Movie Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Title  \\\n",
       "movieId                                       \n",
       "1                          Toy Story (1995)   \n",
       "2                            Jumanji (1995)   \n",
       "3                   Grumpier Old Men (1995)   \n",
       "4                  Waiting to Exhale (1995)   \n",
       "5        Father of the Bride Part II (1995)   \n",
       "\n",
       "                                               Genre  \n",
       "movieId                                               \n",
       "1        Adventure|Animation|Children|Comedy|Fantasy  \n",
       "2                         Adventure|Children|Fantasy  \n",
       "3                                     Comedy|Romance  \n",
       "4                               Comedy|Drama|Romance  \n",
       "5                                             Comedy  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data from MovieLens|GroupLens (http://files.grouplens.org/datasets/movielens/ml-latest-small.zip)\n",
    "\n",
    "movie_data = pd.read_csv('movies.csv',error_bad_lines=False, header=0, usecols=[0,1,2] ,index_col=0, names=['movieId', 'Title','Genre'])\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9125</td>\n",
       "      <td>9125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>9123</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                           Title  Genre\n",
       "count                       9125   9125\n",
       "unique                      9123    902\n",
       "top     War of the Worlds (2005)  Drama\n",
       "freq                           2   1170"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1       31     2.5\n",
       "1       1     1029     3.0\n",
       "2       1     1061     3.0\n",
       "3       1     1129     2.0\n",
       "4       1     1172     4.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings = pd.read_csv('ratings.csv', error_bad_lines=False, usecols=[0,1,2],header=0, names=['userId','movieId', 'rating'] )\n",
    "movie_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100004.000000</td>\n",
       "      <td>100004.000000</td>\n",
       "      <td>100004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>347.011310</td>\n",
       "      <td>12548.664363</td>\n",
       "      <td>3.543608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>195.163838</td>\n",
       "      <td>26369.198969</td>\n",
       "      <td>1.058064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>1028.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>2406.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>520.000000</td>\n",
       "      <td>5418.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>671.000000</td>\n",
       "      <td>163949.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              userId        movieId         rating\n",
       "count  100004.000000  100004.000000  100004.000000\n",
       "mean      347.011310   12548.664363       3.543608\n",
       "std       195.163838   26369.198969       1.058064\n",
       "min         1.000000       1.000000       0.500000\n",
       "25%       182.000000    1028.000000       3.000000\n",
       "50%       367.000000    2406.500000       4.000000\n",
       "75%       520.000000    5418.000000       4.000000\n",
       "max       671.000000  163949.000000       5.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Toy Story (1995)'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def movieTitle(movieId):\n",
    "    title = movie_data.at[movieId, 'Title']\n",
    "    return title\n",
    "movieTitle(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Adventure|Animation|Children|Comedy|Fantasy'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def movieGenre(movieId):  \n",
    "    genre = movie_data.at[movieId, 'Genre']\n",
    "    return  genre\n",
    "movieGenre(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preprocessing for huge dataset (However here Not Required)\n",
    "# to select only those movies whose id is present in movie_data\n",
    "# movie_ratings = movie_ratings[movie_ratings['movieId'].isin(movie_data.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cinema Paradiso (Nuovo cinema Paradiso) (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2105</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tron (1982)</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1953</td>\n",
       "      <td>4.0</td>\n",
       "      <td>French Connection, The (1971)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1339</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Dracula (Bram Stoker's Dracula) (1992)</td>\n",
       "      <td>Fantasy|Horror|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>3671</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Blazing Saddles (1974)</td>\n",
       "      <td>Comedy|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dumbo (1941)</td>\n",
       "      <td>Animation|Children|Drama|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sleepers (1996)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>2150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gods Must Be Crazy, The (1980)</td>\n",
       "      <td>Adventure|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2455</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Fly, The (1986)</td>\n",
       "      <td>Drama|Horror|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId  rating                                           Title  \\\n",
       "4        1     1172     4.0  Cinema Paradiso (Nuovo cinema Paradiso) (1989)   \n",
       "13       1     2105     4.0                                     Tron (1982)   \n",
       "12       1     1953     4.0                   French Connection, The (1971)   \n",
       "8        1     1339     3.5          Dracula (Bram Stoker's Dracula) (1992)   \n",
       "19       1     3671     3.0                          Blazing Saddles (1974)   \n",
       "1        1     1029     3.0                                    Dumbo (1941)   \n",
       "2        1     1061     3.0                                 Sleepers (1996)   \n",
       "14       1     2150     3.0                  Gods Must Be Crazy, The (1980)   \n",
       "17       1     2455     2.5                                 Fly, The (1986)   \n",
       "0        1       31     2.5                          Dangerous Minds (1995)   \n",
       "\n",
       "                               Genre  \n",
       "4                              Drama  \n",
       "13           Action|Adventure|Sci-Fi  \n",
       "12             Action|Crime|Thriller  \n",
       "8    Fantasy|Horror|Romance|Thriller  \n",
       "19                    Comedy|Western  \n",
       "1   Animation|Children|Drama|Musical  \n",
       "2                           Thriller  \n",
       "14                  Adventure|Comedy  \n",
       "17      Drama|Horror|Sci-Fi|Thriller  \n",
       "0                              Drama  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def favMovie(userId, N):\n",
    "    userRatings = movie_ratings[movie_ratings.userId==userId]\n",
    "    sortedRatings = pd.DataFrame.sort_values(userRatings,['rating'] ,ascending=[0])[:N]\n",
    "    sortedRatings['Title'] = sortedRatings['movieId'].apply(movieTitle)\n",
    "    sortedRatings['Genre'] = sortedRatings['movieId'].apply(movieGenre)\n",
    "    return sortedRatings\n",
    "favMovie(1, 10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup Rating Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100004, 3), (9125, 2))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings.shape, movie_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356    341\n",
       "296    324\n",
       "318    311\n",
       "593    304\n",
       "260    291\n",
       "Name: movieId, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userPerMovieID = movie_ratings.movieId.value_counts()\n",
    "userPerMovieID.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9066,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userPerMovieID.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data Preprocessing to obtain less sparse matrix for huge dataset(However here Not Required)\n",
    "\n",
    "    ## Take only those movies which are seen by more than 10 users\n",
    "#movie_ratings = movie_ratings[movie_ratings.index.isin(userPerMovieID[userPerMovieID > 10].index)]\n",
    "#movie_ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5           NaN     NaN     4.0     NaN     NaN     NaN     NaN     NaN   \n",
       "6           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "7           3.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "8           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "9           4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "10          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  9       10       ...    161084  161155  161594  161830  161918  \\\n",
       "userId                    ...                                             \n",
       "1           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "2           NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "5           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "6           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "7           NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "8           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "9           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "10          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  161944  162376  162542  162672  163949  \n",
       "userId                                           \n",
       "1           NaN     NaN     NaN     NaN     NaN  \n",
       "2           NaN     NaN     NaN     NaN     NaN  \n",
       "3           NaN     NaN     NaN     NaN     NaN  \n",
       "4           NaN     NaN     NaN     NaN     NaN  \n",
       "5           NaN     NaN     NaN     NaN     NaN  \n",
       "6           NaN     NaN     NaN     NaN     NaN  \n",
       "7           NaN     NaN     NaN     NaN     NaN  \n",
       "8           NaN     NaN     NaN     NaN     NaN  \n",
       "9           NaN     NaN     NaN     NaN     NaN  \n",
       "10          NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[10 rows x 9066 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userMovieRatingMatrix = pd.pivot_table(movie_ratings, index=['userId'],columns=['movieId'] ,values='rating')\n",
    "userMovieRatingMatrix.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    4.0\n",
       "2    NaN\n",
       "3    4.0\n",
       "4    NaN\n",
       "5    NaN\n",
       "Name: 100, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user1 = 100\n",
    "user2 = 200\n",
    "\n",
    "user1_ratings = userMovieRatingMatrix.transpose()[user1]\n",
    "user1_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    3.0\n",
       "2    3.5\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "Name: 200, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user2_ratings = userMovieRatingMatrix.transpose()[user2]\n",
    "user2_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9972424442973749"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.spatial.distance import hamming\n",
    "# hamming() returns a value which shows the pecentage of disagreement\n",
    "\n",
    "hamming(user1_ratings, user1_ratings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wrapping it up in a function\n",
    "def distance(user1, user2):\n",
    "    try:\n",
    "        user1_ratings = userMovieRatingMatrix.transpose()[user1]\n",
    "        user2_ratings = userMovieRatingMatrix.transpose()[user2]\n",
    "        distance = hamming(user1_ratings, user2_ratings)\n",
    "    except:\n",
    "        distance = np.nan\n",
    "    return distance\n",
    "distance(100,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId\n",
       "1       2\n",
       "2       3\n",
       "3       4\n",
       "4       5\n",
       "5       6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = 1\n",
    "allusers = pd.DataFrame(userMovieRatingMatrix.index)\n",
    "# Removing the activee user\n",
    "allusers = allusers[allusers.userId != user]\n",
    "allusers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>userId</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.99989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  distance\n",
       "1       2   1.00000\n",
       "2       3   1.00000\n",
       "3       4   0.99989\n",
       "4       5   1.00000\n",
       "5       6   1.00000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allusers['distance'] = allusers['userId'].apply(lambda x: distance(user, x))\n",
    "allusers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "579    580\n",
       "451    452\n",
       "14      15\n",
       "563    564\n",
       "72      73\n",
       "149    150\n",
       "508    509\n",
       "118    119\n",
       "55      56\n",
       "438    439\n",
       "Name: userId, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = 10\n",
    "KNearestUsers = allusers.sort_values(['distance'], ascending=True)['userId'][:K]\n",
    "KNearestUsers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "579    580\n",
       "451    452\n",
       "14      15\n",
       "563    564\n",
       "72      73\n",
       "Name: userId, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wrapping it up in a function\n",
    "def nearestNeighbours(user, K=10):\n",
    "    allusers = pd.DataFrame(userMovieRatingMatrix.index)\n",
    "    allusers = allusers[allusers.userId != user]\n",
    "    allusers['distance'] = allusers['userId'].apply(lambda x: distance(user, x))\n",
    "    KNearestUsers = allusers.sort_values(['distance'], ascending=True)['userId'][:K]\n",
    "    return KNearestUsers\n",
    "\n",
    "KNearestNeighbours = nearestNeighbours(1,5)\n",
    "KNearestNeighbours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Top N Recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "15          2.0     2.0     NaN     NaN     4.5     4.0     NaN     NaN   \n",
       "73          5.0     2.5     NaN     NaN     NaN     4.5     NaN     NaN   \n",
       "452         3.5     NaN     1.0     NaN     3.0     4.0     3.0     NaN   \n",
       "564         4.0     4.0     3.0     NaN     3.0     1.0     3.0     NaN   \n",
       "580         4.0     3.5     NaN     NaN     NaN     3.5     NaN     NaN   \n",
       "\n",
       "movieId  9       10       ...    161084  161155  161594  161830  161918  \\\n",
       "userId                    ...                                             \n",
       "15          NaN     3.0   ...       NaN     0.5     NaN     NaN     NaN   \n",
       "73          NaN     3.0   ...       NaN     NaN     3.0     NaN     NaN   \n",
       "452         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "564         NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "580         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  161944  162376  162542  162672  163949  \n",
       "userId                                           \n",
       "15          NaN     NaN     NaN     NaN     NaN  \n",
       "73          NaN     4.5     NaN     NaN     NaN  \n",
       "452         NaN     NaN     NaN     NaN     NaN  \n",
       "564         NaN     NaN     NaN     NaN     NaN  \n",
       "580         NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nearest Neighbours ratings\n",
    "\n",
    "NNratings = userMovieRatingMatrix[userMovieRatingMatrix.index.isin(KNearestNeighbours)]\n",
    "NNratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:242: RuntimeWarning: Mean of empty slice\n",
      "  labels=labels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    3.7\n",
       "2    3.0\n",
       "3    2.0\n",
       "5    3.5\n",
       "6    3.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the average rating of each movie seen by Nearest Neighbours of active user\n",
    "avgRating = NNratings.apply(np.nanmean).dropna()\n",
    "avgRating.head()\n",
    "\n",
    "# warning where the columns of NNratings are completely empty(nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  31, 1029, 1061, 1129, 1172, 1263, 1287, 1293, 1339, 1343, 1371,\n",
       "            1405, 1953, 2105, 2150, 2193, 2294, 2455, 2968, 3671],\n",
       "           dtype='int64', name='movieId')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesAlreadySeen = userMovieRatingMatrix.transpose()[user].dropna().index\n",
    "moviesAlreadySeen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the movies which are already seen by user\n",
    "avgRating = avgRating[~avgRating.index.isin(moviesAlreadySeen)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2177, 2553, 523], dtype='int64', name='movieId')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N=3\n",
    "topNMovieId = avgRating.sort_values(ascending=False).index[:N]\n",
    "topNMovieId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Family Plot (1976)\n",
       "1    Village of the Damned (1960)\n",
       "2         Ruby in Paradise (1993)\n",
       "Name: movieId, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(topNMovieId).apply(movieTitle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Comedy|Thriller\n",
       "1    Horror|Sci-Fi|Thriller\n",
       "2                     Drama\n",
       "Name: movieId, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(topNMovieId).apply(movieGenre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrapping it up in a function\n",
    "def topN(user,N=3):\n",
    "    KnearestUsers = nearestNeighbours(user)\n",
    "    NNRatings = userMovieRatingMatrix[userMovieRatingMatrix.index.isin(KnearestUsers)]\n",
    "    avgRating = NNRatings.apply(np.nanmean).dropna()\n",
    "    moviesAlreadySeen = userMovieRatingMatrix.transpose()[user].dropna().index\n",
    "    avgRating = avgRating[~avgRating.index.isin(moviesAlreadySeen)]\n",
    "    topNMovieId = avgRating.sort_values(ascending=False).index[:N]\n",
    "    return pd.DataFrame({'Movie':pd.Series(topNMovieId).apply(movieTitle), 'Genre':pd.Series(topNMovieId).apply(movieGenre)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>3</td>\n",
       "      <td>2959</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>3</td>\n",
       "      <td>318</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>3</td>\n",
       "      <td>356</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>3</td>\n",
       "      <td>3949</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>3</td>\n",
       "      <td>1197</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Fantasy|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating                             Title  \\\n",
       "130       3     2959     5.0                 Fight Club (1999)   \n",
       "101       3      318     5.0  Shawshank Redemption, The (1994)   \n",
       "103       3      356     5.0               Forrest Gump (1994)   \n",
       "133       3     3949     5.0        Requiem for a Dream (2000)   \n",
       "113       3     1197     5.0        Princess Bride, The (1987)   \n",
       "\n",
       "                                       Genre  \n",
       "130              Action|Crime|Drama|Thriller  \n",
       "101                              Crime|Drama  \n",
       "103                 Comedy|Drama|Romance|War  \n",
       "133                                    Drama  \n",
       "113  Action|Adventure|Comedy|Fantasy|Romance  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favMovie(3,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Movie</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Marathon Man (1976)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shogun Assassin (1980)</td>\n",
       "      <td>Action|Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Love and Death (1975)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>George of the Jungle (1997)</td>\n",
       "      <td>Children|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Unvanquished, The (Aparajito) (1957)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Movie                 Genre\n",
       "0                   Marathon Man (1976)  Crime|Drama|Thriller\n",
       "1                Shogun Assassin (1980)      Action|Adventure\n",
       "2                 Love and Death (1975)                Comedy\n",
       "3           George of the Jungle (1997)       Children|Comedy\n",
       "4  Unvanquished, The (Aparajito) (1957)                 Drama"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To remove the RunTimeWarning error \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) \n",
    "\n",
    "topN(3,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can see that the genre of recommended movies are nearly same as that of favourite movies of active user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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