Docree source file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b5d2a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries like pandas, numpy, matplotlib, seaborn and sklearn \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "418fa917",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading dataset to this notebook\n",
    "df = pd.read_csv('C:\\\\Users\\\\nithi\\\\Downloads\\\\Docereetrain.csv',encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da41217c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>DEVICETYPE</th>\n",
       "      <th>PLATFORM_ID</th>\n",
       "      <th>BIDREQUESTIP</th>\n",
       "      <th>USERPLATFORMUID</th>\n",
       "      <th>USERCITY</th>\n",
       "      <th>USERZIPCODE</th>\n",
       "      <th>USERAGENT</th>\n",
       "      <th>PLATFORMTYPE</th>\n",
       "      <th>CHANNELTYPE</th>\n",
       "      <th>URL</th>\n",
       "      <th>KEYWORDS</th>\n",
       "      <th>TAXONOMY</th>\n",
       "      <th>IS_HCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2</td>\n",
       "      <td>170.173.0.22</td>\n",
       "      <td>6974dcaa-f932-480e-9fb5-c52e20e1393a</td>\n",
       "      <td>Portland</td>\n",
       "      <td>97206.0</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.cancertherapyadvisor.com/home/canc...</td>\n",
       "      <td>General|Endocrine|False|Medicine|Surgery|Urolo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2</td>\n",
       "      <td>65.216.253.25</td>\n",
       "      <td>c12f3f8f-8fcf-484a-90e1-1ac04db8cdcf</td>\n",
       "      <td>Arlington</td>\n",
       "      <td>22202.0</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.cancertherapyadvisor.com/home/deci...</td>\n",
       "      <td>Bone Marrow|Radiography|Chronic|Oncology|Psych...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2</td>\n",
       "      <td>66.232.79.22</td>\n",
       "      <td>a698de4b-e200-46dd-b5fb-40402175ae18</td>\n",
       "      <td>New Meadows</td>\n",
       "      <td>83654.0</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.cancertherapyadvisor.com/home/canc...</td>\n",
       "      <td>General|Endocrine|False|Medicine|Surgery|Urolo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>3</td>\n",
       "      <td>137.54.125.246</td>\n",
       "      <td>45967533-75c8-4fbd-a00c-e6ff20447aaa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229114624.0</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://globalrph.com/medcalcs/warfarin-mainte...</td>\n",
       "      <td>Dental|Total|Clinical|Pharmacology|Physicians|...</td>\n",
       "      <td>2084P0800X</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>7</td>\n",
       "      <td>174.202.231.99</td>\n",
       "      <td>a17e25be-532d-4cf5-b916-9308c8c3961f</td>\n",
       "      <td>Houston</td>\n",
       "      <td>77008.0</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 16_1_1 like...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.cureus.com/articles/58184-a-review...</td>\n",
       "      <td>Critical Care|Emergency Medicine|General Pract...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113932</th>\n",
       "      <td>114933</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2</td>\n",
       "      <td>68.82.97.126</td>\n",
       "      <td>051e4a06-4cb4-4b97-b0b1-d1c6a2b8e2c2</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>19120.0</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.clinicaladvisor.com/home/consultat...</td>\n",
       "      <td>Cardiovascular|Clinical|False|Male|Oncology|Ps...</td>\n",
       "      <td>207RP1001X</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113933</th>\n",
       "      <td>114934</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2</td>\n",
       "      <td>104.172.11.109</td>\n",
       "      <td>3289bba5-4a50-4b61-a5cc-68596bbdc2bf</td>\n",
       "      <td>Van Nuys</td>\n",
       "      <td>914014213.0</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.clinicaladvisor.com/home/features/...</td>\n",
       "      <td>Asthma|Arthritis|General|Clinical|Diagnosis|Rh...</td>\n",
       "      <td>2084P0800X</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113934</th>\n",
       "      <td>114935</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>7</td>\n",
       "      <td>174.21.94.113</td>\n",
       "      <td>9c36e90c-caa7-4323-8431-fb84f8293972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98105.0</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.cureus.com/articles/39455-whats-th...</td>\n",
       "      <td>Rehabilitation|Transplantation|Clinical|Preven...</td>\n",
       "      <td>208000000X</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113935</th>\n",
       "      <td>114936</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2</td>\n",
       "      <td>69.253.129.131</td>\n",
       "      <td>afc27760-31f7-4f6e-aa08-90d8a96525d0</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>19808.0</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 15_6_1 like...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.dermatologyadvisor.com/home/topics...</td>\n",
       "      <td>pain|disease|autoimmune|multiple sclerosis|gra...</td>\n",
       "      <td>2084P0800X</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113936</th>\n",
       "      <td>114937</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2</td>\n",
       "      <td>108.41.233.175</td>\n",
       "      <td>d26887c5-15f9-4b72-80dc-728039529e60</td>\n",
       "      <td>White Plains</td>\n",
       "      <td>10605.0</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 16_1 like M...</td>\n",
       "      <td>Online Medical Journal</td>\n",
       "      <td>Website</td>\n",
       "      <td>https://www.medicalbag.com/</td>\n",
       "      <td>Genetic|Large|Autoimmune|Health|False|Abortion...</td>\n",
       "      <td>2084P0800X</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113937 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID DEVICETYPE  PLATFORM_ID    BIDREQUESTIP  \\\n",
       "0         1001    Desktop            2    170.173.0.22   \n",
       "1         1002    Desktop            2   65.216.253.25   \n",
       "2         1003    Desktop            2    66.232.79.22   \n",
       "3         1004    Desktop            3  137.54.125.246   \n",
       "4         1005     Mobile            7  174.202.231.99   \n",
       "...        ...        ...          ...             ...   \n",
       "113932  114933    Desktop            2    68.82.97.126   \n",
       "113933  114934    Desktop            2  104.172.11.109   \n",
       "113934  114935    Desktop            7   174.21.94.113   \n",
       "113935  114936     Mobile            2  69.253.129.131   \n",
       "113936  114937     Mobile            2  108.41.233.175   \n",
       "\n",
       "                             USERPLATFORMUID      USERCITY  USERZIPCODE  \\\n",
       "0       6974dcaa-f932-480e-9fb5-c52e20e1393a      Portland      97206.0   \n",
       "1       c12f3f8f-8fcf-484a-90e1-1ac04db8cdcf     Arlington      22202.0   \n",
       "2       a698de4b-e200-46dd-b5fb-40402175ae18   New Meadows      83654.0   \n",
       "3       45967533-75c8-4fbd-a00c-e6ff20447aaa           NaN  229114624.0   \n",
       "4       a17e25be-532d-4cf5-b916-9308c8c3961f       Houston      77008.0   \n",
       "...                                      ...           ...          ...   \n",
       "113932  051e4a06-4cb4-4b97-b0b1-d1c6a2b8e2c2  Philadelphia      19120.0   \n",
       "113933  3289bba5-4a50-4b61-a5cc-68596bbdc2bf      Van Nuys  914014213.0   \n",
       "113934  9c36e90c-caa7-4323-8431-fb84f8293972           NaN      98105.0   \n",
       "113935  afc27760-31f7-4f6e-aa08-90d8a96525d0    Wilmington      19808.0   \n",
       "113936  d26887c5-15f9-4b72-80dc-728039529e60  White Plains      10605.0   \n",
       "\n",
       "                                                USERAGENT  \\\n",
       "0       Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...   \n",
       "1       Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...   \n",
       "2       Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...   \n",
       "3       Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...   \n",
       "4       Mozilla/5.0 (iPhone; CPU iPhone OS 16_1_1 like...   \n",
       "...                                                   ...   \n",
       "113932  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6...   \n",
       "113933  Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...   \n",
       "113934  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...   \n",
       "113935  Mozilla/5.0 (iPhone; CPU iPhone OS 15_6_1 like...   \n",
       "113936  Mozilla/5.0 (iPhone; CPU iPhone OS 16_1 like M...   \n",
       "\n",
       "                  PLATFORMTYPE CHANNELTYPE  \\\n",
       "0       Online Medical Journal     Website   \n",
       "1       Online Medical Journal     Website   \n",
       "2       Online Medical Journal     Website   \n",
       "3       Online Medical Journal     Website   \n",
       "4       Online Medical Journal     Website   \n",
       "...                        ...         ...   \n",
       "113932  Online Medical Journal     Website   \n",
       "113933  Online Medical Journal     Website   \n",
       "113934  Online Medical Journal     Website   \n",
       "113935  Online Medical Journal     Website   \n",
       "113936  Online Medical Journal     Website   \n",
       "\n",
       "                                                      URL  \\\n",
       "0       https://www.cancertherapyadvisor.com/home/canc...   \n",
       "1       https://www.cancertherapyadvisor.com/home/deci...   \n",
       "2       https://www.cancertherapyadvisor.com/home/canc...   \n",
       "3       https://globalrph.com/medcalcs/warfarin-mainte...   \n",
       "4       https://www.cureus.com/articles/58184-a-review...   \n",
       "...                                                   ...   \n",
       "113932  https://www.clinicaladvisor.com/home/consultat...   \n",
       "113933  https://www.clinicaladvisor.com/home/features/...   \n",
       "113934  https://www.cureus.com/articles/39455-whats-th...   \n",
       "113935  https://www.dermatologyadvisor.com/home/topics...   \n",
       "113936                        https://www.medicalbag.com/   \n",
       "\n",
       "                                                 KEYWORDS    TAXONOMY  IS_HCP  \n",
       "0       General|Endocrine|False|Medicine|Surgery|Urolo...         NaN     0.0  \n",
       "1       Bone Marrow|Radiography|Chronic|Oncology|Psych...         NaN     0.0  \n",
       "2       General|Endocrine|False|Medicine|Surgery|Urolo...         NaN     0.0  \n",
       "3       Dental|Total|Clinical|Pharmacology|Physicians|...  2084P0800X     1.0  \n",
       "4       Critical Care|Emergency Medicine|General Pract...         NaN     0.0  \n",
       "...                                                   ...         ...     ...  \n",
       "113932  Cardiovascular|Clinical|False|Male|Oncology|Ps...  207RP1001X     1.0  \n",
       "113933  Asthma|Arthritis|General|Clinical|Diagnosis|Rh...  2084P0800X     1.0  \n",
       "113934  Rehabilitation|Transplantation|Clinical|Preven...  208000000X     1.0  \n",
       "113935  pain|disease|autoimmune|multiple sclerosis|gra...  2084P0800X     1.0  \n",
       "113936  Genetic|Large|Autoimmune|Health|False|Abortion...  2084P0800X     1.0  \n",
       "\n",
       "[113937 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90990ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'DEVICETYPE', 'PLATFORM_ID', 'BIDREQUESTIP', 'USERPLATFORMUID',\n",
       "       'USERCITY', 'USERZIPCODE', 'USERAGENT', 'PLATFORMTYPE', 'CHANNELTYPE',\n",
       "       'URL', 'KEYWORDS', 'TAXONOMY', 'IS_HCP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To know which columns are present in the dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9e8b5c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   ID               113937 non-null  int64  \n",
      " 1   DEVICETYPE       113937 non-null  object \n",
      " 2   PLATFORM_ID      113937 non-null  int64  \n",
      " 3   BIDREQUESTIP     113937 non-null  object \n",
      " 4   USERPLATFORMUID  113933 non-null  object \n",
      " 5   USERCITY         107578 non-null  object \n",
      " 6   USERZIPCODE      109345 non-null  float64\n",
      " 7   USERAGENT        113935 non-null  object \n",
      " 8   PLATFORMTYPE     113937 non-null  object \n",
      " 9   CHANNELTYPE      113937 non-null  object \n",
      " 10  URL              113937 non-null  object \n",
      " 11  KEYWORDS         113937 non-null  object \n",
      " 12  TAXONOMY         32313 non-null   object \n",
      " 13  IS_HCP           113936 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(10)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#To know everything about dataset like data types etc..\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff5079af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>PLATFORM_ID</th>\n",
       "      <th>USERZIPCODE</th>\n",
       "      <th>IS_HCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>1.093450e+05</td>\n",
       "      <td>113936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57969.000000</td>\n",
       "      <td>3.574993</td>\n",
       "      <td>1.830240e+07</td>\n",
       "      <td>0.299993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32890.923148</td>\n",
       "      <td>2.569019</td>\n",
       "      <td>1.066908e+08</td>\n",
       "      <td>0.458257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.020000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29485.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.935500e+04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57969.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.321000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>86453.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.709000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>114937.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.970149e+08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID    PLATFORM_ID   USERZIPCODE         IS_HCP\n",
       "count  113937.000000  113937.000000  1.093450e+05  113936.000000\n",
       "mean    57969.000000       3.574993  1.830240e+07       0.299993\n",
       "std     32890.923148       2.569019  1.066908e+08       0.458257\n",
       "min      1001.000000       1.000000  6.020000e+02       0.000000\n",
       "25%     29485.000000       2.000000  1.935500e+04       0.000000\n",
       "50%     57969.000000       2.000000  4.321000e+04       0.000000\n",
       "75%     86453.000000       6.000000  7.709000e+04       1.000000\n",
       "max    114937.000000      16.000000  9.970149e+08       1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get the summary of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "324bd625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID--113937\n",
      "DEVICETYPE--4\n",
      "PLATFORM_ID--15\n",
      "BIDREQUESTIP--33664\n",
      "USERPLATFORMUID--53780\n",
      "USERCITY--4420\n",
      "USERZIPCODE--11278\n",
      "USERAGENT--4288\n",
      "PLATFORMTYPE--5\n",
      "CHANNELTYPE--1\n",
      "URL--5231\n",
      "KEYWORDS--2460\n",
      "TAXONOMY--207\n",
      "IS_HCP--2\n"
     ]
    }
   ],
   "source": [
    "#To know how many unique values of are present in each columns\n",
    "for i in df.columns:\n",
    "    print(i,end='--')\n",
    "    print(df[i].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c09f498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...\n",
       "1         Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...\n",
       "2         Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...\n",
       "3         Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl...\n",
       "4         Mozilla/5.0 (iPhone; CPU iPhone OS 16_1_1 like...\n",
       "                                ...                        \n",
       "113932    Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6...\n",
       "113933    Mozilla/5.0 (Windows NT 6.1; Win64; x64) Apple...\n",
       "113934    Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...\n",
       "113935    Mozilla/5.0 (iPhone; CPU iPhone OS 15_6_1 like...\n",
       "113936    Mozilla/5.0 (iPhone; CPU iPhone OS 16_1 like M...\n",
       "Name: USERAGENT, Length: 113937, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To getting single column info\n",
    "df['USERAGENT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e23e7b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                NaN\n",
       "1                NaN\n",
       "2                NaN\n",
       "3         2084P0800X\n",
       "4                NaN\n",
       "             ...    \n",
       "113932    207RP1001X\n",
       "113933    2084P0800X\n",
       "113934    208000000X\n",
       "113935    2084P0800X\n",
       "113936    2084P0800X\n",
       "Name: TAXONOMY, Length: 113937, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TAXONOMY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "284247f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To encoding categorial to numerical features\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91291d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "list=['DEVICETYPE','PLATFORMTYPE','CHANNELTYPE','KEYWORDS']\n",
    "for i in list:\n",
    "    df[i]=le.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a67f6b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category of Values in Device type [0 1 2 3]\n",
      "Category of Values in Platform type [3 2 1 0 4]\n",
      "Category of Values in Channel type [0]\n"
     ]
    }
   ],
   "source": [
    "#All categorical are converted to numerical values\n",
    "a=df['DEVICETYPE'].unique()\n",
    "print(\"Category of Values in Device type\",a)\n",
    "b=df['PLATFORMTYPE'].unique()\n",
    "print(\"Category of Values in Platform type\",b)\n",
    "c=df['CHANNELTYPE'].unique()\n",
    "print(\"Category of Values in Channel type\",c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b77524c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3VofffSRBg8erISEBDVt2rTeTwZt0aKFrr76as2YMUMpKSl66623tGTJEj3zzDOKiIiQJF166aXq2LGjpk6dquLiYsXHx2vRokX6+uuvKx2vW7duWrhwoebMmaOePXsqKCioyt9En3jiCf373//WoEGD9PjjjyshIUFvv/22Fi9erGeffdblAWXnYubMmbryyis1aNAgTZ06VWFhYZo9e7a2bt2qd955p85PYZakKVOmaO7cuRo5cqR+//vfO2czbd++3WW7Tp06qV27dpo2bZqMMUpISNAnn3yiJUuWVDqmowvyz3/+s8aOHavQ0FB17NhRffv2VXx8vCZNmqQnnnhCoaGhevvttysFKW86fPiwvvnmGxljlJOT43xo3qZNm3T//ffrjjvuqLTP/v373T4huFmzZs5HBjjceuutevTRR/Xcc88pLi5O1157ba3q6tOnj+bMmaPJkyerZ8+e+vWvf60uXbqoqKhIGzZs0GuvvaauXbu6jA06ffq027okeSRAe4IVaoQH+XP0MVCRY8aP4xMWFmaaN29uBgwYYJ5++mmTmZlZaZ+zZxitXr3aXHPNNaZ169bGbrebxMREM2DAAPPxxx+77PfFF1+Yiy66yNjtdiPJjB071uV47mZdVDWbaeTIkeaDDz4wXbp0MWFhYaZNmzZm1qxZlfb/8ccfzdChQ01MTIxp1qyZueeee8zixYsrzZI5fvy4uf76601cXJyx2Wwu55SbWVhbtmwxo0aNMrGxsSYsLMz06NHDzJs3z2Ubdw9LM+bMrJmzt3fnq6++MldccYWJjIw0TZo0Mb179zaffPKJ2+PVZjaTMWUPfbvyyitNeHi4SUhIMLfffrv56KOPKl0Tx3bR0dEmPj7e/M///I/Zv3+/2+sxffp006JFCxMUFORynFWrVpk+ffqYiIgI06xZMzNhwgSzfv36Wn3/Vc1mioyMrLStu/uk4n0dFBRkYmJiTLdu3cydd97pdiZZTbOZbr75Zrd1XnPNNUaSmTx5crXfx9n3gTHGbNy40YwdO9akpqaasLAwExkZaS666CLz+OOPu/y/V91MIUmmqKioymtS3WwmY2qe0Vjbh1TWp0ZYm82YGqaHAAAABDDGzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEvza5hZuXKlRo0apRYtWshms+nDDz90risqKtIjjzzifNpnixYtdOutt1Z6ngYAAGjc/BpmcnNz1aNHD73yyiuV1uXl5Wn9+vX67W9/q/Xr12vhwoX68ccfvfoqegAAYD0BMzXbZrNp0aJFGjNmTJXbrFmzRpdddpn27dun1NTUWh23tLRUBw8eVHR0dL0e7AUAAHzPlD9gskWLFjW+MNVSTwDOysqSzWZTXFxcrfc5ePBgpTcKAwAAa0hPT6/xvWGWCTP5+fmaNm2abrrpJsXExFS5XUFBgcvL/BwNT+np6dXuBwAAAkd2drZatWql6OjoGre1RJgpKirSjTfeqNLSUs2ePbvabWfOnKknn3yy0vKYmBjCDAAAFlObISIBPzW7qKhIN9xwg/bs2aMlS5bUGEimT5+urKws5yc9Pd1HlQIAAH8I6JYZR5D56aeftGzZMiUmJta4j91ul91u90F1AAAgEPg1zJw6dUo7d+50fr1nzx5t3LhRCQkJatGiha6//nqtX79e//73v1VSUqKMjAxJUkJCgsLCwvxVNgAACCB+nZq9fPlyDRo0qNLysWPHasaMGUpLS3O737JlyzRw4MBanSM7O1uxsbHKyspizAwAABZRl5/ffm2ZGThwoKrLUgHyCBwAABDAAn4AMAAAQHUIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIC+q3ZjdWCb/dXu/6mXqk+qgQAgMBHywwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0v4aZlStXatSoUWrRooVsNps+/PBDl/XGGM2YMUMtWrRQkyZNNHDgQH3//ff+KRYAAAQkv4aZ3Nxc9ejRQ6+88orb9c8++6xmzZqlV155RWvWrFFycrKuvPJK5eTk+LhSAAAQqEL8efLhw4dr+PDhbtcZY/Tiiy/qscce07XXXitJeuONN5SUlKQFCxZo4sSJviwVAAAEqIAdM7Nnzx5lZGRo6NChzmV2u10DBgzQqlWrqtyvoKBA2dnZLh8AANBwBWyYycjIkCQlJSW5LE9KSnKuc2fmzJmKjY11flq1auXVOgEAgH8FbJhxsNlsLl8bYyotq2j69OnKyspyftLT071dIgAA8CO/jpmpTnJysqSyFpqUlBTn8szMzEqtNRXZ7XbZ7Xav1wcAAAJDwLbMpKWlKTk5WUuWLHEuKyws1IoVK9S3b18/VgYAAAKJX1tmTp06pZ07dzq/3rNnjzZu3KiEhASlpqZqypQpevrpp9WhQwd16NBBTz/9tCIiInTTTTf5sWoAABBI/Bpm1q5dq0GDBjm/fuCBByRJY8eO1fz58/Xwww/r9OnTmjx5sk6cOKFevXrp888/V3R0tL9KBgAAAcZmjDH+LsKbsrOzFRsbq6ysLMXExPi7nFpZ8O3+atff1CvVR5UAAOAfdfn5HbBjZgAAAGqDMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACwtoMNMcXGxfvOb3ygtLU1NmjRR27Zt9dRTT6m0tNTfpQEAgAAR4u8CqvPMM8/o1Vdf1RtvvKEuXbpo7dq1Gj9+vGJjY3Xffff5uzwAABAAAjrMrF69WqNHj9bIkSMlSW3atNE777yjtWvX+rkyAAAQKAK6m6l///768ssv9eOPP0qSNm3apK+//lojRoyocp+CggJlZ2e7fAAAQMMV0C0zjzzyiLKystSpUycFBwerpKREf/jDH/S///u/Ve4zc+ZMPfnkkz6sEgAA+FNAt8y89957euutt7RgwQKtX79eb7zxhp5//nm98cYbVe4zffp0ZWVlOT/p6ek+rBgAAPhaQLfMPPTQQ5o2bZpuvPFGSVK3bt20b98+zZw5U2PHjnW7j91ul91u92WZAADAjwK6ZSYvL09BQa4lBgcHMzUbAAA4BXTLzKhRo/SHP/xBqamp6tKlizZs2KBZs2bptttu83dpAAAgQAR0mHn55Zf129/+VpMnT1ZmZqZatGihiRMn6vHHH/d3aQAAIEDYjDHG30V4U3Z2tmJjY5WVlaWYmBh/l1MrC77dX+36m3ql+qgSAAD8oy4/vwN6zAwAAEBNCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDS6hVm9uzZ4+k6AAAA6qVeYaZ9+/YaNGiQ3nrrLeXn53u6JgAAgFqrV5jZtGmTLrroIj344INKTk7WxIkT9d1333m6NgAAgBrVK8x07dpVs2bN0oEDBzRv3jxlZGSof//+6tKli2bNmqUjR454uk4AAAC3zmkAcEhIiK655hq9//77euaZZ7Rr1y5NnTpVLVu21K233qpDhw55qk4AAAC3zinMrF27VpMnT1ZKSopmzZqlqVOnateuXVq6dKkOHDig0aNHe6pOAAAAt0Lqs9OsWbM0b9487dixQyNGjNCbb76pESNGKCioLBulpaXpr3/9qzp16uTRYgEAAM5WrzAzZ84c3XbbbRo/frySk5PdbpOamqrXX3/9nIoDAACoSb3CzJIlS5SamupsiXEwxig9PV2pqakKCwvT2LFjPVIkAABAVeo1ZqZdu3Y6evRopeXHjx9XWlraORcFAABQW/UKM8YYt8tPnTql8PDwcyoIAACgLurUzfTAAw9Ikmw2mx5//HFFREQ415WUlOjbb7/VhRde6NECAQAAqlOnMLNhwwZJZS0zW7ZsUVhYmHNdWFiYevTooalTp3q2QgAAgGrUKcwsW7ZMkjR+/Hj9+c9/VkxMjFeKAgAAqK16zWaaN2+ep+sAAACol1qHmWuvvVbz589XTEyMrr322mq3Xbhw4TkXBgAAUBu1DjOxsbGy2WzOPwMAAASCWoeZil1LdDMBAIBAUa/nzJw+fVp5eXnOr/ft26cXX3xRn3/+uccKAwAAqI16hZnRo0frzTfflCSdPHlSl112mV544QWNHj1ac+bM8WiBBw4c0P/7f/9PiYmJioiI0IUXXqh169Z59BwAAMC66hVm1q9fr8svv1yS9MEHHyg5OVn79u3Tm2++qZdeesljxZ04cUL9+vVTaGio/vOf/2jbtm164YUXFBcX57FzAAAAa6vX1Oy8vDxFR0dLkj7//HNde+21CgoKUu/evbVv3z6PFffMM8+oVatWLmN02rRp47HjAwAA66tXy0z79u314YcfKj09Xf/97381dOhQSVJmZqZHH6T38ccf65JLLtH//M//qHnz5rrooov0t7/9zWPHBwAA1levMPP4449r6tSpatOmjXr16qU+ffpIKmulueiiizxW3O7duzVnzhx16NBB//3vfzVp0iTde++9zvE67hQUFCg7O9vlAwAAGi6bqeoV2DXIyMjQoUOH1KNHDwUFlWWi7777TjExMerUqZNHigsLC9Mll1yiVatWOZfde++9WrNmjVavXu12nxkzZujJJ5+stDwrK8syr19Y8O3+atff1CvVR5UAAOAf2dnZio2NrdXP73q1zEhScnKyLrroImeQkaTLLrvMY0FGklJSUnTBBRe4LOvcubP276/6h/306dOVlZXl/KSnp3usHgAAEHjqNQA4NzdXf/zjH/Xll18qMzNTpaWlLut3797tkeL69eunHTt2uCz78ccf1bp16yr3sdvtstvtHjk/AAAIfPUKMxMmTNCKFSt0yy23KCUlxfmaA0+7//771bdvXz399NO64YYb9N133+m1117Ta6+95pXzAQAA66lXmPnPf/6jxYsXq1+/fp6ux8Wll16qRYsWafr06XrqqaeUlpamF198UTfffLNXzwsAAKyjXmEmPj5eCQkJnq7FrauuukpXXXWVT84FAACsp14DgH/3u9/p8ccfd3k/EwAAgD/Uq2XmhRde0K5du5SUlKQ2bdooNDTUZf369es9UhwAAEBN6hVmxowZ4+EyAAAA6qdeYeaJJ57wdB0AAAD1Uu+H5p08eVJ///vfNX36dB0/flxSWffSgQMHPFYcAABATerVMrN582YNGTJEsbGx2rt3r+644w4lJCRo0aJF2rdvX7XvTgIAAPCkerXMPPDAAxo3bpx++uknhYeHO5cPHz5cK1eu9FhxAAAANalXmFmzZo0mTpxYafl5552njIyMcy4KAACgtuoVZsLDw5WdnV1p+Y4dO9SsWbNzLgoAAKC26hVmRo8eraeeekpFRUWSJJvNpv3792vatGm67rrrPFogAABAdeoVZp5//nkdOXJEzZs31+nTpzVgwAC1b99e0dHR+sMf/uDpGgEAAKpUr9lMMTEx+vrrr7Vs2TKtW7dOpaWluvjiizVkyBBP1wcAAFCtOoeZ0tJSzZ8/XwsXLtTevXtls9mUlpam5ORkGWNks9m8UScAAIBbdepmMsbo6quv1oQJE3TgwAF169ZNXbp00b59+zRu3Dhdc8013qoTAADArTq1zMyfP18rV67Ul19+qUGDBrmsW7p0qcaMGaM333xTt956q0eLBAAAqEqdWmbeeecdPfroo5WCjCRdccUVmjZtmt5++22PFQcAAFCTOoWZzZs3a9iwYVWuHz58uDZt2nTORTVW7363X+98t1+lxvi7FAAALKNO3UzHjx9XUlJSleuTkpJ04sSJcy6qMcrMzte0hVskSR2aR+n6ni0VHR7q56oAAAh8dWqZKSkpUUhI1fknODhYxcXF51xUY/Tj4VPOP/+UeUp/WbZTeYVcSwAAalKnlhljjMaNGye73e52fUFBgUeKaox2ZuZIkrq3jNXeo7nKzi/WzsxT6t4yzr+FAQAQ4OoUZsaOHVvjNsxkqp+fMstaZvq1b6ogm00b00/qRG6hn6sCACDw1SnMzJs3z1t1NHo7y8NMh+ZR2pFR1kpzjDADAECN6vVuJnieI8y0bx6lhMgwSdLxPMIMAAA1IcwEgOO5hc5WmHbNopToCDO0zAAAUCPCTABwtMqcF9dEkfYQxZeHmay8IhWXlvqzNAAAAh5hJgBU7GKSpGh7iEKDbTIqCzQAAKBqhJkAcHaYsdlsio8oa51hEDAAANUjzASAn8qfMdOhPMxIOjMImDADAEC1CDMBYNdZLTPSmTDDs2YAAKgeYcbPThUU62BWviT3YYZuJgAAqkeY8TNHq0zTKLviysfJSBVaZnjWDAAA1SLM+NneY7mSpLbNIl2WV2yZMcb4vC4AAKyCMONnR3LKXs6ZFBPustwxm6mwuFS5hSU+rwsAAKsgzPjZ0VNl3UhNo8JclocGBykmvOzVWQwCBgCganV60SQ87+ipspaZplH2SusSIu3Kzi/WsdxCtUqI8HVpCAALvt1f7fqbeqX6qBIACFy0zPjZMWeYCau07syzZgp8WhMAAFZCmPEzRzdTYmTllpnYJqGSpJz8Yp/WBACAlRBm/MzZMhNdOcxE2oMllT2LBgAAuEeY8SNjTJUDgCUp0l42pCm3gNlMAABUhTDjR9n5xSosKZXkfgBwlCPMFNIyAwBAVQgzfuToYoqyhyg8NLjS+sgwR8sMYQYAgKoQZvzIOfjXTReTdGbMzOnCEpXyFGAAANwizPjRsWqeMSNJEeUtM0ZSHk8BBgDALcKMHzkemJcY6b5lJjjIpibl3U90NQEA4B5hxo+cM5ncTMt2ODOjiTADAIA7hBk/cr7KoIqWGenMuBleNgkAgHuEGT86VpuWGWY0AQBQLcKMH1X3kkkHupkAAKgeYcaPahoALElRvNIAAIBqEWb8qFbdTM6nADNmBgAAdwgzfpJfVKKc8taWpm7emO3AmBkAAKpnqTAzc+ZM2Ww2TZkyxd+lnLNjuWWtMqHBNsU0CalyO8bMAABQPcuEmTVr1ui1115T9+7d/V2KRxzNcYyXsctms1W5HVOzAQConiXCzKlTp3TzzTfrb3/7m+Lj4/1djkccyy2fyRRd9eBf6UzLTF5BMe9nAgDADUuEmbvuuksjR47UkCFDaty2oKBA2dnZLp9AdDSn/CWT1YyXkc6MmTEqe+EkAABwVfVgjQDx7rvvav369VqzZk2ttp85c6aefPJJL1d17o7m1vyMGans/UzhoUHKLypVbkGxs6UGAACUCeiWmfT0dN1333166623FB4eXqt9pk+frqysLOcnPT3dy1XWj6NlpqZuJqnCjCZaZgAAqCSgf81ft26dMjMz1bNnT+eykpISrVy5Uq+88ooKCgoUHBzsso/dbpfdXn1rRyA4nlvzA/McouwhOpZbyIwmAADcCOgwM3jwYG3ZssVl2fjx49WpUyc98sgjlYKMlZzIK5IkxUXUomXG+eA8wgwAAGcL6DATHR2trl27uiyLjIxUYmJipeVWcyKvrJspoVZhhlcaAABQlYAeM9OQOcJMfGRojdueeQowY2YAADhbQLfMuLN8+XJ/l+ARJ3Pr0c1EywwAAJXQMuMHhcWlzvcyxTNmBgCAc0KY8YOTp8u6mGw2KbZJLbqZysfM5NHNBABAJYQZPzhZPpMptkmogoOqfi+TQ0T5mJk8WmYAAKiEMOMHJ8rfmF2bLiZJiggtb5nhoXkAAFRCmPEDx0ymuIiau5gkKSKsLMwUlxoVFpd6rS4AAKyIMOMHjgfm1eYZM5IUFhKkYFtZd9TpIlpnAACoiDDjB2daZmoXZmw2m5qEObqaGDcDAEBFhBk/cAwAjq9lN5N0pquJcTMAALgizPjBcccA4Fq8ZNKBMAMAgHuEGT84mVe32UwS07MBAKgKYcYPTtSjm8kxZuY0LTMAALggzPhBXQcAS3QzAQBQFcKMHzgfmleLN2Y7nOlmIswAAFARYcbHSkuNsk7X7TkzUsWnADNmBgCAiggzPpadX6RSU/bnunQzNaGbCQAAtwgzPuYY/BsZFqywkNpf/gg7A4ABAHCHMONj9XnGjCRFhDI1GwAAdwgzPlafZ8xIZ2YznS4qkTHG43UBAGBVhBkfc3Qz1faN2Q6OMFNqpJwCWmcAAHAgzPhYfVtmQoKDFBZc9td1MrfI43UBAGBVhBkfc4yZSajjmBnpzIwmx0P3AAAAYcbn6tvNJJ3paiLMAABwBmHGx+rbzSSdCTOOh+4BAADCjM+deS9T3VtmmpS/0sDxOgQAAECY8bkT5YN36zNm5kw3Ey0zAAA4EGZ87ATdTAAAeBRhxoeMMTp5LgOAQxkADADA2QgzPpRXWKLCklJJ9W2ZKR8zQzcTAABOhBkfcjxjJiwkyNllVBfObiZaZgAAcCLM+JCjiyk+IlQ2m63O+zMAGACAyggzPnQug3+lClOzaZkBAMCJMOND5/KMGelMy0xOfrGKy8feAADQ2BFmfOjEObyXSSp7N5Ojc+ok07MBAJBEmPGpM+9lql+YCbLZFO6Yns1TgAEAkESY8akz72WqXzeTJEXay8bNHCfMAAAgiTDjUyecs5nq1zIjSZHl42YIMwAAlCHM+NC5zmaSpAhHywwzmgAAkESY8SlnmIk8h26mMMbMAABQEWHGhxxvzK7vAGDpzJiZY4QZAAAkEWZ8yiPdTLTMAADggjDjIwXFJcorLJEkJXigZeY4rzQAAEASYcZnHO9lCrJJ0eEh9T7OmdlMBR6pCwAAqyPM+MiZVxmEKSio7i+ZdIhwvJ8pl5YZAAAkwozPOJ4LU9/3Mjnw0DwAAFwRZnzE0c10LuNlpDPdTKeLSnS6fAwOAACNGWHGRyp2M52LsJAghQWX/bXx4DwAAAgzPnPS+SqDc+tmstlszrduHz9FmAEAgDDjI44xLvGR59YyU/EYtMwAAECY8RlPPDDPIaH8dQg8OA8AAKn+DzxBnXiqm0mSEiLtknilQWNijNHKH4/oWG6hosND1SIuXF1axPq7LAAICAHdMjNz5kxdeumlio6OVvPmzTVmzBjt2LHD32XVi6cGAEtSQgQtM43N3mN5+u+2w1q774SW7cjU29/u156juf4uCwACQkCHmRUrVuiuu+7SN998oyVLlqi4uFhDhw5Vbq71/hF3BA9PtMwwZqbx2bD/hCQpNSFCLeObSJI2pp/0Y0UAEDgCupvps88+c/l63rx5at68udatW6df/OIXfqqqfk44njPjgQHAicxmalQKi0u15UCWJGlolySVlkpz/2+Pth7IUmFxqcJCAvp3EgDwuoAOM2fLyir7Bz0hIaHKbQoKClRQcOa9RdnZ2V6vqyYlpUbZ+WVhxhPdTLTMNC4/HMpWQXGp4iNC1SYxUpIUZQ/RqYJifb3ziK7olOTnCgHAvyzzK50xRg888ID69++vrl27VrndzJkzFRsb6/y0atXKh1W6l3W6SMaU/flcX2cgnXmKMGNmGof15V1MF7aKV5DNpiCbTd3OKxv8+/HGg/4sDQACgmXCzN13363NmzfrnXfeqXa76dOnKysry/lJT0/3UYVVc7zhOjo8RKHB537JE6LKW2YIMw1e9uki7cw8JUm6ODXOubxHy7Iw8/m2w7zWAkCjZ4kwc8899+jjjz/WsmXL1LJly2q3tdvtiomJcfn4W2ZOWZhpFm33yPGcLTN5hSotNR45JgLTtkPZMiob+JsYdeb+aZUQofiIUOUVlujL7Yf9VyAABICADjPGGN19991auHChli5dqrS0NH+XVC9HysNMcw+FGce4m1JT1oWFhuvnE3mSpHbNolyW22w253NmVu865vO6ACCQBPQA4LvuuksLFizQRx99pOjoaGVkZEiSYmNj1aRJEz9XV3tHnC0z4R45XlhIkKLDQ5STX6zjeYUeeUUCAlP6idOSpFbxle/3VgkRkqTNP2f5tCYACDQB3TIzZ84cZWVlaeDAgUpJSXF+3nvvPX+XVieebpmRzkzxZhBww5WdX6Sj5ffOeW7CTMu4smXbM7JVUMy4GQCNV0C3zBjTMMaDeHrMjFQWZvYdy+OVBg3Y1p+zZFQ2Ay46vPIsuLiIUCVEhul4bqF+OJSjC1vF+bxGAAgEAd0y01B4o2WmaflgUMex0fBs/PmkJKllfITb9bYKU7S3lG8LAI0RYcYHMnPyJXm2ZSYppuxYmdn5HjsmAsvm9LKxMO7Gyzg4pmhvYtwMgEaMMOMDR7zQzZRUPpj4cDYtMw3VphpaZiSpe8s4SdJmWmYANGKEGS8rLC51vpepuYdmM0lSUkx5mMmhZaYhOpydr0NZ+bJJahFX9X3TvbxlZmfmKeUWFPuoOgAILIQZLzt6qqzlJCTIprgm5/4qA4fm5d1MtMw0TJvK34idFBMue0hwlds1jwlXcky4So209QBdTQAaJ8KMl1WcyRQUZPPYcZNjy35bZ8xMw3Smi6nm5yk5Wmd43gyAxoow42XeGC8jnRkzcyy3UIXFpR49Nvxvy4Gyt727e77M2XqUT8nexLgZAI0UYcbLvDEtWyp7xkhY+UsrMxk30+BsP1QWZlJiah5n1bV8eva28n0AoLEhzHiZN6ZlS2XPGGHcTMN0PLfQ2T2ZVIsw0zklWpK092gub9AG0CgRZrzM0+9lqsjxg45xMw3L9oyyFpbUhAjZQ6se/OvQLMquhMgwlRrpp8wcb5cHAAGHMONl3niVgUOSs2WGMNOQbD9UFkg6JUfXanubzebcdnsGYQZA40OY8TJvjZkpO6bjWTN0MzUkjpaZTikxtd6nU3LZto4gBACNCWHGy7w1m0mq8OA8WmYaFEfrSudatsxIUqcUR8sMg4ABND6EGS8yxni1ZebM+5lomWkoSkqNdpSHmbq0zHQub5n54VB2g3nbPADUFmHGi7JOF6mwpOwZMI63XHsSLTMNz95juSooLlWT0GClJlT9TqazdUiKUpBNOpFXxJvUATQ6hBkvcvxQiW0SqvBazEqpKwYANzyOVpnzk6MVXIcnRoeHBqtN00hJ0g8MAgbQyBBmvMibM5mksvfySFJ2fjHPF2kgHA/Lq8t4GQdHV9MOxs0AaGQIM17kzfEykhRtD1GT8hYfngLcMDhaVTrWI8w4p2czowlAI0OY8aIDJ09LkpJr8RTX+rDZbBW6mhgn0RA4p2Un137wr4NjwDDdTAAaG8KMF+0/lidJSk2s/UDOumrOIOAGI+t0kdKPlwVgxysK6sLRMrMzM4eXjwJoVAgzXrTveK4kqbUXwwwzmhqOH8rHy5wX10RxEWF13r9lfBNFh4eoqMRoZ+YpT5cHAAGLMONFzpaZhEivnSOZGU0NxvcHy8LMBS3q3sUklXU7XlDe1fT9wSyP1QUAgY4w4yX5RSU6VB4wvNkykxLbRJL084nTXjsHfGNbeZjpUs8wU7ZvrKQzwQgAGgPCjJf8fCJPxkiRYcFKjKx7l0FtpTUra/XZfSTXa+eAbzhaUxyBpD66nkfLDIDGhzDjJfucg38jZbPV/uFnddWuaZQkac+xXJWW8hh7qyooLnGOc6lvN5N0JghtO5jN/QCg0SDMeIkjzLSuwyPp6+O8+CYKDbapsLhUB7PoarKqHzNOqbjUKC4iVC1i6z+Vv12zSNlDgpRbWKJ9x/M8WCEABC7CjJfsL/9B4s3xMpIUHGRT60S6mqxu2yFHF1PMObXkhQQHOado09UEoLEgzHjJvmNlwcKbz5hxaFv+Tp49RwkzVuWcyVSHN2VXpct5DAIG0LgQZrzE0cTf2ovTsh3ODALm2SJW9b1zJlP9B/86OGZDbT1AywyAxoEw4wUlpUY/lz/J1dvdTNKZlpndtMxYUkmpcT4w71ymZTtUHARsDIOAATR8hBkvyMjOV2FJqUKCbEo5h8GctdW2WdmMJsbMWNPeY7nKKyyRPSRIaU3PvSWvU3K0goNsOpZbyDu7ADQKhBkvcIyXaRnfRCHB3r/Ejh+AB7NOK7+oxOvng2et23tCktS9ZaxH7pfw0GC1Lw+4m38+ec7HA4BAR5jxgv0VnjHjC4mRYYoJD5ExZb/lw1q+23tcknRpmwSPHbNnm3hJ0pryYwNAQ0aY8YIzg3+9P15GKnsnT1r5b+J76GqynO/2lIeZNM+FmV7lx/p2D2EGQMNHmPECR6DwxeBfBwYBW9Ph7HztP54nm03q2TreY8e9rDzMbD2QpVMFxR47LgAEIsKMF2xMPylJ6nreuU+zrS1nmKFlxlIcrTKdk2MUEx7qseOmxDZRakKESo20lq4mAA0cYcbDDp48rYzsfAUH2dS9pe/CjONZM3uO8qwZK3GMabnMg11MDo5jfkdXE4AGjjDjYev2lc1M6ZwSrYiwEJ+d9/ykskfY/3AoR0UlpT47L86NI2gQZgCg/ggzHrZ+f1mYuTjVc+MfaqN9syjFR4TqdFEJ03EtIut0kXYczpHk2ZlMDo5BwJt+PqnThUzZB9Bw+a7poJFYv/+kJM8O5qyNoCCbeqUl6rPvM/TN7uPq2drzPxzhWev2HZcxZc8JahZt9/jxUxMilBwTrozsfG1IP6G+7Zp6/BwIDAu+3V/jNjf1SvVBJYB/0DLjQflFJdpW/qZiX7fMSFKfdomSpG92H/P5uVF3y7YfkXSmBcXTbDabs6tp9S7uCQANF2HGg7YeyFJRiVHTKLtaxjfx+fl7ty0LM2v3nlBhMeNmAllxSak+3XJIkjS8W4rXzjOwYzNJ0uLNh3hPE4AGizDjQY7Bvz1bx8lms/n8/B2aRykhMoxxMxawatcxHcstVEJkmPqWt6h5w9AuyQoPDdLuo7nawlu0ATRQhBkP8tfgX4egIJt6ty3rVqCrKbB9sumgJGl412SFevH9XVH2EA3pnCRJ+nDDQa+dBwD8iTDjIcUlpVpb/sLAi308+LciR1fTN7uZjhuoCopL9Nn3GZKkq3u08Pr5xlx4niTpk80HVVJKVxOAhocw4yFf/HBYx3IL1TQqTD1axvmtDue4mX3HVVDMdNxAtPLHo8rJL1ZSjN0rU7LP9ovzmykuIlRHcgq0atdRr58PAHyNMOMh//hmnyTpV5e2UliI/y5rh+ZRSo4JV35RqT7ZdMhvdaBq/1ybLkm6qnsLBQV5f2xVWEiQRpYPMl60/oDXzwffKzVGB06e1o6MHG3PyNb+Y7m0wqFR4TkzHrDryCn9385jstmk/73Mv89ysNlsGtevjf74n+2as3ynrr3oPJ/8wETtrNl7XJ9vO6wgW1nw9ZXrerbU29/u10ebDmrigHbqmBzts3PDe/YezdXfvtqtjzcdVE6+6wtFw0OD1KF5tPq1b6rUBN+99BbwB1pmPODtb8oeWDW4U3O1jPf/Pxo390pVTHiIdh3J1efbMvxdDsqVlho99ck2SdKvLk11voLCFy5OjdcvuySppNRoxsffM03b4k7mFeqpT7bpyj+t0Nvf7ldOfrHsIUFqERuu8+KaKCIsWPlFpdpyIEuvrtil+av26PuDzGZDw0XLzDnKyS/SB+vKug1u7t3az9WUiQ4P1di+bfTy0p36y7Jd+mWXZL9MFYerhRsOaMuBLEXZQ/TAlef7/Py/GXmBlu84otW7j+nTLRka2d17z7eBdxQUl+gfq/fppS9/UnZ5S8zAjs3UJjFSbZtGKqR8ZlypMfr5xGmt2XNcG9JP6MfDp3TVy1/rxktbaerQjkqM8vwTpwF/skTLzOzZs5WWlqbw8HD17NlTX331lb9LklQ2g+nuBRuUnV+sNokRGtChmb9LchrfL01NQoO15UCW/sU4Cb/bnpGtpz/9QZJ016D2Xnl9QU1aJURo0oB2kqSn/v290o/n+bwG1I8xRv/efFBDZq3Q7xf/oOz8YnVKjtY/br9M88dfpvOTop1BRpKCbDalJkToup4tdf+Q89W9ZayMkd75Ll0Dn1+uv3+1mwdrokEJ+DDz3nvvacqUKXrssce0YcMGXX755Ro+fLj276/5XSTeZIzRk59s04ofjyg8NEgv/e9FATU2JSEyTLf3T5MkPfzBJn20kUDjLxvTT+pXf/1Gx3MLdUFKjMb3a+O3Wn49sJ3SmkbqcHaBrp2ziq6HAFdSavTZ1kO6ZvYq3b1gg9KPn1bzaLuevb67Ft97uS6vxS9QiVF23Xhpqv45qY+6tIhRTn6xfr/4B/3yxZV657v9yi9i1iOsz2YCvPO8V69euvjiizVnzhznss6dO2vMmDGaOXNmjftnZ2crNjZWWVlZiomJ8Vhdc7/eo6f+vU02m/Tq/+upX3ZJ9tixa3ppXG1fGFdaavTooi16d026gmzSxAHtdEvv1moR5/tXLTRGP5/I07z/26sF3+7X6aISXdgqTm+Mv0yxEaG1Poan7oWKDmfna+zc77Q9I0dR9hCN7dta/693a6XEcl8EgsLiUm1MP6kvfzisxVsO6ecTpyVJEWHBmviLdrrjF2mKCHMdIVDbF02WlBp9sC5dz/13h46eKpRU9ovP8K7JuvKCJF3aJkGRdkYfIDDU5ed3QN+1hYWFWrdunaZNm+ayfOjQoVq1apWfqioTZQ9RSJBNDw/r6NEg40lBQTY9fU032Wxlzctzlu/Sayt368JWcerQPErnxTVRk7Bg2UODFR4SJHtosIJrMbamNsNv6tJG5UjTFWO1KV/qusyxXeX8XeO+xvUYFY/jcjTndqbCdrWrM7+oRCfyCnXgxGl9fzBbu46ckmN2bP/2TfXqLT0VFQA/KJJiwvXexD668821+nbPcf1l2S69umK3OjSP0gUpMUqODVekPUQRYcGKDAtReFhwpb/PiveA7ay1ruuqdvbf4tl/reasLSqvP3v/6n8vq/Px63i+Smev4XylRsorLFFuQbGOnSpQZk6B9hzN1c7MUyquMK06LiJUN/dK1dg+bdQ8Jvzss9RJcJBNv7o0VSO6pei9Nema9397deDkab397X69/e1+2WxSWmKk2jaLUnKsXYmRdoWHBis8NEjhocGyhwQpuEILtGMsns35dfl/VXEbud0GDUvbZlE+ndRwNv//y1qNo0ePqqSkRElJSS7Lk5KSlJHhfpZOQUGBCgoKnF9nZZU1o2dnZ3u0tmEdY9Xh9h5q2zTK48fOy82pdn1dzzdtcGtdkhKuBd/t15q9J7Tmx1yt+fFcKkRt9UpL0Lh+bdS/fVOVFuQpu6DmfSry9L3gYJM054bOWrYjU29/s19r953Qtn252rbvcL2OB8+KaxKifu2bamDH5hrYsbmahAVLKlR2dqHb7Wu6T6TK98oNPZrq2q4JWr37mJZtz9SKH4/ocHaBdh7I084DRzzxbaARmdA/TVM8PLHBcc/WpgMpoMOMw9kzcYwxVc7OmTlzpp588slKy1u18t0zPbztDn8XgFpLl/SBF4/PvdAwpUvaIulVDx6TewXe9MSL0hNeOnZOTo5iY2Or3Sagw0zTpk0VHBxcqRUmMzOzUmuNw/Tp0/XAAw84vy4tLdXx48eVmJhoienJ2dnZatWqldLT0z06xsequB6uuB6uuB6uuB6uuB6urHY9jDHKyclRixY1v8MuoMNMWFiYevbsqSVLluiaa65xLl+yZIlGjx7tdh+73S673XXaa1xcnDfL9IqYmBhL3Gy+wvVwxfVwxfVwxfVwxfVwZaXrUVOLjENAhxlJeuCBB3TLLbfokksuUZ8+ffTaa69p//79mjRpkr9LAwAAASDgw8yvfvUrHTt2TE899ZQOHTqkrl276tNPP1Xr1oHxtF0AAOBfAR9mJGny5MmaPHmyv8vwCbvdrieeeKJSV1ljxfVwxfVwxfVwxfVwxfVw1ZCvR8A/NA8AAKA6Af86AwAAgOoQZgAAgKURZgAAgKURZgAAgKURZvxg9uzZSktLU3h4uHr27Kmvvvqq2u1XrFihnj17Kjw8XG3bttWrr3ryIef+V5frsXz5ctlstkqf7du3+7Bi71i5cqVGjRqlFi1ayGaz6cMPP6xxn4Z+b9T1mjTk+2PmzJm69NJLFR0drebNm2vMmDHasWNHjfs11HukPtejId8fc+bMUffu3Z0PxOvTp4/+85//VLtPQ7o3CDM+9t5772nKlCl67LHHtGHDBl1++eUaPny49u/f73b7PXv2aMSIEbr88su1YcMGPfroo7r33nv1r3/9y8eVe0ddr4fDjh07dOjQIeenQ4cOPqrYe3Jzc9WjRw+98sortdq+od8bUt2viUNDvD9WrFihu+66S998842WLFmi4uJiDR06VLm5uVXu05DvkfpcD4eGeH+0bNlSf/zjH7V27VqtXbtWV1xxhUaPHq3vv//e7fYN7t4w8KnLLrvMTJo0yWVZp06dzLRp09xu//DDD5tOnTq5LJs4caLp3bu312r0pbpej2XLlhlJ5sSJEz6ozn8kmUWLFlW7TUO/N85Wm2vSWO4PY4zJzMw0ksyKFSuq3KYx3SO1uR6N6f4wxpj4+Hjz97//3e26hnZv0DLjQ4WFhVq3bp2GDh3qsnzo0KFatWqV231Wr15daftf/vKXWrt2rYqKirxWqy/U53o4XHTRRUpJSdHgwYO1bNkyb5YZsBryvXGuGsP9kZWVJUlKSEiocpvGdI/U5no4NPT7o6SkRO+++65yc3PVp08ft9s0tHuDMONDR48eVUlJSaU3ficlJVV6M7hDRkaG2+2Li4t19OhRr9XqC/W5HikpKXrttdf0r3/9SwsXLlTHjh01ePBgrVy50hclB5SGfG/UV2O5P4wxeuCBB9S/f3917dq1yu0ayz1S2+vR0O+PLVu2KCoqSna7XZMmTdKiRYt0wQUXuN22od0blnidQUNjs9lcvjbGVFpW0/bulltVXa5Hx44d1bFjR+fXffr0UXp6up5//nn94he/8Gqdgaih3xt11Vjuj7vvvlubN2/W119/XeO2jeEeqe31aOj3R8eOHbVx40adPHlS//rXvzR27FitWLGiykDTkO4NWmZ8qGnTpgoODq7U6pCZmVkpITskJye73T4kJESJiYleq9UX6nM93Ondu7d++uknT5cX8BryveFJDe3+uOeee/Txxx9r2bJlatmyZbXbNoZ7pC7Xw52GdH+EhYWpffv2uuSSSzRz5kz16NFDf/7zn91u29DuDcKMD4WFhalnz55asmSJy/IlS5aob9++bvfp06dPpe0///xzXXLJJQoNDfVarb5Qn+vhzoYNG5SSkuLp8gJeQ743PKmh3B/GGN19991auHChli5dqrS0tBr3acj3SH2uhzsN5f5wxxijgoICt+sa3L3hp4HHjda7775rQkNDzeuvv262bdtmpkyZYiIjI83evXuNMcZMmzbN3HLLLc7td+/ebSIiIsz9999vtm3bZl5//XUTGhpqPvjgA399Cx5V1+vxpz/9ySxatMj8+OOPZuvWrWbatGlGkvnXv/7lr2/BY3JycsyGDRvMhg0bjCQza9Yss2HDBrNv3z5jTOO7N4yp+zVpyPfHr3/9axMbG2uWL19uDh065Pzk5eU5t2lM90h9rkdDvj+mT59uVq5cafbs2WM2b95sHn30URMUFGQ+//xzY0zDvzcIM37wl7/8xbRu3dqEhYWZiy++2GUq4dixY82AAQNctl++fLm56KKLTFhYmGnTpo2ZM2eOjyv2rrpcj2eeeca0a9fOhIeHm/j4eNO/f3+zePFiP1TteY5po2d/xo4da4xpnPdGXa9JQ74/3F0HSWbevHnObRrTPVKf69GQ74/bbrvN+e9os2bNzODBg51BxpiGf2/YjCkf8QMAAGBBjJkBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBUKVx48bJZrPJZrMpNDRUSUlJuvLKKzV37lyVlpY6t2vTpo1zu4qfP/7xj1q3bp1sNluVbzT+5S9/qauvvtp5vjFjxrisz8jI0D333KO2bdvKbrerVatWGjVqlL788stanX/GjBlu11X8DB48WN26dVNhYaHLuT/99FOFhoZq7dq12rt3r8s+8fHx+sUvfqEVK1a4vV4VP8OGDTvXvwoA1QjxdwEAAtuwYcM0b948lZSU6PDhw/rss89033336YMPPtDHH3+skJCyf0aeeuop3XHHHS77RkdHKzIyUj169NC8efPUv39/l/Xp6en64osvtHDhQrfn3rt3r/r166e4uDg9++yz6t69u4qKivTf//5Xd911l7Zv3+7ctqrzG2M0adIk57JLL71Ud955p8u2TZo0UY8ePfTEE09o5syZkqSTJ0/qzjvv1GOPPaZLLrlEe/fulSR98cUX6tKlizIzM/Xoo49qxIgR2rp1q/NFh47rVZHdbq/xOgOoP8IMgGrZ7XYlJydLks477zxdfPHF6t27twYPHqz58+drwoQJksqCg2O7s91+++169NFH9dJLLykyMtK5fP78+WrWrJlGjhzpdr/JkyfLZrPpu+++c9mvS5cuuu2221y2re78UVFRzj8HBwe73Xb+/PkaOnSoxowZo169emnKlClKSUnRb37zG5ftEhMTlZycrOTkZP31r39Vy5Yt9fnnn2vixImVrhcA36CbCUCdXXHFFerRo0eVLSpnu/nmm1VUVKR//vOfzmXGGM2fP19jx451tu5UdPz4cX322We66667XIKMQ1xcXL3rd2fgwIGaPHmyxo4dq3/+8596//339eabb7qtzSEiIkKSVFRU5NFaANQNYQZAvXTq1MnZ9SJJjzzyiKKiolw+y5cvlyQlJCRozJgxLt0vy5cv1+7duyu1sDjs3LlTxhh16tSpVvVUd/7amjlzpmw2m2688UY9/fTT6ty5c5Xb5ubmavr06QoODtaAAQOcy//9739XquN3v/tdneoAUDd0MwGoF2OMbDab8+uHHnpI48aNc9nmvPPOc/759ttv19ChQ7Vz5061b99ec+fOVb9+/dSxY8cqjy/J5RzVqen8tdGkSRM9+OCDuv/++3Xfffe53aZv374KCgpSXl6eUlJSNH/+fHXr1s25ftCgQZozZ47LPgkJCXWqA0DdEGYA1MsPP/zgHPQqSU2bNlX79u2r3H7IkCFq3bq15s+fr4cfflgLFy7UK6+8UuX2HTp0kM1m0w8//FBphpM7NZ2/tkJCQhQcHFxliHrvvfd0wQUXKC4uTomJiZXWR0ZGeqQOALVHNxOAOlu6dKm2bNmi6667rtb72Gw2jR8/Xm+88YYWLFigoKAg3XDDDVVun5CQoF/+8pf6y1/+otzc3ErrT548WZ/Sz1mrVq3Url07t0EGgH/QMgOgWgUFBcrIyHCZmj1z5kxdddVVuvXWW53b5eTkKCMjw2XfiIgIxcTEOL8eP368nnrqKT366KO68cYb3Q7srWj27Nnq27evLrvsMj311FPq3r27iouLtWTJEs2ZM0c//PBDnc7vC47rVVFISIiaNm3q0zqAxoSWGQDV+uyzz5SSkqI2bdpo2LBhWrZsmV566SV99NFHCg4Odm73+OOPKyUlxeXz8MMPuxwrNTVVQ4YM0YkTJ6oc+FtRWlqa1q9fr0GDBunBBx9U165ddeWVV+rLL7+sNC6lNuf3Bcf1qvg5+/k6ADzLZhyj7AAAACyIlhkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBp/x8TbZbaXt4WJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the distribution of data for the below three columns using seaborn\n",
    "list=['DEVICETYPE','PLATFORMTYPE','CHANNELTYPE']\n",
    "for i in list:\n",
    "    sns.distplot(df[i])\n",
    "    plt.title(\"Distribution of data in\" +i)\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4fbc10e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   DEVICETYPE    113937 non-null  int32  \n",
      " 1   PLATFORM_ID   113937 non-null  int64  \n",
      " 2   PLATFORMTYPE  113937 non-null  int32  \n",
      " 3   CHANNELTYPE   113937 non-null  int32  \n",
      " 4   KEYWORDS      113937 non-null  int32  \n",
      " 5   IS_HCP        113936 non-null  float64\n",
      "dtypes: float64(1), int32(4), int64(1)\n",
      "memory usage: 3.5 MB\n"
     ]
    }
   ],
   "source": [
    "#As below columns are not useful to predict helathcare professionals. so, we can drop that columns\n",
    "x_t=df.drop(['ID','BIDREQUESTIP','USERPLATFORMUID','USERCITY','USERZIPCODE','USERAGENT','URL','TAXONOMY',],axis=1)\n",
    "x_t.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6469050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check any null values present in target column\n",
    "x_t['IS_HCP'].isnull().sum()\n",
    "#we are getting one null value. so, we have to remove that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0dc3c7df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#We are filling that na value with 1\n",
    "x_t['IS_HCP']=x_t['IS_HCP'].fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "facc5d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now no values are present\n",
    "x_t['IS_HCP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0b787ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting y to target column for prediction\n",
    "x_train=x_t.drop(['IS_HCP'],axis=1)\n",
    "y_train=x_t['IS_HCP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47cc0255",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To read test dataset\n",
    "df1=pd.read_csv('C:\\\\Users\\\\nithi\\\\Downloads\\\\Doceree-HCP_Test.csv',encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "201aedaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "list=['DEVICETYPE','PLATFORMTYPE','CHANNELTYPE','KEYWORDS']\n",
    "for i in list:\n",
    "    df1[i]=le.fit_transform(df1[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29f572ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28493 entries, 0 to 28492\n",
      "Data columns (total 12 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               28493 non-null  int64  \n",
      " 1   DEVICETYPE       28493 non-null  int32  \n",
      " 2   PLATFORM_ID      28493 non-null  int64  \n",
      " 3   BIDREQUESTIP     28493 non-null  object \n",
      " 4   USERPLATFORMUID  28493 non-null  object \n",
      " 5   USERCITY         26934 non-null  object \n",
      " 6   USERZIPCODE      27378 non-null  float64\n",
      " 7   USERAGENT        28492 non-null  object \n",
      " 8   PLATFORMTYPE     28493 non-null  int32  \n",
      " 9   CHANNELTYPE      28493 non-null  int32  \n",
      " 10  URL              28493 non-null  object \n",
      " 11  KEYWORDS         28493 non-null  int32  \n",
      "dtypes: float64(1), int32(4), int64(2), object(5)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#to get to know info about test datset\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bfb49b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#samething like we are dropping unusual columns from testdataset also\n",
    "x_test=df1.drop(['ID','BIDREQUESTIP','USERPLATFORMUID','USERCITY','USERZIPCODE','USERAGENT','URL'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a5093ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the Random forest classifier algorithm and creating instance\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(n_estimators=200,max_depth=4,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "199e926c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fitting the model for training dataset\n",
    "model1=rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b35281e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To get to know the which feature is more important for prediciton\n",
    "i_df=pd.DataFrame({ \n",
    "    \"Feature Name\" : x_train.columns,\n",
    "    \"Importance\" : rf.feature_importances_\n",
    "})    \n",
    "f1=i_df.sort_values(by=\"Importance\",ascending=False)\n",
    "f2=f1.head(10)\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(data=f2,x=\"Importance\",y=\"Feature Name\")\n",
    "plt.title(\"Importance of Features for this robust model\")\n",
    "plt.xlabel(\"Importance\",fontsize=16)\n",
    "plt.ylabel(\"Feature Name\",fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b636913",
   "metadata": {},
   "outputs": [],
   "source": [
    "#classifying the values for test dataset\n",
    "y_pred=model1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1037b994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing predicted values\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dbdda3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dataframe for predicted values\n",
    "b=pd.DataFrame(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ef67a076",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Predicted Values are:          0\n",
      "0      0.0\n",
      "1      0.0\n",
      "2      0.0\n",
      "3      0.0\n",
      "4      0.0\n",
      "...    ...\n",
      "28488  0.0\n",
      "28489  0.0\n",
      "28490  0.0\n",
      "28491  0.0\n",
      "28492  0.0\n",
      "\n",
      "[28493 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"The Predicted Values are:\",b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3cd89fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=df1['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c820659f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        115501\n",
       "1        115502\n",
       "2        115503\n",
       "3        115504\n",
       "4        115505\n",
       "          ...  \n",
       "28488    143989\n",
       "28489    143990\n",
       "28490    143991\n",
       "28491    143992\n",
       "28492    143993\n",
       "Name: ID, Length: 28493, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b83d306",
   "metadata": {},
   "outputs": [],
   "source": [
    "frames=[a,b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0394d9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "res=pd.concat(frames,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "03908889",
   "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>ID</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>115501</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115502</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>115503</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115504</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115505</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28488</th>\n",
       "      <td>143989</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28489</th>\n",
       "      <td>143990</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28490</th>\n",
       "      <td>143991</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28491</th>\n",
       "      <td>143992</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28492</th>\n",
       "      <td>143993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28493 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID    0\n",
       "0      115501  0.0\n",
       "1      115502  0.0\n",
       "2      115503  0.0\n",
       "3      115504  0.0\n",
       "4      115505  0.0\n",
       "...       ...  ...\n",
       "28488  143989  0.0\n",
       "28489  143990  0.0\n",
       "28490  143991  0.0\n",
       "28491  143992  0.0\n",
       "28492  143993  0.0\n",
       "\n",
       "[28493 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e2e96a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finally saving our final results to csv format\n",
    "res.to_csv(\"C:\\\\Users\\\\nithi\\\\Downloads\\\\submission.csv\")"
   ]
  }
 ],
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