Python Project-2_Movie lens Research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-20f623867f1f>:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  movies = pd.read_csv(\"C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2\\\\movies.dat\", sep=\"::\", names=['MovieID', 'Title', 'Genres'] )\n"
     ]
    }
   ],
   "source": [
    "#Input movies dataset\n",
    "movies = pd.read_csv(\"C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2\\\\movies.dat\", sep=\"::\", names=['MovieID', 'Title', 'Genres'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   MovieID                               Title                        Genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the sample movies dataset\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-49-7bed20d38662>:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  ratings = pd.read_csv(\"C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2\\\\ratings.dat\", sep=\"::\", names=['UserID', 'MovieID', 'Rating', 'Timestamp'] )\n"
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       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
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       "      <td>978302109</td>\n",
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       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
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      "text/plain": [
       "   UserID  MovieID  Rating  Timestamp\n",
       "0       1     1193       5  978300760\n",
       "1       1      661       3  978302109\n",
       "2       1      914       3  978301968\n",
       "3       1     3408       4  978300275\n",
       "4       1     2355       5  978824291"
      ]
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     "execution_count": 49,
     "metadata": {},
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   ],
   "source": [
    "#Input ratings dataset\n",
    "ratings = pd.read_csv(\"C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2\\\\ratings.dat\", sep=\"::\", names=['UserID', 'MovieID', 'Rating', 'Timestamp'] )\n",
    "\n",
    "#Read the sample ratings dataset\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-bedd54aca3ee>:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  users = pd.read_csv(\"C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2\\\\users.dat\", sep=\"::\", names=['UserID', 'Gender', 'Age', 'Occupation', 'Zip-code'] )\n"
     ]
    }
   ],
   "source": [
    "#Input users dataset\n",
    "users = pd.read_csv(\"C:\\\\Users\\\\NITHIN\\\\Documents\\\\02)Data Science with Python\\\\Data-Science-with-Python-Project-2\\\\users.dat\", sep=\"::\", names=['UserID', 'Gender', 'Age', 'Occupation', 'Zip-code'] )"
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Gender</th>\n",
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       "      <td>48067</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
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       "      <td>55455</td>\n",
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      "text/plain": [
       "   UserID Gender  Age  Occupation Zip-code\n",
       "0       1      F    1          10    48067\n",
       "1       2      M   56          16    70072\n",
       "2       3      M   25          15    55117\n",
       "3       4      M   45           7    02460\n",
       "4       5      M   25          20    55455"
      ]
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     "execution_count": 51,
     "metadata": {},
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   ],
   "source": [
    "#Read the sample users dataset\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>UserID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Occupation</th>\n",
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       "      <td>1193</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
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       "      <th>1</th>\n",
       "      <td>661</td>\n",
       "      <td>James and the Giant Peach (1996)</td>\n",
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       "      <td>914</td>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>3408</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
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       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
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      "text/plain": [
       "   MovieID                                   Title  UserID  Age Gender  \\\n",
       "0     1193  One Flew Over the Cuckoo's Nest (1975)       1    1      F   \n",
       "1      661        James and the Giant Peach (1996)       1    1      F   \n",
       "2      914                     My Fair Lady (1964)       1    1      F   \n",
       "3     3408                  Erin Brockovich (2000)       1    1      F   \n",
       "4     2355                    Bug's Life, A (1998)       1    1      F   \n",
       "\n",
       "   Occupation  Rating  \n",
       "0          10       5  \n",
       "1          10       3  \n",
       "2          10       3  \n",
       "3          10       4  \n",
       "4          10       5  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge the ratings and users with movieID and UserID\n",
    "ratings_user = pd.merge(ratings,users, on=['UserID'])\n",
    "ratings_movie = pd.merge(ratings,movies, on=['MovieID'])\n",
    "\n",
    "master_data = pd.merge(ratings_user,ratings_movie,\n",
    "                       on=['UserID', 'MovieID', 'Rating'])[['MovieID', 'Title', 'UserID', 'Age', 'Gender', 'Occupation', \"Rating\"]]\n",
    "\n",
    "master_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
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     "execution_count": 53,
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     "data": {
      "image/png": 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6OnA/sF3SVknLO5taRET0i3bPhtoEvN/2S2y/BLiQ4hGrERExD7TbLI60/eWpCdsTwJEdySgiIvpOuwPc35H0P4C/Kqd/H3i4MylFRES/aXfP4m3ACcDny9fxwFs7lVRERPSXlnsWkp4PvAv4l8A24ELb/68biUVERP+o2rO4BhihaBSnA3/U8YwiIqLvVI1ZLLO9Ep66bfg9nU8pIiL6TdWexVOHnGwf6nAuERHRp6r2LF4p6UD5WcAR5bQoruQ+qqPZRUREX2jZLGwf1mp+RETMD+2eOhsREfNYmkVERFRKs4iIiEpdbxaSXi7pvobXAUnvk/RhSY80xF/fsM7FknZK2iHptG7nHBEx37V7b6g5Y3sHsApA0mEUT+G7keL2IR+z/ceNy0taBqwFlgMvAv5e0ssaHrsaEREd1uvDUKcCD9n+botlzgTGbR+0/TCwEzipK9lFRAQAst27jUtXAffavlzSh4H1wAFgC8V9qB6TdDlwt+3rynWuBG6z/bkm37cB2AAwPDy8enx8vOX2JycnGRoamsOK+ssg1LftkcebxlcuPLrlep2sbbY5zZU61waD8Xs5W3WobWxsbKvtkenxnjULSc8D9gDLbe+TNAz8EDDwUWCB7bdJ+iRw17RmcavtG1p9/8jIiLds2dIyh4mJCUZHR3/5YvrUINS35KIvNo3vuuQNLdfrZG2zzWmu1Lk2GIzfy9mqQ22SmjaLXh6GOp1ir2IfgO19tp+0/XOKp/BNHWraDSxuWG8RRZOJiIgu6WWzWAdcPzUhaUHDvDdSPO8b4GZgraTDJZ0ILCU3NIyI6Kqunw0FIOlXgX8PvLMh/L8kraI4DLVrap7t7ZI2Aw8Ah4DzcyZURER39aRZ2P4p8GvTYm9psfxGYGOn84qIiOZ6fepsREQMgDSLiIiolGYRERGV0iwiIqJSmkVERFRKs4iIiEppFhERUSnNIiIiKqVZREREpTSLiIiolGYRERGV0iwiIqJSmkVERFTqyV1nIyK6oR+eDFgX2bOIiIhKPWkWknZJ2ibpPklbythxkm6X9O3y/diG5S+WtFPSDkmn9SLniIj5rJd7FmO2VzU8GPwi4A7bS4E7ymkkLQPWAsuBNcCnJB3Wi4QjIuarfjoMdSZwTfn5GuCshvi47YO2HwZ2Aif1IL+IiHlLtru/Uelh4DGK523/ue1Nkn5s+5iGZR6zfayky4G7bV9Xxq8EbrP9uSbfuwHYADA8PLx6fHy8ZR6Tk5MMDQ3NWV39ZhDq2/bI403jKxce3XK9TtY225zmSp1rg+7+Xna73kH4N1dlbGxsa8MRn6f06myoU2zvkfRC4HZJ32qxrJrEmnY425uATQAjIyMeHR1tmcTExARVywyyQahv/Uxnq5wz2nK9TtY225zmSp1rg+7+Xnaj3sYzri5c+SSXfvUnxTZqdsZVTw5D2d5Tvu8HbqQ4rLRP0gKA8n1/ufhuYHHD6ouAPd3LNiIiut4sJB0p6QVTn4HfBu4HbgbOKxc7D7ip/HwzsFbS4ZJOBJYC93Q364iI+a0Xh6GGgRslTW3/s7a/JOkfgc2S3g58D3gzgO3tkjYDDwCHgPNtP9mDvCMi5q2uNwvb3wFe2ST+I+DUGdbZCGzscGoRETGDfjp1NiIi+lSaRUREVEqziIiISmkWERFRKc0iIiIqpVlERESlPPwoImIAdfvBTtmziIiISmkWERFRKc0iIiIqpVlERESlNIuIiKiUZhEREZXSLCIiolKaRUREVEqziIiISr14rOpiSV+W9KCk7ZIuKOMflvSIpPvK1+sb1rlY0k5JOySd1u2cIyLmu17c7uMQcKHte8tncW+VdHs572O2/7hxYUnLgLXAcuBFwN9LelkerRoR0T1d37Owvdf2veXnJ4AHgYUtVjkTGLd90PbDwE7gpM5nGhERU2S7dxuXlgB3AiuA9wPrgQPAFoq9j8ckXQ7cbfu6cp0rgdtsf67J920ANgAMDw+vHh8fb7n9yclJhoaG5qqcvjMI9W175PGm8ZULj265Xidrm21Oc6XOtUF3fy+7UW/jNoaPgH0/m/ttVG230S+73bGxsa22R6bHe3bXWUlDwA3A+2wfkHQF8FHA5fulwNsANVm9aYezvQnYBDAyMuLR0dGWOUxMTFC1zCAbhPrWz3TnzHNGW67Xydpmm9NcqXNt0N3fy27U27iNC1ce4tJtz5nzbVRtt1GnttuTs6EkPZeiUXzG9ucBbO+z/aTtnwOf5ulDTbuBxQ2rLwL2dDPfiIj5rut7FpIEXAk8aPuyhvgC23vLyTcC95efbwY+K+kyigHupcA9ncyx2/eJj4jod704DHUK8BZgm6T7ytgHgXWSVlEcYtoFvBPA9nZJm4EHKM6kOj9nQkVEdFfXm4Xtr9J8HOLWFutsBDZ2LKmIiGgpV3BHRESlNIuIiKiUZhEREZXSLCIiolKaRUREVEqziIiISmkWERFRKc0iIiIqpVlERESlNIuIiKiUZhEREZXSLCIiolKaRUREVEqziIiISmkWERFRKc0iIiIqDUyzkLRG0g5JOyVd1Ot8IiLmk4FoFpIOAz4JnA4so3gE67LeZhURMX8MRLMATgJ22v6O7X8GxoEze5xTRMS8Idu9zqGSpLOBNbbfUU6/Bfg3tt8zbbkNwIZy8uXAjoqvPh744Ryn20/qXF9qG1x1rq8Otb3E9gnTg8/pRSazoCaxZ3Q525uATW1/qbTF9sgvk1g/q3N9qW1w1bm+Otc2KIehdgOLG6YXAXt6lEtExLwzKM3iH4Glkk6U9DxgLXBzj3OKiJg3BuIwlO1Dkt4D/C1wGHCV7e1z8NVtH7IaUHWuL7UNrjrXV9vaBmKAOyIiemtQDkNFREQPpVlERESledss6nT7EElXSdov6f6G2HGSbpf07fL92F7mOFuSFkv6sqQHJW2XdEEZr0t9z5d0j6RvlPX9YRmvRX1Q3IFB0tcl3VJO16m2XZK2SbpP0pYyVpv6Gs3LZlHD24dcDayZFrsIuMP2UuCOcnoQHQIutP0K4GTg/PJnVZf6DgKvtf1KYBWwRtLJ1Kc+gAuABxum61QbwJjtVQ3XV9StPmCeNgtqdvsQ23cCj04LnwlcU36+Bjirq0nNEdt7bd9bfn6C4o/OQupTn21PlpPPLV+mJvVJWgS8AfiLhnAtamuhlvXN12axEPh+w/TuMlYnw7b3QvEHF3hhj/P5pUlaArwK+Bo1qq88THMfsB+43Xad6vsT4L8BP2+I1aU2KBr730naWt5uCOpV31MG4jqLDmjr9iHRPyQNATcA77N9QGr2IxxMtp8EVkk6BrhR0ope5zQXJJ0B7Le9VdJor/PpkFNs75H0QuB2Sd/qdUKdMl/3LObD7UP2SVoAUL7v73E+sybpuRSN4jO2P1+Ga1PfFNs/BiYoxp/qUN8pwO9K2kVxqPe1kq6jHrUBYHtP+b4fuJHiEHdt6ms0X5vFfLh9yM3AeeXn84CbepjLrKnYhbgSeND2ZQ2z6lLfCeUeBZKOAF4HfIsa1Gf7YtuLbC+h+Df2f2z/PjWoDUDSkZJeMPUZ+G3gfmpS33Tz9gpuSa+nOJ46dfuQjT1OadYkXQ+MUtweeR/wIeALwGbgxcD3gDfbnj4I3vck/SbwFWAbTx/3/iDFuEUd6vt1ikHQwyj+522z7Y9I+jVqUN+U8jDUf7F9Rl1qk/RSir0JKA7pf9b2xrrUN928bRYREdG++XoYKiIinoU0i4iIqJRmERERldIsIiKiUppFRERUSrOImGOS3ijJkv5Vr3OJmCtpFhFzbx3wVYoL0SJqIc0iYg6V97A6BXg7ZbOQ9CuSPlU+r+IWSbdKOruct1rSP5Q3ovvbqdtERPSbNIuIuXUW8CXb/wQ8Kuk3gDcBS4CVwDuAV8NT97z6BHC27dXAVcDA3kkg6m2+3nU2olPWUdxGBoqb562jeEbF/7b9c+AHkr5czn85sILibqVQ3PJjb3fTjWhPmkXEHCnvCfRaYIUkU/zxN0/fP+gZqwDbbb+6SylGzFoOQ0XMnbOBa22/xPYS24uBh4EfAr9Xjl0MU9z0EWAHcIKkpw5LSVrei8QjqqRZRMyddTxzL+IG4EUUz1C5H/hzijvmPl4+0vds4H9K+gZwH/Bvu5duRPty19mILpA0ZHuyPFR1D8UT1n7Q67wi2pUxi4juuKV8yNHzgI+mUcSgyZ5FRERUyphFRERUSrOIiIhKaRYREVEpzSIiIiqlWURERKX/D2L5c4DFfGWOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#User age distribution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "users['Age'].hist(bins=50)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Population')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "1     112\n",
       "18    448\n",
       "25    790\n",
       "35    423\n",
       "45    143\n",
       "50    108\n",
       "56     53\n",
       "Name: MovieID, dtype: int64"
      ]
     },
     "execution_count": 54,
     "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": [
    "#User rating of the movie “Toy Story”\n",
    "\n",
    "res = master_data[master_data.Title == \"Toy Story (1995)\"]\n",
    "\n",
    "plt.plot(res.groupby(\"Age\")[\"MovieID\"].count(),'--bo')\n",
    "res.groupby(\"Age\")[\"MovieID\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19501c7280>"
      ]
     },
     "execution_count": 55,
     "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": [
    "#Top 25 movies by viewership rating\n",
    "\n",
    "res = master_data.groupby(\"Title\").size().sort_values(ascending=False)[:25]\n",
    "plt.ylabel(\"Title\")\n",
    "plt.xlabel(\"Viewership Count\")\n",
    "res.plot(kind=\"barh\")\n",
    "#res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>UserID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>440667</th>\n",
       "      <td>1258</td>\n",
       "      <td>Shining, The (1980)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440668</th>\n",
       "      <td>1270</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440669</th>\n",
       "      <td>1617</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440670</th>\n",
       "      <td>1625</td>\n",
       "      <td>Game, The (1997)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440671</th>\n",
       "      <td>1644</td>\n",
       "      <td>I Know What You Did Last Summer (1997)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440672</th>\n",
       "      <td>1645</td>\n",
       "      <td>Devil's Advocate, The (1997)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440673</th>\n",
       "      <td>1805</td>\n",
       "      <td>Wild Things (1998)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440674</th>\n",
       "      <td>1892</td>\n",
       "      <td>Perfect Murder, A (1998)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440675</th>\n",
       "      <td>800</td>\n",
       "      <td>Lone Star (1996)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440676</th>\n",
       "      <td>2338</td>\n",
       "      <td>I Still Know What You Did Last Summer (1998)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440677</th>\n",
       "      <td>1711</td>\n",
       "      <td>Midnight in the Garden of Good and Evil (1997)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440678</th>\n",
       "      <td>3176</td>\n",
       "      <td>Talented Mr. Ripley, The (1999)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440679</th>\n",
       "      <td>2389</td>\n",
       "      <td>Psycho (1998)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440680</th>\n",
       "      <td>1589</td>\n",
       "      <td>Cop Land (1997)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440681</th>\n",
       "      <td>2713</td>\n",
       "      <td>Lake Placid (1999)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440682</th>\n",
       "      <td>3386</td>\n",
       "      <td>JFK (1991)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440683</th>\n",
       "      <td>1783</td>\n",
       "      <td>Palmetto (1998)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440684</th>\n",
       "      <td>350</td>\n",
       "      <td>Client, The (1994)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440685</th>\n",
       "      <td>1092</td>\n",
       "      <td>Basic Instinct (1992)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440686</th>\n",
       "      <td>1097</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>2696</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MovieID                                           Title  UserID  Age  \\\n",
       "440667     1258                             Shining, The (1980)    2696   25   \n",
       "440668     1270                       Back to the Future (1985)    2696   25   \n",
       "440669     1617                        L.A. Confidential (1997)    2696   25   \n",
       "440670     1625                                Game, The (1997)    2696   25   \n",
       "440671     1644          I Know What You Did Last Summer (1997)    2696   25   \n",
       "440672     1645                    Devil's Advocate, The (1997)    2696   25   \n",
       "440673     1805                              Wild Things (1998)    2696   25   \n",
       "440674     1892                        Perfect Murder, A (1998)    2696   25   \n",
       "440675      800                                Lone Star (1996)    2696   25   \n",
       "440676     2338    I Still Know What You Did Last Summer (1998)    2696   25   \n",
       "440677     1711  Midnight in the Garden of Good and Evil (1997)    2696   25   \n",
       "440678     3176                 Talented Mr. Ripley, The (1999)    2696   25   \n",
       "440679     2389                                   Psycho (1998)    2696   25   \n",
       "440680     1589                                 Cop Land (1997)    2696   25   \n",
       "440681     2713                              Lake Placid (1999)    2696   25   \n",
       "440682     3386                                      JFK (1991)    2696   25   \n",
       "440683     1783                                 Palmetto (1998)    2696   25   \n",
       "440684      350                              Client, The (1994)    2696   25   \n",
       "440685     1092                           Basic Instinct (1992)    2696   25   \n",
       "440686     1097               E.T. the Extra-Terrestrial (1982)    2696   25   \n",
       "\n",
       "       Gender  Occupation  Rating  \n",
       "440667      M           7       4  \n",
       "440668      M           7       2  \n",
       "440669      M           7       4  \n",
       "440670      M           7       4  \n",
       "440671      M           7       2  \n",
       "440672      M           7       4  \n",
       "440673      M           7       4  \n",
       "440674      M           7       4  \n",
       "440675      M           7       5  \n",
       "440676      M           7       2  \n",
       "440677      M           7       4  \n",
       "440678      M           7       4  \n",
       "440679      M           7       4  \n",
       "440680      M           7       3  \n",
       "440681      M           7       1  \n",
       "440682      M           7       1  \n",
       "440683      M           7       4  \n",
       "440684      M           7       3  \n",
       "440685      M           7       4  \n",
       "440686      M           7       3  "
      ]
     },
     "execution_count": 56,
     "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": [
    "#Find the ratings for all the movies reviewed by for a particular user of user id = 2696\n",
    "\n",
    "res = master_data[master_data.UserID == 2696]\n",
    "\n",
    "plt.scatter(y=res.Title, x=res.Rating)\n",
    "\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Action</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>...</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>War</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Western</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Animation Children's Comedy Adventure Fantasy Romance Drama Action Crime  \\\n",
       "0         0          0      0         0       0       0     1      0     0   \n",
       "1         1          1      0         0       0       0     0      0     0   \n",
       "2         0          0      0         0       0       1     0      0     0   \n",
       "3         0          0      0         0       0       0     1      0     0   \n",
       "4         1          1      1         0       0       0     0      0     0   \n",
       "\n",
       "  Thriller  ... Sci-Fi Documentary War Musical Mystery Film-Noir Western  \\\n",
       "0        0  ...      0           0   0       0       0         0       0   \n",
       "1        0  ...      0           0   0       1       0         0       0   \n",
       "2        0  ...      0           0   0       1       0         0       0   \n",
       "3        0  ...      0           0   0       0       0         0       0   \n",
       "4        0  ...      0           0   0       0       0         0       0   \n",
       "\n",
       "  Gender Age Rating  \n",
       "0      F   1      5  \n",
       "1      F   1      3  \n",
       "2      F   1      3  \n",
       "3      F   1      4  \n",
       "4      F   1      5  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Engineering\n",
    "\n",
    "val = movies.Genres.str.split(\"|\")\n",
    "\n",
    "res_col = []\n",
    "for v in val:\n",
    "    for i in v:\n",
    "        if i not in res_col:\n",
    "            res_col.append(i)\n",
    "\n",
    "res_col.append(\"Gender\")\n",
    "res_col.append(\"Age\")\n",
    "res_col.append(\"Rating\")\n",
    "\n",
    "df = pd.DataFrame(columns=res_col)\n",
    "\n",
    "res = master_data.merge(movies, on = ['MovieID'], how=\"left\")[[\"Genres\",\"Rating\",\"Gender\", \"Age\"]]\n",
    "\n",
    "for index, row in res.head(20000).iterrows():\n",
    "    tmp = row.Genres.split(\"|\") \n",
    "    \n",
    "    for i in tmp:\n",
    "       # print(i)\n",
    "        df.loc[index,i] = 1\n",
    "        df.loc[index,\"Gender\"] = res.loc[index,\"Gender\"]\n",
    "        df.loc[index,\"Age\"] = res.loc[index,\"Age\"]\n",
    "        df.loc[index,\"Rating\"] = res.loc[index,\"Rating\"]\n",
    "         \n",
    "#         var = res.loc[index, \"Rating\"]\n",
    "#         if var == 1:\n",
    "#             df.loc[index,\"Rating\"] = \"one\" \n",
    "#         elif var == 2:\n",
    "#             df.loc[index,\"Rating\"] = \"two\"\n",
    "#         elif var == 3:\n",
    "#             df.loc[index,\"Rating\"] = \"three\"\n",
    "#         elif var == 4:\n",
    "#             df.loc[index,\"Rating\"] = \"four\"\n",
    "#         else:\n",
    "#             df.loc[index,\"Rating\"] = \"five\"\n",
    "     \n",
    "    df.loc[index,df.columns[~df.columns.isin(tmp+[\"Gender\",\"Rating\",\"Age\"])]] = 0\n",
    "\n",
    "df.head()\n",
    "    \n",
    "\n",
    "#df.loc[i,\"Animation\"] = 1\n",
    "\n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NITHIN\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn import datasets \n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "X = df[df.columns[~df.columns.isin([\"Rating\"])]]\n",
    "y = df.Rating\n",
    "\n",
    "# dividing X, y into train and test data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) \n",
    "\n",
    "number = LabelEncoder()\n",
    "X_train.Gender = number.fit_transform(X_train[\"Gender\"].astype(\"str\"))\n",
    "X_test.Gender = number.fit_transform(X_test[\"Gender\"].astype(\"str\"))\n",
    "y_train = number.fit_transform(y_train.astype(\"int\"))\n",
    "y_test = number.fit_transform(y_test.astype(\"int\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SVM\n",
    "\n",
    "from sklearn.svm import SVC \n",
    "svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) \n",
    "svm_predictions = svm_model_linear.predict(X_test) \n",
    "  \n",
    "# model accuracy for X_test   \n",
    "accuracy = svm_model_linear.score(X_test, y_test) \n",
    "  \n",
    "# creating a confusion matrix \n",
    "cm = confusion_matrix(y_test, svm_predictions) \n",
    "accuracy\n",
    "#cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3102"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "knn = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train) \n",
    "  \n",
    "# accuracy on X_test \n",
    "accuracy = knn.score(X_test, y_test) \n",
    "  \n",
    "# creating a confusion matrix \n",
    "knn_predictions = knn.predict(X_test)  \n",
    "cm = confusion_matrix(y_test, knn_predictions) \n",
    "\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2788"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Naive Bayes classifier \n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "gnb = GaussianNB().fit(X_train, y_train) \n",
    "gnb_predictions = gnb.predict(X_test) \n",
    "  \n",
    "# accuracy on X_test \n",
    "accuracy = gnb.score(X_test, y_test)  \n",
    "  \n",
    "# creating a confusion matrix \n",
    "cm = confusion_matrix(y_test, gnb_predictions) \n",
    "\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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