Janta_NLP_Hackathon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "######### Importing Libraries ##############\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat_1 = pd.read_csv(r'C:\\Users\\NAVIN\\Desktop\\Janta Hack\\train.csv') ## Reading train dataset\n",
    "dat_2 = pd.read_csv(r'C:\\Users\\NAVIN\\Desktop\\Janta Hack\\game_overview.csv') ## Reading game overview\n",
    "test_dat = pd.read_csv(r'C:\\Users\\NAVIN\\Desktop\\Janta Hack\\test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_id            int64\n",
       "title               object\n",
       "year               float64\n",
       "user_review         object\n",
       "user_suggestion      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17494, 5)\n",
      "   review_id                        title    year  \\\n",
      "0          1  Spooky's Jump Scare Mansion  2016.0   \n",
      "1          2  Spooky's Jump Scare Mansion  2016.0   \n",
      "2          3  Spooky's Jump Scare Mansion  2016.0   \n",
      "3          4  Spooky's Jump Scare Mansion  2015.0   \n",
      "4          5  Spooky's Jump Scare Mansion  2015.0   \n",
      "\n",
      "                                         user_review  user_suggestion  \n",
      "0  I'm scared and hearing creepy voices.  So I'll...                1  \n",
      "1  Best game, more better than Sam Pepper's YouTu...                1  \n",
      "2  A littly iffy on the controls, but once you kn...                1  \n",
      "3  Great game, fun and colorful and all that.A si...                1  \n",
      "4  Not many games have the cute tag right next to...                1  \n"
     ]
    }
   ],
   "source": [
    "print(dat_1.shape)\n",
    "print(dat_1.head()) ## Top rows of train _dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 5)\n",
      "                              title                          developer  \\\n",
      "0       Spooky's Jump Scare Mansion                       Lag Studios    \n",
      "1                    Sakura Clicker                      Winged Cloud    \n",
      "2                           WARMODE                           WARTEAM    \n",
      "3                   Fractured Space              Edge Case Games Ltd.    \n",
      "4  Counter-Strike: Global Offensive  Valve, Hidden Path Entertainment    \n",
      "\n",
      "               publisher                                               tags  \\\n",
      "0           Lag Studios   ['Horror', 'Free to Play', 'Cute', 'First-Pers...   \n",
      "1          Winged Cloud   ['Nudity', 'Anime', 'Free to Play', 'Mature', ...   \n",
      "2               WARTEAM   ['Early Access', 'Free to Play', 'FPS', 'Multi...   \n",
      "3  Edge Case Games Ltd.   ['Space', 'Multiplayer', 'Free to Play', 'PvP'...   \n",
      "4                 Valve   ['FPS', 'Multiplayer', 'Shooter', 'Action', 'T...   \n",
      "\n",
      "                                            overview  \n",
      "0  Can you survive 1000 rooms of cute terror? Or ...  \n",
      "1  The latest entry in the Sakura series is more ...  \n",
      "2  Free to play shooter about the confrontation o...  \n",
      "3  Take the helm of a gigantic capital ship and g...  \n",
      "4  Counter-Strike: Global Offensive (CS: GO) expa...  \n"
     ]
    }
   ],
   "source": [
    "print(dat_2.shape)\n",
    "print(dat_2.head()) ## To rows of test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title        64\n",
       "developer    59\n",
       "publisher    54\n",
       "tags         64\n",
       "overview     62\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_2.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "review_id            0\n",
      "title                0\n",
      "year               178\n",
      "user_review          0\n",
      "user_suggestion      0\n",
      "dtype: int64\n",
      "review_id       0\n",
      "title           0\n",
      "year           67\n",
      "user_review     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## Checking missing values ##\n",
    "print(dat_1.isnull().sum())\n",
    "print(test_dat.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Imputing missing values by mode ##\n",
    "dat_1['year'] = dat_1['year'].astype('object')\n",
    "test_dat['year'] = test_dat['year'].astype('object')\n",
    "dat_1['year'] = dat_1['year'].fillna(dat_1['year'].mode().iloc[0])\n",
    "test_dat['year'] = test_dat['year'].fillna(test_dat['year'].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "review_id          0\n",
      "title              0\n",
      "year               0\n",
      "user_review        0\n",
      "user_suggestion    0\n",
      "dtype: int64\n",
      "review_id      0\n",
      "title          0\n",
      "year           0\n",
      "user_review    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## Checking missing values ##\n",
    "print(dat_1.isnull().sum())\n",
    "print(test_dat.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>user_review</th>\n",
       "      <th>user_suggestion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>I'm scared and hearing creepy voices.  So I'll...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Best game, more better than Sam Pepper's YouTu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>A littly iffy on the controls, but once you kn...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Great game, fun and colorful and all that.A si...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Not many games have the cute tag right next to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id                        title    year  \\\n",
       "0          1  Spooky's Jump Scare Mansion  2016.0   \n",
       "1          2  Spooky's Jump Scare Mansion  2016.0   \n",
       "2          3  Spooky's Jump Scare Mansion  2016.0   \n",
       "3          4  Spooky's Jump Scare Mansion  2015.0   \n",
       "4          5  Spooky's Jump Scare Mansion  2015.0   \n",
       "\n",
       "                                         user_review  user_suggestion  \n",
       "0  I'm scared and hearing creepy voices.  So I'll...                1  \n",
       "1  Best game, more better than Sam Pepper's YouTu...                1  \n",
       "2  A littly iffy on the controls, but once you kn...                1  \n",
       "3  Great game, fun and colorful and all that.A si...                1  \n",
       "4  Not many games have the cute tag right next to...                1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Checking top rows of Train dataset ###\n",
    "dat_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>title</th>\n",
       "      <th>developer</th>\n",
       "      <th>publisher</th>\n",
       "      <th>tags</th>\n",
       "      <th>overview</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>Lag Studios</td>\n",
       "      <td>Lag Studios</td>\n",
       "      <td>['Horror', 'Free to Play', 'Cute', 'First-Pers...</td>\n",
       "      <td>Can you survive 1000 rooms of cute terror? Or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sakura Clicker</td>\n",
       "      <td>Winged Cloud</td>\n",
       "      <td>Winged Cloud</td>\n",
       "      <td>['Nudity', 'Anime', 'Free to Play', 'Mature', ...</td>\n",
       "      <td>The latest entry in the Sakura series is more ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WARMODE</td>\n",
       "      <td>WARTEAM</td>\n",
       "      <td>WARTEAM</td>\n",
       "      <td>['Early Access', 'Free to Play', 'FPS', 'Multi...</td>\n",
       "      <td>Free to play shooter about the confrontation o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fractured Space</td>\n",
       "      <td>Edge Case Games Ltd.</td>\n",
       "      <td>Edge Case Games Ltd.</td>\n",
       "      <td>['Space', 'Multiplayer', 'Free to Play', 'PvP'...</td>\n",
       "      <td>Take the helm of a gigantic capital ship and g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>Valve, Hidden Path Entertainment</td>\n",
       "      <td>Valve</td>\n",
       "      <td>['FPS', 'Multiplayer', 'Shooter', 'Action', 'T...</td>\n",
       "      <td>Counter-Strike: Global Offensive (CS: GO) expa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              title                          developer  \\\n",
       "0       Spooky's Jump Scare Mansion                       Lag Studios    \n",
       "1                    Sakura Clicker                      Winged Cloud    \n",
       "2                           WARMODE                           WARTEAM    \n",
       "3                   Fractured Space              Edge Case Games Ltd.    \n",
       "4  Counter-Strike: Global Offensive  Valve, Hidden Path Entertainment    \n",
       "\n",
       "               publisher                                               tags  \\\n",
       "0           Lag Studios   ['Horror', 'Free to Play', 'Cute', 'First-Pers...   \n",
       "1          Winged Cloud   ['Nudity', 'Anime', 'Free to Play', 'Mature', ...   \n",
       "2               WARTEAM   ['Early Access', 'Free to Play', 'FPS', 'Multi...   \n",
       "3  Edge Case Games Ltd.   ['Space', 'Multiplayer', 'Free to Play', 'PvP'...   \n",
       "4                 Valve   ['FPS', 'Multiplayer', 'Shooter', 'Action', 'T...   \n",
       "\n",
       "                                            overview  \n",
       "0  Can you survive 1000 rooms of cute terror? Or ...  \n",
       "1  The latest entry in the Sakura series is more ...  \n",
       "2  Free to play shooter about the confrontation o...  \n",
       "3  Take the helm of a gigantic capital ship and g...  \n",
       "4  Counter-Strike: Global Offensive (CS: GO) expa...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>user_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1603</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Nice graphics, new maps, weapons and models. B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1604</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>I would not recommend getting into this at its...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1605</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Edit 11/12/18I have tried playing CS:GO recent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1606</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>The game is great. But the community is the wo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1607</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>I thank TrulyRazor for buying this for me a lo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id                             title    year  \\\n",
       "0       1603  Counter-Strike: Global Offensive  2015.0   \n",
       "1       1604  Counter-Strike: Global Offensive  2018.0   \n",
       "2       1605  Counter-Strike: Global Offensive  2018.0   \n",
       "3       1606  Counter-Strike: Global Offensive  2015.0   \n",
       "4       1607  Counter-Strike: Global Offensive  2015.0   \n",
       "\n",
       "                                         user_review  \n",
       "0  Nice graphics, new maps, weapons and models. B...  \n",
       "1  I would not recommend getting into this at its...  \n",
       "2  Edit 11/12/18I have tried playing CS:GO recent...  \n",
       "3  The game is great. But the community is the wo...  \n",
       "4  I thank TrulyRazor for buying this for me a lo...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    9968\n",
       "0    7526\n",
       "Name: user_suggestion, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking class imbalance ##\n",
    "dat_1[\"user_suggestion\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Coverting into text data ###\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "stp = stopwords.words(\"English\")\n",
    "stp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Removing punctuation and topwords\n",
    "def text_process(sugg):\n",
    "    nopunc = [char for char in sugg if char not in string.punctuation]\n",
    "    nopunc = \"\".join(nopunc)\n",
    "    return [word for word in nopunc.split() if word not in stp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Extracting user review and user suggestion ###\n",
    "dat_tr = dat_1\n",
    "dat_ts = test_dat\n",
    "\n",
    "## Making all the characters into lower case##\n",
    "dat_tr['user_review'] = dat_tr['user_review'].apply(str.lower)\n",
    "dat_ts['user_review'] = dat_ts['user_review'].apply(str.lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Removing stopwords ##\n",
    "dc_tr = dat_tr.user_review.apply(text_process)\n",
    "dc_tr = pd.concat([dc_tr,dat_tr['user_suggestion']],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>user_review</th>\n",
       "      <th>user_suggestion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17489</th>\n",
       "      <td>[arguably, single, greatest, mmorp, exists, to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17490</th>\n",
       "      <td>[older, game, sure, charm, holds, special, pla...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17491</th>\n",
       "      <td>[frist, started, playing, everquest, 2, amazin...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17492</th>\n",
       "      <td>[cool, game, thing, really, pisses, ridable, t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17493</th>\n",
       "      <td>[game, since, little, kid, always, since, stil...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             user_review  user_suggestion\n",
       "17489  [arguably, single, greatest, mmorp, exists, to...                1\n",
       "17490  [older, game, sure, charm, holds, special, pla...                1\n",
       "17491  [frist, started, playing, everquest, 2, amazin...                1\n",
       "17492  [cool, game, thing, really, pisses, ridable, t...                1\n",
       "17493  [game, since, little, kid, always, since, stil...                1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_tr.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>user_review</th>\n",
       "      <th>user_suggestion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[somewhere, light, hearted, happy, parody, afr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>[nice, elements, overall, quite, boring, repet...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>[boring, jump, scares, 0, effect, youre, one, ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>[hell, rubbish, theres, nothing, go, one, door...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>[bad, game, waaaay, slow, practically, nothing...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           user_review  user_suggestion\n",
       "8    [somewhere, light, hearted, happy, parody, afr...                0\n",
       "91   [nice, elements, overall, quite, boring, repet...                0\n",
       "93   [boring, jump, scares, 0, effect, youre, one, ...                0\n",
       "99   [hell, rubbish, theres, nothing, go, one, door...                0\n",
       "100  [bad, game, waaaay, slow, practically, nothing...                0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend = dc_tr[dc_tr['user_suggestion']==1]\n",
    "not_recommend = dc_tr[dc_tr['user_suggestion']==0]\n",
    "\n",
    "not_recommend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f9e5b03a90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Word Cloud ##\n",
    "### Build the word cloud\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "cloud = WordCloud().generate(str(recommend.user_review))\n",
    "\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f9e5bb89b0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cloud = WordCloud().generate(str(not_recommend.user_review))\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>review_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>user_review</th>\n",
       "      <th>user_suggestion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>im scared and hearing creepy voices  so ill pa...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>best game more better than sam peppers youtube...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>a littly iffy on the controls but once you kno...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>great game fun and colorful and all thata side...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Spooky's Jump Scare Mansion</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>not many games have the cute tag right next to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id                        title    year  \\\n",
       "0          1  Spooky's Jump Scare Mansion  2016.0   \n",
       "1          2  Spooky's Jump Scare Mansion  2016.0   \n",
       "2          3  Spooky's Jump Scare Mansion  2016.0   \n",
       "3          4  Spooky's Jump Scare Mansion  2015.0   \n",
       "4          5  Spooky's Jump Scare Mansion  2015.0   \n",
       "\n",
       "                                         user_review  user_suggestion  \n",
       "0  im scared and hearing creepy voices  so ill pa...                1  \n",
       "1  best game more better than sam peppers youtube...                1  \n",
       "2  a littly iffy on the controls but once you kno...                1  \n",
       "3  great game fun and colorful and all thata side...                1  \n",
       "4  not many games have the cute tag right next to...                1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Removing punctuation and numbers ##\n",
    "import re\n",
    "\n",
    "for i in range(0,dat_tr.shape[0]):\n",
    "    dat_tr.loc[i,'user_review']= re.sub(r'['+string.punctuation+']','',dat_tr.loc[i,'user_review'])\n",
    "    dat_tr.loc[i,'user_review']= re.sub(r'\\d+','',dat_tr.loc[i,'user_review'])\n",
    "\n",
    "dat_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>review_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>user_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1603</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>nice graphics new maps weapons and models but ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1604</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>i would not recommend getting into this at its...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1605</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>edit i have tried playing csgo recently and it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1606</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>the game is great but the community is the wor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1607</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>i thank trulyrazor for buying this for me a lo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id                             title    year  \\\n",
       "0       1603  Counter-Strike: Global Offensive  2015.0   \n",
       "1       1604  Counter-Strike: Global Offensive  2018.0   \n",
       "2       1605  Counter-Strike: Global Offensive  2018.0   \n",
       "3       1606  Counter-Strike: Global Offensive  2015.0   \n",
       "4       1607  Counter-Strike: Global Offensive  2015.0   \n",
       "\n",
       "                                         user_review  \n",
       "0  nice graphics new maps weapons and models but ...  \n",
       "1  i would not recommend getting into this at its...  \n",
       "2  edit i have tried playing csgo recently and it...  \n",
       "3  the game is great but the community is the wor...  \n",
       "4  i thank trulyrazor for buying this for me a lo...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,dat_ts.shape[0]):\n",
    "    dat_ts.loc[i,'user_review']= re.sub('['+string.punctuation+']','',dat_ts.loc[i,'user_review'])\n",
    "    dat_ts.loc[i,'user_review']= re.sub(r'\\d+','',dat_ts.loc[i,'user_review'])\n",
    "\n",
    "dat_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Removing numbers ###\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_id             int64\n",
       "title                object\n",
       "year                float64\n",
       "user_review          object\n",
       "user_suggestion    category\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_tr['user_suggestion'] = dat_tr['user_suggestion'].astype('category')\n",
    "dat_tr.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>title_AdventureQuest 3D</th>\n",
       "      <th>title_Aura Kingdom</th>\n",
       "      <th>title_Black Squad</th>\n",
       "      <th>title_Bless Online</th>\n",
       "      <th>title_Bloons TD Battles</th>\n",
       "      <th>title_Brawlhalla</th>\n",
       "      <th>title_Business Tour - Board Game with Online Multiplayer</th>\n",
       "      <th>title_Champions Online</th>\n",
       "      <th>title_Counter-Strike: Global Offensive</th>\n",
       "      <th>...</th>\n",
       "      <th>title_Tree of Savior (English Ver.)</th>\n",
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       "      <th>title_VEGA Conflict</th>\n",
       "      <th>title_WARMODE</th>\n",
       "      <th>title_War Thunder</th>\n",
       "      <th>title_World of Guns: Gun Disassembly</th>\n",
       "      <th>title_World of Tanks Blitz</th>\n",
       "      <th>title_World of Warships</th>\n",
       "      <th>title_Yu-Gi-Oh! Duel Links</th>\n",
       "      <th>title_theHunter Classic</th>\n",
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       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id  title_AdventureQuest 3D  title_Aura Kingdom  title_Black Squad  \\\n",
       "0          1                        0                   0                  0   \n",
       "1          2                        0                   0                  0   \n",
       "2          3                        0                   0                  0   \n",
       "3          4                        0                   0                  0   \n",
       "4          5                        0                   0                  0   \n",
       "\n",
       "   title_Bless Online  title_Bloons TD Battles  title_Brawlhalla  \\\n",
       "0                   0                        0                 0   \n",
       "1                   0                        0                 0   \n",
       "2                   0                        0                 0   \n",
       "3                   0                        0                 0   \n",
       "4                   0                        0                 0   \n",
       "\n",
       "   title_Business Tour - Board Game with Online Multiplayer  \\\n",
       "0                                                  0          \n",
       "1                                                  0          \n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "\n",
       "   title_Champions Online  title_Counter-Strike: Global Offensive  ...  \\\n",
       "0                       0                                       0  ...   \n",
       "1                       0                                       0  ...   \n",
       "2                       0                                       0  ...   \n",
       "3                       0                                       0  ...   \n",
       "4                       0                                       0  ...   \n",
       "\n",
       "   title_Tree of Savior (English Ver.)  title_Trove  title_VEGA Conflict  \\\n",
       "0                                    0            0                    0   \n",
       "1                                    0            0                    0   \n",
       "2                                    0            0                    0   \n",
       "3                                    0            0                    0   \n",
       "4                                    0            0                    0   \n",
       "\n",
       "   title_WARMODE  title_War Thunder  title_World of Guns: Gun Disassembly  \\\n",
       "0              0                  0                                     0   \n",
       "1              0                  0                                     0   \n",
       "2              0                  0                                     0   \n",
       "3              0                  0                                     0   \n",
       "4              0                  0                                     0   \n",
       "\n",
       "   title_World of Tanks Blitz  title_World of Warships  \\\n",
       "0                           0                        0   \n",
       "1                           0                        0   \n",
       "2                           0                        0   \n",
       "3                           0                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   title_Yu-Gi-Oh! Duel Links  title_theHunter Classic  \n",
       "0                           0                        0  \n",
       "1                           0                        0  \n",
       "2                           0                        0  \n",
       "3                           0                        0  \n",
       "4                           0                        0  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Creating dummy variables of title ###\n",
    "dat = pd.concat([dat_1.loc[:,['review_id','title']],test_dat.loc[:,['review_id','title']]],axis = 0,ignore_index = True)\n",
    "dat = pd.get_dummies(dat)\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17494, 69)\n",
      "(8045, 68)\n"
     ]
    }
   ],
   "source": [
    "## Merging with data ##\n",
    "dat_tr = pd.merge(dat_tr,dat,on = \"review_id\")\n",
    "dat_ts = pd.merge(dat_ts,dat,on = \"review_id\")\n",
    "\n",
    "print(dat_tr.shape)\n",
    "print(dat_ts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>user_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1603</td>\n",
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       "      <td>2015.0</td>\n",
       "      <td>nice graphics new maps weapons and models but ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1604</td>\n",
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       "      <td>2018.0</td>\n",
       "      <td>i would not recommend getting into this at its...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1605</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>edit i have tried playing csgo recently and it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1606</td>\n",
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       "      <td>the game is great but the community is the wor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1607</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>i thank trulyrazor for buying this for me a lo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id                             title    year  \\\n",
       "0       1603  Counter-Strike: Global Offensive  2015.0   \n",
       "1       1604  Counter-Strike: Global Offensive  2018.0   \n",
       "2       1605  Counter-Strike: Global Offensive  2018.0   \n",
       "3       1606  Counter-Strike: Global Offensive  2015.0   \n",
       "4       1607  Counter-Strike: Global Offensive  2015.0   \n",
       "\n",
       "                                         user_review  \n",
       "0  nice graphics new maps weapons and models but ...  \n",
       "1  i would not recommend getting into this at its...  \n",
       "2  edit i have tried playing csgo recently and it...  \n",
       "3  the game is great but the community is the wor...  \n",
       "4  i thank trulyrazor for buying this for me a lo...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   review_id                        title    year  \\\n",
      "0          1  Spooky's Jump Scare Mansion  2016.0   \n",
      "1          2  Spooky's Jump Scare Mansion  2016.0   \n",
      "2          3  Spooky's Jump Scare Mansion  2016.0   \n",
      "3          4  Spooky's Jump Scare Mansion  2015.0   \n",
      "4          5  Spooky's Jump Scare Mansion  2015.0   \n",
      "\n",
      "                                         user_review user_suggestion  \\\n",
      "0  im scared and hearing creepy voices  so ill pa...               1   \n",
      "1  best game more better than sam peppers youtube...               1   \n",
      "2  a littly iffy on the controls but once you kno...               1   \n",
      "3  great game fun and colorful and all thata side...               1   \n",
      "4  not many games have the cute tag right next to...               1   \n",
      "\n",
      "   title_AdventureQuest 3D  title_Aura Kingdom  title_Black Squad  \\\n",
      "0                        0                   0                  0   \n",
      "1                        0                   0                  0   \n",
      "2                        0                   0                  0   \n",
      "3                        0                   0                  0   \n",
      "4                        0                   0                  0   \n",
      "\n",
      "   title_Bless Online  title_Bloons TD Battles  ...  \\\n",
      "0                   0                        0  ...   \n",
      "1                   0                        0  ...   \n",
      "2                   0                        0  ...   \n",
      "3                   0                        0  ...   \n",
      "4                   0                        0  ...   \n",
      "\n",
      "   title_Tree of Savior (English Ver.)  title_Trove  title_VEGA Conflict  \\\n",
      "0                                    0            0                    0   \n",
      "1                                    0            0                    0   \n",
      "2                                    0            0                    0   \n",
      "3                                    0            0                    0   \n",
      "4                                    0            0                    0   \n",
      "\n",
      "   title_WARMODE  title_War Thunder  title_World of Guns: Gun Disassembly  \\\n",
      "0              0                  0                                     0   \n",
      "1              0                  0                                     0   \n",
      "2              0                  0                                     0   \n",
      "3              0                  0                                     0   \n",
      "4              0                  0                                     0   \n",
      "\n",
      "   title_World of Tanks Blitz  title_World of Warships  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           0                        0   \n",
      "3                           0                        0   \n",
      "4                           0                        0   \n",
      "\n",
      "   title_Yu-Gi-Oh! Duel Links  title_theHunter Classic  \n",
      "0                           0                        0  \n",
      "1                           0                        0  \n",
      "2                           0                        0  \n",
      "3                           0                        0  \n",
      "4                           0                        0  \n",
      "\n",
      "[5 rows x 69 columns]\n",
      "   review_id                             title    year  \\\n",
      "0       1603  Counter-Strike: Global Offensive  2015.0   \n",
      "1       1604  Counter-Strike: Global Offensive  2018.0   \n",
      "2       1605  Counter-Strike: Global Offensive  2018.0   \n",
      "3       1606  Counter-Strike: Global Offensive  2015.0   \n",
      "4       1607  Counter-Strike: Global Offensive  2015.0   \n",
      "\n",
      "                                         user_review  title_AdventureQuest 3D  \\\n",
      "0  nice graphics new maps weapons and models but ...                        0   \n",
      "1  i would not recommend getting into this at its...                        0   \n",
      "2  edit i have tried playing csgo recently and it...                        0   \n",
      "3  the game is great but the community is the wor...                        0   \n",
      "4  i thank trulyrazor for buying this for me a lo...                        0   \n",
      "\n",
      "   title_Aura Kingdom  title_Black Squad  title_Bless Online  \\\n",
      "0                   0                  0                   0   \n",
      "1                   0                  0                   0   \n",
      "2                   0                  0                   0   \n",
      "3                   0                  0                   0   \n",
      "4                   0                  0                   0   \n",
      "\n",
      "   title_Bloons TD Battles  title_Brawlhalla  ...  \\\n",
      "0                        0                 0  ...   \n",
      "1                        0                 0  ...   \n",
      "2                        0                 0  ...   \n",
      "3                        0                 0  ...   \n",
      "4                        0                 0  ...   \n",
      "\n",
      "   title_Tree of Savior (English Ver.)  title_Trove  title_VEGA Conflict  \\\n",
      "0                                    0            0                    0   \n",
      "1                                    0            0                    0   \n",
      "2                                    0            0                    0   \n",
      "3                                    0            0                    0   \n",
      "4                                    0            0                    0   \n",
      "\n",
      "   title_WARMODE  title_War Thunder  title_World of Guns: Gun Disassembly  \\\n",
      "0              0                  0                                     0   \n",
      "1              0                  0                                     0   \n",
      "2              0                  0                                     0   \n",
      "3              0                  0                                     0   \n",
      "4              0                  0                                     0   \n",
      "\n",
      "   title_World of Tanks Blitz  title_World of Warships  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           0                        0   \n",
      "3                           0                        0   \n",
      "4                           0                        0   \n",
      "\n",
      "   title_Yu-Gi-Oh! Duel Links  title_theHunter Classic  \n",
      "0                           0                        0  \n",
      "1                           0                        0  \n",
      "2                           0                        0  \n",
      "3                           0                        0  \n",
      "4                           0                        0  \n",
      "\n",
      "[5 rows x 68 columns]\n"
     ]
    }
   ],
   "source": [
    "##################### Using Textblob #####################\n",
    "print(dat_tr.head())\n",
    "print(dat_ts.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "######### Using Textblob ############\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     year                                        user_review user_suggestion  \\\n",
      "0  2016.0  im scared and hearing creepy voices  so ill pa...               1   \n",
      "1  2016.0  best game more better than sam peppers youtube...               1   \n",
      "2  2016.0  a littly iffy on the controls but once you kno...               1   \n",
      "3  2015.0  great game fun and colorful and all thata side...               1   \n",
      "4  2015.0  not many games have the cute tag right next to...               1   \n",
      "\n",
      "   title_AdventureQuest 3D  title_Aura Kingdom  title_Black Squad  \\\n",
      "0                        0                   0                  0   \n",
      "1                        0                   0                  0   \n",
      "2                        0                   0                  0   \n",
      "3                        0                   0                  0   \n",
      "4                        0                   0                  0   \n",
      "\n",
      "   title_Bless Online  title_Bloons TD Battles  title_Brawlhalla  \\\n",
      "0                   0                        0                 0   \n",
      "1                   0                        0                 0   \n",
      "2                   0                        0                 0   \n",
      "3                   0                        0                 0   \n",
      "4                   0                        0                 0   \n",
      "\n",
      "   title_Business Tour - Board Game with Online Multiplayer  ...  \\\n",
      "0                                                  0         ...   \n",
      "1                                                  0         ...   \n",
      "2                                                  0         ...   \n",
      "3                                                  0         ...   \n",
      "4                                                  0         ...   \n",
      "\n",
      "   title_Tree of Savior (English Ver.)  title_Trove  title_VEGA Conflict  \\\n",
      "0                                    0            0                    0   \n",
      "1                                    0            0                    0   \n",
      "2                                    0            0                    0   \n",
      "3                                    0            0                    0   \n",
      "4                                    0            0                    0   \n",
      "\n",
      "   title_WARMODE  title_War Thunder  title_World of Guns: Gun Disassembly  \\\n",
      "0              0                  0                                     0   \n",
      "1              0                  0                                     0   \n",
      "2              0                  0                                     0   \n",
      "3              0                  0                                     0   \n",
      "4              0                  0                                     0   \n",
      "\n",
      "   title_World of Tanks Blitz  title_World of Warships  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           0                        0   \n",
      "3                           0                        0   \n",
      "4                           0                        0   \n",
      "\n",
      "   title_Yu-Gi-Oh! Duel Links  title_theHunter Classic  \n",
      "0                           0                        0  \n",
      "1                           0                        0  \n",
      "2                           0                        0  \n",
      "3                           0                        0  \n",
      "4                           0                        0  \n",
      "\n",
      "[5 rows x 67 columns]\n",
      "     year                                        user_review  \\\n",
      "0  2015.0  nice graphics new maps weapons and models but ...   \n",
      "1  2018.0  i would not recommend getting into this at its...   \n",
      "2  2018.0  edit i have tried playing csgo recently and it...   \n",
      "3  2015.0  the game is great but the community is the wor...   \n",
      "4  2015.0  i thank trulyrazor for buying this for me a lo...   \n",
      "\n",
      "   title_AdventureQuest 3D  title_Aura Kingdom  title_Black Squad  \\\n",
      "0                        0                   0                  0   \n",
      "1                        0                   0                  0   \n",
      "2                        0                   0                  0   \n",
      "3                        0                   0                  0   \n",
      "4                        0                   0                  0   \n",
      "\n",
      "   title_Bless Online  title_Bloons TD Battles  title_Brawlhalla  \\\n",
      "0                   0                        0                 0   \n",
      "1                   0                        0                 0   \n",
      "2                   0                        0                 0   \n",
      "3                   0                        0                 0   \n",
      "4                   0                        0                 0   \n",
      "\n",
      "   title_Business Tour - Board Game with Online Multiplayer  \\\n",
      "0                                                  0          \n",
      "1                                                  0          \n",
      "2                                                  0          \n",
      "3                                                  0          \n",
      "4                                                  0          \n",
      "\n",
      "   title_Champions Online  ...  title_Tree of Savior (English Ver.)  \\\n",
      "0                       0  ...                                    0   \n",
      "1                       0  ...                                    0   \n",
      "2                       0  ...                                    0   \n",
      "3                       0  ...                                    0   \n",
      "4                       0  ...                                    0   \n",
      "\n",
      "   title_Trove  title_VEGA Conflict  title_WARMODE  title_War Thunder  \\\n",
      "0            0                    0              0                  0   \n",
      "1            0                    0              0                  0   \n",
      "2            0                    0              0                  0   \n",
      "3            0                    0              0                  0   \n",
      "4            0                    0              0                  0   \n",
      "\n",
      "   title_World of Guns: Gun Disassembly  title_World of Tanks Blitz  \\\n",
      "0                                     0                           0   \n",
      "1                                     0                           0   \n",
      "2                                     0                           0   \n",
      "3                                     0                           0   \n",
      "4                                     0                           0   \n",
      "\n",
      "   title_World of Warships  title_Yu-Gi-Oh! Duel Links  \\\n",
      "0                        0                           0   \n",
      "1                        0                           0   \n",
      "2                        0                           0   \n",
      "3                        0                           0   \n",
      "4                        0                           0   \n",
      "\n",
      "   title_theHunter Classic  \n",
      "0                        0  \n",
      "1                        0  \n",
      "2                        0  \n",
      "3                        0  \n",
      "4                        0  \n",
      "\n",
      "[5 rows x 66 columns]\n"
     ]
    }
   ],
   "source": [
    "#### Creating Textdata\n",
    "txt_dt_tr = pd.DataFrame(dat_tr.iloc[:,2:len(dat_tr)])\n",
    "txt_dt_ts = pd.DataFrame(dat_ts.iloc[:,2:len(dat_ts)])\n",
    "print(txt_dt_tr.head())\n",
    "print(txt_dt_ts.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "########## Creating two new variables polarity score and subjectivity ############\n",
    "def polarity(text):\n",
    "    try:\n",
    "        return TextBlob(text).polarity\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "def subjectivity(text):\n",
    "    try:\n",
    "        return TextBlob(text).subjectivity\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "txt_dt_tr['polarity'] = txt_dt_tr['user_review'].apply(polarity)\n",
    "txt_dt_tr['subjectivity'] = txt_dt_tr['user_review'].apply(subjectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_dt_ts['polarity'] = txt_dt_ts['user_review'].apply(polarity)\n",
    "txt_dt_ts['subjectivity'] = txt_dt_ts['user_review'].apply(subjectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     year                                        user_review  user_suggestion  \\\n",
      "0  2016.0  im scared and hearing creepy voices  so ill pa...                1   \n",
      "1  2016.0  best game more better than sam peppers youtube...                1   \n",
      "2  2016.0  a littly iffy on the controls but once you kno...                1   \n",
      "3  2015.0  great game fun and colorful and all thata side...                1   \n",
      "4  2015.0  not many games have the cute tag right next to...                1   \n",
      "\n",
      "   polarity  subjectivity  \n",
      "0 -0.080556      0.677778  \n",
      "1  0.228571      0.471429  \n",
      "2  0.011374      0.577554  \n",
      "3  0.214286      0.550000  \n",
      "4  0.045408      0.457313  \n",
      "     year                                        user_review  polarity  \\\n",
      "0  2015.0  nice graphics new maps weapons and models but ...  0.239394   \n",
      "1  2018.0  i would not recommend getting into this at its... -0.204293   \n",
      "2  2018.0  edit i have tried playing csgo recently and it...  0.023715   \n",
      "3  2015.0  the game is great but the community is the wor... -0.067803   \n",
      "4  2015.0  i thank trulyrazor for buying this for me a lo...  0.006981   \n",
      "\n",
      "   subjectivity  \n",
      "0      0.559091  \n",
      "1      0.394949  \n",
      "2      0.513000  \n",
      "3      0.603409  \n",
      "4      0.501034  \n"
     ]
    }
   ],
   "source": [
    "print(txt_dt_tr.head())\n",
    "print(txt_dt_ts.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     year user_suggestion  title_AdventureQuest 3D  title_Aura Kingdom  \\\n",
      "0  2016.0               1                        0                   0   \n",
      "1  2016.0               1                        0                   0   \n",
      "2  2016.0               1                        0                   0   \n",
      "3  2015.0               1                        0                   0   \n",
      "4  2015.0               1                        0                   0   \n",
      "\n",
      "   title_Black Squad  title_Bless Online  title_Bloons TD Battles  \\\n",
      "0                  0                   0                        0   \n",
      "1                  0                   0                        0   \n",
      "2                  0                   0                        0   \n",
      "3                  0                   0                        0   \n",
      "4                  0                   0                        0   \n",
      "\n",
      "   title_Brawlhalla  title_Business Tour - Board Game with Online Multiplayer  \\\n",
      "0                 0                                                  0          \n",
      "1                 0                                                  0          \n",
      "2                 0                                                  0          \n",
      "3                 0                                                  0          \n",
      "4                 0                                                  0          \n",
      "\n",
      "   title_Champions Online  ...  title_VEGA Conflict  title_WARMODE  \\\n",
      "0                       0  ...                    0              0   \n",
      "1                       0  ...                    0              0   \n",
      "2                       0  ...                    0              0   \n",
      "3                       0  ...                    0              0   \n",
      "4                       0  ...                    0              0   \n",
      "\n",
      "   title_War Thunder  title_World of Guns: Gun Disassembly  \\\n",
      "0                  0                                     0   \n",
      "1                  0                                     0   \n",
      "2                  0                                     0   \n",
      "3                  0                                     0   \n",
      "4                  0                                     0   \n",
      "\n",
      "   title_World of Tanks Blitz  title_World of Warships  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           0                        0   \n",
      "3                           0                        0   \n",
      "4                           0                        0   \n",
      "\n",
      "   title_Yu-Gi-Oh! Duel Links  title_theHunter Classic  polarity  subjectivity  \n",
      "0                           0                        0 -0.080556      0.677778  \n",
      "1                           0                        0  0.228571      0.471429  \n",
      "2                           0                        0  0.011374      0.577554  \n",
      "3                           0                        0  0.214286      0.550000  \n",
      "4                           0                        0  0.045408      0.457313  \n",
      "\n",
      "[5 rows x 68 columns]\n",
      "     year  title_AdventureQuest 3D  title_Aura Kingdom  title_Black Squad  \\\n",
      "0  2015.0                        0                   0                  0   \n",
      "1  2018.0                        0                   0                  0   \n",
      "2  2018.0                        0                   0                  0   \n",
      "3  2015.0                        0                   0                  0   \n",
      "4  2015.0                        0                   0                  0   \n",
      "\n",
      "   title_Bless Online  title_Bloons TD Battles  title_Brawlhalla  \\\n",
      "0                   0                        0                 0   \n",
      "1                   0                        0                 0   \n",
      "2                   0                        0                 0   \n",
      "3                   0                        0                 0   \n",
      "4                   0                        0                 0   \n",
      "\n",
      "   title_Business Tour - Board Game with Online Multiplayer  \\\n",
      "0                                                  0          \n",
      "1                                                  0          \n",
      "2                                                  0          \n",
      "3                                                  0          \n",
      "4                                                  0          \n",
      "\n",
      "   title_Champions Online  title_Counter-Strike: Global Offensive  ...  \\\n",
      "0                       0                                       1  ...   \n",
      "1                       0                                       1  ...   \n",
      "2                       0                                       1  ...   \n",
      "3                       0                                       1  ...   \n",
      "4                       0                                       1  ...   \n",
      "\n",
      "   title_VEGA Conflict  title_WARMODE  title_War Thunder  \\\n",
      "0                    0              0                  0   \n",
      "1                    0              0                  0   \n",
      "2                    0              0                  0   \n",
      "3                    0              0                  0   \n",
      "4                    0              0                  0   \n",
      "\n",
      "   title_World of Guns: Gun Disassembly  title_World of Tanks Blitz  \\\n",
      "0                                     0                           0   \n",
      "1                                     0                           0   \n",
      "2                                     0                           0   \n",
      "3                                     0                           0   \n",
      "4                                     0                           0   \n",
      "\n",
      "   title_World of Warships  title_Yu-Gi-Oh! Duel Links  \\\n",
      "0                        0                           0   \n",
      "1                        0                           0   \n",
      "2                        0                           0   \n",
      "3                        0                           0   \n",
      "4                        0                           0   \n",
      "\n",
      "   title_theHunter Classic  polarity  subjectivity  \n",
      "0                        0  0.239394      0.559091  \n",
      "1                        0 -0.204293      0.394949  \n",
      "2                        0  0.023715      0.513000  \n",
      "3                        0 -0.067803      0.603409  \n",
      "4                        0  0.006981      0.501034  \n",
      "\n",
      "[5 rows x 67 columns]\n"
     ]
    }
   ],
   "source": [
    "txt_dt_tr = txt_dt_tr.drop(columns = ['user_review'])\n",
    "txt_dt_ts = txt_dt_ts.drop(columns = ['user_review'])\n",
    "print(txt_dt_tr.head())\n",
    "print(txt_dt_ts.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13120, 67)\n",
      "(13120,)\n",
      "(4374, 67)\n",
      "(4374,)\n"
     ]
    }
   ],
   "source": [
    "##### Splitting Data in Train and Test #####\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = txt_dt_tr.drop(columns = ['user_suggestion'])\n",
    "y = txt_dt_tr.loc[:,'user_suggestion']\n",
    "\n",
    "x_trn, x_tst, y_trn, y_tst = train_test_split(x,y,test_size = 0.25, random_state = 101)\n",
    "\n",
    "### Train ###\n",
    "print(x_trn.shape)\n",
    "print(y_trn.shape)\n",
    "\n",
    "### Test ###\n",
    "print(x_tst.shape)\n",
    "print(y_tst.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 32, 35, 38, 41, 44, 47, 50, 53, 56, 60, None], 'min_samples_split': [1, 2, 3, 4], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 1000, stop = 1200, num = 200)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(1, 60, num = 20)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [1,2,3,4]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  7.3min\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed: 13.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 1086, 'min_samples_split': 3, 'min_samples_leaf': 4, 'max_features': 'auto', 'max_depth': 35, 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "randm = RandomForestClassifier(random_state = 43)\n",
    "random = RandomizedSearchCV(estimator = randm, param_distributions = random_grid, cv = 5, verbose=2, random_state=42, n_jobs = -1)\n",
    "random.fit(x_trn,y_trn)\n",
    "\n",
    "print(random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Random Forest Model #####\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "randm = RandomForestClassifier(n_estimators = 1086,min_samples_split = 3,max_features = 'auto',min_samples_leaf = 4,max_depth = 35,bootstrap = True,random_state = 73)\n",
    "\n",
    "#random = RandomizedSearchCV(estimator = randm, param_distributions = random_grid, cv = 5, verbose=2, random_state=42, n_jobs = -1)\n",
    "#random.fit(x_trn,y_trn)\n",
    "\n",
    "# print results\n",
    "#print(random.best_params_)\n",
    "\n",
    "## Fitting the model ##\n",
    "randm_model = randm.fit(x_trn,y_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Fitting the model ##\n",
    "# randm_model = randm.fit(x_trn,y_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting Values ##\n",
    "pred_value_rf = randm.predict(x_tst)\n",
    "pred_value_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, roc_auc_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1291,  570],\n",
       "       [ 364, 2149]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Confusion matrix ##\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "\n",
    "confusion_matrix(y_tst, pred_value_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.780     0.694     0.734      1861\n",
      "           1      0.790     0.855     0.821      2513\n",
      "\n",
      "    accuracy                          0.786      4374\n",
      "   macro avg      0.785     0.774     0.778      4374\n",
      "weighted avg      0.786     0.786     0.784      4374\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Classification Report ##\n",
    "print(classification_report(y_tst, pred_value_rf, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "########### GBM Model ###########\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "gbm = GradientBoostingClassifier(learning_rate=0.05, n_estimators=500,max_depth=3, min_samples_split=4,min_samples_leaf = 1,max_features='sqrt', random_state=43)\n",
    "gbm_mdl = gbm.fit(x_trn,y_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.781     0.706     0.742      1861\n",
      "           1      0.797     0.853     0.824      2513\n",
      "\n",
      "    accuracy                          0.791      4374\n",
      "   macro avg      0.789     0.780     0.783      4374\n",
      "weighted avg      0.790     0.791     0.789      4374\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_gbm = gbm_mdl.predict(x_tst)\n",
    "print(classification_report(y_tst, pred_gbm,digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   review_id  0\n",
       "0       1603  1\n",
       "1       1604  0\n",
       "2       1605  0\n",
       "3       1606  1\n",
       "4       1607  1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Final Prediction ####\n",
    "\n",
    "final_predict = pd.DataFrame(gbm_mdl.predict(txt_dt_ts))\n",
    "\n",
    "\n",
    "finl_sub = pd.concat([test_dat['review_id'],final_predict[0]],axis = 1)\n",
    "finl_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "finl_sub.to_csv(r'C:\\Users\\NAVIN\\Desktop\\Janta Hack\\final_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NAVIN\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:823: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.05, 'n_estimators': 500}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_test3 = {'learning_rate':[0.05,0.011,0.01], 'n_estimators':[500,750,1000]}\n",
    "\n",
    "tuning = GridSearchCV(estimator =GradientBoostingClassifier(max_depth=4, subsample=1,max_features='sqrt', random_state=10), \n",
    "            param_grid = p_test3, scoring='accuracy',n_jobs=4,iid=False, cv=5)\n",
    "tuning.fit(x_trn,y_trn)\n",
    "tuning.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NAVIN\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:823: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_test2 = {'max_depth':[2,3,4,5,6,7,8,9,10] }\n",
    "tuning = GridSearchCV(estimator =GradientBoostingClassifier(learning_rate=0.05,n_estimators=500, min_samples_split=2, min_samples_leaf=1, subsample=1,max_features='sqrt', random_state=10), \n",
    "            param_grid = p_test2, scoring='accuracy',n_jobs=4,iid=False, cv=5)\n",
    "tuning.fit(x_trn,y_trn)\n",
    "tuning.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NAVIN\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:823: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 1, 'min_samples_split': 4}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_test4 = {'min_samples_split':[2,4,6,8,10,20,40,60,100], 'min_samples_leaf':[1,3,5,7,9]}\n",
    "\n",
    "tuning = GridSearchCV(estimator =GradientBoostingClassifier(learning_rate=0.05, n_estimators=500,max_depth=3, subsample=1,max_features='sqrt', random_state=10), \n",
    "            param_grid = p_test4, scoring='accuracy',n_jobs=4,iid=False, cv=5)\n",
    "tuning.fit(x_trn,y_trn)\n",
    "tuning.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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