+oQXWjv5Bunp6mTY6YoCugJ6BQW7s3Mba7r383cQ2Xnv4uGHrz79yez/Xremmu9/xD9NbeNXhLcMSt5ZZubT/+UfrOVi5pJ/psxsjv7OkpmL3szmaWtT3WpQGnbkuMW7Tk88V1s/PzNH/+59XJqE657G/fZbld7zvucJ6H0+t+aXOXpeByaPDH2sWNb8UTG2ZRJOF3yz5nQ2/ZEe/uoaUkt90bQgtrAFu2fj4MGfjx+Jt23mmrz90WXs2w+SW5BH8Pv3QE1y7dgt/3ryTq1Zs5Pr1W0udZijb+3J85N5nuWdLHw8/28/nlu1k8ZbeYYldy/T37n+mub8vfpTqTaviz2yPHqdySJSOirrJzOaY2YMFr51mdqmZXWlmGwvmn13JPFPRFDHoQXf8MN4iPXu7u9iy7pbQZS43QPczjwxzRvXP62Z/hNbM/jeItWXHM23sUWWL2TfYT58LL5z63QD3bV9Zttgjkf5c7R2UHhLSHQSgoznLFXOPIJvwnO6dfQPcvXXXfvNu3fhsyfKLY+m2PvYM7l8V/mWziuskps/e/zufPquRhoboKw2RQ58H5AbULaSayD3VTf8d6xlY0oXrq70rlBUtrp1zq51z85xz84DjgB7gV8Hir+1b5pz7XcWSTEnDxEPDC+zRbcOfTJ0zOBDf//OZrr8MUyYjh8PGHMGnT/4t8w99NW3ZCcxqn8975n21rI/hW/xs/FMqDh/VUbbYI5ELps8kG/Lc8qaGBi6YPrMCGSUzf1wbo0IK6N0Dg0xpSR7lsbmxgdbG/fXjmoen1+TozNCCcEJzdXa/qSaOOi7DvFOyTJvVyEtOzDL3pPg2KOlR/E8/XnsFXL0y2LmdvmsfYPDuJxi4dQ19P6m9p0JV03WQhcBa59yGSidysNiEocNmNs47oQKZ1Dej24+gbeLcyOWtbTOGL5kRxKLOa3lg8yJ29T/Nhp0r2NrzZFnjNcbsFY8deyRHtZWvS8pIpDWT5boTT+Ol7eNpKihYT544mbnt1Tlgz9N9A+wNOePeM5jj5xs2J+qbGxt4z+zJz+0QWxsbePeRk0ucZTh/7Bp6SvWm9T1s6I7vxjDSMTNeMDvD/NOamHl0JvasNUDz6PjPO2RKNZVDpSO3dYDe67ez9xtP0397N26w+s/QDy7tgtzzebond5Lr2hWjqD6qyU1vAm4oeH+xmT1kZtea2bhKJVUMDQtO3X9G8ygaj1VxXQ7aJhwTMteYOP1VTJy+cNjzqXc27+5kUecPcOQbvv5cL9etuILd/eXr9nR8+9E0RjRVn37Re8sWdyQztXUMx43voK+gYL1jSxcrdwxPV4m0jG/Ohj4pBOCh7X475fOnT+Bnp87hqvkzuOm0FzF3XEI1ViLW7RpaRO8ehF919gxL/JFC28T4cmcgodtILeJyjr4fbSe3qg+3ZZCBP/UwcFcN+Kox5LvKVFO5mkxVZGtmTcA5wL67/q4GjgDmAZuAr0To3m1mS8xsydatw3PzSRJucIDcPbc/P6Ohkew5F2LZ8o5eN1LZ/tS9Q+bNfeU1zHn5Z7AGPQyn1GzdM/QsdY5BNuxYUbaY9+1YySDh/YDfdv8VDDhdzi0Hm0Meuxc2rxpoNONds6ZGLvPl0JYmTuhoY0x2+J4acXxH+M2WfbnqP8NYS2Rb4n0w0Fd/29ttGcRt37/tzK2u/v78mROmQtPzv8GGORNomDQ8B7uloiqKa+As4H7n3GYA59xm59ygyz/u4XvA8WEi59x3nXMLnHMLOjqqo99lbv1q3LaCy5C5QXKdj0ULxEHRMvYF+73PNI1ldPusCmVT/8xqfynZhv37sDbQwNS2OWWLuWpXdE+xHQPdPLB9ddlij2QWTj6MwnLkkGyW4ydURzsbRk/EjZivnDJhmDNJxzuOHMPrZ7SQLdjY2QY4Z3pr5ZKqQ7pjhj8H2LOr/opra2+AA87r2aTqP+nUMHkMze9bQOasWWT/8cVkX390pVNKTbUU12+moEuImU0pWHY+UL3j7vqge1PKxoxjL6ZlbL7PbWO2jSMWXE5DY/INTKI4RmVGc8n8q2lvzo/gN6pxDG998acY21y+Aub0iQtil7fo+y4LC8Z38PljX8ZJEw/lNVOm8q3jTmJ0pnqvwIU95WThoeN5w/Sho01WE02NxiVHj+XmMzu45Og2LjxiNN89aQIvaq/ebV2LDIQ/cOg5qvQR7geFjWoge+5YGJVfOTs8Q3ZhbZwBtrZmMscdRuORE7CE/vTViLkKj1xlZq3AE8BM59yOYN515LuEOKATeI9zblPc5yxYsMAtWbKkvMl64AYH6L/+W7itT+VnjGqh6cKLsfbqPntSyzjn6N3dRXbUBBoz8QNFiNKRc4M02PBcPr/xydv45vqf0X9AF5BJTeO45YSvDksOorrZurePi+5Zzo5gIJmJzVl++PKXMDaiL7YYWWxZN8DKRdEV9skXNZNpqpbzjaXF9Tvc7hwN7Rokp5SY2VLnXOjZn4q3Os65HmDCAfPeWqF0DhprzJB98/vJrV6G6+ulcc5cbMzYZKEoGjNj1JjDK53GiGO4CmuAC6a+koaGBr685vr95r91Wu08Al+Ul45RTXz/xBdza9c2MmacdViHCmvxHJNmZuh9eY61d+9/gN6YhaPObKrbwhrAsoapsB5W1PKUAWtqovElL6t0GkLUFa/qOIEfP3Erm3q3AXDYqA5ePenECmclqolDRzXz9pk60BbhTJvbRNvEQdYu7qN/Dxx2TIbpx6r7jSg9Fe8WUiqqpVuIEKJ8dA/0cPvW+wA4s+NljMnopi8hhBDDT1V3CxFCCF/GZFo5b8pplU5DCCGEiKR+OxkJIYQQQggxzKi4FkIIIYQQokSouBZCCCGEEKJEqLgWQgghhBCiRKi4FkIIIYQQokSouBZCCCGEEKJEqLgWQgghhBCiRKi4FkIIIYQQokSouBZCCCGEEKJEqLgWQgghhBCiRKi4FkIIIYQQokSYc67SOZQEM9sKbIj5l4nAtiI/vlLaSsauRW0lY2uda0Nbydha59rQVjJ2LWorGVvrXBvaSsYup/YFzrmO0CXOuRHxApbUmrZW89b20jpXq7ZW89Y6a3tVq7ZW89Y6a3uVU6tuIUIIIYQQQpQIFddCCCGEEEKUiJFUXH+3BrWVjF2L2krG1jrXhraSsbXOtaGtZOxa1FYytta5NrSVjF0Rbd3c0CiEEEIIIUSlGUlnroUQQgghhCgrdV9cm9lrzGy1ma0xs8tTaq81sy1mtryIuNPM7P/M7BEzW2FmH0yhHWVmfzOzZYH2U0XEbzSzB8zsN0VoO83sYTN70MyWpNS2m9lNZrYqWPcTPXVzgnj7XjvN7NIUcT8UbKvlZnaDmY1Kof1goFvhEzPMF2Y23sxuM7PHgr/jUmjfGMTOmdmClHG/FGzrh8zsV2bWnlL/mUD7oJktMrPDfLUFyy4zM2dmE1PEvdLMNhZ832eniWtmlwS/6xVmdlWKuD8riNlpZg+GaWP088xs8b7fhpkdn0J7rJndE/y2fm1mYyO0oW2Hj8ditIkei9EmeixG6+uv2PYyzmMxsRM9Fhc3yWMxcRM9FqP19VeUPtFjFrF/8fRXlNbHX1FaH39FaX39FbtPTfBXVGwff0XG9fBXVFyvNixGn+ixGK1XGxb87341iI+/YrRe+8gIbZp95IFaL3+FcjCPVqn2F9AIrAVmAk3AMuDoFPpTgfnA8iJiTwHmB9NtwKO+sQEDxgTTWeBe4ISU8T8M/AT4TRG5dwITi9zmPwLeGUw3Ae1Ffm9PkX+GpM//Hw6sB1qC9zcC/+ypPQZYDrQCGeB24Mi0vgCuAi4Ppi8HvphCexQwB/gTsCBl3FcBmWD6i1FxY/RjC6b/FfiOrzaYPw34A/lnzId6JiLulcBlHt9PmPb04HtqDt5PSpNzwfKvAJ9MGXsRcFYwfTbwpxTa+4DTgumLgM9EaEPbDh+PxWgTPRajTfRYjNbXX5HtZZLHYmIneixGm+ixuJyTPBYT19dfUfpEjxGxf/H0V5TWx19RWh9/RWl9/RW5T/XwV1RsH39FaX38lVgHRPkrIXaix2K0Xm1YsHy/GsTHXzFar31khDbNPvJArZe/wl71fub6eGCNc26dc64P+Clwrq/YOXcn8EwxgZ1zm5xz9wfTu4BHyBeBPlrnnOsO3maDl3fneDObCrwW+H6qpA+S4Cj2VOAaAOdcn3NuexEftRBY65yLGxToQDJAi5llyBfKXZ66o4DFzrke59wA8Gfg/DhBhC/OJX9gQfD3PF+tc+4R59zqpEQjtIuCvAEWA1NT6ncWvB1NhM9ifgtfA/4tSpegTSRC+z7gC8653uB/tqSNa2YGXADckDK2A/adrTmECJ9FaOcAdwbTtwGvj9BGtR2JHovS+ngsRpvosRitr7/i2stYjx1kWxulTfRYUtw4j8Voff0VpU/0WMz+xcdfoVpPf0VpffwVpfX1V9w+NclfRe+PY7Q+/oqNm9SGxegTPRaj9WrDImoQr31kmNZ3Hxmh9dpHRmi9/BVGvRfXhwNPFLx/Es9Gt5SY2QzgpeSP/nw1jcHlni3Abc45by3wdfKNRS6FphAHLDKzpWb27hS6mcBW4AfBpZXvm9noIuK/iZii50CccxuBLwOPA5uAHc65RZ7y5cCpZjbBzFrJH8lPS5kvwKHOuU1BPpuASUV8xsFyEfD7tCIz+6yZPQH8E/DJFLpzgI3OuWVpYwZcHFxyuzbuEmEIs4FTzOxeM/uzmb2siNinAJudc4+l1F0KfCnYXl8GPp5Cuxw4J5h+Ix4+O6DtSOWxYtodD22ixw7UpvVXoT6tx0Ly9vbYAdpUHovYXl4eO0B7KSn9dYDey2MR+xcvfx3MvslDG+mvKK2vv8L0vv6KyTvRXxFaL38lbK9Ef0XoL8XDYxFa3zbs6wytQXzbrzCtL0nauPYrVFvs/rHei2sLmed95FGSBMzGAL8ALj3gKCgW59ygc24e+aOs483sGM94fw9scc4tLSbfgJOcc/OBs4APmNmpnroM+UvhVzvnXgrsJn/5xxszayL/4/15Cs048kfFLwQOA0ab2YU+WufcI+QvFd0G3Eq+69BArKgKMbNPkM/7x2m1zrlPOOemBdqLPeO1Ap8gRWNzAFcDRwDzyB8QfSWFNgOMI3+Z8qPAjcFZnDS8mRQHcAW8D/hQsL0+RHCVxpOLyP+elpK/lN8X98/Fth3l0vp4LEybxl+F+iCWt8dCYnt7LETr7bGYbZ3osRBtKn+F6L08Vuz+pZzaJH9FaX39FaKfi6e/ImJ7+StC6+WvhG2d6K8IvZfHIrSJ/jqYGqSc2jh/xWmL2T/uE9btCzgR+EPB+48DH0/5GTMoos+1e76v0h+ADx/kelyBR//U4H8/T/4MfSf5fss9wPUHEfvKFLEnA50F708Bfpsy3rnAopSaNwLXFLx/G/DtItf3c8D70/oCWA1MCaanAKvTegq//mRDtMDbgXuA1rR5H7DsBXFeL9QCLyF/RqMzeA2Qv3IwuYi4sb+xkG19K/CKgvdrgY4U2ysDbAamFvE974DnHmFqwM4it/Vs4G8x2iFth6/HwrS+HovS+ngsLq6nv/bTp/GYR+y47yJsW3t5LGZ7JXosIm4afyWtc6zHCv7vCuAyX3+FaX39FaX18VdcXB9/hej/w9dfHrEj/RWxrb3bsIjt5d2GhcT29ljCOof6i4gaxMdfUVoff8Vpk/yVFDetv5yr/z7X9wFHmtkLgzOibwJuGY7AwVHoNcAjzrmvptR2WHBHq5m1AGcCq3y0zrmPO+emOudmkF/fO5xzXmdxg3ijzaxt3zT5mwG8npbinHsKeMLM5gSzFgIrfWMHFHNG8XHgBDNrDbb7QvJ9EL0ws0nB3+nA64qID3lfvT2Yfjvwv0V8RmrM7DXAx4BznHM9ReiPLHh7Dv4+e9g5N8k5NyPw2pPkb7B6yjPulIK35+PpsYCbgTOCz5lN/sbZbSn0ZwKrnHNPptDsows4LZg+A/DuVlLgswbg34HvRPxfVNuR6LGDbHdCtT4ei9F6+StM7+uxmNiJHovZXjeT4LGEbR3rsRitl79i1jnRYzH7Fx9/Fb1vitJ6+itK6+uvMP0Dnv6Kiu3jr6jtdTPJ/orb1oltWIw+0WMx65zor5gaJNFfB1O/RGl9/BWjLWr/uO9D6/pFvg/to+SPDD+RUnsD+cs9/eR/eP+SQnsy+S4oDwEPBq+zPbVzgQcC7XJinmiQ8DmvIOXTQsj3m14WvFYUsc3mAUuC3G8GxqXQtgJPA4cUsa6fCoy/HLiO4C5sT+1d5A8ClgELi/EFMAH4I/mG6o/A+BTa84PpXvJnI/6QQruG/H0F+zwWeTdzhP4XwTZ7CPg1+ZvQUv8WiHnCTETc64CHg7i3EJzR8NQ2kT8Tshy4HzgjTc7AD4H3Fvk9nwwsDbxyL3BcCu0HybdFjwJfIDh7FKINbTt8PBajTfRYjDbRYzFaX38ltpdRHouJneixGG2ix+JyJsFjMXF9/RWlT/QYEfsX/PwVpfXxV5TWx19RWl9/Je5TifZXVGwff0VpffwVmXOSvxJiJ3osRuvVhhV8zit4/skbXvvICK3XPjJC672PDNF6+SvspREahRBCCCGEKBH13i1ECCGEEEKIYUPFtRBCCCGEECVCxbUQQgghhBAlQsW1EEIIIYQQJULFtRBCCCGEECVCxbUQQgghhBAlQsW1EELUOWY2xsw6zewtBfPazOxxM3uDmX3UzJab2S4zW29mH61kvkIIUcuouBZCiDrHOdcNvBv4LzPrCGZfBSxxzt1EfhjktwHjgNcAF5vZmyqSrBBC1DgaREYIIUYIZvZDoBn4H/Kjjx3jnNsU8n/fIL9/uGR4MxRCiNpHZ66FEGLk8CHyw/veBFwWUVgbcAqwYnhTE0KI+kDFtRBCjBCcc8+SL5pbgV9G/NuV5PcNPximtIQQoq5QcS2EECMEM7sQmAHcDnwxZPnF5Ptev9Y51zu82QkhRH2gPtdCCDECMLNJ5M9aXwCsCqbPc87dGSy/CPg0cKpzbl3FEhVCiBpHxbUQQowAzOxGYIdz7l3B+3cCHwXmAm8AvgKc7px7pHJZCiFE7aPiWggh6hwzOw/4NnC0c257wfw/AouBtwBTgcKuINc75947jGkKIURdoOJaCCGEEEKIEqEbGoUQQgghhCgRKq6FEEIIIYQoESquhRBCCCGEKBEqroUQQgghhCgRKq6FEEIIIYQoESquhRBCCCGEKBEqroUQQgghhCgRKq6FEEIIIYQoESquhRBCCCGEKBH/Dy1rApzZGCE4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X2\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.stripplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X3\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.violinplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SqsRwLUmS1MAyk6985SvMmjWr3qV0CYZrSZKkBrZx40Z+97vf8S//8i/1LqVLMFxLkiR1AatWOTdELRiuJUmSuoDMrHcJXYLhWpIkqYG1herSbUfU0QzXkiRJDcwR69oyXEuSJDWwYrEIGLJrxXAtSZLUBdgWUhuGa0mSpC7AkevaMFxLkiQ1MEesa8twLUmS1MAM17VluJYkSWpghYJxr5b8a0uSJElVYriWJEnqAmwPqQ3DtSRJklQlhmtJkqQG1jZi7VR8tWG4liRJkqrEcC1JkiRVieFakiRJqhLDtSRJklQlhmtJkiSpSgzXkiRJUpUYriVJkqQqMVxLkiRJVWK4liRJkqrEcC1JkiRVSU3CdUQcFBH3RsS8iJgTEZ8oL/9iRDwXEdPLj/Mq9vlcRCyIiCcj4pxa1ClJkiTtieYanacF+HRmTo2IvsCUiJhYXvfvmflvlRtHxDHApcCxwIHAPRExKjNba1SvJEmStMtqMnKdmcsyc2r5+TpgHjBsB7tcANyUmZszcxGwADip4yuVJEmSdl/Ne64jYiQwDnisvOijETEzIq6PiP3Ky4YBz1bstpQdh3FJkiSp7moariOiD3Ar8MnMXAt8BzgMGAssA77etmk7u2c7x7sqIiZHxOSVK1d2TNGSJEnSTqpZuI6IbpSC9Y2Z+SuAzFyRma2ZWQR+wP+2fiwFDqrYfTjw/LbHzMzvZ+b4zBw/aNCgjv0FJEmSpNdRq9lCArgOmJeZ36hYPrRis3cBs8vP7wQujYgeEXEIcAQwqRa1SpIkSburVrOFnAa8D5gVEdPLyz4PXBYRYym1fCwGPgSQmXMi4mZgLqWZRq52phBJkiR1djUJ15n5IO33Uf9mB/t8BfhKhxUlSZIkVZl3aJQkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJTUJ1xFxUETcGxHzImJORHyivPxrEfFERMyMiNsion95+ciI2BQR08uP79aiTkmSJGlP1GrkugX4dGYeDZwMXB0RxwATgdGZeTzwFPC5in2ezsyx5ceHa1SnJEmStNtqEq4zc1lmTi0/XwfMA4Zl5u8zs6W82aPA8FrUI0mSJHWEmvdcR8RIYBzw2DarrgDurnh9SERMi4j7IuL07RzrqoiYHBGTV65c2TEFS5IkSTuppuE6IvoAtwKfzMy1Fcu/QKl15MbyomXAiMwcB3wK+FlE9Nv2eJn5/cwcn5njBw0a1PG/gCRJkrQDNQvXEdGNUrC+MTN/VbH8A8A7gPdmZgJk5ubMfKn8fArwNDCqVrVKkiRJu6NWs4UEcB0wLzO/UbH8XODvgHdm5saK5YMioqn8/FDgCGBhLWqVJEmSdldzjc5zGvA+YFZETC8v+zxwLdADmFjK3zxanhnkDOBLEdECtAIfzsxVNapVkiRJ2i01CdeZ+SAQ7az6zXa2v5VSC4kkSZK01/AOjZIkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqpCbhOiIOioh7I2JeRMyJiE+Ul+8fERMjYn75534V+3wuIhZExJMRcU4t6pQkSZL2RK1GrluAT2fm0cDJwNURcQzwWeAPmXkE8Ifya8rrLgWOBc4Fvh0RTTWqVZIkSdotNQnXmbksM6eWn68D5gHDgAuAH5c3+zFwYfn5BcBNmbk5MxcBC4CTalGrJEmStLtq3nMdESOBccBjwODMXAalAA4cUN5sGPBsxW5Ly8u2PdZVETE5IiavXLmyQ+uWJEmSXk9Nw3VE9AFuBT6ZmWt3tGk7y/JPFmR+PzPHZ+b4QYMGVatMSZIkabfULFxHRDdKwfrGzPxVefGKiBhaXj8UeKG8fClwUMXuw4Hna1WrJEmStDtqNVtIANcB8zLzGxWr7gQ+UH7+AeCOiuWXRkSPiDgEOAKYVItaJUmSpN3VXKPznAa8D5gVEdPLyz4P/Ctwc0RcCTwDXASQmXMi4mZgLqWZRq7OzNYa1SpJkiTtlpqE68x8kPb7qAHesp19vgJ8pcOKkiRJkqrMOzRKkiRJVWK4liRJkqrEcC1JkiRVieFakiRJqhLDtSRJklQlhmtJkiSpSgzXkiRJUpUYriVJkqQqMVxLkiRJVWK4liRJkqrEcC1JkiRVieFakiRJqhLDtSRJklQlhmtJkiSpSgzXkiRJUpUYriVJkqQqMVxLkiRJVWK4liRJkqrEcC1JkiRVieFakiRJqhLDtSRJklQlhmtJkiSpSgzXkiRJUpUYriVJkqQqMVxLkiRJVWK4liRJkqrEcC1JkiRVSU3CdURcHxEvRMTsimW/iIjp5cfiiJheXj4yIjZVrPtuLWqUJEmS9lRzjc5zA/BfwE/aFmTmJW3PI+LrwJqK7Z/OzLE1qk2SJEmqipqE68y8PyJGtrcuIgK4GHhzLWqRJEnqSjKz3iV0KZ2h5/p0YEVmzq9YdkhETIuI+yLi9HoVJkmS1ChK45nqaLVqC9mRy4CfV7xeBozIzJci4g3A7RFxbGau3XbHiLgKuApgxIgRNSlWkiRJ2p66jlxHRDPwbuAXbcsyc3NmvlR+PgV4GhjV3v6Z+f3MHJ+Z4wcNGlSLkiVJkvZKtofURr3bQs4GnsjMpW0LImJQRDSVnx8KHAEsrFN9kiRJDcG2kNqo1VR8PwceAY6MiKURcWV51aW8tiUE4AxgZkTMAG4BPpyZq2pRpyRJUqMpFov1LqFLqdVsIZdtZ/nl7Sy7Fbi1o2uSJEnqCtrCtW0htVHvthBJkiR1oNbW1nqX0KUYriVJkhpY28i1Pde1sdPhOiK+ERFjO7AWSZIkVVnbyLVtIbWxKyPX3YDfRcTsiPi7iBjeUUVJkiSpOhy5rq2dDteZ+THgQOCzwFhgXkTcExHvj4g+HVSfJEmS9kDbiLUj17WxSz3XmdmamXeVZ/84GRgE3AAsj4gfRsSwDqhRkiRJu8mp+Gprl8J1RPSLiCsj4l7gfuAx4HTgaGA9cHf1S5QkSZL2Djs9z3VE3AKcQylUfxe4PTM3V6z/FLCm6hVKkiRpj9lzXRu7chOZR4GPZuby9lZmZjEiBlenLEmSJFVDW6i257o2djpcZ+a/7cQ2G/esHEmSJFVTW7h25Lo2vImMJElSAzNU15bhWpIkqYEVCqW4Z1tIbRiuJUmSGlhbqHYEuzYM15IkSQ3Mea5ry3AtSZLUwLxDY20ZriVJkhpYS0sLYFtIrRiuJUmSGlhbuFZtGK4lSZIaWFu4ti2kNgzXkiRJDay1tRWwLaRWDNeSJEkNbOvWrfUuoUsxXEuSJDWwtnBtW0htGK4lSZIamCPXtWW4liRJamBt4dqe69owXEuSJDUw20Jqy3AtSZLUwF69iUyd6+gqDNeSJEkNrG0qPtWG4VqSJKmBeROZ2jJcS5IkNbBisQh4QWOt1CRcR8T1EfFCRMyuWPbFiHguIqaXH+dVrPtcRCyIiCcj4pxa1ChJktSI2sK1aqNWI9c3AOe2s/zfM3Ns+fEbgIg4BrgUOLa8z7cjoqlGdUqSJDWUtnYQ20JqoybhOjPvB1bt5OYXADdl5ubMXAQsAE7qsOIkSZIaWNsFjbaF1Ea9e64/GhEzy20j+5WXDQOerdhmaXnZn4iIqyJickRMXrlyZUfXKkmStNdxxLq26hmuvwMcBowFlgFfLy9v72NVu++KzPx+Zo7PzPGDBg3qkCIlSZL2Zm0914bs2qhbuM7MFZnZmplF4Af8b+vHUuCgik2HA8/Xuj5JkiRpV9UtXEfE0IqX7wLaZhK5E7g0InpExCHAEcCkWtcnSZIk7armWpwkIn4OnAkMjIilwD8CZ0bEWEotH4uBDwFk5pyIuBmYC7QAV2emtxaSJElSp1eTcJ2Zl7Wz+LodbP8V4CsdV5EkSVLX4DzXtVXv2UIkSZLUgbyQsbYM15IkSQ2sbZ5rQ3ZtGK4lSZIa2KZNmwDYunWrAbsGDNeSJEkNbMOGDQC0Fots2bKlztU0PsO1JElSA2sL1wDr1q2rYyVdg+FakiSpgb388suvPl+zZk0dK+kaDNeSJEkNbNWLL9Kr7fmqVXWtpSswXEuSJDWwl19+mcHl54brjme4liRJalDFYpG169czqPzatpCOZ7iWJElqUBs2bKBYLLI/EMDatWvrXVLDM1xLkiQ1qLaR6l5Az0LBcF0DhmtJkqQGtX79egB6lh9tr9VxDNeSJEkNqm1e657APpnOc10DhmtJkqQG1TZSvQ+lcL3WCxo7nOFakiSpQbX1WD9CafTacN3xDNeSJEkNqu2CxhcpXdToBY0dr7neBUiS9l7XXnstCxYsqHcZ6oQOP/xwPv7xj9e7jC5v7dq1BKVp+HoB6zdupLW1laampjpX1rgcuZYkSWpQa9asoS1G9wLSixo7nCPXkqTd5sik1LmtXr361ZHU3hXL+vfvX6eKGp8j15IkSQ1q1UsvvTpy3RauX3755XqV0yUYriVJkhrUsmXLXm1T6F/+uXz58jpV0zUYriVJkhrQunXrWL9hw6vhel9KFzY+99xzdayq8dlzLUmS1ICeffZZALqVXzcT7FcIli5dWr+iugBHriVJkhrQU089BUD3imWDi0WenDevPgV1EYZrSZKkBjRv3jx6FwqvaVMYDjy3bJk3k+lAhmtJkqQGNG/uXIYVi69ZNrz884knnqh9QV2E4VqSJKnBbN68mWeefZah2ywfUv759NNP17qkLqMm4Toiro+IFyJidsWyr0XEExExMyJui4j+5eUjI2JTREwvP75bixolSZIaxZIlSygWi6+G6Ta9CPYtFFiwYEFd6uoKajVyfQNw7jbLJgKjM/N44CngcxXrns7MseXHh2tUoyRJUkOYP38+AIPbWTe4WOSpJ5+sbUFdSE3CdWbeD6zaZtnvM7Ol/PJR/rcNSJIkSXtg1qxZ9C4UGNjOuhHAkmeeYc2aNbUuq0voLD3XVwB3V7w+JCKmRcR9EXF6vYqSJEnaG02bMoURxSJB/Mm6keWfM2fOrGlNXUXdw3VEfAFoAW4sL1oGjMjMccCngJ9FRL/t7HtVREyOiMkrV66sTcGSJEmd2KJFi1i2YsWrIXpbw4BuETz00EM1rKrrqGu4jogPAO8A3puZCZCZmzPzpfLzKcDTwKj29s/M72fm+MwcP2jQoFqVLUmS1Gldf9119IhgzHbWNxOckMnvfvtb79bYAeoWriPiXODvgHdm5saK5YMioqn8/FDgCGBhfaqUJEnaezzxxBPcd//9nJpJ73ZaQtpMAAqZ/OhHP6pdcV1Erabi+znwCHBkRCyNiCuB/wL6AhO3mXLvDGBmRMwAbgE+nJmr2j2wJEmSgNLc1t/4+tfpFQVOfZ1t+xKcnMk9EycyadKkmtTXVTS//iZ7LjMva2fxddvZ9lbg1o6tSJIkqXFkJtdccw1PPPkklwH77GDUus0EYH4E//gP/8D3fvADRowY0eF1dgV1v6BRkiRJe+ZnP/sZEydO5C3AMTsRrAF6ELw3EzZv5rN/93esW7euY4vsIgzXkiRJe7FbbrmF73/vexxHaTR6V/QnuKxYZNnzz/Ppv/1bVq9e3QEVdi2Ga0mSpL1QsVjkO9/5Dtdeey1HAe+Cdue1fj0HE1ySydPz5/ORD32I5557ruq1diWGa0mSpL3Mli1b+PKXv8zPf/5zTgIuBbrtRrBuczTB5Zm8vGIFH/7Qh5g3b17Vau1qDNeSJEl7kfnz5/PhD32IP/zhD7yV0g1DCnsQrNuMIPjrYpHC+vV89OqrufHGG2lpadnj43Y1hmtJkqS9wJYtW/jhD3/IVR/8IMsXLeIy4Axit1pBtmcQwVXFIoe3tPC9732Pj3z4wzz99NNVO35XYLiWJEnq5ObOncuVV1zBT37yE44rFvlYsbjTs4Lsqj4ElwEXA0sXLOCDf/3X3HDDDWzdurVDztdoajLPtTqfxYsX81/f+haTJ0/e5X3HjBnDR6++miOOOKIDKpMkSW0WLlzIDTfcwB//+Ef2LRR4HzCqg0J1pSA4Dji0WOTXwPXXX89v7rqL919+Oeeeey7NzUbI7YnMrHcNVTF+/PjcnaDY1SxcuJAbb7yRe+65B5q6sWXgKLLQtNP7R7GV7i8tILe+wplnnslf/uVfMmrUqA6sWJKkrmfRokWvhuruwMmZvImduznMtq6jlPWu3INQPp/kDxE8l8mQwYP5wOWXc84553TZkB0RUzJzfLvrDNeNr6WlhYcffphf3XYbU6dMIZq6sfmAo9g6dAx022c3DriZbstm0uOFeWTLFo4fM4Z3v+tdvOlNb6J79+7V/wUkSeoiFi1axI9vuIF7y6H6jZmcBvTag2BcjXANkCRPAf8TwfOZDB0yhA9cfjlve9vbulzINlx3QZnJvHnzuOeee/j9xHtYu2Y10aMPmwcdydYDjt69UL2tls10e+FJuq+cB6+so0/ffrz17Ldw9tlnc+yxx1Io2NIvSdLraWlp4cEHH+T2229n6tSp9IioSqhuU61w3SZJngTuLYfs/ffbjz975zv5sz/7Mw444ICqnKOzM1x3Ea2trcyePZsHHniAP953Hy+sWAGFJlr2PYiWQaNo7T8cogMCbxZpWvM8zSufotvqZ8hiCwMHDmLChDM4/fTTOf7447vcJ9pquvbaa1mwYEG9y1AndPjhh/Pxj3+83mVI2k0rV67kv//7v/nvO+7kpZdXsW+hwPhikROB3lXsq652uG6TJPOBx4D5QBQKnHrqqVx44YWMHz++oQfZdhSuTTx7udWrV/P444/z6KOP8sijj7J+3Tqi0ERLvwNpOfQMWvY7GJp7dGwRUaC1/3Ba+w9nc8sWmlYvYflLi/jVbXdw66230qt3b05+4xs5+eSTOemkk9h///07th5JkjqpzGTq1KncdtttPPjgg2SxyOHAOcCRxWJV5quulSAYBYwCXiZ5vFhk6sMP8+CDDzJs6FAueNe7OO+88+jXr1+9S60pR673Mps3b2bWrFlMmTKFSZMeZ8GC+WQm0b0nW/oNo7X/CFr3HQ7NnaD3uXUrTWuW0rT6WbqvWUpu2QjAoYcdxhtPOok3vOENHHfccfTs2bPOhUqS1LE2b97MxIkTufkXv2DxkiX0KhQ4oTxKvX8HB+qOGrluTwvJHGBSBM9k0qNbN95+/vlcdNFFHHTQQR1+/lqxLWQvtnnzZubNm8e0adOYOnUqs+fMobWlBaJAsc8BtOw7jNZ9h1PsPaBjWj6qJZPCxpdoWr2U5rXPUVi/AopFmpqaOPqYY3jDCScwduxYRo8eTY8eHTzSLklSjaxatYrbb7+d2371K9asXcuQCE7NZDR7drvyXVHLcF1pOcmjwIwIWjI55ZRTuOSSSxg3bhwRe88IfXsM13uRTZs2MWfOHGbMmMH06dOZM3cuLeVJ27P3QLb2G0qx34G09h0CTd3qXO0eaG2had1yCmufp9u6ZcSGFyGTpuZmjj76aMaNHcuYMWMYPXo0vXr1qne1kiTtksWLF3PTTTcx8fe/Z2tLC0cCpwKHQFXvqLgz6hWu26wnmQRMKhTYUCxy+GGHcfEll3D22WfvtddkGa47sbVr1zJr1ixmzpzJtOnTeeqppyi2tkIE2XsgLX0G09pvaClMd3TvdD21bKFp/Ypy2F5BbFhZGu0uFDj88CMYO3YMY8aM4bjjjqN///71rlaSpO168skn+ciHP0wUi4zL5GRKtxWvh9+QTC0/H1p+nFenWraSzAQejgIvZJFzzz2Xz3/+83WpZU8ZrjuRl156iZkzZzJjxgymTZvO4sWLyEwoNFHsPZCWPkMo9htCa5/BnaNvul5at1JYv4KmtctpXr+CpvUvkMVWAEYcPJITxpVGtseMGcPAgQPrXKwkSSVbtmzhr6+8klXPPstHikX61PkCxetIFle8Hkn9RrDbJMlE4AHgn//5n3nTm95U13p2h7OF1NGqVauYNm0a06ZNY8rUqTy3dCkA0dSNlj4H0DrsBFr7DqHYZxAU/Od4VVM3ivsOp7jvcLYCFFspbFhJ09rlLFq9nGfv+g233347AEMPPJA3nHAC48aNY9y4cYZtSVLdXH/99SxesoT3Q92DdWcVBG8mWRDB1776VY477jj23XffepdVNaa5Ktu4cSPTpk1j8uTJTJ4yhSWLFwMQzd3Z2mcwxYNOorXfEIq9BkIDz/9YdYUmin2HUOw7pBS2s0hhw0sU1i3n2bXLWP7bidx1110ADD/oIE4cP57x48czbtw4+vTpU9fSJUldw9atW/nFL35BP2BIvYvp5JqAwzN5YM0a7r77bi699NJ6l1Q1hus9lJksXryYhx9+mEcffZRZs2dTbG0lmppL/dIHnUhrv6EUew/s3LN57G2iQLHPIIp9BtEy9Dg2Z5HCxlUU1j7PkrXP89ydd3HbbbdRKBQ45thjOeXkkzn11FM59NBD9/orlCVJnVO3bt34zGc+w79//et8q7WVdxeLjHL0+k9sJLkdmAecdNJJvP3tb69zRdVlz/VuKBaLzJ07lz/+8Y/cd/8DrFi+rLSi94DSXNP7DqfY9wDbPOqp2Eph/Qs0rVlKt7XPEetfBGDQAQdwxumnM2HCBI477jiamprqXKgkqdEsXryYL/7f/8vCxYs5FXgr0FynkN3Zeq4Xk9xSKLAhgqs+9CEuvvjivfJOjl7QWCVLlizh7rvv5ve/n8iLL64s3wlxKC39D6a1/wiyR+8OPX+tdF/yCABbDj6lzpVUT2zZSNPqZ2h6+Rm6rX2OLLay3/4DeOvZb+Htb387hx12WL1LlCQ1kM2bN/Otb32L22+/nQGFAucUixxFfabhW1zxeiT1CddrSe4BpgNDhwzhi1/6EkcddVTN66gWw/UeaG1t5YEHHuCXv7yFWbNmQgSt+w5n64BDae1/cEPO6LHP3FLv8ivHvKPOlXSQ1i00rX6W5hefpnntUigWOfroY7joovdw5pln7rVzbkqSOp9JkyZx7X/+J888+yyHELydZGgNw229w/UWkoeAByMoFgpcdPHFfOADH9jr72FhuN4Nmckf/vAHfnjd9Tz/3FLYpx9bBh3J1kFHQLe9+w2xI92XPELzyqcAKPYaQLH3gIYawf4TW1+h+cX59Fj5BGxaw+DBQ7jiir/inHPO2Su/ppIkdT4tLS3ceeedXP/DH7Ju/XrGAWcDfWsQcusVrpNkBnBPocCaYpEJEybwkY98hAMPPLDDz10Lhutd9Oyzz/Iv//KvzJ49i+w9gM1Dx9K6/8Fd4oLEfebeRdO65a++bu07pHFHsCtl0rT6GXo8P41Y/yKjjjySL3z+8xxyyCH1rkyS1CDWrVvHT37yE2695RaGFJMP1SCD1StczyK5GThy1Cg++rGPMWbMmA4/Zy3tKFw3flrcRY899hhXXHklc558is2HnsHGYy+kdcAhXSJYd2kRtO53MBuPuYBXDjuT+Yue4a8/+EHuu+++elcmSWoQffv25eqrr+aDV13F0iyymsYY4GzPHGDAfvvxve9/v+GC9esxMVaYO3cun/3c53ilqQ8bjn03LYNGgdO2dS0RtA48nPWj38XmHv35x3/8R6ZOnfr6+0mStJNOO+00AJ6scx0dpaV8g5hT3/SmLtliWZPfOCKuj4gXImJ2xbL9I2JiRMwv/9yvYt3nImJBRDwZEefUosbM5JprvkZrc082HnVuw8z8od3UrRebjjyXYo9+fPWaaygWi/WuSJLUIPr27Uu35maW1buQDrIK2JzJgAED6l1KXdTq48QNwLnbLPss8IfMPAL4Q/k1EXEMcClwbHmfb0dEh09GvGzZMhYufJrNg0dD8z4dfbrOq3ULPXv25D3veQ89e/aE1i31rqh+mrqzeejxLHv+eRYtWlTvaiRJDWDr1q38w9//PbS2clK9i+kgA4FRwP/7yU+YPn16naupvZqE68y8n9IHmUoXAD8uP/8xcGHF8psyc3NmLgIWQMe//7Zu3Vp60tSto0/VqUXLFs4//3w+/vGPc/755xMtXThcA1l+P2zZ0rX/DpKk6rj22muZOWsWF2ZyYIPevbFAcBGwf8I/fOELLF++/HX3aST1bIQZnJnLAMo/DygvHwY8W7Hd0vKyPxERV0XE5IiYvHLlyj0qZvjw4QwYOJDuL8yDLtwCkM3d+fWvf821117Lr3/9a7IB5/HeaVmk+4q59O3Xz5vMSJL22OOPP84dd9zBm4DjaxSsX4HXfCP9Sk3OCvsQXJZFNm/YwNe+9rUanbVz6Ixd5u2929q9nDYzv5+Z4zNz/KBBg/bopE1NTXz8Yx8j1q+kx9P3QrFlj46312rqzqZNm7jlllvYtGkTNHXRcF1spfvCByisXcbffOQjdO/eRf8OkqSqufGnP6VfocBbanjOV+A130jXKlwDDCI4vVjk8ccfZ/78+TU8c33VM1yviIihAOWfL5SXLwUOqthuOPB8LQo666yzuPrqq2letYhec/+bwoYXa3FadTKxcRW95t1Ftxfnc8UVV3D++efXuyRJ0l7uiSeeYOq0aZxaLNJcw3aQfeA130jX+qqyE4EeEfzsZz+r8Znrp57h+k7gA+XnHwDuqFh+aUT0iIhDgCOASbUq6pJLLuGrX/0q+3Uv0nP2HXR/+o/EpjW1Or3qKF5ZS/eFD9Br9m30K2zmy1/+Mpdffnm9y5Ik7eVaW1v59re+xT4RvKHG594HXvONdK3DdU+C8Znc+z//w9y5c2t89vqo1VR8PwceAY6MiKURcSXwr8BbI2I+8NbyazJzDnAzMBf4LXB1ZrbWos42p5xyCj+78UYuu+xSeq19hl4zf0mPpybStHopNMgdLben2HsA2dSNbOpGa98hFHs3+DQ6mRTWPE+P+X+g18xfss/LT/OeP/9zfnbjjUyYMKHe1UmSGsBNN93E9BkzODeTfRr0IsYdmQD0i+CfvvhFNm7cWO9yOpy3P38dq1at4tZbb+X2O+5g3dq1sE9ftux/KC0DDiN77teQN5npvuQRALYcfEqdK+k4sWk1zS89TfeXnoZX1tKrd28ueOc7ueiiixg4cGC9y5MkNYh58+bxNx/5CEcVi1wCRI3Ddb1uf76txSTXA+ecey6f+9zniL08P+3o9ueG6520ZcsWHnjgAX7zm98wefJkMhN69WdL/4Np3e9gir0HNWTQbhiZFDa+RNPLS+j+8hLYuIqIYOzYsZx33nmceeaZ9OjRo95VSpIaQLFYZNq0adxxxx08+MAD9Mrk6mKRXnUItZ0lXAPcQ3IfMGL4cN554YWce+659OvXry617CnDdZWtWrWK++67jz/+8T5mzJhOsVgkuvdiS79htPYfTmu/YdCtC9+IprNoeYWmNc/TtGYp3dc+R27eQEQwevRxnHXWmUyYMIE9nWVGkqQ2q1ev5u677+bO22/nuWXL6FUoMLZY5BSgf50CbWcK10WSGcDjETybSbfmZs486ywuuOACjjvuuL1qNNtw3YHWrl3Lo48+ysMPP8xjkx5nw/p1AGTvgWztdyDFfgfS2ndwl785TU0UWyisW0HT2ufptnYZsWElZNKzV2/eeNKJnHrqqZx88sn079+/3pVKkhpEa2srM2bM4M477+T+++6jpbWVg6N0Ed+xQLc691h3pnBdaTnJ48DMCF7JZOTBB/POCy7g7LPP3iv+f9pwXSOtra08+eSTPP744zz++OPMmTOH1tZWiALFPoNo6TuE1n5DKfYxbFdFawuF9S/QtG4ZzWuXUdiwEoqtFJqaOProozlx/HhOPPFEjj76aJqbm+tdrSSpQWzcuJFJkybx8MMP88hDD7Fm3Tp6RoExWWQ8MLgThNc2nTVct9lCMovSaPZzmRQiOPbYY3nT6adz6qmnMmLEiE45om24rpNNmzYxe/Zspk6dypQpU3nqqScpFoulsN17IC19B1PsO7Q0st1sv+/ratlC0/oVFNYup3n98nKYLhIRHHHEKE44YRwnnHACxx9/PL169ap3tZKkBrJixQoeeughHnrwQaZNn05LSws9CwWOKBY5CjgS6N6JQmubzh6uKy0jmQs8GcGycj4dNnQop5WD9vHHH99pBssM153Exo0bmT17NjNmzGDqtGk88cQTtLaU7gSZvQfQ0mdwafq7vkPI7oZDtm6iad1ymtYtp3ndCmLjS6ULE5uaOHLUkYwbN5YxY8Zw3HHH0adPn3pXK0lqMAsWLODee+/loQcfZOGiRQAMKBQ4qljkSGAE0NRJg2qbvSlcV1pN8iTwJLAwgtZM+vTqxRtPOYXTTjuN008/va4TERiuO6nNmzczd+5cZs6cyfTp05k9ew6bN5dvTNpzX7b2KY9s9xtK9mj88BhbNlBYu4ymdcvptn45bFwNQLfu3Tn22GMZO2YMY8aM4ZhjjqFnz571LVaS1NAmTZrEZz7zGcjkYIIjSY6kdEvvvcneGq4rbSZZQCloP1UosKFY5OQ3vpGvXnNN3VpGDNd7iZaWFp566ilmzJjBjBkzmD5jBhs3bCit3KcfW/sOobXfgRT7DSW7965vsdWwdSNNa5fRtOb5Upgu3wmzZ89ejBlzPGPGjOH444/nqKOOols3e9QlSbWxevVqLn//+2las5Yrs0jvvSyMVmqEcF2pSPIw8DvgE5/4BH/+539elzp2FK47R+OKAGhubuaYY47hmGOO4bLLLqNYLLJw4UKmTZtWfkxnw8qnShv32o+tfYfSuu9wWvsN3TsukGxtoWndMprWPEe3dc/DhlUA9OzVm3HjxjB27FjGjRvH4YcfTlNTU52LlSR1RZnJNV/9KmvWrOFDmXt1sG5EBYLTSBYB3/7WtzjhhBM45JBD6l3WaxiuO7FCocDhhx/O4YcfzkUXXURraytPP/00U6ZMYfLkyUyfMYOtK+ZCoUBrnyG09D+I1v1GkPvsW+/SXxWvrKNp9RKaVz9L87rlZLGVbt26c/zxxzF+/EW84Q1v4IgjjjBMS5I6hWXLlvHgQw9RAH4NjCQ5BDiIznnBYleRJC8Ci4FFwLJCga0tLdx222186lOfqm9x27AtZC+2ZcsWZs2axaRJk3jo4Ud4Zsni0ope+5XuHLn/IRR77V/zO0fGxlU0r1pE99VLXh2dHjZ8OKedeionnXQSY8aM8W6IkqRO6/HHH2fSpElMnzaN+fPnU8ykAAyLYGSWwvYIoMdeELZ/QzK1/Hxo+XHeXlB3kWQl/xumlxQKrC8WAdi/f3/GnnACY8eO5ayzzmLffWs/qGjPdRexbNkyHn74Ye67/35mTJ9eukV7z/5sGXAYLQOP6NCLImPLBppfXED3lxbAxpdfvRPihAlncNpppzFs2LAOO7ckSR1lw4YNzJo1i+nTpzN92jSefPJJWotFCsDQirA9BOgHRCcMrtdRynqdudd6SzlMP0MpUC8pX7gIMHDAAMaVw/TYsWMZPnx43ee+Nlx3QatXr+a+++5j4j33MHPGDABa+x/E1gOOprX/QdUZzc6ksPY5uq2YS/PqZyGTY0eP5q1nn82ECRMYMGDAnp9DkqROZOPGjcyZM+fVsD1v3jxaWlsB6BEFBpIMymQgMKj82J/6TtnXmcL1hnKIrny8WCiwuhykAYYccMCrI9Njx45l6NChdQ/T2zJcd3HPP/88d999N3fe+d+8/PIq6LUfrwwdQ+uAQyEKu37ATJpWLaLHshnEhpfot29//uwd53P++eczfPjw6v8CkiR1Ups3b2bevHksWrSIJUuWsGTxYhYvWsRLL7/86jZNEQyIYGCx+JrQPZDatJbUOlwXSdZAuyF6Y0WI7tGtGyNGjODgQw7h4IMP5uCDD+boo49m8ODBNalzTxiuBcDWrVu59957+X8//SlLFi8mew9gy+DRZGEXrmvNFnqsmEusX8mw4Qfxvr98L2effTbdu3fvuMIlSdrLbNiwoRS2lyzhmWeeYcmSJSxeuJDnly8v3a25rH+hwMBi8dWwfQClvuhqhu6OCtdtFxmuoCJAR/AisLUiX+7bty8HjxxZepRD9MEHH8wBBxxAobAbg3ydgOFar1EsFrn33nv59ne+y8oXVuzy/vsPGMCHrrqKt73tbc7yIUnSLti6dSvPPfccixcvfjV4L160iGeeeYbNW7YAUAAGRzAik4MoXTzZn93v565WuN5C8hylvuhngKXbjEQPOeAARowcyciKED1ixAj69++/R+ftjAzXateWLVtYunTpLu83bNgwZ/uQJKmKisUiK1euZOHChcyZM4fZs2Yxd+5cXtm8GYB+hQLDi0VGUArbQ4HmnQzLuxOus9za8QzwLPBMBMszaYvSI4YPZ/TxxzN69GhGjRrFiBEj2GeffXb6+Hs7byKjdnXv3p1DDz203mVIktTlFQoFBg8ezODBgznllFOA0p2bFy5cyOzZs5kzZw6zZs5k7orSN87NERyYcBDJCOBw9nwe7mXlm7O0jUqvKY9K9+jenWOOOYa3HHcco0eP5thjj6Vfv357dK5GZriWJEnqhJqbmxk1ahSjRo3i3e9+NwAvvvhiaWR79mxmz5rFY089xUMtLexXKHB+sciRuxGw15P8FphRfj3kgAM48fjjOfbYYxk9ejSHHXYYzc1Gxp3lX0qSJGkvMXDgQCZMmMCECROAUovntGnT+K9vfpOfPvMMR5GcD/TfiZBdJHkcuCcKtBSC97/3vVx44YUMHDiwY3+JBme4liRJ2kt1796dN77xjbzhDW/g5ptv5kfXX883t25lQiansf35tZ8nuSOC5zM5YdxYPvWpTzFixIjaFt+gDNeSJEl7uebmZv7iL/6Ct7zlLfznf/4nEx98kP7A8dvZ/qeFAs19+/KPn/wkb37zmzvdTVr2Znvn5IKSJEn6E4MHD+Zzn/scAOt3sN36YpF3vPOdvOUtbzFYV5nhWpIkqYF069YNgNbtrC+SJHgDuA5iuJYkSWoga9asAdjuJY1ZXte2narLcC1JktRAbr/9dgI4djvrmwiOBn57991s2rSphpV1DYZrSZKkBrFp0ybuuP12jgb228F0fKcC6zds4Le//W3Nausq6hquI+LIiJhe8VgbEZ+MiC9GxHMVy8+rZ52SJEl7g8cee4z1GzZw8utsNwIYGsHvf/e7WpTVpdR1Kr7MfBIYCxARTcBzwG3AXwH/npn/Vr/qJEmS9i4rV64EYPDrbBcEQzJZWr6duqqnM7WFvAV4OjOX1LsQSZKkvdGqVatoiqDnTmzbG3h5zRoys6PL6lI6U7i+FPh5xeuPRsTMiLg+IvarV1GSJEl7i1deeYUC8MpObLsVaG1tZevWrR1cVdfSKcJ1RHQH3gn8srzoO8BhlFpGlgFf385+V0XE5IiY3PY1iCRJUld1/vnn0wI8+DrbrSGZEsE555zjfNdV1inCNfB2YGpmrgDIzBWZ2ZqZReAHwEnt7ZSZ38/M8Zk5ftCgQTUsV5IkqfM5/PDDOfvss3kkgrVsv93jfwCamrjiiitqVltX0VnC9WVUtIRExNCKde8CZte8IkmSpL3QlVdeSRYK3AXlezG+1iKSacC73/1uhgwZUvP6Gl3dw3VE9ALeCvyqYvE1ETErImYCZwF/W5fiJEmS9jIHHnggH/rwh5kHPLbNug0kvywUGDZsmKPWHaSuU/EBZOZGYMA2y95Xp3IkSZL2ehdddBFTJk/mt5MmcWwmvYEiya3AK4UC//GlL9GrV696l9mQ6j5yLUmSpOoqFAp8/gtfYN/+/VkdwXkEc4H5wN9cfTVHHHFEvUtsWIZrSZKkBtS/f3/e9/7380wmz5I8EsHQIUO48MIL611aQzNcS5IkNai3v/3t9OrZk1uAZzJ5z0UX0dTUVO+yGprhWpIkqUH16tWL884/n1VARHDeeefVu6SGZ7iWJElqYEcddRQA3bt3p3fv3nWupvEZriVJkhrY0KFDX38jVY3hWpIkqYEddNBBALaE1Ejd57mWJElSx+nfvz8///nPGTRoUL1L6RIM15IkSQ1u2LBh9S6hy7AtRJIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVGK4lSZKkKjFcS5IkSVViuJYkSZKqxHAtSZIkVYnhWpIkSaoSw7UkSZJUJYZrSZIkqUoM15IkSVKVRGbWu4aqiIiVwJJ619FABgIv1rsIqR2+N9WZ+f5UZ+V7s7oOzsxB7a1omHCt6oqIyZk5vt51SNvyvanOzPenOivfm7VjW4gkSZJUJYZrSZIkqUoM19qe79e7AGk7fG+qM/P9qc7K92aN2HMtSZIkVYkj15IkSVKVGK71GhFxbkQ8GRELIuKz9a5HahMR10fECxExu961SJUi4qCIuDci5kXEnIj4RL1rktpExD4RMSkiZpTfn/9U75oanW0helVENAFPAW8FlgKPA5dl5ty6FiYBEXEGsB74SWaOrnc9UpuIGAoMzcypEdEXmAJc6H871RlERAC9M3N9RHQDHgQ+kZmP1rm0huXItSqdBCzIzIWZuQW4CbigzjVJAGTm/cCqetchbSszl2Xm1PLzdcA8YFh9q5JKsmR9+WW38sOR1Q5kuFalYcCzFa+X4v9BSNJOi4iRwDjgsTqXIr0qIpoiYjrwAjAxM31/diDDtSpFO8v8dCtJOyEi+gC3Ap/MzLX1rkdqk5mtmTkWGA6cFBG21nUgw7UqLQUOqng9HHi+TrVI0l6j3Mt6K3BjZv6q3vVI7cnM1cAfgXPrW0ljM1yr0uPAERFxSER0By4F7qxzTZLUqZUvGLsOmJeZ36h3PVKliBgUEf3Lz3sCZwNP1LWoBme41qsyswX4KPA7Shfk3JyZc+pblVQSET8HHgGOjIilEXFlvWuSyk4D3ge8OSKmlx/n1bsoqWwocG9EzKQ0iDYxM++qc00Nzan4JEmSpCpx5FqSJEmqEsO1JEmSVCWGa0mSJKlKDNeSJElSlRiuJUmSpCoxXEuSJElVYriWpAYXEX0iYnFE/EXFsr4R8UxEvKdiWfeIeCIiltanUkna+xmuJanBZeZ64CrgPyNiUHnxNcDkzLylYtPPAC/Uuj5JaiTeREaSuoiIuAHoAXwPuBUYnZnLyusOAX4DfAr4QWYOr1edkrQ3M1xLUhcREfsBc4FuwGcy80cV6+4CrgNeBn5quJak3WNbiCR1EZn5MjAH6AX8qm15RLwLaM7M2+pVmyQ1CsO1JHUREfGXwEjgHuCr5WW9KfVff6x+lUlS47AtRJK6gIg4gNKo9cXAE+XnFwJrgceBl8qbdgf2BVYCJ2fm4lrXKkl7M8O1JHUBEXEzsCYzP1h+/deUZgc5HtivYtNTgf8CTgBWZmZrrWuVpL2ZbSGS1OAi4kLgTZTCNACZ+UNgKfB/M3N52wNYBRTLrw3WkrSLHLmWJEmSqsSRa0mSJKlKDNeSJElSlRiuJUmSpCoxXEuSJElVYriWJEmSqsRwLUmSJFWJ4VqSJEmqEsO1JEmSVCWGa0mSJKlK/n/qn/8MArH7TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X4\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.violinplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X5\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.stripplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X6\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=\"X8\"\n",
    "colu_order=np.sort(train_new[var].unique()).tolist()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(x=var,y=\"y\",data=train_new,order=colu_order)\n",
    "plt.xlabel(var,fontsize=12)\n",
    "plt.ylabel(\"y\",fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As the categorical variables are in string , so we have to do label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
       "      <th>X382</th>\n",
       "      <th>X383</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130.81</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.53</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.26</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.62</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.02</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 365 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        y  X0  X1  X2  X3  X4  X5  X6  X8  X10  ...  X375  X376  X377  X378  \\\n",
       "0  130.81  32  23  17   0   3  24   9  14    0  ...     0     0     1     0   \n",
       "1   88.53  32  21  19   4   3  28  11  14    0  ...     1     0     0     0   \n",
       "2   76.26  20  24  34   2   3  27   9  23    0  ...     0     0     0     0   \n",
       "3   80.62  20  21  34   5   3  27  11   4    0  ...     0     0     0     0   \n",
       "4   78.02  20  23  34   5   3  12   3  13    0  ...     0     0     0     0   \n",
       "\n",
       "   X379  X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0     0  \n",
       "1     0     0     0     0     0     0  \n",
       "2     0     0     1     0     0     0  \n",
       "3     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 365 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder(train_new)\n",
    "train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "      <th>X10</th>\n",
       "      <th>X12</th>\n",
       "      <th>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
       "      <th>X382</th>\n",
       "      <th>X383</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 364 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X0  X1  X2  X3  X4  X5  X6  X8  X10  X12  ...  X375  X376  X377  X378  \\\n",
       "0  21  23  34   5   3  26   0  22    0    0  ...     0     0     0     1   \n",
       "1  42   3   8   0   3   9   6  24    0    0  ...     0     0     1     0   \n",
       "2  21  23  17   5   3   0   9   9    0    0  ...     0     0     0     1   \n",
       "3  21  13  34   5   3  31  11  13    0    0  ...     0     0     0     1   \n",
       "4  45  20  17   2   3  30   8  12    0    0  ...     1     0     0     0   \n",
       "\n",
       "   X379  X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0     0  \n",
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       "3     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 364 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder(test_new)\n",
    "test_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_new.drop('y',axis=1)\n",
    "y_train =  train_new['y'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction using PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components= 10)\n",
    "pca.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 10)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_x_train= pca.transform(x_train)\n",
    "pca_x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 10)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = test_new\n",
    "pca_x_test = pca.transform(x_test)\n",
    "pca_x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test= train_test_split(pca_x_train,y_train,test_size=0.25,random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and testing the data using XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the final feature set\n",
    "f_train = xgb.DMatrix(x_train, label = y_train)\n",
    "f_valid = xgb.DMatrix(x_test, label = y_test)\n",
    "f_test = xgb.DMatrix(pca_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the parameters for XGB\n",
    "params = {}\n",
    "params['objective'] = 'reg:linear'\n",
    "params['eta'] = 0.02\n",
    "params['max_depth'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:26:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/objective/regression_obj.cu:171: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\ttrain-rmse:98.75449\ttrain-r2:-64.48366\tpred-rmse:99.65597\tpred-r2:-49.73256\n",
      "[10]\ttrain-rmse:80.93552\ttrain-r2:-42.98426\tpred-rmse:81.88810\tpred-r2:-33.25483\n",
      "[20]\ttrain-rmse:66.40878\ttrain-r2:-28.61217\tpred-rmse:67.41641\tpred-r2:-22.21729\n",
      "[30]\ttrain-rmse:54.57908\ttrain-r2:-19.00191\tpred-rmse:55.66895\tpred-r2:-14.83093\n",
      "[40]\ttrain-rmse:44.96291\ttrain-r2:-12.57463\tpred-rmse:46.15663\tpred-r2:-9.88299\n",
      "[50]\ttrain-rmse:37.16322\ttrain-r2:-8.27355\tpred-rmse:38.47375\tpred-r2:-6.56152\n",
      "[60]\ttrain-rmse:30.85887\ttrain-r2:-5.39410\tpred-rmse:32.30477\tpred-r2:-4.33106\n",
      "[70]\ttrain-rmse:25.78884\ttrain-r2:-3.46563\tpred-rmse:27.37907\tpred-r2:-2.82928\n",
      "[80]\ttrain-rmse:21.73907\ttrain-r2:-2.17323\tpred-rmse:23.48134\tpred-r2:-1.81661\n",
      "[90]\ttrain-rmse:18.53255\ttrain-r2:-1.30616\tpred-rmse:20.44097\tpred-r2:-1.13444\n",
      "[100]\ttrain-rmse:16.01896\ttrain-r2:-0.72301\tpred-rmse:18.09551\tpred-r2:-0.67271\n",
      "[110]\ttrain-rmse:14.07097\ttrain-r2:-0.32943\tpred-rmse:16.31180\tpred-r2:-0.35920\n",
      "[120]\ttrain-rmse:12.59114\ttrain-r2:-0.06451\tpred-rmse:14.98226\tpred-r2:-0.14666\n",
      "[130]\ttrain-rmse:11.47344\ttrain-r2:0.11609\tpred-rmse:13.99401\tpred-r2:-0.00038\n",
      "[140]\ttrain-rmse:10.63352\ttrain-r2:0.24077\tpred-rmse:13.26196\tpred-r2:0.10155\n",
      "[150]\ttrain-rmse:10.00513\ttrain-r2:0.32785\tpred-rmse:12.73034\tpred-r2:0.17213\n",
      "[160]\ttrain-rmse:9.54619\ttrain-r2:0.38810\tpred-rmse:12.34084\tpred-r2:0.22202\n",
      "[170]\ttrain-rmse:9.20665\ttrain-r2:0.43086\tpred-rmse:12.05431\tpred-r2:0.25772\n",
      "[180]\ttrain-rmse:8.95564\ttrain-r2:0.46147\tpred-rmse:11.85000\tpred-r2:0.28267\n",
      "[190]\ttrain-rmse:8.77064\ttrain-r2:0.48349\tpred-rmse:11.69138\tpred-r2:0.30175\n",
      "[200]\ttrain-rmse:8.62581\ttrain-r2:0.50040\tpred-rmse:11.58212\tpred-r2:0.31474\n",
      "[210]\ttrain-rmse:8.51270\ttrain-r2:0.51342\tpred-rmse:11.49905\tpred-r2:0.32453\n",
      "[220]\ttrain-rmse:8.42336\ttrain-r2:0.52358\tpred-rmse:11.43552\tpred-r2:0.33198\n",
      "[230]\ttrain-rmse:8.35130\ttrain-r2:0.53170\tpred-rmse:11.39204\tpred-r2:0.33705\n",
      "[240]\ttrain-rmse:8.28993\ttrain-r2:0.53856\tpred-rmse:11.35622\tpred-r2:0.34121\n",
      "[250]\ttrain-rmse:8.23677\ttrain-r2:0.54445\tpred-rmse:11.33314\tpred-r2:0.34388\n",
      "[260]\ttrain-rmse:8.19248\ttrain-r2:0.54934\tpred-rmse:11.31757\tpred-r2:0.34569\n",
      "[270]\ttrain-rmse:8.15266\ttrain-r2:0.55371\tpred-rmse:11.30180\tpred-r2:0.34751\n",
      "[280]\ttrain-rmse:8.11993\ttrain-r2:0.55729\tpred-rmse:11.29071\tpred-r2:0.34879\n",
      "[290]\ttrain-rmse:8.09084\ttrain-r2:0.56045\tpred-rmse:11.27961\tpred-r2:0.35007\n",
      "[300]\ttrain-rmse:8.06125\ttrain-r2:0.56366\tpred-rmse:11.27168\tpred-r2:0.35098\n",
      "[310]\ttrain-rmse:8.03323\ttrain-r2:0.56669\tpred-rmse:11.26777\tpred-r2:0.35143\n",
      "[320]\ttrain-rmse:8.00574\ttrain-r2:0.56965\tpred-rmse:11.26282\tpred-r2:0.35200\n",
      "[330]\ttrain-rmse:7.98009\ttrain-r2:0.57240\tpred-rmse:11.25667\tpred-r2:0.35271\n",
      "[340]\ttrain-rmse:7.95605\ttrain-r2:0.57498\tpred-rmse:11.25047\tpred-r2:0.35342\n",
      "[350]\ttrain-rmse:7.92820\ttrain-r2:0.57795\tpred-rmse:11.24575\tpred-r2:0.35396\n",
      "[360]\ttrain-rmse:7.90067\ttrain-r2:0.58087\tpred-rmse:11.24039\tpred-r2:0.35458\n",
      "[370]\ttrain-rmse:7.87568\ttrain-r2:0.58352\tpred-rmse:11.23693\tpred-r2:0.35498\n",
      "[380]\ttrain-rmse:7.85200\ttrain-r2:0.58602\tpred-rmse:11.23533\tpred-r2:0.35516\n",
      "[390]\ttrain-rmse:7.82944\ttrain-r2:0.58840\tpred-rmse:11.23244\tpred-r2:0.35549\n",
      "[400]\ttrain-rmse:7.80641\ttrain-r2:0.59081\tpred-rmse:11.23009\tpred-r2:0.35576\n",
      "[410]\ttrain-rmse:7.78224\ttrain-r2:0.59334\tpred-rmse:11.22551\tpred-r2:0.35629\n",
      "[420]\ttrain-rmse:7.76478\ttrain-r2:0.59517\tpred-rmse:11.22540\tpred-r2:0.35630\n",
      "[430]\ttrain-rmse:7.74258\ttrain-r2:0.59748\tpred-rmse:11.21922\tpred-r2:0.35701\n",
      "[440]\ttrain-rmse:7.72070\ttrain-r2:0.59975\tpred-rmse:11.21312\tpred-r2:0.35771\n",
      "[450]\ttrain-rmse:7.69742\ttrain-r2:0.60216\tpred-rmse:11.21104\tpred-r2:0.35795\n",
      "[460]\ttrain-rmse:7.67667\ttrain-r2:0.60430\tpred-rmse:11.20934\tpred-r2:0.35814\n",
      "[470]\ttrain-rmse:7.65762\ttrain-r2:0.60626\tpred-rmse:11.20766\tpred-r2:0.35833\n",
      "[480]\ttrain-rmse:7.63482\ttrain-r2:0.60860\tpred-rmse:11.20506\tpred-r2:0.35863\n",
      "[490]\ttrain-rmse:7.61404\ttrain-r2:0.61073\tpred-rmse:11.20232\tpred-r2:0.35894\n",
      "[500]\ttrain-rmse:7.59257\ttrain-r2:0.61292\tpred-rmse:11.19855\tpred-r2:0.35938\n",
      "[510]\ttrain-rmse:7.56212\ttrain-r2:0.61602\tpred-rmse:11.19556\tpred-r2:0.35972\n",
      "[520]\ttrain-rmse:7.53925\ttrain-r2:0.61834\tpred-rmse:11.19449\tpred-r2:0.35984\n",
      "[530]\ttrain-rmse:7.51631\ttrain-r2:0.62066\tpred-rmse:11.19443\tpred-r2:0.35985\n",
      "[540]\ttrain-rmse:7.49145\ttrain-r2:0.62316\tpred-rmse:11.19156\tpred-r2:0.36018\n",
      "[550]\ttrain-rmse:7.46843\ttrain-r2:0.62548\tpred-rmse:11.18867\tpred-r2:0.36051\n",
      "[560]\ttrain-rmse:7.44984\ttrain-r2:0.62734\tpred-rmse:11.18659\tpred-r2:0.36074\n",
      "[570]\ttrain-rmse:7.42630\ttrain-r2:0.62969\tpred-rmse:11.18557\tpred-r2:0.36086\n",
      "[580]\ttrain-rmse:7.40600\ttrain-r2:0.63171\tpred-rmse:11.18472\tpred-r2:0.36096\n",
      "[590]\ttrain-rmse:7.38767\ttrain-r2:0.63353\tpred-rmse:11.18413\tpred-r2:0.36102\n",
      "[600]\ttrain-rmse:7.36853\ttrain-r2:0.63543\tpred-rmse:11.18111\tpred-r2:0.36137\n",
      "[610]\ttrain-rmse:7.34273\ttrain-r2:0.63798\tpred-rmse:11.17822\tpred-r2:0.36170\n",
      "[620]\ttrain-rmse:7.32495\ttrain-r2:0.63973\tpred-rmse:11.17704\tpred-r2:0.36183\n",
      "[630]\ttrain-rmse:7.30800\ttrain-r2:0.64140\tpred-rmse:11.17953\tpred-r2:0.36155\n",
      "[640]\ttrain-rmse:7.28395\ttrain-r2:0.64375\tpred-rmse:11.17536\tpred-r2:0.36203\n",
      "[650]\ttrain-rmse:7.26784\ttrain-r2:0.64533\tpred-rmse:11.17620\tpred-r2:0.36193\n",
      "[660]\ttrain-rmse:7.24842\ttrain-r2:0.64722\tpred-rmse:11.17707\tpred-r2:0.36183\n",
      "[670]\ttrain-rmse:7.22871\ttrain-r2:0.64914\tpred-rmse:11.17639\tpred-r2:0.36191\n",
      "[680]\ttrain-rmse:7.20491\ttrain-r2:0.65144\tpred-rmse:11.17278\tpred-r2:0.36232\n",
      "[690]\ttrain-rmse:7.18532\ttrain-r2:0.65333\tpred-rmse:11.17173\tpred-r2:0.36244\n",
      "[700]\ttrain-rmse:7.16517\ttrain-r2:0.65528\tpred-rmse:11.16952\tpred-r2:0.36269\n",
      "[710]\ttrain-rmse:7.14309\ttrain-r2:0.65740\tpred-rmse:11.16619\tpred-r2:0.36307\n",
      "[720]\ttrain-rmse:7.12473\ttrain-r2:0.65916\tpred-rmse:11.16423\tpred-r2:0.36330\n",
      "[730]\ttrain-rmse:7.09860\ttrain-r2:0.66165\tpred-rmse:11.16284\tpred-r2:0.36345\n",
      "[740]\ttrain-rmse:7.07844\ttrain-r2:0.66357\tpred-rmse:11.16542\tpred-r2:0.36316\n",
      "[750]\ttrain-rmse:7.05689\ttrain-r2:0.66561\tpred-rmse:11.16440\tpred-r2:0.36328\n",
      "[760]\ttrain-rmse:7.03895\ttrain-r2:0.66731\tpred-rmse:11.16385\tpred-r2:0.36334\n",
      "[770]\ttrain-rmse:7.01584\ttrain-r2:0.66949\tpred-rmse:11.16382\tpred-r2:0.36334\n",
      "[779]\ttrain-rmse:6.99936\ttrain-r2:0.67105\tpred-rmse:11.16477\tpred-r2:0.36323\n"
     ]
    }
   ],
   "source": [
    "# Predicting the score\n",
    "# Creating a function for the same\n",
    "\n",
    "def scorer(m, w):\n",
    "    labels = w.get_label()\n",
    "    return 'r2', r2_score(labels, m)\n",
    "\n",
    "final_set = [(f_train, 'train'), (f_valid, 'pred')]\n",
    "\n",
    "P = xgb.train(params, f_train, 1000, final_set, early_stopping_rounds=50, feval=scorer, maximize=True, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 82.45813 ,  94.956276,  89.14604 , ..., 101.18644 , 107.139946,\n",
       "        97.557365], dtype=float32)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting on test set\n",
    "p_test = P.predict(f_test)\n",
    "p_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
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       "      <th>0</th>\n",
       "      <td>82.458130</td>\n",
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      "text/plain": [
       "            y\n",
       "0   82.458130\n",
       "1   94.956276\n",
       "2   89.146042\n",
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     "execution_count": 53,
     "metadata": {},
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   ],
   "source": [
    "Predicted_Data = pd.DataFrame()\n",
    "Predicted_Data['y'] = p_test\n",
    "Predicted_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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