Correlation Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Movie Recommender using Correlation Analysis of Movie Ratings\n",
    "#Sanjana Rai - 18BCS0116\n",
    "#Naveena Koneru - 18BCS0131\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  age sex  occupation    zip\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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ucolnames = ['id', 'age', 'sex', 'occupation', 'zip']\n",
    "url=r\"C:\\Users\\sanja\\OneDrive\\Documents\\Data Mining\\ml-100k\\u.user\"\n",
    "users = pd.read_csv(url, names=ucolnames, sep='|' , encoding='latin-1')\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>movieID</th>\n",
       "      <th>rating</th>\n",
       "      <th>unixTimestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   userID  movieID  rating  unixTimestamp\n",
       "0     196      242       3      881250949\n",
       "1     186      302       3      891717742\n",
       "2      22      377       1      878887116\n",
       "3     244       51       2      880606923\n",
       "4     166      346       1      886397596"
      ]
     },
     "execution_count": 57,
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   ],
   "source": [
    "rcolnames = ['userID', 'movieID', 'rating', 'unixTimestamp']\n",
    "url=r\"C:\\Users\\sanja\\OneDrive\\Documents\\VIT\\4th Sem\\Data Mining\\DA3\\ml-100k\\u.data\"\n",
    "ratings = pd.read_csv(url, sep='\\t', names=rcolnames, encoding='latin-1')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieID</th>\n",
       "      <th>movie title</th>\n",
       "      <th>release date</th>\n",
       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieID        movie title release date  video release date  \\\n",
       "0        1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1        2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2        3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3        4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4        5     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            IMDb URL  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "   Adventure  Animation  Children's  ...  Fantasy  Film-Noir  Horror  Musical  \\\n",
       "0          0          1           1  ...        0          0       0        0   \n",
       "1          1          0           0  ...        0          0       0        0   \n",
       "2          0          0           0  ...        0          0       0        0   \n",
       "3          0          0           0  ...        0          0       0        0   \n",
       "4          0          0           0  ...        0          0       0        0   \n",
       "\n",
       "   Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0       0         0    0        0  \n",
       "1        0        0       0         1    0        0  \n",
       "2        0        0       0         1    0        0  \n",
       "3        0        0       0         0    0        0  \n",
       "4        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icolnames = ['movieID', 'movie title' ,'release date','video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure', 'Animation', 'Children\\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']\n",
    "url=r\"C:\\Users\\sanja\\OneDrive\\Documents\\VIT\\4th Sem\\Data Mining\\DA3\\ml-100k\\u.item\"\n",
    "items = pd.read_csv(url, sep='|', names=icolnames, encoding='latin-1')\n",
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 5)\n"
     ]
    },
    {
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       "      <td>1</td>\n",
       "      <td>24</td>\n",
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       "      <td>85711</td>\n",
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       "      <td>53</td>\n",
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       "      <td>94043</td>\n",
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       "      <th>3</th>\n",
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       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
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       "      <td>15213</td>\n",
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      "text/plain": [
       "   id  age sex  occupation    zip\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(users.shape)\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((90570, 4), (9430, 4))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcolnames = ['userID', 'movieID', 'rating', 'unixTimestamp']\n",
    "url=r\"C:\\Users\\sanja\\OneDrive\\Documents\\VIT\\4th Sem\\Data Mining\\DA3\\ml-100k\\ua.base\"\n",
    "url2=r\"C:\\Users\\sanja\\OneDrive\\Documents\\VIT\\4th Sem\\Data Mining\\DA3\\ml-100k\\ua.test\"\n",
    "ratings_base = pd.read_csv(url, sep='\\t', names=rcolnames, encoding='latin-1')\n",
    "ratings_test = pd.read_csv(url2, sep='\\t', names=rcolnames, encoding='latin-1')\n",
    "ratings_base.shape, ratings_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Dataset: \n",
      "(943, 5)\n",
      "Rating dataset: \n",
      "(100000, 4)\n",
      "Item Dataset: \n",
      "(1682, 24)\n"
     ]
    }
   ],
   "source": [
    "print(\"User Dataset: \")\n",
    "print(users.shape)\n",
    "print(\"Rating dataset: \")\n",
    "print(ratings.shape)\n",
    "print(\"Item Dataset: \")\n",
    "print(items.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>movieID</th>\n",
       "      <th>rating</th>\n",
       "      <th>unixTimestamp</th>\n",
       "      <th>movie title</th>\n",
       "      <th>release date</th>\n",
       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "      <td>24-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Kolya%20(1996)</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?L%2EA%2E+Conf...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "      <td>Heavyweights (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Heavyweights%...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "      <td>Legends of the Fall (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Legends%20of%...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "      <td>Jackie Brown (1997)</td>\n",
       "      <td>01-Jan-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-11...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID  movieID  rating  unixTimestamp                 movie title  \\\n",
       "0     196      242       3      881250949                Kolya (1996)   \n",
       "1     186      302       3      891717742    L.A. Confidential (1997)   \n",
       "2      22      377       1      878887116         Heavyweights (1994)   \n",
       "3     244       51       2      880606923  Legends of the Fall (1994)   \n",
       "4     166      346       1      886397596         Jackie Brown (1997)   \n",
       "\n",
       "  release date  video release date  \\\n",
       "0  24-Jan-1997                 NaN   \n",
       "1  01-Jan-1997                 NaN   \n",
       "2  01-Jan-1994                 NaN   \n",
       "3  01-Jan-1994                 NaN   \n",
       "4  01-Jan-1997                 NaN   \n",
       "\n",
       "                                            IMDb URL  unknown  Action  ...  \\\n",
       "0    http://us.imdb.com/M/title-exact?Kolya%20(1996)        0       0  ...   \n",
       "1  http://us.imdb.com/M/title-exact?L%2EA%2E+Conf...        0       0  ...   \n",
       "2  http://us.imdb.com/M/title-exact?Heavyweights%...        0       0  ...   \n",
       "3  http://us.imdb.com/M/title-exact?Legends%20of%...        0       0  ...   \n",
       "4  http://us.imdb.com/M/title-exact?imdb-title-11...        0       0  ...   \n",
       "\n",
       "   Fantasy  Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  \\\n",
       "0        0          0       0        0        0        0       0         0   \n",
       "1        0          1       0        0        1        0       0         1   \n",
       "2        0          0       0        0        0        0       0         0   \n",
       "3        0          0       0        0        0        1       0         0   \n",
       "4        0          0       0        0        0        0       0         0   \n",
       "\n",
       "   War  Western  \n",
       "0    0        0  \n",
       "1    0        0  \n",
       "2    0        0  \n",
       "3    1        1  \n",
       "4    0        0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings1=ratings.merge(items,on='movieID', how='left')\n",
    "ratings1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>'Til There Was You (1997)</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
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      "text/plain": [
       "                             rating\n",
       "movie title                        \n",
       "'Til There Was You (1997)  2.333333\n",
       "1-900 (1994)               2.600000\n",
       "101 Dalmatians (1996)      2.908257\n",
       "12 Angry Men (1957)        4.344000\n",
       "187 (1997)                 3.024390"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgratings=pd.DataFrame(ratings1.groupby('movie title')['rating'].mean())\n",
    "avgratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>12 Angry Men (1957)</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <td>2.772727</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
       "      <td>3.341463</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <td>2.166667</td>\n",
       "      <td>6</td>\n",
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       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Á köldum klaka (Cold Fever) (1994)</th>\n",
       "      <td>3.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1664 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         rating  total ratings\n",
       "movie title                                                   \n",
       "'Til There Was You (1997)              2.333333              9\n",
       "1-900 (1994)                           2.600000              5\n",
       "101 Dalmatians (1996)                  2.908257            109\n",
       "12 Angry Men (1957)                    4.344000            125\n",
       "187 (1997)                             3.024390             41\n",
       "...                                         ...            ...\n",
       "Young Guns II (1990)                   2.772727             44\n",
       "Young Poisoner's Handbook, The (1995)  3.341463             41\n",
       "Zeus and Roxanne (1997)                2.166667              6\n",
       "unknown                                3.444444              9\n",
       "Á köldum klaka (Cold Fever) (1994)     3.000000              1\n",
       "\n",
       "[1664 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgratings['total ratings']=pd.DataFrame(ratings1.groupby('movie title')['rating'].count())\n",
    "avgratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "movie title  'Til There Was You (1997)  1-900 (1994)  101 Dalmatians (1996)  \\\n",
       "userID                                                                        \n",
       "1                                  NaN           NaN                    2.0   \n",
       "2                                  NaN           NaN                    NaN   \n",
       "3                                  NaN           NaN                    NaN   \n",
       "4                                  NaN           NaN                    NaN   \n",
       "5                                  NaN           NaN                    2.0   \n",
       "\n",
       "movie title  12 Angry Men (1957)  187 (1997)  2 Days in the Valley (1996)  \\\n",
       "userID                                                                      \n",
       "1                            5.0         NaN                          NaN   \n",
       "2                            NaN         NaN                          NaN   \n",
       "3                            NaN         2.0                          NaN   \n",
       "4                            NaN         NaN                          NaN   \n",
       "5                            NaN         NaN                          NaN   \n",
       "\n",
       "movie title  20,000 Leagues Under the Sea (1954)  \\\n",
       "userID                                             \n",
       "1                                            3.0   \n",
       "2                                            NaN   \n",
       "3                                            NaN   \n",
       "4                                            NaN   \n",
       "5                                            NaN   \n",
       "\n",
       "movie title  2001: A Space Odyssey (1968)  \\\n",
       "userID                                      \n",
       "1                                     4.0   \n",
       "2                                     NaN   \n",
       "3                                     NaN   \n",
       "4                                     NaN   \n",
       "5                                     4.0   \n",
       "\n",
       "movie title  3 Ninjas: High Noon At Mega Mountain (1998)  \\\n",
       "userID                                                     \n",
       "1                                                    NaN   \n",
       "2                                                    1.0   \n",
       "3                                                    NaN   \n",
       "4                                                    NaN   \n",
       "5                                                    NaN   \n",
       "\n",
       "movie title  39 Steps, The (1935)  ...  Yankee Zulu (1994)  \\\n",
       "userID                             ...                       \n",
       "1                             NaN  ...                 NaN   \n",
       "2                             NaN  ...                 NaN   \n",
       "3                             NaN  ...                 NaN   \n",
       "4                             NaN  ...                 NaN   \n",
       "5                             NaN  ...                 NaN   \n",
       "\n",
       "movie title  Year of the Horse (1997)  You So Crazy (1994)  \\\n",
       "userID                                                       \n",
       "1                                 NaN                  NaN   \n",
       "2                                 NaN                  NaN   \n",
       "3                                 NaN                  NaN   \n",
       "4                                 NaN                  NaN   \n",
       "5                                 NaN                  NaN   \n",
       "\n",
       "movie title  Young Frankenstein (1974)  Young Guns (1988)  \\\n",
       "userID                                                      \n",
       "1                                  5.0                3.0   \n",
       "2                                  NaN                NaN   \n",
       "3                                  NaN                NaN   \n",
       "4                                  NaN                NaN   \n",
       "5                                  4.0                NaN   \n",
       "\n",
       "movie title  Young Guns II (1990)  Young Poisoner's Handbook, The (1995)  \\\n",
       "userID                                                                     \n",
       "1                             NaN                                    NaN   \n",
       "2                             NaN                                    NaN   \n",
       "3                             NaN                                    NaN   \n",
       "4                             NaN                                    NaN   \n",
       "5                             NaN                                    NaN   \n",
       "\n",
       "movie title  Zeus and Roxanne (1997)  unknown  \\\n",
       "userID                                          \n",
       "1                                NaN      4.0   \n",
       "2                                NaN      NaN   \n",
       "3                                NaN      NaN   \n",
       "4                                NaN      NaN   \n",
       "5                                NaN      4.0   \n",
       "\n",
       "movie title  Á köldum klaka (Cold Fever) (1994)  \n",
       "userID                                           \n",
       "1                                           NaN  \n",
       "2                                           NaN  \n",
       "3                                           NaN  \n",
       "4                                           NaN  \n",
       "5                                           NaN  \n",
       "\n",
       "[5 rows x 1664 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movieUser=ratings1.pivot_table(index='userID', columns='movie title', values='rating')\n",
    "movieUser.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of movie title\n",
       "'Til There Was You (1997)                0.104828\n",
       "1-900 (1994)                                  NaN\n",
       "101 Dalmatians (1996)                    0.426573\n",
       "12 Angry Men (1957)                      0.185675\n",
       "187 (1997)                               0.883883\n",
       "                                           ...   \n",
       "Young Guns II (1990)                     0.139279\n",
       "Young Poisoner's Handbook, The (1995)   -0.386474\n",
       "Zeus and Roxanne (1997)                 -0.866025\n",
       "unknown                                  0.866025\n",
       "Á köldum klaka (Cold Fever) (1994)            NaN\n",
       "Length: 1664, dtype: float64>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation=movieUser.corrwith(movieUser['Lion King, The (1994)'])\n",
    "correlation.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Correlation</th>\n",
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       "    <tr>\n",
       "      <th>movie title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>0.104828</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.426573</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.185675</td>\n",
       "      <td>125</td>\n",
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       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.883883</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <td>0.260395</td>\n",
       "      <td>93</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                             Correlation  total ratings\n",
       "movie title                                            \n",
       "'Til There Was You (1997)       0.104828              9\n",
       "101 Dalmatians (1996)           0.426573            109\n",
       "12 Angry Men (1957)             0.185675            125\n",
       "187 (1997)                      0.883883             41\n",
       "2 Days in the Valley (1996)     0.260395             93"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation=pd.DataFrame(correlation, columns=['Correlation'])\n",
    "recommendation.dropna(inplace=True)\n",
    "recommendation=recommendation.join(avgratings['total ratings'])\n",
    "recommendation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lion King, The (1994)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ice Storm, The (1997)</td>\n",
       "      <td>0.709736</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manchurian Candidate, The (1962)</td>\n",
       "      <td>0.544610</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Craft, The (1996)</td>\n",
       "      <td>0.518510</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>0.516434</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                        movie title  Correlation  total ratings\n",
       "0             Lion King, The (1994)     1.000000            220\n",
       "1             Ice Storm, The (1997)     0.709736            108\n",
       "2  Manchurian Candidate, The (1962)     0.544610            131\n",
       "3                 Craft, The (1996)     0.518510            104\n",
       "4       Beauty and the Beast (1991)     0.516434            202"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recsubset=recommendation[recommendation['total ratings']>100].sort_values(\"Correlation\",ascending=False).reset_index()\n",
    "recsubset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>104</td>\n",
       "      <td>26-Apr-1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>01-Jan-1991</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>0.514440</td>\n",
       "      <td>219</td>\n",
       "      <td>01-Jan-1992</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>G.I. Jane (1997)</td>\n",
       "      <td>0.498312</td>\n",
       "      <td>175</td>\n",
       "      <td>01-Jan-1997</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",
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       "      <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>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Firm, The (1993)</td>\n",
       "      <td>0.471582</td>\n",
       "      <td>151</td>\n",
       "      <td>01-Jan-1993</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>0.464787</td>\n",
       "      <td>125</td>\n",
       "      <td>01-Jan-1964</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Wag the Dog (1997)</td>\n",
       "      <td>0.463378</td>\n",
       "      <td>137</td>\n",
       "      <td>09-Jan-1998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mrs. Doubtfire (1993)</td>\n",
       "      <td>0.456042</td>\n",
       "      <td>192</td>\n",
       "      <td>01-Jan-1993</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cinderella (1950)</td>\n",
       "      <td>0.448823</td>\n",
       "      <td>129</td>\n",
       "      <td>01-Jan-1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Philadelphia Story, The (1940)</td>\n",
       "      <td>0.426997</td>\n",
       "      <td>104</td>\n",
       "      <td>01-Jan-1940</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>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>0.426778</td>\n",
       "      <td>452</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>101 Dalmatians (1996)</td>\n",
       "      <td>0.426573</td>\n",
       "      <td>109</td>\n",
       "      <td>27-Nov-1996</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kiss the Girls (1997)</td>\n",
       "      <td>0.416352</td>\n",
       "      <td>143</td>\n",
       "      <td>01-Jan-1997</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ulee's Gold (1997)</td>\n",
       "      <td>0.413299</td>\n",
       "      <td>184</td>\n",
       "      <td>01-Jan-1997</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ulee's Gold (1997)</td>\n",
       "      <td>0.413299</td>\n",
       "      <td>184</td>\n",
       "      <td>01-Jan-1997</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cop Land (1997)</td>\n",
       "      <td>0.413155</td>\n",
       "      <td>175</td>\n",
       "      <td>01-Jan-1997</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>...</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         movie title  Correlation  total ratings release date  \\\n",
       "0              Lion King, The (1994)     1.000000            220  01-Jan-1994   \n",
       "1              Ice Storm, The (1997)     0.709736            108  01-Jan-1997   \n",
       "2              Ice Storm, The (1997)     0.709736            108  01-Jan-1997   \n",
       "3   Manchurian Candidate, The (1962)     0.544610            131  01-Jan-1962   \n",
       "4                  Craft, The (1996)     0.518510            104  26-Apr-1996   \n",
       "5        Beauty and the Beast (1991)     0.516434            202  01-Jan-1991   \n",
       "6                     Aladdin (1992)     0.514440            219  01-Jan-1992   \n",
       "7                   G.I. Jane (1997)     0.498312            175  01-Jan-1997   \n",
       "8                   Firm, The (1993)     0.471582            151  01-Jan-1993   \n",
       "9                My Fair Lady (1964)     0.464787            125  01-Jan-1964   \n",
       "10                Wag the Dog (1997)     0.463378            137  09-Jan-1998   \n",
       "11             Mrs. Doubtfire (1993)     0.456042            192  01-Jan-1993   \n",
       "12                 Cinderella (1950)     0.448823            129  01-Jan-1950   \n",
       "13    Philadelphia Story, The (1940)     0.426997            104  01-Jan-1940   \n",
       "14                  Toy Story (1995)     0.426778            452  01-Jan-1995   \n",
       "15             101 Dalmatians (1996)     0.426573            109  27-Nov-1996   \n",
       "16             Kiss the Girls (1997)     0.416352            143  01-Jan-1997   \n",
       "17                Ulee's Gold (1997)     0.413299            184  01-Jan-1997   \n",
       "18                Ulee's Gold (1997)     0.413299            184  01-Jan-1997   \n",
       "19                   Cop Land (1997)     0.413155            175  01-Jan-1997   \n",
       "\n",
       "    Action  Adventure  Animation  Children's  Comedy  Crime  ...  Fantasy  \\\n",
       "0        0          0          1           1       0      0  ...        0   \n",
       "1        0          0          0           0       0      0  ...        0   \n",
       "2        0          0          0           0       0      0  ...        0   \n",
       "3        0          0          0           0       0      0  ...        0   \n",
       "4        0          0          0           0       0      0  ...        0   \n",
       "5        0          0          1           1       0      0  ...        0   \n",
       "6        0          0          1           1       1      0  ...        0   \n",
       "7        1          0          0           0       0      0  ...        0   \n",
       "8        0          0          0           0       0      0  ...        0   \n",
       "9        0          0          0           0       0      0  ...        0   \n",
       "10       0          0          0           0       1      0  ...        0   \n",
       "11       0          0          0           0       1      0  ...        0   \n",
       "12       0          0          1           1       0      0  ...        0   \n",
       "13       0          0          0           0       1      0  ...        0   \n",
       "14       0          0          1           1       1      0  ...        0   \n",
       "15       0          0          0           1       1      0  ...        0   \n",
       "16       0          0          0           0       0      1  ...        0   \n",
       "17       0          0          0           0       0      0  ...        0   \n",
       "18       0          0          0           0       0      0  ...        0   \n",
       "19       0          0          0           0       0      1  ...        0   \n",
       "\n",
       "    Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  \\\n",
       "0           0       0        1        0        0       0         0    0   \n",
       "1           0       0        0        0        0       0         0    0   \n",
       "2           0       0        0        0        0       0         0    0   \n",
       "3           1       0        0        0        0       0         1    0   \n",
       "4           0       1        0        0        0       0         0    0   \n",
       "5           0       0        1        0        0       0         0    0   \n",
       "6           0       0        1        0        0       0         0    0   \n",
       "7           0       0        0        0        0       0         0    1   \n",
       "8           0       0        0        0        0       0         1    0   \n",
       "9           0       0        1        0        1       0         0    0   \n",
       "10          0       0        0        0        0       0         0    0   \n",
       "11          0       0        0        0        0       0         0    0   \n",
       "12          0       0        1        0        0       0         0    0   \n",
       "13          0       0        0        0        1       0         0    0   \n",
       "14          0       0        0        0        0       0         0    0   \n",
       "15          0       0        0        0        0       0         0    0   \n",
       "16          0       0        0        0        0       0         1    0   \n",
       "17          0       0        0        0        0       0         0    0   \n",
       "18          0       0        0        0        0       0         0    0   \n",
       "19          0       0        0        1        0       0         0    0   \n",
       "\n",
       "    Western  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         0  \n",
       "7         0  \n",
       "8         0  \n",
       "9         0  \n",
       "10        0  \n",
       "11        0  \n",
       "12        0  \n",
       "13        0  \n",
       "14        0  \n",
       "15        0  \n",
       "16        0  \n",
       "17        0  \n",
       "18        0  \n",
       "19        0  \n",
       "\n",
       "[20 rows x 22 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recsubset=recsubset.merge(items,on='movie title', how='left')\n",
    "recsubset.drop(recsubset.columns[[3,5,6,7]],axis=1,inplace=True)\n",
    "recsubset.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>movieID</th>\n",
       "      <th>movie title</th>\n",
       "      <th>release date</th>\n",
       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>1678</td>\n",
       "      <td>Mat' i syn (1997)</td>\n",
       "      <td>06-Feb-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Mat%27+i+syn+...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>1679</td>\n",
       "      <td>B. Monkey (1998)</td>\n",
       "      <td>06-Feb-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?B%2E+Monkey+(...</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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>1680</td>\n",
       "      <td>Sliding Doors (1998)</td>\n",
       "      <td>01-Jan-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?Sliding+Doors+(1998)</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>1681</td>\n",
       "      <td>You So Crazy (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?You%20So%20Cr...</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>1682</td>\n",
       "      <td>Scream of Stone (Schrei aus Stein) (1991)</td>\n",
       "      <td>08-Mar-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Schrei%20aus%...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieID                                movie title release date  \\\n",
       "1677     1678                          Mat' i syn (1997)  06-Feb-1998   \n",
       "1678     1679                           B. Monkey (1998)  06-Feb-1998   \n",
       "1679     1680                       Sliding Doors (1998)  01-Jan-1998   \n",
       "1680     1681                        You So Crazy (1994)  01-Jan-1994   \n",
       "1681     1682  Scream of Stone (Schrei aus Stein) (1991)  08-Mar-1996   \n",
       "\n",
       "      video release date                                           IMDb URL  \\\n",
       "1677                 NaN  http://us.imdb.com/M/title-exact?Mat%27+i+syn+...   \n",
       "1678                 NaN  http://us.imdb.com/M/title-exact?B%2E+Monkey+(...   \n",
       "1679                 NaN      http://us.imdb.com/Title?Sliding+Doors+(1998)   \n",
       "1680                 NaN  http://us.imdb.com/M/title-exact?You%20So%20Cr...   \n",
       "1681                 NaN  http://us.imdb.com/M/title-exact?Schrei%20aus%...   \n",
       "\n",
       "      unknown  Action  Adventure  Animation  Children's  ...  Fantasy  \\\n",
       "1677        0       0          0          0           0  ...        0   \n",
       "1678        0       0          0          0           0  ...        0   \n",
       "1679        0       0          0          0           0  ...        0   \n",
       "1680        0       0          0          0           0  ...        0   \n",
       "1681        0       0          0          0           0  ...        0   \n",
       "\n",
       "      Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  \\\n",
       "1677          0       0        0        0        0       0         0    0   \n",
       "1678          0       0        0        0        1       0         1    0   \n",
       "1679          0       0        0        0        1       0         0    0   \n",
       "1680          0       0        0        0        0       0         0    0   \n",
       "1681          0       0        0        0        0       0         0    0   \n",
       "\n",
       "      Western  \n",
       "1677        0  \n",
       "1678        0  \n",
       "1679        0  \n",
       "1680        0  \n",
       "1681        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          movie title  total ratings  Correlation\n",
      "0               Lion King, The (1994)            220     1.000000\n",
      "1               Ice Storm, The (1997)            108     0.709736\n",
      "2    Manchurian Candidate, The (1962)            131     0.544610\n",
      "3                   Craft, The (1996)            104     0.518510\n",
      "4         Beauty and the Beast (1991)            202     0.516434\n",
      "..                                ...            ...          ...\n",
      "329                      Kolya (1996)            117    -0.218362\n",
      "330               Jackie Brown (1997)            126    -0.223684\n",
      "331             Reservoir Dogs (1992)            148    -0.234065\n",
      "332       Seven Years in Tibet (1997)            155    -0.266655\n",
      "333                  Edge, The (1997)            113    -0.588489\n",
      "\n",
      "[334 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(recsubset,columns=['movie title','total ratings','Correlation'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               total ratings  Correlation\n",
      "total ratings       1.000000     0.009469\n",
      "Correlation         0.009469     1.000000\n"
     ]
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "print (corrMatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib' has no attribute 'show'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-85-38332369b980>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0msn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorrMatrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'show'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149effb8>"
      ]
     },
     "execution_count": 87,
     "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": [
    "%matplotlib inline\n",
    "ratings['rating'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14846a18>"
      ]
     },
     "execution_count": 92,
     "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": [
    "avgratings['total ratings'].hist(bins=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x14d29af0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.jointplot(x='rating', y='total ratings', data=avgratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "aladdin_user_rating = movieUser['Aladdin (1992)']\n",
    "tlk_user_rating = movieUser['Lion King, The (1994)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "1    4.0\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    4.0\n",
       "Name: Aladdin (1992), dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aladdin_user_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "1    3.0\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "Name: Lion King, The (1994), dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tlk_user_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie title\n",
       "'Til There Was You (1997)   -0.034483\n",
       "1-900 (1994)                      NaN\n",
       "101 Dalmatians (1996)        0.385118\n",
       "12 Angry Men (1957)          0.030291\n",
       "187 (1997)                   0.609919\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_to_aladdin=movieUser.corrwith(aladdin_user_rating)\n",
    "similar_to_aladdin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie title\n",
       "'Til There Was You (1997)    0.104828\n",
       "1-900 (1994)                      NaN\n",
       "101 Dalmatians (1996)        0.426573\n",
       "12 Angry Men (1957)          0.185675\n",
       "187 (1997)                   0.883883\n",
       "dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_to_tlk=movieUser.corrwith(tlk_user_rating)\n",
    "similar_to_tlk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}