Naive_Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB as GNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       user_id  movie_id  rating  unix_timestamp\n",
      "24         308         1       4       887736532\n",
      "454        287         1       5       875334088\n",
      "957        148         1       4       877019411\n",
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      "1333         5         1       4       875635748\n",
      "1364       109         1       4       880563619\n",
      "1379       181         1       3       878962392\n",
      "1523        95         1       5       879197329\n",
      "1928       268         1       3       875742341\n",
      "2496       189         1       5       893264174\n",
      "2731       145         1       3       882181396\n",
      "3056       158         1       4       880132443\n",
      "3241        67         1       3       875379445\n",
      "3411       232         1       4       880062302\n",
      "3528       150         1       4       878746441\n",
      "3579       289         1       3       876789736\n",
      "4145       117         1       4       880126083\n",
      "4160        49         1       2       888068651\n",
      "4790       223         1       4       891549324\n",
      "5767        56         1       4       892683248\n",
      "6041        17         1       4       885272579\n",
      "6208       340         1       5       884990988\n",
      "6450       177         1       3       880130699\n",
      "7067       194         1       4       879539127\n",
      "7127       250         1       4       883263374\n",
      "7602       213         1       2       878870719\n",
      "7910       350         1       4       882345734\n",
      "7922       348         1       4       886523078\n",
      "7975       131         1       4       883681384\n",
      "...        ...       ...     ...             ...\n",
      "95015      495         1       4       888632943\n",
      "95101      582         1       4       882961257\n",
      "95178      892         1       5       886608185\n",
      "95280      701         1       4       891447139\n",
      "95630      721         1       5       877137877\n",
      "95857      880         1       4       880166744\n",
      "96320      927         1       5       879191524\n",
      "96366      434         1       4       886724590\n",
      "96370      865         1       1       880143424\n",
      "96388      552         1       3       879221716\n",
      "96875      923         1       3       880387306\n",
      "97000      692         1       4       876953340\n",
      "97574      917         1       3       882910888\n",
      "97627       21         1       5       874951244\n",
      "97690      902         1       5       879465583\n",
      "97776      748         1       4       879455040\n",
      "97857      938         1       4       891356314\n",
      "98332      932         1       4       891249932\n",
      "98516      941         1       5       875049144\n",
      "98713      907         1       5       880158712\n",
      "99261      265         1       5       875320247\n",
      "99282      709         1       4       879847730\n",
      "99359      445         1       3       891199749\n",
      "99373      398         1       5       875652927\n",
      "99387      682         1       4       888523054\n",
      "99423      895         1       4       879437950\n",
      "99445      747         1       5       888639138\n",
      "99631      786         1       4       882841828\n",
      "99704      800         1       4       887646283\n",
      "99941      593         1       3       875659150\n",
      "\n",
      "[452 rows x 4 columns]\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
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       "      <th>2</th>\n",
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       "      <td>23</td>\n",
       "      <td>M</td>\n",
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      ],
      "text/plain": [
       "   user_id  age sex  occupation zip_code\n",
       "0        1   24   M  technician    85711\n",
       "1        2   53   F       other    94043\n",
       "2        3   23   M      writer    32067\n",
       "3        4   24   M  technician    43537\n",
       "4        5   33   F       other    15213"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading users file:\n",
    "u_cols = ['user_id','age','sex','occupation','zip_code']\n",
    "users = pd.read_csv('ml-100k/u.user', sep='|', \\\n",
    "                    names=u_cols,encoding='latin-1')\n",
    "\n",
    "#Reading ratings file:\n",
    "r_cols = ['user_id','movie_id','rating','unix_timestamp']\n",
    "df = pd.read_csv('ml-100k/u.data',sep='\\t',names=r_cols,\\\n",
    "                     encoding='latin-1')\n",
    "\n",
    "#Reading the movie file:\n",
    "item_cols = ['movie id','movie title','release date',\n",
    "'video release date','IMDb URL','unknown','Action',\n",
    "'Adventure','Animation','Childrens','Comedy','Crime',\n",
    "'Documentary','Drama','Fantasy','Film-Noir','Horror',\n",
    "'Musical','Mystery','Romance ','Sci-Fi','Thriller',\n",
    "'War' ,'Western']\n",
    "\n",
    "movies = pd.read_csv('ml-100k/u.item', sep='|',names=item_cols,\\\n",
    "                   encoding='latin-1')\n",
    "\n",
    "df2  = df.copy()\n",
    "\n",
    "df2.head()\n",
    "\n",
    "groups = df.groupby(\"movie_id\", as_index=False)\n",
    "for group in groups:\n",
    "    toy_stor = group\n",
    "    break\n",
    "toy_story = toy_stor[1].copy()\n",
    "print((toy_story) )\n",
    "\n",
    "users_rating = users.copy()\n",
    "\n",
    "movies.head()\n",
    "\n",
    "df_1_toy = users.copy()\n",
    "\n",
    "df_1_toy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1_toy.occupation.unique()\n",
    "occupations = ['technician', 'other', 'writer', 'executive', 'administrator',\n",
    "       'student', 'lawyer', 'educator', 'scientist', 'entertainment',\n",
    "       'programmer', 'librarian', 'homemaker', 'artist', 'engineer',\n",
    "       'marketing', 'none', 'healthcare', 'retired', 'salesman', 'doctor']\n",
    "nums = list(range(len(occupations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1_toy.sex.replace(['M', 'F'], [1, 0], inplace=True)\n",
    "df_1_toy.occupation.replace(occupations, nums, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
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      "text/plain": [
       "   user_id  age  sex  occupation zip_code\n",
       "0        1   24    1           0    85711\n",
       "1        2   53    0           1    94043\n",
       "2        3   23    1           2    32067\n",
       "3        4   24    1           0    43537\n",
       "4        5   33    0           1    15213"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1_toy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_movie = pd.merge(df_1_toy,toy_story)\n",
    "user_movie = pd.merge(df_1_toy,toy_story,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 5)\n"
     ]
    }
   ],
   "source": [
    "print(df_1_toy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>91344</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>8</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>05201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>01002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>6</td>\n",
       "      <td>90703</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>877888877.0</td>\n",
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       "      <td>11</td>\n",
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       "      <td>1</td>\n",
       "      <td>30329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>06405</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>29206</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>882140487.0</td>\n",
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       "      <td>14</td>\n",
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       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>55106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>97301</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>879455635.0</td>\n",
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       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>877717833.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>06355</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>885272579.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37212</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>880130802.0</td>\n",
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       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>02138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>95660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>879667963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>874951244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>40206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>48197</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>874784615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>94533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>55107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>885853415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>21044</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>891350625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>30030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>55369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>94043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>55436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>914</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>08105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>915</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>60614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>916</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>N2L5N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>880843361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>917</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>20006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>882910888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>918</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>70116</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>891987059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>919</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14216</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>875289321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>920</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>90008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>921</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>98801</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>879379601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>922</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21114</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>891448551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>923</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>E2E3R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>880387306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>924</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11753</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>884371535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>925</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>49036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>926</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>01701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>927</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>879191524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>928</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>55408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>929</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>53711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>878402162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>930</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>07310</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>879534525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>931</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>33556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>932</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>06437</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>891249932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>933</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>48105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>874854294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>934</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>22902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>891225958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>935</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>66221</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>884472064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>936</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>886832453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>937</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>98072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>938</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>891356314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>939</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>33319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>940</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>02215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>941</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>97229</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>875049144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>942</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>78209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>943</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>77841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>943 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  age  sex  occupation zip_code  movie_id  rating  unix_timestamp\n",
       "0          1   24    1           0    85711       1.0     5.0     874965758.0\n",
       "1          2   53    0           1    94043       1.0     4.0     888550871.0\n",
       "2          3   23    1           2    32067       NaN     NaN             NaN\n",
       "3          4   24    1           0    43537       NaN     NaN             NaN\n",
       "4          5   33    0           1    15213       1.0     4.0     875635748.0\n",
       "5          6   42    1           3    98101       1.0     4.0     883599478.0\n",
       "6          7   57    1           4    91344       NaN     NaN             NaN\n",
       "7          8   36    1           4    05201       NaN     NaN             NaN\n",
       "8          9   29    1           5    01002       NaN     NaN             NaN\n",
       "9         10   53    1           6    90703       1.0     4.0     877888877.0\n",
       "10        11   39    0           1    30329       NaN     NaN             NaN\n",
       "11        12   28    0           1    06405       NaN     NaN             NaN\n",
       "12        13   47    1           7    29206       1.0     3.0     882140487.0\n",
       "13        14   45    1           8    55106       NaN     NaN             NaN\n",
       "14        15   49    0           7    97301       1.0     1.0     879455635.0\n",
       "15        16   21    1           9    10309       1.0     5.0     877717833.0\n",
       "16        17   30    1          10    06355       1.0     4.0     885272579.0\n",
       "17        18   35    0           1    37212       1.0     5.0     880130802.0\n",
       "18        19   40    1          11    02138       NaN     NaN             NaN\n",
       "19        20   42    0          12    95660       1.0     3.0     879667963.0\n",
       "20        21   26    1           2    30068       1.0     5.0     874951244.0\n",
       "21        22   25    1           2    40206       NaN     NaN             NaN\n",
       "22        23   30    0          13    48197       1.0     5.0     874784615.0\n",
       "23        24   21    0          13    94533       NaN     NaN             NaN\n",
       "24        25   39    1          14    55107       1.0     5.0     885853415.0\n",
       "25        26   49    1          14    21044       1.0     3.0     891350625.0\n",
       "26        27   40    0          11    30030       NaN     NaN             NaN\n",
       "27        28   32    1           2    55369       NaN     NaN             NaN\n",
       "28        29   41    1          10    94043       NaN     NaN             NaN\n",
       "29        30    7    1           5    55436       NaN     NaN             NaN\n",
       "..       ...  ...  ...         ...      ...       ...     ...             ...\n",
       "913      914   44    0           1    08105       NaN     NaN             NaN\n",
       "914      915   50    1           9    60614       NaN     NaN             NaN\n",
       "915      916   27    1          14    N2L5N       1.0     4.0     880843361.0\n",
       "916      917   22    0           5    20006       1.0     3.0     882910888.0\n",
       "917      918   40    1           8    70116       1.0     3.0     891987059.0\n",
       "918      919   25    1           1    14216       1.0     4.0     875289321.0\n",
       "919      920   30    0          13    90008       NaN     NaN             NaN\n",
       "920      921   20    0           5    98801       1.0     3.0     879379601.0\n",
       "921      922   29    0           4    21114       1.0     5.0     891448551.0\n",
       "922      923   21    1           5    E2E3R       1.0     3.0     880387306.0\n",
       "923      924   29    1           1    11753       1.0     5.0     884371535.0\n",
       "924      925   18    0          19    49036       NaN     NaN             NaN\n",
       "925      926   49    1           9    01701       NaN     NaN             NaN\n",
       "926      927   23    1          10    55428       1.0     5.0     879191524.0\n",
       "927      928   21    1           5    55408       NaN     NaN             NaN\n",
       "928      929   44    1           8    53711       1.0     3.0     878402162.0\n",
       "929      930   28    0           8    07310       1.0     3.0     879534525.0\n",
       "930      931   60    1           7    33556       NaN     NaN             NaN\n",
       "931      932   58    1           7    06437       1.0     4.0     891249932.0\n",
       "932      933   28    1           5    48105       1.0     3.0     874854294.0\n",
       "933      934   61    1          14    22902       1.0     2.0     891225958.0\n",
       "934      935   42    1          20    66221       1.0     3.0     884472064.0\n",
       "935      936   24    1           1    32789       1.0     4.0     886832453.0\n",
       "936      937   48    1           7    98072       NaN     NaN             NaN\n",
       "937      938   38    0           0    55038       1.0     4.0     891356314.0\n",
       "938      939   26    0           5    33319       NaN     NaN             NaN\n",
       "939      940   32    1           4    02215       NaN     NaN             NaN\n",
       "940      941   20    1           5    97229       1.0     5.0     875049144.0\n",
       "941      942   48    0          11    78209       NaN     NaN             NaN\n",
       "942      943   22    1           5    77841       NaN     NaN             NaN\n",
       "\n",
       "[943 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_movie1 = user_movie.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#user_movie1 = user_movie.drop(['unix_timestamp','rating','zip_code'],axis=1)\n",
    "#user_movie = user_movie.drop(['unix_timestamp','rating','zip_code'],axis=1)\n",
    "user_movie1 = user_movie.drop(['unix_timestamp','rating','zip_code','occupation'],axis=1)\n",
    "user_movie = user_movie.drop(['unix_timestamp','rating','zip_code','occupation'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['user_id', 'age', 'sex', 'movie_id']\n",
      "['user_id', 'age', 'sex', 'movie_id']\n"
     ]
    }
   ],
   "source": [
    "print(list(user_movie))\n",
    "print(list(user_movie1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = user_movie1[:564].copy()\n",
    "test = user_movie1[564:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age  sex  movie_id\n",
       "0        1   24    1       1.0\n",
       "1        2   53    0       1.0\n",
       "2        3   23    1       0.0\n",
       "3        4   24    1       0.0\n",
       "4        5   33    0       1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels_train = user_movie['movie_id'][:564].copy()\n",
    "labels_test = user_movie['movie_id'][564:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['movie_id'],axis=1)\n",
    "test = test.drop(['movie_id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   user_id  age  sex\n",
      "0        1   24    1\n",
      "1        2   53    0\n",
      "2        3   23    1\n",
      "3        4   24    1\n",
      "4        5   33    0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(564, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.head())\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    1.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    1.0\n",
       "Name: movie_id, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564    0.0\n",
       "565    0.0\n",
       "566    1.0\n",
       "567    0.0\n",
       "568    1.0\n",
       "Name: movie_id, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 564 entries, 0 to 563\n",
      "Data columns (total 3 columns):\n",
      "user_id    564 non-null int64\n",
      "age        564 non-null int64\n",
      "sex        564 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 17.6 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(564, 3)\n",
      "(564,)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(labels_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "GNB = GNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time: 0.005545616149902344s.\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "GNB.fit(train, labels_train)\n",
    "print(\"Training Time: \", time()-t0, \"s.\", sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Time: 0.00422215461730957s.\n"
     ]
    }
   ],
   "source": [
    "t1 = time()\n",
    "nb_pred = GNB.predict(test)\n",
    "approx_pred = GNB.predict_proba(test)\n",
    "print(\"Testing Time: \", time()-t1, \"s.\", sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5620052770448549.\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \", accuracy_score(labels_test, nb_pred), \".\", sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.45885088 0.54114912]\n",
      "[0.58711922 0.41288078]\n"
     ]
    }
   ],
   "source": [
    "print(approx_pred[0])\n",
    "print(approx_pred[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(nb_pred[0])\n",
    "print(nb_pred[7])"
   ]
  }
 ],
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