test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"train.tsv\",sep='\\t')\n",
    "test_data = pd.read_csv(\"test.tsv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150211 entries, 0 to 150210\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                     Non-Null Count   Dtype \n",
      "---  ------                                     --------------   ----- \n",
      " 0   Number_of_Shops_Around_ATM                 150211 non-null  int64 \n",
      " 1   ATM_Zone                                   150211 non-null  object\n",
      " 2   No_of_Other_ATMs_in_1_KM_radius            150211 non-null  int64 \n",
      " 3   Estimated_Number_of_Houses_in_1_KM_Radius  150211 non-null  int64 \n",
      " 4   ATM_Placement                              150211 non-null  object\n",
      " 5   ATM_TYPE                                   150211 non-null  object\n",
      " 6   ATM_Location_TYPE                          150211 non-null  object\n",
      " 7   ATM_looks                                  150211 non-null  object\n",
      " 8   ATM_Attached_to                            150211 non-null  object\n",
      " 9   Average_Wait_Time                          150211 non-null  int64 \n",
      " 10  Day_Type                                   150211 non-null  object\n",
      " 11  rating                                     150211 non-null  int64 \n",
      " 12  revenue                                    150211 non-null  int64 \n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 14.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# train_data.head()\n",
    "# train_data.describe()\n",
    "train_data.info()\n",
    "# train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2836 entries, 0 to 2835\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                     Non-Null Count  Dtype \n",
      "---  ------                                     --------------  ----- \n",
      " 0   Number_of_Shops_Around_ATM                 2836 non-null   int64 \n",
      " 1   ATM_Zone                                   2836 non-null   object\n",
      " 2   No_of_Other_ATMs_in_1_KM_radius            2836 non-null   int64 \n",
      " 3   Estimated_Number_of_Houses_in_1_KM_Radius  2836 non-null   int64 \n",
      " 4   ATM_Placement                              2836 non-null   object\n",
      " 5   ATM_TYPE                                   2836 non-null   object\n",
      " 6   ATM_Location_TYPE                          2836 non-null   object\n",
      " 7   ATM_looks                                  2836 non-null   object\n",
      " 8   ATM_Attached_to                            2836 non-null   object\n",
      " 9   Average_Wait_Time                          2836 non-null   int64 \n",
      " 10  Day_Type                                   2836 non-null   object\n",
      " 11  rating                                     2836 non-null   int64 \n",
      " 12  revenue                                    2836 non-null   int64 \n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 288.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.select_dtypes(\"object\")\n",
    "bool_features = train_data[train_data[train_data.columns[train_data.apply(lambda x: len(x.unique())) == 2]].isin([0,1,\"Y\",\"N\"])].dropna(axis=1).columns.values\n",
    "bool_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Average_Wait_Time', 'Estimated_Number_of_Houses_in_1_KM_Radius',\n",
       "       'No_of_Other_ATMs_in_1_KM_radius', 'Number_of_Shops_Around_ATM',\n",
       "       'rating', 'revenue'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = np.setdiff1d(train_data.select_dtypes(include=[\"int64\",\"float64\"]).columns.values, bool_features)\n",
    "numerical_features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_data[['Average_Wait_Time', 'Estimated_Number_of_Houses_in_1_KM_Radius',\n",
    "    'No_of_Other_ATMs_in_1_KM_radius', 'Number_of_Shops_Around_ATM',\n",
    "    'rating', 'revenue']].corr(), annot=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3dT1QH96WL5g9c32fhxcREZGCoZIrfSaRTA8hKLCT8r5O2uLxsB56ryZgeXir7+D+e8BrC2bPbO6J9xMREZHCopIrvSKRTO8JzMi77QsM7cMIA4HJ4W1b2hPJ9NvAXODljV8XzJ65utfTiYiISK9SyZVuSyTT1WxeaA8DRkUaqnPKCMr3vsA5GwcTyfRigsI7l6D8vrxg9swFfR9PREREukolV3ZaIpmeAswEjgAOBxKRBup5u4S3j28cSCTTC4B0eHtMyxxEREQKm0qudEoimT4E+ER42zfiOFFIABeHt6ZEMv0oQeG9b8HsmbVRBhMREZGtqeRKhxLJdAnwIYJSezqwW6SBCstA4JTwdl0imX6VTbO8zy2YPTMbZTgRERFRyZU8iWS6EvgoQbE9FaiONlG/cUB4uwxYlUim7wNuBp5YMHumRxlMCpOZDQduAPYjOA3e54G3gTsIfmuwADjT3deYmQE/A04GmoDPuftL4evMAr4bvuzV7n5LOH4owZ/BAcD9wCXurj+LIlJUdDEIIZFMH55Ipm8GVgD3AReggttVo4BZwGPAO4lk+juJZHpCxJmk8PwMeNDd9wYOBN4EksAj7j4FeCR8DHASMCW8XQhcB2BmI4ErCNbFzwCuMLMR4T7XAV/I2+/EPvhMIiIFRTO5RSq86MLZwBeBgyOOE1d7AD8Arkwk0w8BvwLSC2bPzEUbS6JkZsOADwOfA3D3VqDVzE4Djgk3uwV4HPg2cBpwazgT+5yZDTez8eG2D7v76vB1HwZONLPHgaHu/lw4fivBkqMHev/TiYgUDpXcIpNIpvcAvkow29gjF16QHSol+FXzycD8RDL9P8CNC2bPXBNtLInIZILfmvzWzA4EXgQuAca6+9Jwm2XA2PD+RGBx3v614dj2xms7GBcpSGf/7qyoI/S52869PeoIRUHLFYpEIpk+MpFM/xn4J0HJVcGNxmTgv4C6RDJ9QyKZPiDqQNLnyoBDgOvc/WCCS1In8zcIZ217dQ2tmV1oZnPMbM6KFSt6861ERCKhmdwYSyTTpQQHkX2D4Jy2UjgGEKx9/nwimf5f4PIFs2e+FXEm6Ru1QK27/yN8/CeCkrvczMa7+9JwOUJ9+HwdwXmbN5oUjtWxaXnDxvHHw/FJHWy/GXe/HrgeYPr06TooTURiRzO5MZVIpj8O1AB3oYJbyAw4A3gtkUz/NpFM61RtMefuy4DFZjY1HDoOeAO4l2AZEeHXe8L79wLnWeAIIBMua3gIOMHMRoQHnJ0APBQ+12BmR4RnZjgv77VERIqGZnJjJpFMHwr8mM1neKTwlRIciHR2Ipn+NfCDBbNnLo82kvSirwB/MLMK4D3gfIJJhzvN7AJgIXBmuO39BOu55xGcQux8AHdfbWZXAS+E21258SA04EtsOoXYA+igMxEpQiq5MRHOAP6A4IwJFnEc6boKggL0+UQyfS3wnwtmz1wbbSTpae4+F5jewVPHdbCtE1xpr6PXuQm4qYPxOQTn4BURKVoquf1cIpkeBnyH4GCyqojjSM8ZRHBxiYsSyfR/AT9bMHtmU8SZRERE+g2tye2nEsl0eSKZ/irwLvAtVHDjagTwH8C7iWT681GHERER6S9UcvuhRDL9CYIDVX5GcIUtib9xwI2JZPqvOjhNRERkx7RcoR9JJNOjCK6adUbUWSQyxxOcieHbwHULZs/UqZ9EREQ6oJncfiKRTJ8MvIYKrsBg4JfAo+EV7ERERGQLmsktcIlkehDw38CFUWeRgnMM8Goimf534NoFs2fmIs4jIiJSMDSTW8ASyfSRwCuo4Mq2DQR+CjyZSKan7mhjERGRYqGZ3AKUSKYrgO8TnDVBP4hIZ3wQmJtIpq8AfrJg9sxs1IFERESipAJVYBLJ9P7A8wTXstd/H9kZVcCPgEcSyXR11GFERESipBJVQBLJ9DcILtF5YNRZpF87GpiTSKYPijqIiIhIVFRyC0Aima5IJNO3Aj8GKqPOI7GwK/B0Ipn+dNRBREREoqCSG7FEMj0aeAQ4N+osEjsDgdsTyfQPE8m0/l8XEZGion/4IpRIpvcGngOOijqLxFoS+L9EMj0s6iAiIiJ9RSU3Iolk+qPAs4BO5i994WTg+fAHKxERkdhTyY1AIpn+N+ABYHjEUaS47AX8I5FMz4w6iIiISG9Tye1DiWS6JJFM/zfwK3SOYonGUODeRDL97aiDiIiI9CaV3D6SSKYHA38BvhZxFJESYHYimf7PqIOIiIj0Fs0m9oHwxPwPAQdHnUUkz/9LJNMDga8smD3Tow4jIiLSkzST28vCgvsoKrhSmC4GbtQpxkREJG70D1svyiu4+0WdRWQ7zgduSyTT+s2OiIjEhkpuL1HBlX7m08AfEsl0adRBREREeoJKbi8Ir2Kmgiv9zZnALVq6ICIicaB/zHpYIpkeSnCQmQqu9EfnEKzRtaiDiIiIdIdKbg9KJNMDgPuAQ6LOItINnwN+raIrIiL9mUpuD0kk0+XA/wIfijqLSA/4AvAfUYcQERHpKpXcHhCuYfwdcFLUWUR6UDKRTH826hAiIiJdoZLbM35EcHS6SNzckEimD486hIiIyM5Sye2mRDL9aeCbUecQ6SWVwF8SyfSkqIOIiIjsDJXcbkgk0/sDN0adQ6SXjQPuDS8BLCIi0i/oCkddlEimhwN3A4MijtJv1F73eUoqBkBJCVZSyvhZ15DdsI6V9/yI9obllA0dy+jTk5RWDd5sv/ZMPSvu/gHuOchmGXLoKQw5+GQAlt2WJLt+DVZWAcDYM6+idNBwGl78PxrnPkDp0GrG/Mt3sdJymmtfp+ntZxh53Bf6/LPHwMEE59A9c8HsmR51GBERkR1Rye2C8ECzPwB7RJ2lvxn7mf+gdOCw9x83PHcXVYkDGXbEp8g8dxcNz93FiGPO32yf0sEjGPfZH2Nl5eRaN7DkxosZsOfhlA0ZBcDoU75J5fgpm+2z/vXHGf/5X5B59k42zH+JAXvMIPP07Yw+9Vu9/yHj6wwgBVwRcQ4REZEd0nKFrkkBJ0cdIg6a5v2DQfsdB8Cg/Y6j6Z3nttrGSsuxsnIAPNsG3pmJRIdsFm9rwUrKWP/6YwzYfTqlA4b0ZPxidHkimT4z6hAiIiI7opK7kxLJ9GnAd6PO0S+ZUX/n91h68yWsm/sgANn1aykbPBKA0kEjyK5f2+Gu7Q0rWHLTl6n7n/MZdsQn35/FBVh1/zUs+e1XWPv0H/GwAA855BSW/u4bZBtWUDlxHxpr/saQQ2b27ucrDgbcnEimD406iIiIyPZoucJOSCTTU4FbCf6hl5007pwfUTZkNNn1a1l+x3cpH7X5Aftmts1vbNnQaiZ8/he0r1vFiruvZuDUIykdNILRH/8mZUNGk2tpYsVffsj61x9l8H7HMXi/Yxm837EArH36jww99ONseO9F1r/2CKVDqxlx7AWY6We8LhpAcMaFAxfMnrk66jAiIiId0b/ynZRIpocQHGg2NOos/VXZkNEAlA4azsC9PkDLkn9SOmg47Y1BT2pvXE3JoOE7eI1RlI/ejebFr2/2miWVAxk07Whal/5zs+3b162idek/GbjXB2h44W5Gn/ZtSioH0bzglR7+dEVnEvDzqEOIiIhsi0pu590A7BN1iP4q19pMrqXp/fvN81+mono3Bu55OOtfewSA9a89wsA9t77uQHvDSnJtLQBkmxtpqX2D8lGT8FyWbFMGAM+2s2He85SP3m2zfdc++XuGHXVOsE17C5iBWXBfuuvsRDL9iahDiIiIdETLFTohkUx/EtDBNt2QbVrLij9fHTzI5Rg07WgG7H4oFeOnsPKe2TS++lfKho5h9GlJAFqWvkPj3AcYddJXaVu1mDWPbTod8dAZ/0JFdYJcazP1d34Pz2Uhl6MqcSCDD/zY+9u1Ln8XgMpxewIwaJ9jWHrjlykdOpphh5/RR5889n6VSKafXDB75sqog4iIiOQz79SR6sUrPB/umwQnxBeRrd21YPZM/RDYj02fPt3nzJkTdYzYOPHyO6KO0OcevKrrV7Y/+3dn9WCS/uG2c2+POkKsmNmL7j59y3EtV9ixH6OCK7I9nwovby0iIlIwVHK3I5FMHwtcEHUOkX7gl4lkemzUIURERDZSyd2GRDI9ALg+6hwi/cQo4NdRhxAREdlIJXfbvo8u2yuyM05LJNPnRh1CREQEVHI7lEimDwG+HnUOkX7o2kQyPSHqECIiIiq5W0gk02XAjUBp1FlE+qHhwC+iDiEiIqKSu7VvAgdFHUKkH/tEIpn+YNQhRESkuKnk5kkk07sDV0SdQyQGfhR1ABERKW4quZv7PlAVdQiRGDgqkUyfGnWIQmVmC8ysxszmmtmccGykmT1sZu+EX0eE42Zm15rZPDN71cwOyXudWeH275jZrLzxQ8PXnxfua33/KUVEoqWSG0ok0/sAZ0edQyRG/iORTGtt+7Z9xN0PyrtKTxJ4xN2nAI+EjwFOAqaEtwuB6yAoxQS/eTocmAFcsbEYh9t8IW+/E3v/44iIFBaV3E1S6Psh0pP2BWbtcCvZ6DTglvD+LcDpeeO3euA5YLiZjQc+Bjzs7qvdfQ3wMHBi+NxQd3/Og+u235r3WiIiRUOlDkgk0wcAn4o6h0gMfT+RTGsJ0NYc+KuZvWhmF4ZjY919aXh/GbDxCnITgcV5+9aGY9sbr+1gXESkqKjkBr4PaM2aSM+bBHw16hAF6Ch3P4RgKcLFZvbh/CfDGVjvzQBmdqGZzTGzOStWrOjNtxIRiUTRl9zwwg+nR51DJMaSiWR6xI43Kx7uXhd+rQfuJlhTuzxcakD4tT7cvA7YJW/3SeHY9sYndTC+ZYbr3X26u0+vrq7uiY8lIlJQir7kAldGHUAk5kYAl0UdolCY2SAzG7LxPnAC8BpwL5vWMM8C7gnv3wucF55l4QggEy5reAg4wcxGhAecnQA8FD7XYGZHhGdVOC/vtUREikZRl9xEMn0EMDPqHCJF4CuJZHrSjjcrCmOBp8zsFeB5IO3uDwKzgePN7B3go+FjgPuB94B5wG+ALwG4+2rgKuCF8HZlOEa4zQ3hPu8CD/TB5xIRKShlUQeImGZxRfpGFfAN4GtRB4mau78HHNjB+CrguA7GHbh4G691E3BTB+NzgP26HVZEpB8r2pncRDL9IeD4qHOIFJHPJ5LpIVGHEBGR4lC0JRe4POoAIkVmKPD5qEOIiEhxKMqSm0im9yJY8yYifesriWS6KP/eERGRvlWs/9h8EZ0XVyQKewAfjzqEiIjEX9GV3EQyPQBdalQkSpdEHUBEROKv6EoucBbBeTtFJBofSSTTU6IOISIi8VaMJfeLUQcQES6MOoCIiMRbUZXcRDJ9AHBY1DlEhFmJZLoi6hAiIhJfRVVygc9FHUBEAKgGTo86hIiIxFfRlNxEMl0GnBN1DhF5n5YsiIhIrymakgucBIyJOoSIvO/YRDI9PuoQIiIST8VUcnXaMJHCYuicuSIi0kuKouQmkunh6B9TkUJ0atQBREQknoqi5AInADqSW6TwHJdIpgdFHUJEROKnWEruiVEHEJEOVRH8ECoiItKjiqXkfizqACKyTVqyICIiPS72JTe8AMSEqHOIyDbNTCTTsf+7SERE+lYx/MOipQoiha0a+GDUIUREJF5UckWkEGjJgoiI9KhYl9xEMj0YODLqHCKyQyq5IiLSo2JdcoHj0KnDRPqDqYlkeq+oQ4iISHzEveRqqYJI/3FK1AFERCQ+4l5ydeowkf7jiKgDiIhIfMS25CaS6anA5KhziEinHRp1ABERiY/YllzgQ1EHEJGdsnsimR4RdQgREYmHOJfcA6MOICI77ZCoA4iISDzEueQeEHUAEdlp06MOICIi8RDnkrt/1AFEZKdpXa6IiPSIWJbcRDI9CdDaPpH+RyVXRER6RCxLLprFFemvdPCZiIj0iLiWXK3HFem/NJsrIiLdFteSq5lckf5LJVdERLotriVXM7ki/ZdKroiIdFvsSm4imS4H9o46h4h0mX5IFRGRbotdySUouOVRhxCRLpsYdQAREen/4lhytR5XpH8bnEimh0QdQkRE+rc4ltzJUQcQkW4bH3UAERHp3+JYckdHHUBEuk0lV0REukUlV0QK0YSoA4iISP8Wx5I7KuoAItJtmskVEZFuKevMRmZWA/gWwxlgDnC1u6/q6WDdoJlckf6vX8zkmtkj7n7cjsZERKTvdarkAg8AWeC28PFZwEBgGXAz8PEeT9Z1Krki/V9Bz+SaWRXB34GjzWwEYOFTQ9Ep0ERECkJnS+5H3f2QvMc1ZvaSux9iZp/tjWDdoJIr0v8VdMkF/g24lGDG+UU2ldwG4BcRZRIRkTydLbmlZjbD3Z8HMLPDgNLwufZeSdYFiWS6AtD5NUX6v4JeruDuPwN+ZmZfcfefR51HRES21tmS+6/ATWY2mGDGogH4VzMbBPywt8J1gWZxReKh0GdyAXD3n5vZB4EEeX+fuvutkYUSERGgkyXX3V8A9jezYeHjTN7Td/ZGsC7SmRVE4mFoIpmuWDB7ZmvUQbbHzH4H7AHMJThuAYKDdFVyRUQi1tmzK1QCnyScrTALlp+5+5W9lqxrNJMrEh/lQEGXXGA6MM3dtzz7zA6ZWSnBGWrq3P0UM5sM3E7ww/qLwLnu3hr+/XsrcCiwCvi0uy8IX+My4AKCgv1Vd38oHD8R+BnBsrIb3H129z6miEj/09nz5N4DnEaw/nZ93q3QaCZXJD7Kow7QCa8B47q47yXAm3mPfwT81N33BNYQlFfCr2vC8Z+G22Fm0wjOdLMvcCLwP2ZWGpbnXwInAdOAz4TbiogUlc6uyZ3k7if2apKeMSDqACLSYzr791OURgNvmNnzQMvGQXc/dXs7mdkkYCbwA+DrFvx67Fjg7HCTW4AUcB3BBEMqHP8T8Itw+9OA2929BZhvZvOAGeF289z9vfC9bg+3faNbn1REpJ/p7D8iz5jZ/u5e06tpum+nf2UoIgWrP5TcVBf3uwb4FpvOBjMKWOvuG89WU8um8+1OBBYDuHu7mWXC7ScCz+W9Zv4+i7cYP7yLOUVE+q3O/iNyFPA5M5tPMFthgLv7Ab2WrGtUckXio+CXK7j7Ezu7j5mdAtS7+4tmdkyPh+p8jguBCwF23XXXqGKIiPSazpbck3o1Rc/JRR2g2JSSzZbT3lpOe1vFxpu1ZYP7bdkK2tsrrTVbQXt7FW25SlqzldaWraTNK2nLVtLqldaWq6SNyuB5Kq3dK2izSlqpoN0rabNya7cK2q2cdpvnA9bf0Tyqbc/aEqYsa60c1dg20DadjF9iYuGQcRb8Rr9wmdk6Nv1wXUFQzNe7+9Dt7HYkcKqZnQxUEVwl7WfAcDMrC2dzJwF14fZ1wC5ArZmVAcMIDkDbOL5R/j7bGn+fu18PXA8wffp0TRCISOxst+Sa2VB3bwDW9VGe7uq3f1GXkm2voL2tnLa2crKtlbS1l1t7eyVt7eW0t1fSlq2wtvagCAZfq6w1FxTFNq+kLVdprR4+TyVtXmltVNJGBW1eQZtV0E6FBUWxnPaSctopJ1taZtmSMrIlZWRLS8mVlJItLcVLS8iVlZArLcHLDC+3979SRvgPuhmlBGuh+2w99DRg6vDy92ZNG7vhzyUl+w5s9sweS33Rvot87dRachNX+pChG5hU4ozpq0zS83Zbt7zgf2h19/cvPpO3TvaIHexzGXBZuM8xwDfd/Rwzuws4g+AMC7MIDvgFuDd8/Gz4/KPu7mZ2L3Cbmf03wcUzpgDPE/zANyU8W0MdwcFpG9f6iogUjR3N5N4GnEJwOhtn89kyB3bvpVxdlSujvb2cbGs5be0VtLd2PLPYlg3L4fszi1W05Spoy1XR6hXWlqsKymGukjaqrJUK2rySdgsKY3tJubVTQZuVk91YGEs2lsWwKJYFX3NlJe8XRi8zvKwELwcvs2DWp5ygLJYR/PfQwXOdMLWtbfenF9Zm/7161OPpQQOPrJlcsn/N5M23Gdzka/Za4ounLfK1e9U5E1YxbPAGJpX0k7NwLG1r47KlS1mZbceAM4cP59wRI7fa7vmm9fywvp52d0aUlnLrrrsxv7WFry9Z8v42tW1tfGXUaM4bOZKfrKjnycb17F1VyezxwYXF7s1kWJvNct7IrV8/QgVzNcXOCE8j9hczuwJIduElvg3cbmZXAy8DN4bjNwK/Cw8sW01QWnH3183sToIDytqBi909C2BmXwYeIjiF2E3u/nrXP5mISP+03ZLr7qeEXydvb7tCsaDq7HZ4vyxKzJVC6ewVq475TMO6ty8YN6akpaRkSv7zjQNtxEt72oiX9tx8v2GNvmJqnddNW+SZKXVeMn41wwa1sJsFvwYuGGVmfGvMGKZVVbE+l+WMBQv4wMBB7FlZ+f42DdksVy5fzvWTdmFCeTmr2oNeOLmikrsTwf+2WXeOeXcexw0Zwrpsljeam/nL5Mlcvmwp/2xpZtfyCu5uyHD9pF06zBGh7I43iZaZ/UvewxKC8+Y2d3Z/d38ceDy8/x6bzo6Qv00z8Klt7P8DgjM0bDl+P3B/Z3OIiMRRZy8G8Yi7H7ejsQKwIeoA0vcObGmd+szC2tavja1+4u8Dqo4iOE/oNmUGW/XzU636+ambj4/K+LKpdV63zyJvnLLES8etYcSAVna1TUfA96nqsjKqy4L/RQeVlLJ7ZSX17e2bldx0QwPHDx7ChPLgGK1RZVv/L/1cUxO7llcwsbyc9bks7e64O805pwzjt6tXc87wEZRbwS1r7g8zuR/Pu98OLCBYsiAiIhHb0ZrcKmAgMNrMRrBpucJQNp2qppCo5BapCqj45fIVRz9TVVXz5XHVQ9rMEjv7GquG2bhnhtm4Z/JPm+/uY9dSO7XWl05b5E17LPXSMWsZXdXGbtaHS0vq2lp5s7mZA6qqNhtf0NZKuzuzFi1kfS7HuSNGctqwzSek729o4OShwXFQg0pK+fCgwfzLwgUcMXAgQ0pLebV5A18cXZAXCyz0q53h7udHnUFERDq2o5ncfwMuJTio4UU2ldwG4Be9F6vLVHKL3Aebm/d/emFt0xfHVj/xYlXlh7FuTk+a2fIRTFo+wib9ff+8Yffc+NUs3HuxL5+2yJt2X+bl1RmqK9rZzaBy2y+489bnclxSV8dlY8YyuHTzSeqsO683N3PTLrvSksvxmUULOXDAABIVFQC0uvPY+ka+Vl39/j4XjBrFBaOCZcmXL1vKV0ZX86e1a3m6aT1TKyu5aFRBFN62fd56sxCvqriZ8KIOPyc4YwLAk8Al7l4bXSoREYEdr8n9GfAzM/uKu/+8jzJ1R1PUASR6A9wH3rys/ui/DRzw8jfHjB6TNevx3zq4WcmSUey2ZJTt9uhBm8ZLcp6dtJL5ey/25fss9ubdl3nlqAbGlGfZzbqwVrzNnUvr6jhl6DCOH7L1qomxZeUMG1TKwJISBpaUMH3gQN5qaX6/5D7Z2Mi0ykpGd7CM4Y3mZhxIVFTw0xUr+M0uu/CdpUtZ0Nr6/v4RWtOVncxsF+BWYCzBwbHXh3+P9ZbfEhygu3HN7GfDseN78T1FRKQTOvWPrrv/3Mz2Izh7U1Xe+K29FayLVkUdQArHR5s2HPzkwtqGz48f+9RblRVH9cV75kqsdNEYJi8aY5P/euim8dKst+1Wz7y9F3v9Pou9NbHcB4xsZExZll0tOAJ+K+7O5cuWsntlBZ/bxlkPjh08mKvrl9PuTps7r27YwKy8MzDcv27TUoUt/XzlCr4/bhzt7mTDs++VGDTnCuLMXau7uF878A13f8nMhgAvmtnD7t5bl7Stdvff5j2+2cwu7aX3EhGRndDZA8+uAI4hKLn3E1wc4imCGZNCspzgiOztHngkxWOI+9C7liw76u7Bg55PjR6ZyJlFcu7cbKmVvzeePd8bb3ven3f8fHm7NyeWM2+fxb5yn8Xevlu9DxjeyPjSHJNe2rDB7m1oYK+KSj6xYD4Al46uZml7GwBnDR/BHpWVHDVoEKcvmE8JcMaw4UwJD0xryuV4Zv16UmPHbZXnb+vWsW9VFWPKggPW9q6s4rT589mrspK9t1j3G5EulVx3XwosDe+vM7M3CY4f6K2Su8rMPgv8MXz8GfTDtohIQejsr0/PAA4EXnb3881sLPD73ovVRalMltSwemB81FGksHyicf2MY5o2rD53wthnF5aXfyDqPBu1lVnVOxOZ+s5Em3pv3iUEKlt9/e7Lhiy8YtG0NXvXevsuK3zQsPVMKHUmbPkaF4wcxQUjtz7178CSEp6dsleH7/vRIUP4aN7yh2+NKbjrZnR1Jvd9Fhx8eDDwj26n2bbPE6zJ/SnB8ohngM/14vuJiEgndbbkNrt7zszazWwoUM/ml40sJEtQyZUOjMjlRt5Xu/QDtw4d8uyPRw7f24MzhhSklgob9OauTHtz182PmxvQ7A17LvVF0xb5mql15Cat8CFDNzCxxBkbUdTesqw7O5vZYOB/gUvDqzb2liuBWe6+JnzfkcCPCcqviIhEaIclN7xU5atmNhz4DcFZFhoJLjFZiJYAh+5wKyla5zWs+8DH1jctP3vC2Bfqy8oOizrPzthQZUNrJtt+W13dbYOvnVLni6Yt9rV71ToTVzE0vLpbQZwqoQuW7HiTjplZOUHB/YO7/7nnInXogI0FF8DdV5vZwb38niIi0gk7LLnhNdJnuPta4Fdm9iAw1N1f7fV0XdPlfxyleIzNZsc+snjJ2F8MH/bkr4cPPYjgIKV+q3GADX95Txv+8tZXd1u5V50vnrbI1+21xBm/muGDmtnVYHgkQTtvaVd2Cn8ovxF4093/u2cjdajEzEZsMZOrKy6KiBSAzv5l/JKZHebuL7j7gt4M1ANUcqXTvrw286HTGtfXnjNh7Lw1paWxm4HLDLbRL0y10S9seXW3Bl+2V3Bp48YpS7xk3GpGRnl1tw50qeQSnK/2XKDGzOaGY98JL3PbG34CPGtmd4WPP0UHl9kVEZG+19mSezhwjpktBNYTXBTC3f2AXkvWdSq5slN2aW+f9MSiuomzR4544rahg2dg1mdXMovKqqE27tmhNu7ZfTYfH7PW66Yu9qXTFnvjnku9bMwaRoVXdxvYxxG79P+xuz/FpovW9Dp3v9XM5gDHhkP/0ounKxMRkZ3Q2ZL7sV5N0bNUcmWnGdhlq9cc/cl1jfPPmzC2aX1Jyb5RZ4pC/XCbWD/cJj659dXdFk2t9WXhpY0rqjOMqmgn0dNXd8szr5det8eFpVbFVkSkwHT2YhALeztID1oUdQDpv/Zqa5v89MLa7HerRz1+36CBH8Qs8kt/RS28utuuS0bZro8duGm8JOfZiSuZv3etL5+2yFsmL/OK0euoLg8ubVzejbes3+etN7t0xTMREZGN4niAxNtAK1D05US6phRKf7hi1TGfaVj39gXjxlhzSUnHJ5stcrkSK108hsmLx9jkhw/ZNF6a9bZdVzBvn0W+Yp/F3pKo96qR6xi7vau7beGtXgstIiJFI34lN5VpIzXsLaAQ1wtLP3JAS+vUpxfWtn5jzOjHHx844EOY6Up6nZAttfL549hz/rjNr+5W1u4tieXMm7bYV+292Ft3q/eBIxoZV5pjF9t8He3bfR5aRERiJ34lN/AKKrnSAyqg4uf1K4/5R1Xl618cN2Zgm9nkHe8lHWkvs8p5E5k6b6KRf3W3ijZv2mMpC/cJym/7wBafs8+2X0ZERKRT4lpyC/UcvtJPHd7csu8zC2s3fHFs9d/nVFV+iOB8rNIDWstt4Ju7sk/e1d1+cFqUgUREJBZKog7QS16JOoDET5X7gN8uq//wNfUr55a610adJ8bmRh1ARET6P83kiuyk45o2HPzkwtqGC8aPferNyoqjeup1s+uz1P22jubaZsyMiRdMZOCem05Pm23KUvvrWtpWt+FZZ/RJoxnxoREAvHb+a1RNqgKgfFQ5u126GwCLf7WY5tpmhhw0hHFnjAOg/t56qiZWMfTQoT0VvSfV1cyqWRl1CBER6f/iWXJTmeWkhi0HxkYdReJpiPvQO5csO+qewYNe+N7okbvmzLr9Z23pbUsZvP9gdv3yruTac3iLb/b8qkdWUTmxkt2+thvtDe28c9k7DPvAMErKSiipKGHPqza/pm/z4mZKKkqYcvUU5v/XfLJNWXKtOTa8u4Exp47pbtzeMjfqACIiEg9xXa4Ams2VPnBa4/rDnlhUV7FbW9sz3XmdbFOW9W+vZ8SHg5nZkrISSgdtfjIHMyPXnMPdybXkKB1UipVsZ2lwKeRac3jO8XaHEqj/cz1jPlGwBRfg5agDiIhIPMS55GpdrvSJ4bnciPtql37wW6vWPGvuq7vyGq0rWikbUkbdDXXM+9486m6qI9eS22ybkceNpGVJC29f+jbzvjuP8WePf7/k5tpyzEvN490r36XhxQYAqiZUUTakjHeveJehBw2ldXkr7s6AREFftXhu1AFERCQe4rlcIfAU8M2oQ0jxOLdh3Qc+tr6p/uwJY59fXlY2Y8d75MnBhoUbGP/Z8QzcYyBL/7CUFfetYOwnN62CaHytkapdq0h8O0FrfSsL/msBe07dk9IBpUz9yVTKR5TTWt/K/B/Np3KXSirHVDL+nPHv77/wpwuZ8LkJ1N9bT/PiZgbvO5iRx4zssc/fQ56LOoCIiMRDnGdyHwOyUYeQ4jImmx3zt8VLZnxpzdqncG/o7H5lI8ooH1HOwD2CA82GTh/KhoUbNttmzZNrGHroUMyMyrGVVFRX0LK0BYDyEcFVdCvGVDBo70E0L2zebN+GlxqoSlSRa8nRuqKVXS/elYY5DVvNFkespmZWTV3UIUREJB7iW3JTmQbghahjSHH64tqGo+6vXdowIpvt1BrT8uHllI8qf7+0Nr7RSNWEqs22qRhVQeMbjQC0Z9ppWdpCRXUF2fVZcm1BWW1f107TvCYqJ1S+v5+3O6v+uorqk6vJtW4qte+v1S0cD0YdQERE4iPOyxUAHgGO2OFWIr1gl/b2SU8sqpv4o5HDn/jD0CEzMNvuYtjx54xn8a8X4+1ORXUFk/51EqsfDZb4jjx2JNWnVlN7Qy3vfPcdcBh35jjKhpTR9E4TdbfUYWa4O6NPHk3VxE0FedUjqxh+5HBKKkuo2qUKb3Xe+e47DDlgyFYHt0XsgagDiIhIfMS95P4N+PeoQ0jxMrDk6rVHn7Fu/fxzJ4xd31hSst+2th2w2wD2TG1+GrCRx25aM1s+opzJ/2/rqwoPnDKQKVdP2WaG0R8bvSmPGbt8cZed+gx9pJFgHb2IiEiPiO9yhcAzQFPUIUT2bGub/NTC2n1OXdf4BO6tUecpQI/UzKppizqEiIjER7xLbirTimaHpECUQukPVq4++o9Lli+syuXejjpPgdF6XBER6VHxLrmBv0UdQCTffq2tU55ZWLv7R9Y3PYG7zgASUMkVEZEepZIrEoFyKL+2fuXRNy6rf6vcfX7UeSL2Vs2smgVRhxARkXgphpI7F1gcdQiRjsxobtn3mYW14w7b0Px33AvqfF59SLO4IiLS4+JfclMZB26POobItlS5D7hpWf2Hf1a/8pVS99qo80RAJVdERHpc/Etu4I9RBxDZkWObNhz01MLaYdNaWp6MOksf2gA8EXUIERGJn+IouanMy8CbUccQ2ZHB7kPuWLL8Qz9YsfKFEvflUefpA4/VzKpp3vFmIiIiO6c4Sm5As7nSb5za2HTYE4vqKia3tj0TdZZe9oeoA4iISDyp5IoUqOG53Ih765Z+MLlq9bPmvirqPL1gLfDnqEOIiEg8FU/JTWXmAS9EHUNkZ53T0PiBRxbX5ca1tz8fdZYedruWKoiISG8pnpIbuC3qACJdUZ3NVT+8eMmMi9esfQr3hqjz9JCbog4gIiLxVWwl9w4gF3UIka66aG3DUQ/ULlk3Mpt9Keos3VRTM6tGv1kREZFeU1wlN5VZCtwfdQyR7pjUnp34+KK6g8/NNPwd96ao83TRb6MOICIi8VZcJTfw06gDiHSXgX1r9doP/6Vuaf3gbK4m6jw7aQNwy87uZGYLzKzGzOaa2ZxeyCUiIjFSfCU3lXkUeDXqGCI9YY+29sRTi2r3PX1d4xO4t0Sdp5P+UDOrZnUX9/2Iux/k7tN7NJGIiMRO8ZXcwDVRBxDpKaVQctXK1UffvmT5oqpc7u2o83TCL6IOICIi8VesJfc2oBiuJiVFZN/W1inPLKzd/bj1TU/g3h51nm14smZWzStd3NeBv5rZi2Z2YU+GEhGR+CnOkpvKtADXRR1DpKeVQ/k19SuPvmlZ/T8r3N+LOk8HujOLe5S7HwKcBFxsZh/uoUx9ysyqzOx5M3vFzF43s++H45PN7B9mNs/M7jCzinC8Mnw8L3w+kfdal4Xjb5vZx/LGTwzH5plZss8/pIhIASjOkhu4DugvaxhFdsphzS3Tnlm4eMLhG5qfwN2jzhOqoxtXOHP3uvBrPXA3MKOHcvW1FuBYdz8QOAg40cyOAH4E/NTd9wTWABeE218ArAnHfxpuh5lNA84C9gVOBP7HzErNrBT4JcEPA9OAz4TbiogUleItualMPfCHqGOI9JZKp+qGZfVH/7x+5Stl7oujzgP8sGZWTZeWUZjZIDMbsvE+cALwWk+G6yseaAwfloc3B44F/hSO3wKcHt4/jU1no/gTcJyZWTh+u7u3uPt8YB5B8Z8BzHP399y9Fbg93FZEpKgUb8kNXBN1AJHedkzThoOeWlg7Yr+WlicjjPEecH039h8LPGVmrwDPA2l3f7BHkkUgnHGdC9QDDwPvAmt901rqWmBieH8isBggfD4DjMof32KfbY1vmeFCM5tjZnNWrFjRQ59MRKRwFHfJTWVq2DRzIhJbg9wH/3HJ8g/9sH7lnBL3ZRFEuLxmVk1bV3cOZyUPDG/7uvsPejJcX3P3rLsfBEwimHndO4IM17v7dHefXl1d3ddvLyLS64q75Ab+HSjUI9FFetQp65um/31RXdXurW1P9+HbzgX+2Ifv12+4+1rgMeADwHAzKwufmkSwhpnw6y4A4fPDgFX541vss61xEZGiopKbyvwTuDHqGCJ9ZVguN/yeuqVHXrZy9bPmvqoP3vI7NbNqCuXgt8iZWbWZDQ/vDwCOB94kKLtnhJvNAu4J798bPiZ8/lEPDia8FzgrPPvCZGAKwVKOF4Ap4dkaKggOTru31z+YiEiBUckNpID1UYcQ6Utnr2v8wKOL63Lj29r/0Ytv80TNrJoHevH1+6PxwGNm9ipBIX3Y3e8Dvg183czmEay53fjD943AqHD860ASwN1fB+4E3gAeBC4Ol0G0A18GHiIoz3eG24qIFJWyHW9SBFKZZaSGXUOwdEGkaIzO5qr/Wruk+tfDhj79ixHD9sNsWA+/xbd7+PX6PXd/FTi4g/H36OC0aO7eDHxqG6/1A2Cr9cnufj9wf7fDioj0Y5rJ3eQ/gZVRhxCJwr9lGo58sHbJ+pHZ7Es9+LJ318yq6c1ZYhERkW1Syd0olWmggxkRkWIxsT074fFFdQfPyjT8HffuLt/Jot+MiIhIhFRyN/c/wIKoQ4hExcC+uXrth++pW7pySDb3ajde6paaWTVv9lgwERGRnaSSmy+VaQW+FXUMkajt3ta+21OLavf7xLrGJ3Df2ctfNwNX9EYuERGRzlLJ3VIqcxc63Y4IJVBy5crVR9++ZPmiAbncWzux6zU1s2pqey2YiIhIJ6jkduxLBJfOFCl6+7a2Tnl6Ye0eH13f9DibLju7Lf8Evt8XuURERLZHJbcjqUwdWrYg8r5yKP9p/cpjfrus/p8VOX93G5s5cEHNrJrmvswmIiLSEZXcbfsN8ETUIaKWzTkH/7qRU25rAmD+mhyH39DInteu49N/aqI1u/WFrFY15fjILesZ/B8NfPn+DZs9d+Lv13PgrxrZ938auei+DWRzwf7ffriZA65r5Ly7N23/+1dbuea5nV0OKr1penPLtGcWLZ74gQ0bnsA9t8XTv6yZVfNUJMFERES2oJK7LamMA18gOIimaP3sH63sM3rTH5Nv/62Zrx1RybyvDmFElXHjS21b7VNVZlz1kUp+fELVVs/d+amBvHLRYF774iBWNDl3vdFOptl5aVmWV784mIpSqFmeZUOb89u5bVx8WEWvfj7ZeZVO1fXLVhz9i+UrasrcF4XDCwivxCUiIlIIVHK3J5V5h+CSv0WptiFH+p12/vWQoGi6O4/Oz3LGtOBCebMOLOcvb29dcgdVGEftWkZVB9fTG1ppALTnoDULBpQYtGWD129qc8pL4cfPtPKVGRWUl1qvfT7pnqM3NB/41MLakfs3t/wd+NeaWTW6NLaIiBQMldwd+wnwctQhonDpg83850erKAl75qoNzvAqKAsHJg0toa5h6+UKO/Kx369nzI/XMaQCzphWxpBK4+QpZRz86/WMH1zCsErjH3VZTt+7vCc/jvSCQe6Db1u6vKZmVs0jUWcRERHJp5K7I6lMO3A+UFSLQ+/7ZxtjBhmHTijt8dd+6LODWPqNIbRk4dH5WQC+dWQlcy8azE8+VsXlj7Vw5UcqueGlVs68q4mr/15U3/r+5i3g/0UdQkREZEsquZ2RyrwCfDPqGH3p6UVZ7n27ncQ16zjrTxt4dH47lzzYzNpmaA8PFqttyDFxaNeWE1SVGadNLeOeLZY7vLw0iztMHVXCXW+0ceenBvLumhzvrMp2+zNJj2sDziGV2bDDLUVERPqYSm5npTK/AP4cdYy+8sOPVlH79SEsuHQIt58xgGMnl/GHfxnIRyaX8qc3glOl3vJKG6dN7fySgsZWZ+m64ID89pyTfqedvUdvPlN8+WMtXHVsJW05yIbH7pcATVsv/ZXopUhlXoo6hIiISEc6ODRItuMC4BAgEXGOyPzoo1Wc9acmvvtoMwePL+WCg4OSe+/bbcxZkuXKjwRnVEhcs46GFqc1C395q52/njuQUQOMU29voqUdcg4fSZRy0fRNJfkvb7UxfUIJE4YEP3sdNK6U/a9r5ICxJRw4rueXTUi33AfMjjqEiIjItpj7zh84VNRSww4DngQqo44iEpG3gcNJZXRVwJiYPn26z5kzJ+oYsXHi5XdEHaHPPXjVp7u879m/O6sHk/QPt517e9QRYsXMXnT36VuOa7nCzkplXgC+EnUMkYg0AKer4IqISKFTye2KVOY3wI1RxxDpYw58llTmraiDiIiI7IhKbtddDOj3e1JMUqQy/xd1CBERkc5Qye2qVKYFOB1YHHESkb5wN3BV1CFEREQ6SyW3O1KZOuBjwOqoo4j0ojeAWaQyOkpVRET6DZXc7kpl3gROBZqjjiLSC+qB00hl1kUdREREZGeo5PaEVOZp4CxAl+WSOFkLnEAqMy/qICIiIjtLJbenpDL3EByMJhIHjcBJ4SWtRURE+h2V3J6UyvwauDLqGCLd1EKwROG5qIOIiIh0lUpuT0tlrgBuiDqGSBe1A2eSyjwadRAREZHuUMntHRcBt0UdQmQn5QjOonBv1EFERES6SyW3N6QyWeBc4DdRRxHZCV8kldEPZyIiEgsqub0llcmRylwIXBN1FJEdcOCrpDLXRx1ERESkp6jk9rZU5mvoSlFSuFqBc0hlfh51EBERkZ6kktsXUpnvAd+KOobIFhqBU0hl/hh1EBERkZ6mkttXUpn/Ar5E8KthkaitAD5CKvNw1EFERER6g0puX0plrgM+R3CaJpGozAeOJJWZE3UQERGR3qKS29dSmVuBE4BVUUeRovQK8EFSmXeiDiIiItKbVHKjkMo8BhwGvBp1FCkqjwEfJpVZFnUQERGR3qaSG5VUZj7wQeBPUUeRovAT4ARSmYaog4iIiPQFldwopTLrgTOB76ED0qR3NACfJJX5JqmM1oKLiEjRUMmNWirjpDJXAacD6yJOI/FSA0wnlflz1EFERET6mkpuoUhl7gWOAHRAkPSE3wFH6AAzEREpViq5hSSVeQM4BPhN1FGk32oBLiKVOY9UpinqMCIiIlFRyS00qUwjqcyFwExAR8HLzngXOIpU5tdRBxEREYmaSm6hSmXuB/ZDZ1+QHcsRnD1hf13gQUREJFAWdQDZjlRmFfApUsM+C/wcGB5tIClArwOfJ5V5PuogIiIihUQzuf1BKvN7YH/gkaijSMFoA64CDlHBFRER2ZpKbn+RytQCxwMXAisjTiPRegk4jFTme6QyrVGHERERKUQquf1JcE7d3wBTgGsIZvOkeDQDlwGHk8q8EnUYERGRQqaS2x+lMmtJZb4GHAA8FHUc6XU54GZgL1KZ2bpyWf9mZruY2WNm9oaZvW5ml4TjI83sYTN7J/w6Ihw3M7vWzOaZ2atmdkjea80Kt3/HzGbljR9qZjXhPteamfX9JxURiZZKbn+WyrxFKnMi8HF0EYm4ehA4iFTmfFKZxVGHkR7RDnzD3acRXADmYjObBiSBR9x9CsH6+2S4/UkEv72ZQrBc6ToISjFwBXA4MAO4YmMxDrf5Qt5+J/bB5xIRKSgquXGQytxHcLqx/wc0RJxGesZLwEdJZU4ilamJOoz0HHdf6u4vhffXAW8CE4HTgFvCzW4huNQ34fitHngOGG5m44GPAQ+7+2p3XwM8DJwYPjfU3Z9zdwduzXstEZGioZIbF6lMK6nMj4E9gB8AmYgTSdcsBD4LTCeV0dk0Ys7MEsDBwD+Ase6+NHxqGTA2vD8RyJ/Frw3Htjde28H4lu99oZnNMbM5K1as6P6HEREpMCq5cZPKrCSV+S6wK/AdoD7iRNI5i4BLgamkMn8glfGI80gvM7PBwP8Cl7r7Zr+BCWdge/XPgLtf7+7T3X16dXV1b76ViEgkVHLjKpVpIJX5IZAALmHzGR8pHC8AZwF7kMr8jFSmJepA0vvMrJyg4P7B3f8cDi8PlxoQft34A2odsEve7pPCse2NT+pgXESkqKjkxl0qs4FU5lqCZQz/ig5QKwQ54B7gw6QyM0hl7tAZE4pHeKaDG4E33f2/8566F9h4hoRZBH9GNo6fF55l4QggEy5reAg4wcxGhAecnQA8FD7XYGZHhO91Xt5riYgUDV3Wt1ikMm3AjaSG3QycAlwAnAyURhmryDQRnArsGlIZ/bBRvI4EzgVqzGxuOPYdYDZwp5ldQLA2+8zwufsJ/l+dR/Bn6HwAd19tZlcR/DYA4Ep3Xx3e/xLBn7UBwAPhTUSkqKjkFptUJkswq3MPqWHjCWaMzgf2ijRXvL1OcLT8jaQyq3e0scSbuz8FbOu8tcd1sL0DF2/jtW4CbupgfA7BGVdERIqWSm4xS2WWEswezSY17EMEs7tnAIMizRUPq4A/AjeTyrwYdRgREZFio5IrgVTmSeBJUsO+QnAg1GcJfq2q5Qydt45glvwO4KFwiYiIiIhEQCVXNpfKrAN+A/yG1LDRBOt3TyM4qGVglNEK1AqCk/D/CXiAVKY54jwiIiKCSq5sTyqzkuDglZtJDRsAHENwedCNlxktRhuAJwmK7cPAqzqnrYiISOFRyZXOSWU2sOko7UtIDdsdOB44ApgB7E08T0nnwMtsKrVPa7ZWRESk8KnkStekMu8Bvw5vkBo2FJhOUHgPD79OiCpeF7UDbwNzw9srwEukMqsizCQiIiJdoJIrPSOVaQAeDW/h2LCJBGV3H2AysHv4dRei/bPnBFeT+iebyuxc4DVdcUxERCQeVHKl96QydcDd4S1vfFgZQdHdsvgOA4Z0cBvMts8rCtAKtOTdmoClbLrE6cZbbfh1qc58ICIiEm8qudL3gkvYzg9vj+5ga0gNM4KiO4Sg7G4qtKlMa+8FFRERkf5KJVcKX3D2gnXhTURERGSH4ng0vHSRmZWa2ctmdl/UWURERES6QyVX8l0CvBl1CBEREZHuUskVAMxsEjATuCHqLCIiIiLdpZIrG10DfAvIRZxDREREpNtUcgUzOwWod/cXo84iIiIi0hNUcgXgSOBUM1sA3A4ca2a/jzaSiIiISNep5Arufpm7T3L3BHAW8Ki7fzbiWCIiIiJdppIrIiIiIrGji0HIZtz9ceDxiGOIiIiIdItmckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFREREJHZUckVEREQkdlRyRURERCR2VHJFRPqQmd1kZvVm9lre2Egze9jM3gm/jgjHzcyuNbN5ZvaqmR2St8+scPt3zGxW3vihZlYT7nOtmVnffkIRkcKgkisi0rduBk7cYiwJPOLuU4BHwscAJwFTwtuFwHUQlGLgCuBwYAZwxcZiHG7zhbz9tnwvEZGiUBZ1ABGRYuLufzezxBbDpwHHhPdvAR4Hvh2O3+ruDjxnZsPNbHy47cPuvhrAzB4GTjSzx4Gh7v5cOH4rcDrwQO99IhHpSz87+9dRR+hzl9z2b13aTzO5IiLRG+vuS8P7y4Cx4f2JwOK87WrDse2N13YwLiJSdFRyRUQKSDhr6739PmZ2oZnNMbM5K1as6O23ExHpcyq5IiLRWx4uQyD8Wh+O1wG75G03KRzb3vikDsa34u7Xu/t0d59eXV3dIx9CRKSQqOSKiETvXmDjGRJmAffkjZ8XnmXhCCATLmt4CDjBzEaEB5ydADwUPtdgZkeEZ1U4L++1RESKig48ExHpQ2b2R4IDx0abWS3BWRJmA3ea2QXAQuDMcPP7gZOBeUATcD6Au682s6uAF8Ltrtx4EBrwJYIzOAwgOOBMB52JSFFSyRUR6UPu/pltPHVcB9s6cPE2Xucm4KYOxucA+3Uno4hIHGi5goiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjEjkquiIiIiMSOSq6IiIiIxI5KroiIiIjETlnUAUREpLDNmT4j6gh9bvqc56OOICLdpJlcEREREYkdlVwRERERiR2VXBERERGJHZVcEREREYkdlVwRERERiR2VXBERERGJHZVcEREREYkdlVwRERERiR2VXBERERGJHZVcEREREYkdlVwRERERiR2VXBERERGJHZVcEREREYkdlVwRERERiR2VXBERERGJHZVcEREREYkdlVwRERERiR2VXBGRGDKzE83sbTObZ2bJqPOIiPQ1lVwRkZgxs1Lgl8BJwDTgM2Y2LdpUIiJ9SyVXRCR+ZgDz3P09d28FbgdOiziTiEifUskVEYmficDivMe14ZiISNEwd486g4iI9CAzOwM40d3/NXx8LnC4u385b5sLgQvDh1OBt/s86I6NBlZGHaKf0fds5+j7tXMK9fu1m7tXbzlYFkUSERHpVXXALnmPJ4Vj73P364Hr+zLUzjKzOe4+Peoc/Ym+ZztH36+d09++X1quICISPy8AU8xssplVAGcB90acSUSkT2kmV0QkZty93cy+DDwElAI3ufvrEccSEelTKrkiIjHk7vcD90edo5sKejlFgdL3bOfo+7Vz+tX3SweeiYiIiEjsaE2uiIiIiMSOSq6IiBQUM9vFzB4zszfM7HUzuyTqTIXMzKrM7HkzeyX8fn0/6kz9gZmVmtnLZnZf1FkKnZktMLMaM5trZnOiztNZWpMrIiKFph34hru/ZGZDgBfN7GF3fyPqYAWqBTjW3RvNrBx4yswecPfnog5W4C4B3gSGRh2kn/iIuxfiOXK3STO5IiJSUNx9qbu/FN5fR1BEdMW2bfBAY/iwPLzpgJvtMLNJwEzghqizSO9RyRURkYJlZgngYOAfEUcpaOGv3ucC9cDD7q7v1/ZdA3wLyEWco79w4K9m9mJ4tcR+QSVXREQKkpkNBv4XuNTdG6LOU8jcPevuBxFc3W6Gme0XcaSCZWanAPXu/mLUWfqRo9z9EOAk4GIz+3DUgTpDJVdERApOuLb0f4E/uPufo87TX7j7WuAx4MSIoxSyI4FTzWwBcDtwrJn9PtpIhc3d68Kv9cDdwIxoE3WOSq6IiBQUMzPgRuBNd//vqPMUOjOrNrPh4f0BwPHAW5GGKmDufpm7T3L3BMElrx91989GHKtgmdmg8ABQzGwQcALwWrSpOkdnVxARkUJzJHAuUBOuMwX4TngVN9naeOAWMyslmLy60911WizpKWOBu4OfPSkDbnP3B6ON1Dm64pmIiIiIxI6WK4iIiIhI7KjkioiIiEjsqOSKiIiISOyo5IqIiIhI7KjkioiIiEjsqOSKiIhIjzKzS81sYN7j+zeey1ekr+gUYiIiIrLTwot2mLvnOnhuATDd3Vf2eTCRkGZyRUREpFPMLGFmb5vZrQRXvbrRzOaY2etm9v1wm68CE4DHzOyxcGyBmY0O93/TzH4T7vPX8CptmNlhZvaqmc01s/8ys35xVS0pXCq5IiIisjOmAP/j7vsC33D36cABwNFmdoC7XwssAT7i7h/Zxv6/DPdfC3wyHP8t8G/ufhCQ7eXPIEVAJVdERER2xkJ3fy68f6aZvQS8DOwLTOvE/vPdfW54/0UgEa7XHeLuz4bjt/VgXilSZVEHEBERkX5lPYCZTQa+CRzm7mvM7GagqhP7t+TdzwIDejyhCJrJFRERka4ZSlB4M2Y2Fjgp77l1wJDOvpC7rwXWmdnh4dBZPRVSipdmckVERGSnufsrZvYy8BawGHg67+nrgQfNbMk21uV25ALgN2aWA54AMj0aWIqOTiEmIiIikTOzwe7eGN5PAuPd/ZKIY0k/pplcERERKQQzzewygm6yEPhctHGkv9NMroiIiIjEjg48ExEREZHYUckVERERkdhRyRURERGR2FHJFREREZHYUckVERERkdhRyRURERGR2Pn/H+RNf6iYisYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(12,8))\n",
    "\n",
    "ax[0] = train_data.rating.value_counts().plot.pie(explode=[0,0,0,0], ax=ax[0], autopct=\"%1.2f%%\")\n",
    "ax[0].set_title(\"Rating Proportion\")\n",
    "\n",
    "ax[1] = sns.countplot(x=\"rating\", data = train_data, palette=\"Set1\")\n",
    "ax[1].set_title(\"Rating Count\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.log(train_data.revenue)\n",
    "fig, ax = plt.subplots(figsize=(18,8))\n",
    "ax = sns.histplot(x, bins=100, color=\"b\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number_of_Shops_Around_ATM</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM_Zone</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_of_Other_ATMs_in_1_KM_radius</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimated_Number_of_Houses_in_1_KM_Radius</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM_Placement</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM_TYPE</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM_Location_TYPE</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM_looks</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM_Attached_to</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average_Wait_Time</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_Type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Total  Percent\n",
       "Number_of_Shops_Around_ATM                     0      0.0\n",
       "ATM_Zone                                       0      0.0\n",
       "No_of_Other_ATMs_in_1_KM_radius                0      0.0\n",
       "Estimated_Number_of_Houses_in_1_KM_Radius      0      0.0\n",
       "ATM_Placement                                  0      0.0\n",
       "ATM_TYPE                                       0      0.0\n",
       "ATM_Location_TYPE                              0      0.0\n",
       "ATM_looks                                      0      0.0\n",
       "ATM_Attached_to                                0      0.0\n",
       "Average_Wait_Time                              0      0.0\n",
       "Day_Type                                       0      0.0\n",
       "rating                                         0      0.0\n",
       "revenue                                        0      0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train_data.isnull().sum().sort_values(ascending=False)\n",
    "percent = train_data.isnull().sum()/ train_data.isnull().count().sort_values(ascending=False)\n",
    "missing = pd.concat([total,percent], axis=1, keys=[\"Total\", \"Percent\"])\n",
    "new_data = train_data[missing[missing.Percent < 0.05].index]\n",
    "new_data = new_data.dropna()\n",
    "missing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Number_of_Shops_Around_ATM</th>\n",
       "      <th>ATM_Zone</th>\n",
       "      <th>No_of_Other_ATMs_in_1_KM_radius</th>\n",
       "      <th>Estimated_Number_of_Houses_in_1_KM_Radius</th>\n",
       "      <th>ATM_Placement</th>\n",
       "      <th>ATM_TYPE</th>\n",
       "      <th>ATM_Location_TYPE</th>\n",
       "      <th>ATM_looks</th>\n",
       "      <th>ATM_Attached_to</th>\n",
       "      <th>Average_Wait_Time</th>\n",
       "      <th>Day_Type</th>\n",
       "      <th>rating</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>8450</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>209500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>9600</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>184300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>11250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>231500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>9550</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>143600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>14260</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>255600</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150206</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>9790</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>149500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150207</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>36500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>197200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150208</th>\n",
       "      <td>123</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>5664</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>278700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150209</th>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>11065</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>282800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150210</th>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>76</td>\n",
       "      <td>14112</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>184600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150211 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Number_of_Shops_Around_ATM  ATM_Zone  No_of_Other_ATMs_in_1_KM_radius  \\\n",
       "0                               66         3                               65   \n",
       "1                               26         3                               80   \n",
       "2                               65         3                               68   \n",
       "3                               80         3                               60   \n",
       "4                               66         3                               84   \n",
       "...                            ...       ...                              ...   \n",
       "150206                          30         3                               79   \n",
       "150207                          22         3                               65   \n",
       "150208                         123         3                               40   \n",
       "150209                          64         3                               86   \n",
       "150210                          89         3                               76   \n",
       "\n",
       "        Estimated_Number_of_Houses_in_1_KM_Radius  ATM_Placement  ATM_TYPE  \\\n",
       "0                                            8450              0         3   \n",
       "1                                            9600              0         3   \n",
       "2                                           11250              0         2   \n",
       "3                                            9550              0         2   \n",
       "4                                           14260              0         2   \n",
       "...                                           ...            ...       ...   \n",
       "150206                                       9790              0         3   \n",
       "150207                                      36500              0         2   \n",
       "150208                                       5664              0         2   \n",
       "150209                                      11065              0         2   \n",
       "150210                                      14112              0         2   \n",
       "\n",
       "        ATM_Location_TYPE  ATM_looks  ATM_Attached_to  Average_Wait_Time  \\\n",
       "0                       2          1                0                  3   \n",
       "1                       2          1                0                  3   \n",
       "2                       2          1                0                  3   \n",
       "3                       2          1                0                  3   \n",
       "4                       2          1                0                  4   \n",
       "...                   ...        ...              ...                ...   \n",
       "150206                  2          1                3                  3   \n",
       "150207                  0          0                0                  4   \n",
       "150208                  2          1                0                  2   \n",
       "150209                  2          1                0                  3   \n",
       "150210                  2          1                3                  3   \n",
       "\n",
       "        Day_Type  rating  revenue  \n",
       "0              3       4   209500  \n",
       "1              3       3   184300  \n",
       "2              3       4   231500  \n",
       "3              3       4   143600  \n",
       "4              3       4   255600  \n",
       "...          ...     ...      ...  \n",
       "150206         3       3   149500  \n",
       "150207         3       3   197200  \n",
       "150208         3       5   278700  \n",
       "150209         3       5   282800  \n",
       "150210         3       3   184600  \n",
       "\n",
       "[150211 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "le = LabelEncoder()\n",
    "for col in new_data.select_dtypes(\"object\").columns:\n",
    "    new_data[col] = le.fit_transform(new_data[col])\n",
    "\n",
    "new_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increase Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150211, 105)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(\n",
    "    degree=2,\n",
    "    interaction_only=False,\n",
    "    include_bias=True\n",
    ")\n",
    "poly.fit_transform(new_data).shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso = IsolationForest(contamination=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ycap = iso.fit_predict(new_data.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = ycap != -1\n",
    "new_data = new_data[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150211, 13), (135248, 13))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape,new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prep(data):\n",
    "    new_data = data.copy()\n",
    "    total = new_data.isnull().sum().sort_values(ascending=False)\n",
    "    percent = new_data.isnull().sum()/ new_data.isnull().count().sort_values(ascending=False)\n",
    "    missing = pd.concat([total,percent], axis=1, keys=[\"Total\", \"Percent\"])\n",
    "    new_data = new_data[missing[missing.Percent<0.05].index]\n",
    "    new_data = new_data.dropna()\n",
    "    le = LabelEncoder()\n",
    "    for col in new_data.select_dtypes(\"object\").columns:\n",
    "        new_data[col] = le.fit_transform(new_data[col])\n",
    "    iso = IsolationForest(contamination=0.1)\n",
    "    ycap = iso.fit_predict(new_data.values)\n",
    "    mask = ycap != -1\n",
    "    new_data = new_data[mask]\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = prep(train_data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model\n",
    "from sklearn.feature_selection import SelectKBest, SelectPercentile, SelectFromModel, SequentialFeatureSelector\n",
    "\n",
    "# estimator\n",
    "## regression\n",
    "from sklearn.feature_selection import f_regression, mutual_info_regression, r_regression\n",
    "\n",
    "## classification\n",
    "from sklearn.feature_selection import f_classif, mutual_info_classif, chi2\n",
    "\n",
    "# basic modal\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter methods\n",
    "    Basic methods\n",
    "    Univariate methods\n",
    "    Information gain\n",
    "    Fisher score\n",
    "    Correlation Matrix with Heatmap\n",
    "Wrapper methods\n",
    "    Forward selection\n",
    "    Backward elimination\n",
    "    Exhaustive feature selection\n",
    "    Recursive feature elimination\n",
    "    Recursive Feature Elimination\n",
    "    Recursive feature elimination with cross validation\n",
    "Embedded methods\n",
    "    Lasso\n",
    "    Ridge\n",
    "    Tree Importance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter\n",
    "\n",
    "1. Basic methods\n",
    "2. Univariate feature selection\n",
    "3. Information gain\n",
    "set of all features -> select the best subset of features -> learn Algorithm -> Performance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic methods"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove constant values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    66,      3,     65, ...,      3,      4, 209500],\n",
       "       [    26,      3,     80, ...,      3,      3, 184300],\n",
       "       [    65,      3,     68, ...,      3,      4, 231500],\n",
       "       ...,\n",
       "       [   123,      3,     40, ...,      3,      5, 278700],\n",
       "       [    64,      3,     86, ...,      3,      5, 282800],\n",
       "       [    89,      3,     76, ...,      3,      3, 184600]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "filter = VarianceThreshold(threshold=0)\n",
    "filter.fit_transform(new_data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove quasi-constant features Quasi-constant features are those features that have the same value for the large part of the observations in the dataset. They do not vary much within the dataset and hence, they do not have much information. They will not contribute much to the prediction task. We can remove these features as they do not have much predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    66,      3,     65, ...,      3,      4, 209500],\n",
       "       [    26,      3,     80, ...,      3,      3, 184300],\n",
       "       [    65,      3,     68, ...,      3,      4, 231500],\n",
       "       ...,\n",
       "       [   123,      3,     40, ...,      3,      5, 278700],\n",
       "       [    64,      3,     86, ...,      3,      5, 282800],\n",
       "       [    89,      3,     76, ...,      3,      3, 184600]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = VarianceThreshold(threshold=0.01)\n",
    "filter.fit_transform(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = new_data[new_data.columns[filter.get_support()]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_clf_train = new_data[\"rating\"]\n",
    "y_reg_train = new_data[\"revenue\"]\n",
    "X_clf_train = new_data.drop(\"rating\",axis=1)\n",
    "X_reg_train = new_data.drop(\"revenue\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectKBest(score_func=&lt;function f_regression at 0x180d13370&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function f_regression at 0x180d13370&gt;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(score_func=<function f_regression at 0x180d13370>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = SelectKBest(f_regression, k=10)\n",
    "selector.fit(X_reg_train, y_reg_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Number_of_Shops_Around_ATM</th>\n",
       "      <th>ATM_Zone</th>\n",
       "      <th>No_of_Other_ATMs_in_1_KM_radius</th>\n",
       "      <th>Estimated_Number_of_Houses_in_1_KM_Radius</th>\n",
       "      <th>ATM_TYPE</th>\n",
       "      <th>ATM_Location_TYPE</th>\n",
       "      <th>ATM_Attached_to</th>\n",
       "      <th>Average_Wait_Time</th>\n",
       "      <th>Day_Type</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>8450</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>9600</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>11250</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>9550</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>14260</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150205</th>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>6000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150206</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>9790</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150208</th>\n",
       "      <td>123</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>5664</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150209</th>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>11065</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150210</th>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>76</td>\n",
       "      <td>14112</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135203 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Number_of_Shops_Around_ATM  ATM_Zone  No_of_Other_ATMs_in_1_KM_radius  \\\n",
       "0                               66         3                               65   \n",
       "1                               26         3                               80   \n",
       "2                               65         3                               68   \n",
       "3                               80         3                               60   \n",
       "4                               66         3                               84   \n",
       "...                            ...       ...                              ...   \n",
       "150205                          51         4                               50   \n",
       "150206                          30         3                               79   \n",
       "150208                         123         3                               40   \n",
       "150209                          64         3                               86   \n",
       "150210                          89         3                               76   \n",
       "\n",
       "        Estimated_Number_of_Houses_in_1_KM_Radius  ATM_TYPE  \\\n",
       "0                                            8450         3   \n",
       "1                                            9600         3   \n",
       "2                                           11250         2   \n",
       "3                                            9550         2   \n",
       "4                                           14260         2   \n",
       "...                                           ...       ...   \n",
       "150205                                       6000         3   \n",
       "150206                                       9790         3   \n",
       "150208                                       5664         2   \n",
       "150209                                      11065         2   \n",
       "150210                                      14112         2   \n",
       "\n",
       "        ATM_Location_TYPE  ATM_Attached_to  Average_Wait_Time  Day_Type  \\\n",
       "0                       2                0                  3         3   \n",
       "1                       2                0                  3         3   \n",
       "2                       2                0                  3         3   \n",
       "3                       2                0                  3         3   \n",
       "4                       2                0                  4         3   \n",
       "...                   ...              ...                ...       ...   \n",
       "150205                  2                0                  4         3   \n",
       "150206                  2                3                  3         3   \n",
       "150208                  2                0                  2         3   \n",
       "150209                  2                0                  3         3   \n",
       "150210                  2                3                  3         3   \n",
       "\n",
       "        rating  \n",
       "0            4  \n",
       "1            3  \n",
       "2            4  \n",
       "3            4  \n",
       "4            4  \n",
       "...        ...  \n",
       "150205       3  \n",
       "150206       3  \n",
       "150208       5  \n",
       "150209       5  \n",
       "150210       3  \n",
       "\n",
       "[135203 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reg_train[X_reg_train.columns[selector.get_support()]]"
   ]
  }
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