Movie Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d635b8d",
   "metadata": {},
   "source": [
    "# Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "933ff7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "d9f2b4ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from fuzzywuzzy import process  # to match the string value\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "6cdda794",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies=pd.read_csv('movies.csv', usecols=['movieId','title'], dtype={'movieId':'int32','title':'str'})\n",
    "df_ratings=pd.read_csv('ratings.csv', usecols=['userId','movieId','rating'],dtype={'userId':'int32','movieId':'int32','rating':'float32'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "8dc342f4",
   "metadata": {},
   "outputs": [
    {
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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>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title\n",
       "0        1                    Toy Story (1995)\n",
       "1        2                      Jumanji (1995)\n",
       "2        3             Grumpier Old Men (1995)\n",
       "3        4            Waiting to Exhale (1995)\n",
       "4        5  Father of the Bride Part II (1995)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "f26c2bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9742, 2)"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "519c1749",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9742 entries, 0 to 9741\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   movieId  9742 non-null   int32 \n",
      " 1   title    9742 non-null   object\n",
      "dtypes: int32(1), object(1)\n",
      "memory usage: 114.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "3f7a092a",
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   "outputs": [
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       "       userId  movieId  rating\n",
       "49449     318    49396     3.5\n",
       "38284     263     1120     4.0\n",
       "95558     600     3083     4.0\n",
       "80886     510     1466     2.0\n",
       "83489     532      293     3.0"
      ]
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     "execution_count": 296,
     "metadata": {},
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   ],
   "source": [
    "df_ratings.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "ccc2a0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100836, 3)"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "728ca248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100836 entries, 0 to 100835\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   userId   100836 non-null  int32  \n",
      " 1   movieId  100836 non-null  int32  \n",
      " 2   rating   100836 non-null  float32\n",
      "dtypes: float32(1), int32(2)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "df_ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "0deaf356",
   "metadata": {},
   "outputs": [
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       "             movieId\n",
       "count    9742.000000\n",
       "mean    42200.353623\n",
       "std     52160.494854\n",
       "min         1.000000\n",
       "25%      3248.250000\n",
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    "df_movies.describe()"
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  {
   "cell_type": "code",
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   "id": "a3a38b93",
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       "              title\n",
       "count          9742\n",
       "unique         9737\n",
       "top     Emma (1996)\n",
       "freq              2"
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    "df_movies.describe(include = 'object')"
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      "text/plain": [
       "              userId        movieId         rating\n",
       "count  100836.000000  100836.000000  100836.000000\n",
       "mean      326.127564   19435.295718       3.501557\n",
       "std       182.618491   35530.987199       1.042541\n",
       "min         1.000000       1.000000       0.500000\n",
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       "50%       325.000000    2991.000000       3.500000\n",
       "75%       477.000000    8122.000000       4.000000\n",
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     "execution_count": 301,
     "metadata": {},
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   "source": [
    "df_ratings.describe()"
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  {
   "cell_type": "code",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</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>1.5</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",
       "      <td>2.0</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",
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       "      <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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>193581</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
       "      <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>193583</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>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",
       "      <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>193585</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>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",
       "      <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>193587</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>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",
       "      <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>193609</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>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",
       "      <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>9724 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   11   12   13   14   \\\n",
       "movieId                                                                         \n",
       "1        4.0  NaN  NaN  NaN  4.0  NaN  4.5  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2        NaN  NaN  NaN  NaN  NaN  4.0  NaN  4.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3        4.0  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4        NaN  NaN  NaN  NaN  NaN  3.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  3.0   \n",
       "5        NaN  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193583   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193585   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193587   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193609   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "userId   15   ...  596  597  598  599  600  601  602  603  604  605  606  607  \\\n",
       "movieId       ...                                                               \n",
       "1        2.5  ...  4.0  4.0  NaN  3.0  2.5  4.0  NaN  4.0  3.0  4.0  2.5  4.0   \n",
       "2        NaN  ...  NaN  NaN  NaN  2.5  4.0  NaN  4.0  NaN  5.0  3.5  NaN  NaN   \n",
       "3        NaN  ...  NaN  NaN  NaN  1.5  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4        NaN  ...  NaN  NaN  NaN  NaN  1.5  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "5        NaN  ...  NaN  NaN  NaN  NaN  2.5  NaN  NaN  NaN  3.0  NaN  NaN  NaN   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193583   NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193585   NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193587   NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "193609   NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "userId   608  609  610  \n",
       "movieId                 \n",
       "1        2.5  3.0  5.0  \n",
       "2        2.0  NaN  NaN  \n",
       "3        2.0  NaN  NaN  \n",
       "4        NaN  NaN  NaN  \n",
       "5        NaN  NaN  NaN  \n",
       "...      ...  ...  ...  \n",
       "193581   NaN  NaN  NaN  \n",
       "193583   NaN  NaN  NaN  \n",
       "193585   NaN  NaN  NaN  \n",
       "193587   NaN  NaN  NaN  \n",
       "193609   NaN  NaN  NaN  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sparse Matrix\n",
    "#         Users\n",
    "#        [4,4,5] A\n",
    "#Movies  [3,3,4] B ==   Cos(A,B) => 0.95 \n",
    "#       [3,2,1]\n",
    "\n",
    "movies_users=df_ratings.pivot(index='movieId', columns='userId',values='rating')\n",
    "movies_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "5b661366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>userId</th>\n",
       "      <th>1</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193581</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193583</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193585</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193587</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193609</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9724 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   11   12   13   14   \\\n",
       "movieId                                                                         \n",
       "1        4.0  0.0  0.0  0.0  4.0  0.0  4.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2        0.0  0.0  0.0  0.0  0.0  4.0  0.0  4.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3        4.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4        0.0  0.0  0.0  0.0  0.0  3.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0   \n",
       "5        0.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193583   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193585   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193587   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193609   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "userId   15   ...  596  597  598  599  600  601  602  603  604  605  606  607  \\\n",
       "movieId       ...                                                               \n",
       "1        2.5  ...  4.0  4.0  0.0  3.0  2.5  4.0  0.0  4.0  3.0  4.0  2.5  4.0   \n",
       "2        0.0  ...  0.0  0.0  0.0  2.5  4.0  0.0  4.0  0.0  5.0  3.5  0.0  0.0   \n",
       "3        0.0  ...  0.0  0.0  0.0  1.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4        0.0  ...  0.0  0.0  0.0  0.0  1.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "5        0.0  ...  0.0  0.0  0.0  0.0  2.5  0.0  0.0  0.0  3.0  0.0  0.0  0.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193583   0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193585   0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193587   0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "193609   0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "userId   608  609  610  \n",
       "movieId                 \n",
       "1        2.5  3.0  5.0  \n",
       "2        2.0  0.0  0.0  \n",
       "3        2.0  0.0  0.0  \n",
       "4        0.0  0.0  0.0  \n",
       "5        0.0  0.0  0.0  \n",
       "...      ...  ...  ...  \n",
       "193581   0.0  0.0  0.0  \n",
       "193583   0.0  0.0  0.0  \n",
       "193585   0.0  0.0  0.0  \n",
       "193587   0.0  0.0  0.0  \n",
       "193609   0.0  0.0  0.0  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_users=df_ratings.pivot(index='movieId', columns='userId',values='rating').fillna(0)\n",
    "# pivot table\n",
    "movies_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "df0492f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    0\n",
       "title      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "61d0ee57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     0\n",
       "movieId    0\n",
       "rating     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "186d5b45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t4.0\n",
      "  (0, 4)\t4.0\n",
      "  (0, 6)\t4.5\n",
      "  (0, 14)\t2.5\n",
      "  (0, 16)\t4.5\n",
      "  (0, 17)\t3.5\n",
      "  (0, 18)\t4.0\n",
      "  (0, 20)\t3.5\n",
      "  (0, 26)\t3.0\n",
      "  (0, 30)\t5.0\n",
      "  (0, 31)\t3.0\n",
      "  (0, 32)\t3.0\n",
      "  (0, 39)\t5.0\n",
      "  (0, 42)\t5.0\n",
      "  (0, 43)\t3.0\n",
      "  (0, 44)\t4.0\n",
      "  (0, 45)\t5.0\n",
      "  (0, 49)\t3.0\n",
      "  (0, 53)\t3.0\n",
      "  (0, 56)\t5.0\n",
      "  (0, 62)\t5.0\n",
      "  (0, 63)\t4.0\n",
      "  (0, 65)\t4.0\n",
      "  (0, 67)\t2.5\n",
      "  (0, 70)\t5.0\n",
      "  :\t:\n",
      "  (9700, 337)\t2.5\n",
      "  (9701, 337)\t3.0\n",
      "  (9702, 183)\t4.0\n",
      "  (9702, 247)\t3.5\n",
      "  (9703, 317)\t2.5\n",
      "  (9704, 209)\t1.0\n",
      "  (9705, 461)\t2.5\n",
      "  (9706, 49)\t3.5\n",
      "  (9707, 337)\t1.5\n",
      "  (9708, 337)\t4.0\n",
      "  (9709, 337)\t1.0\n",
      "  (9710, 337)\t1.5\n",
      "  (9711, 337)\t1.0\n",
      "  (9712, 337)\t1.0\n",
      "  (9713, 183)\t4.5\n",
      "  (9714, 183)\t3.5\n",
      "  (9715, 183)\t3.0\n",
      "  (9716, 183)\t4.0\n",
      "  (9717, 183)\t4.0\n",
      "  (9718, 183)\t3.5\n",
      "  (9719, 183)\t4.0\n",
      "  (9720, 183)\t3.5\n",
      "  (9721, 183)\t3.5\n",
      "  (9722, 183)\t3.5\n",
      "  (9723, 330)\t4.0\n"
     ]
    }
   ],
   "source": [
    "mat_movies_users=csr_matrix(movies_users.values)\n",
    "print(mat_movies_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "93860062",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cosine Similarity\n",
    "model_knn= NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "2b210b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine', n_neighbors=20)"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "5b3e63b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine', n_neighbors=20)"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.fit(mat_movies_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "05578aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommender(movie_name, data,model, n_recommendations ):\n",
    "    print(model.fit(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "09b3bcf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the Movie Name : spider man\n",
      "NearestNeighbors(algorithm='brute', metric='cosine', n_neighbors=20)\n"
     ]
    }
   ],
   "source": [
    "recommender(input('Enter the Movie Name : '), mat_movies_users, model_knn,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "da30b75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommender(movie_name, data,model, n_recommendations ):\n",
    "    print(model.fit(data))\n",
    "    print(process.extractOne(movie_name, df_movies['title']))\n",
    "    idx = process.extractOne(movie_name, df_movies['title'])[2]\n",
    "    print(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "a87c1c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the Movie Name : spiderman\n",
      "NearestNeighbors(algorithm='brute', metric='cosine', n_neighbors=20)\n",
      "('Spider-Man (2002)', 80, 3819)\n",
      "3819\n"
     ]
    }
   ],
   "source": [
    "recommender(input('Enter the Movie Name : '), mat_movies_users, model_knn,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fda3215",
   "metadata": {},
   "source": [
    "## Movie Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "1e47c56f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the Movie Name : spider man\n",
      "Movie Selected:  Spider-Man (2002) Index:  3819\n",
      "Recommended Movies : \n",
      "3819                                    Spider-Man (2002)\n",
      "1611                                 Avengers, The (1998)\n",
      "5564        Riki-Oh: The Story of Ricky (Lik Wong) (1991)\n",
      "5386                     Christmas with the Kranks (2004)\n",
      "4703                                     Project X (1987)\n",
      "5205    Human Condition I, The (Ningen no joken I) (1959)\n",
      "3546                          Spriggan (Supurigan) (1998)\n",
      "2858                            Autumn in New York (2000)\n",
      "3390                                           K-9 (1989)\n",
      "3941                                    Swarm, The (1978)\n",
      "3419                 Return of the Musketeers, The (1989)\n",
      "6268                                   Half Nelson (2006)\n",
      "9185                                    Spellbound (2011)\n",
      "5552                                        Ju Dou (1990)\n",
      "7310    Mortadelo & Filemon: The Big Adventure (La gra...\n",
      "5483                                     Dark Star (1974)\n",
      "6264                           Night Listener, The (2006)\n",
      "5478                                          Duel (1971)\n",
      "3743                              Oh, God! Book II (1980)\n",
      "9264                                          Hush (2016)\n",
      "Name: title, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Recommender(movie_name) => List of Movies recommended\n",
    "\n",
    "def recommender(movie_name, data,model, n_recommendations ):\n",
    "    model.fit(data)\n",
    "    idx=process.extractOne(movie_name, df_movies['title'])[2]\n",
    "    print('Movie Selected: ',df_movies['title'][idx], 'Index: ',idx)\n",
    "    print('Recommended Movies : ')\n",
    "    distances, indices=model.kneighbors(data[idx], n_neighbors=n_recommendations)\n",
    "    for i in indices:\n",
    "        print(df_movies['title'][i])\n",
    "    \n",
    "recommender(input('Enter the Movie Name : '), mat_movies_users, model_knn,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff32dff8",
   "metadata": {},
   "source": [
    "# Content Based Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "19ab46c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import difflib  #to find closest value in the dataset\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer #to convert text into feature vectors\n",
    "from sklearn.metrics.pairwise import cosine_similarity #to find similarity score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "7d8e9ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',30)\n",
    "df_movies = pd.read_csv('moviesdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "ebc29e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>4679</td>\n",
       "      <td>0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>287815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>Call + Response</td>\n",
       "      <td>Call + Response is a first of its kind feature...</td>\n",
       "      <td>0.062089</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2008-10-10</td>\n",
       "      <td>0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>Released</td>\n",
       "      <td>A FILM ABOUT THE WORLD'S 27 MILLION MOST TERRI...</td>\n",
       "      <td>Call + Response</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'Justin Dillon', 'gender': 0, 'depar...</td>\n",
       "      <td>Justin Dillon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>679</td>\n",
       "      <td>64000000</td>\n",
       "      <td>Comedy Crime</td>\n",
       "      <td>http://www.littleman-movie.com/</td>\n",
       "      <td>9072</td>\n",
       "      <td>baby adoption married couple small person crim...</td>\n",
       "      <td>en</td>\n",
       "      <td>Little Man</td>\n",
       "      <td>After leaving the prison, the dwarf criminal C...</td>\n",
       "      <td>12.421504</td>\n",
       "      <td>[{\"name\": \"Revolution Studios\", \"id\": 497}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2006-08-31</td>\n",
       "      <td>101595121</td>\n",
       "      <td>98.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Big things come in small packages</td>\n",
       "      <td>Little Man</td>\n",
       "      <td>5.3</td>\n",
       "      <td>261</td>\n",
       "      <td>Marlon Wayans Shawn Wayans Kerry Washington Jo...</td>\n",
       "      <td>[{'name': 'Lisa Beach', 'gender': 1, 'departme...</td>\n",
       "      <td>Keenen Ivory Wayans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>4703</td>\n",
       "      <td>150000</td>\n",
       "      <td>Comedy Drama Romance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39141</td>\n",
       "      <td>independent film</td>\n",
       "      <td>en</td>\n",
       "      <td>Tadpole</td>\n",
       "      <td>Beautiful, sophisticated women are all over Os...</td>\n",
       "      <td>8.570328</td>\n",
       "      <td>[{\"name\": \"IFC Productions\", \"id\": 26468}, {\"n...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2002-11-01</td>\n",
       "      <td>2891288</td>\n",
       "      <td>78.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Everyone says he should date girls his own age...</td>\n",
       "      <td>Tadpole</td>\n",
       "      <td>5.5</td>\n",
       "      <td>19</td>\n",
       "      <td>Aaron Stanford Kate Mara Bebe Neuwirth John Ri...</td>\n",
       "      <td>[{'name': 'John Sloss', 'gender': 2, 'departme...</td>\n",
       "      <td>Gary Winick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>3276</td>\n",
       "      <td>8000000</td>\n",
       "      <td>Comedy Drama</td>\n",
       "      <td>http://focusfeatures.com/its_kind_of_a_funny_s...</td>\n",
       "      <td>43923</td>\n",
       "      <td>suicide depression independent film coming of ...</td>\n",
       "      <td>en</td>\n",
       "      <td>It's Kind of a Funny Story</td>\n",
       "      <td>A clinically depressed teenager gets a new sta...</td>\n",
       "      <td>13.143045</td>\n",
       "      <td>[{\"name\": \"Focus Features\", \"id\": 10146}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>6491240</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Sometimes what's in your head isn't as crazy a...</td>\n",
       "      <td>It's Kind of a Funny Story</td>\n",
       "      <td>6.9</td>\n",
       "      <td>672</td>\n",
       "      <td>Keir Gilchrist Emma Roberts Zach Galifianakis ...</td>\n",
       "      <td>[{'name': 'Carrie Stewart', 'gender': 0, 'depa...</td>\n",
       "      <td>Ryan Fleck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>3999</td>\n",
       "      <td>2159280</td>\n",
       "      <td>Drama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209274</td>\n",
       "      <td>nun mine jew jewish poland</td>\n",
       "      <td>pl</td>\n",
       "      <td>Ida</td>\n",
       "      <td>Poland, 1962. Anna is a novice, an orphan brou...</td>\n",
       "      <td>13.265432</td>\n",
       "      <td>[{\"name\": \"Danish Film Institute\", \"id\": 125},...</td>\n",
       "      <td>[{\"iso_3166_1\": \"DK\", \"name\": \"Denmark\"}, {\"is...</td>\n",
       "      <td>2013-10-25</td>\n",
       "      <td>10700000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>[{\"iso_639_1\": \"pl\", \"name\": \"Polski\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ida</td>\n",
       "      <td>7.1</td>\n",
       "      <td>260</td>\n",
       "      <td>Agata Trzebuchowska Agata Kulesza Joanna Kulig...</td>\n",
       "      <td>[{'name': 'Kristian Eidnes Andersen', 'gender'...</td>\n",
       "      <td>Pawe\\u0142 Pawlikowski</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index    budget                genres  \\\n",
       "4679   4679         0           Documentary   \n",
       "679     679  64000000          Comedy Crime   \n",
       "4703   4703    150000  Comedy Drama Romance   \n",
       "3276   3276   8000000          Comedy Drama   \n",
       "3999   3999   2159280                 Drama   \n",
       "\n",
       "                                               homepage      id  \\\n",
       "4679                                                NaN  287815   \n",
       "679                     http://www.littleman-movie.com/    9072   \n",
       "4703                                                NaN   39141   \n",
       "3276  http://focusfeatures.com/its_kind_of_a_funny_s...   43923   \n",
       "3999                                                NaN  209274   \n",
       "\n",
       "                                               keywords original_language  \\\n",
       "4679                                                NaN                en   \n",
       "679   baby adoption married couple small person crim...                en   \n",
       "4703                                   independent film                en   \n",
       "3276  suicide depression independent film coming of ...                en   \n",
       "3999                         nun mine jew jewish poland                pl   \n",
       "\n",
       "                  original_title  \\\n",
       "4679             Call + Response   \n",
       "679                   Little Man   \n",
       "4703                     Tadpole   \n",
       "3276  It's Kind of a Funny Story   \n",
       "3999                         Ida   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "4679  Call + Response is a first of its kind feature...    0.062089   \n",
       "679   After leaving the prison, the dwarf criminal C...   12.421504   \n",
       "4703  Beautiful, sophisticated women are all over Os...    8.570328   \n",
       "3276  A clinically depressed teenager gets a new sta...   13.143045   \n",
       "3999  Poland, 1962. Anna is a novice, an orphan brou...   13.265432   \n",
       "\n",
       "                                   production_companies  \\\n",
       "4679                                                 []   \n",
       "679   [{\"name\": \"Revolution Studios\", \"id\": 497}, {\"...   \n",
       "4703  [{\"name\": \"IFC Productions\", \"id\": 26468}, {\"n...   \n",
       "3276          [{\"name\": \"Focus Features\", \"id\": 10146}]   \n",
       "3999  [{\"name\": \"Danish Film Institute\", \"id\": 125},...   \n",
       "\n",
       "                                   production_countries release_date  \\\n",
       "4679                                                 []   2008-10-10   \n",
       "679   [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2006-08-31   \n",
       "4703  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2002-11-01   \n",
       "3276  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2010-07-06   \n",
       "3999  [{\"iso_3166_1\": \"DK\", \"name\": \"Denmark\"}, {\"is...   2013-10-25   \n",
       "\n",
       "        revenue  runtime                                   spoken_languages  \\\n",
       "4679          0     86.0                                                 []   \n",
       "679   101595121     98.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "4703    2891288     78.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...   \n",
       "3276    6491240    101.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "3999   10700000     80.0            [{\"iso_639_1\": \"pl\", \"name\": \"Polski\"}]   \n",
       "\n",
       "        status                                            tagline  \\\n",
       "4679  Released  A FILM ABOUT THE WORLD'S 27 MILLION MOST TERRI...   \n",
       "679   Released                  Big things come in small packages   \n",
       "4703  Released  Everyone says he should date girls his own age...   \n",
       "3276  Released  Sometimes what's in your head isn't as crazy a...   \n",
       "3999  Released                                                NaN   \n",
       "\n",
       "                           title  vote_average  vote_count  \\\n",
       "4679             Call + Response           8.0           1   \n",
       "679                   Little Man           5.3         261   \n",
       "4703                     Tadpole           5.5          19   \n",
       "3276  It's Kind of a Funny Story           6.9         672   \n",
       "3999                         Ida           7.1         260   \n",
       "\n",
       "                                                   cast  \\\n",
       "4679                                                NaN   \n",
       "679   Marlon Wayans Shawn Wayans Kerry Washington Jo...   \n",
       "4703  Aaron Stanford Kate Mara Bebe Neuwirth John Ri...   \n",
       "3276  Keir Gilchrist Emma Roberts Zach Galifianakis ...   \n",
       "3999  Agata Trzebuchowska Agata Kulesza Joanna Kulig...   \n",
       "\n",
       "                                                   crew  \\\n",
       "4679  [{'name': 'Justin Dillon', 'gender': 0, 'depar...   \n",
       "679   [{'name': 'Lisa Beach', 'gender': 1, 'departme...   \n",
       "4703  [{'name': 'John Sloss', 'gender': 2, 'departme...   \n",
       "3276  [{'name': 'Carrie Stewart', 'gender': 0, 'depa...   \n",
       "3999  [{'name': 'Kristian Eidnes Andersen', 'gender'...   \n",
       "\n",
       "                    director  \n",
       "4679           Justin Dillon  \n",
       "679      Keenen Ivory Wayans  \n",
       "4703             Gary Winick  \n",
       "3276              Ryan Fleck  \n",
       "3999  Pawe\\u0142 Pawlikowski  "
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "52a70bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 24)"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns in the data frame\n",
    "df_movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "472a7251",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4803 entries, 0 to 4802\n",
      "Data columns (total 24 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   index                 4803 non-null   int64  \n",
      " 1   budget                4803 non-null   int64  \n",
      " 2   genres                4775 non-null   object \n",
      " 3   homepage              1712 non-null   object \n",
      " 4   id                    4803 non-null   int64  \n",
      " 5   keywords              4391 non-null   object \n",
      " 6   original_language     4803 non-null   object \n",
      " 7   original_title        4803 non-null   object \n",
      " 8   overview              4800 non-null   object \n",
      " 9   popularity            4803 non-null   float64\n",
      " 10  production_companies  4803 non-null   object \n",
      " 11  production_countries  4803 non-null   object \n",
      " 12  release_date          4802 non-null   object \n",
      " 13  revenue               4803 non-null   int64  \n",
      " 14  runtime               4801 non-null   float64\n",
      " 15  spoken_languages      4803 non-null   object \n",
      " 16  status                4803 non-null   object \n",
      " 17  tagline               3959 non-null   object \n",
      " 18  title                 4803 non-null   object \n",
      " 19  vote_average          4803 non-null   float64\n",
      " 20  vote_count            4803 non-null   int64  \n",
      " 21  cast                  4760 non-null   object \n",
      " 22  crew                  4803 non-null   object \n",
      " 23  director              4773 non-null   object \n",
      "dtypes: float64(3), int64(5), object(16)\n",
      "memory usage: 900.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "a881a5b1",
   "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>popularity</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4803.000000</td>\n",
       "      <td>4801.000000</td>\n",
       "      <td>4803.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.492301</td>\n",
       "      <td>106.875859</td>\n",
       "      <td>6.092172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.816650</td>\n",
       "      <td>22.611935</td>\n",
       "      <td>1.194612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.668070</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.921594</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.313505</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>875.581305</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        popularity      runtime  vote_average\n",
       "count  4803.000000  4801.000000   4803.000000\n",
       "mean     21.492301   106.875859      6.092172\n",
       "std      31.816650    22.611935      1.194612\n",
       "min       0.000000     0.000000      0.000000\n",
       "25%       4.668070    94.000000      5.600000\n",
       "50%      12.921594   103.000000      6.200000\n",
       "75%      28.313505   118.000000      6.800000\n",
       "max     875.581305   338.000000     10.000000"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_float = df_movies.describe(include='float64')\n",
    "df_float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "03b3c625",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "corr = df_float.corr()\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(corr, cmap='PiYG', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "c8f90b9f",
   "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>index</th>\n",
       "      <th>budget</th>\n",
       "      <th>id</th>\n",
       "      <th>revenue</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4803.000000</td>\n",
       "      <td>4.803000e+03</td>\n",
       "      <td>4803.000000</td>\n",
       "      <td>4.803000e+03</td>\n",
       "      <td>4803.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2401.000000</td>\n",
       "      <td>2.904504e+07</td>\n",
       "      <td>57165.484281</td>\n",
       "      <td>8.226064e+07</td>\n",
       "      <td>690.217989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1386.651002</td>\n",
       "      <td>4.072239e+07</td>\n",
       "      <td>88694.614033</td>\n",
       "      <td>1.628571e+08</td>\n",
       "      <td>1234.585891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1200.500000</td>\n",
       "      <td>7.900000e+05</td>\n",
       "      <td>9014.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2401.000000</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>14629.000000</td>\n",
       "      <td>1.917000e+07</td>\n",
       "      <td>235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3601.500000</td>\n",
       "      <td>4.000000e+07</td>\n",
       "      <td>58610.500000</td>\n",
       "      <td>9.291719e+07</td>\n",
       "      <td>737.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4802.000000</td>\n",
       "      <td>3.800000e+08</td>\n",
       "      <td>459488.000000</td>\n",
       "      <td>2.787965e+09</td>\n",
       "      <td>13752.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index        budget             id       revenue    vote_count\n",
       "count  4803.000000  4.803000e+03    4803.000000  4.803000e+03   4803.000000\n",
       "mean   2401.000000  2.904504e+07   57165.484281  8.226064e+07    690.217989\n",
       "std    1386.651002  4.072239e+07   88694.614033  1.628571e+08   1234.585891\n",
       "min       0.000000  0.000000e+00       5.000000  0.000000e+00      0.000000\n",
       "25%    1200.500000  7.900000e+05    9014.500000  0.000000e+00     54.000000\n",
       "50%    2401.000000  1.500000e+07   14629.000000  1.917000e+07    235.000000\n",
       "75%    3601.500000  4.000000e+07   58610.500000  9.291719e+07    737.000000\n",
       "max    4802.000000  3.800000e+08  459488.000000  2.787965e+09  13752.000000"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int = df_movies.describe(include='int64')\n",
    "df_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "b735e963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_int.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(corr, cmap='PiYG', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "8900b777",
   "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>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4775</td>\n",
       "      <td>1712</td>\n",
       "      <td>4391</td>\n",
       "      <td>4803</td>\n",
       "      <td>4803</td>\n",
       "      <td>4800</td>\n",
       "      <td>4803</td>\n",
       "      <td>4803</td>\n",
       "      <td>4802</td>\n",
       "      <td>4803</td>\n",
       "      <td>4803</td>\n",
       "      <td>3959</td>\n",
       "      <td>4803</td>\n",
       "      <td>4760</td>\n",
       "      <td>4803</td>\n",
       "      <td>4773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1168</td>\n",
       "      <td>1691</td>\n",
       "      <td>4219</td>\n",
       "      <td>37</td>\n",
       "      <td>4801</td>\n",
       "      <td>4800</td>\n",
       "      <td>3697</td>\n",
       "      <td>469</td>\n",
       "      <td>3280</td>\n",
       "      <td>544</td>\n",
       "      <td>3</td>\n",
       "      <td>3944</td>\n",
       "      <td>4800</td>\n",
       "      <td>4741</td>\n",
       "      <td>4776</td>\n",
       "      <td>2349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Drama</td>\n",
       "      <td>http://www.missionimpossible.com/</td>\n",
       "      <td>independent film</td>\n",
       "      <td>en</td>\n",
       "      <td>Out of the Blue</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Based on a true story.</td>\n",
       "      <td>The Host</td>\n",
       "      <td>William Shatner Leonard Nimoy DeForest Kelley ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Steven Spielberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>370</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>4505</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>2977</td>\n",
       "      <td>10</td>\n",
       "      <td>3171</td>\n",
       "      <td>4795</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres                           homepage          keywords  \\\n",
       "count    4775                               1712              4391   \n",
       "unique   1168                               1691              4219   \n",
       "top     Drama  http://www.missionimpossible.com/  independent film   \n",
       "freq      370                                  4                55   \n",
       "\n",
       "       original_language   original_title  \\\n",
       "count               4803             4803   \n",
       "unique                37             4801   \n",
       "top                   en  Out of the Blue   \n",
       "freq                4505                2   \n",
       "\n",
       "                                                 overview  \\\n",
       "count                                                4800   \n",
       "unique                                               4800   \n",
       "top     In the 22nd century, a paraplegic Marine is di...   \n",
       "freq                                                    1   \n",
       "\n",
       "       production_companies  \\\n",
       "count                  4803   \n",
       "unique                 3697   \n",
       "top                      []   \n",
       "freq                    351   \n",
       "\n",
       "                                     production_countries release_date  \\\n",
       "count                                                4803         4802   \n",
       "unique                                                469         3280   \n",
       "top     [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2006-01-01   \n",
       "freq                                                 2977           10   \n",
       "\n",
       "                                spoken_languages    status  \\\n",
       "count                                       4803      4803   \n",
       "unique                                       544         3   \n",
       "top     [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "freq                                        3171      4795   \n",
       "\n",
       "                       tagline     title  \\\n",
       "count                     3959      4803   \n",
       "unique                    3944      4800   \n",
       "top     Based on a true story.  The Host   \n",
       "freq                         3         2   \n",
       "\n",
       "                                                     cast  crew  \\\n",
       "count                                                4760  4803   \n",
       "unique                                               4741  4776   \n",
       "top     William Shatner Leonard Nimoy DeForest Kelley ...    []   \n",
       "freq                                                    6    28   \n",
       "\n",
       "                director  \n",
       "count               4773  \n",
       "unique              2349  \n",
       "top     Steven Spielberg  \n",
       "freq                  27  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_object = df_movies.describe(include= 'object')\n",
    "df_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "fd3d8f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting relevant features for movie suggestion\n",
    "selected_features = ['genres','keywords','tagline','cast','director']\n",
    "\n",
    "# replacing the null values with null string\n",
    "for sf in selected_features:\n",
    "    df_movies[sf] = df_movies[sf].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "beee293c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                      0\n",
       "budget                     0\n",
       "genres                     0\n",
       "homepage                3091\n",
       "id                         0\n",
       "keywords                   0\n",
       "original_language          0\n",
       "original_title             0\n",
       "overview                   3\n",
       "popularity                 0\n",
       "production_companies       0\n",
       "production_countries       0\n",
       "release_date               1\n",
       "revenue                    0\n",
       "runtime                    2\n",
       "spoken_languages           0\n",
       "status                     0\n",
       "tagline                    0\n",
       "title                      0\n",
       "vote_average               0\n",
       "vote_count                 0\n",
       "cast                       0\n",
       "crew                       0\n",
       "director                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "79ba260c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining all the selected features\n",
    "combined_features = df_movies['genres']+' '+df_movies['keywords']+' '+df_movies['tagline']+' '+df_movies['cast']+' '+df_movies['director']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "177ea316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Action Adventure Fantasy Science Fiction cultu...\n",
       "1       Adventure Fantasy Action ocean drug abuse exot...\n",
       "2       Action Adventure Crime spy based on novel secr...\n",
       "3       Action Crime Drama Thriller dc comics crime fi...\n",
       "4       Action Adventure Science Fiction based on nove...\n",
       "                              ...                        \n",
       "4798    Action Crime Thriller united states\\u2013mexic...\n",
       "4799    Comedy Romance  A newlywed couple's honeymoon ...\n",
       "4800    Comedy Drama Romance TV Movie date love at fir...\n",
       "4801      A New Yorker in Shanghai Daniel Henney Eliza...\n",
       "4802    Documentary obsession camcorder crush dream gi...\n",
       "Length: 4803, dtype: object"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "e8603c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 2432)\t0.17272411194153\n",
      "  (0, 7755)\t0.1128035714854756\n",
      "  (0, 13024)\t0.1942362060108871\n",
      "  (0, 10229)\t0.16058685400095302\n",
      "  (0, 8756)\t0.22709015857011816\n",
      "  (0, 14608)\t0.15150672398763912\n",
      "  (0, 16668)\t0.19843263965100372\n",
      "  (0, 14064)\t0.20596090415084142\n",
      "  (0, 13319)\t0.2177470539412484\n",
      "  (0, 17290)\t0.20197912553916567\n",
      "  (0, 17007)\t0.23643326319898797\n",
      "  (0, 13349)\t0.15021264094167086\n",
      "  (0, 11503)\t0.27211310056983656\n",
      "  (0, 11192)\t0.09049319826481456\n",
      "  (0, 16998)\t0.1282126322850579\n",
      "  (0, 15261)\t0.07095833561276566\n",
      "  (0, 4945)\t0.24025852494110758\n",
      "  (0, 14271)\t0.21392179219912877\n",
      "  (0, 3225)\t0.24960162956997736\n",
      "  (0, 16587)\t0.12549432354918996\n",
      "  (0, 14378)\t0.33962752210959823\n",
      "  (0, 5836)\t0.1646750903586285\n",
      "  (0, 3065)\t0.22208377802661425\n",
      "  (0, 3678)\t0.21392179219912877\n",
      "  (0, 5437)\t0.1036413987316636\n",
      "  :\t:\n",
      "  (4801, 17266)\t0.2886098184932947\n",
      "  (4801, 4835)\t0.24713765026963996\n",
      "  (4801, 403)\t0.17727585190343226\n",
      "  (4801, 6935)\t0.2886098184932947\n",
      "  (4801, 11663)\t0.21557500762727902\n",
      "  (4801, 1672)\t0.1564793427630879\n",
      "  (4801, 10929)\t0.13504166990041588\n",
      "  (4801, 7474)\t0.11307961713172225\n",
      "  (4801, 3796)\t0.3342808988877418\n",
      "  (4802, 6996)\t0.5700048226105303\n",
      "  (4802, 5367)\t0.22969114490410403\n",
      "  (4802, 3654)\t0.262512960498006\n",
      "  (4802, 2425)\t0.24002350969074696\n",
      "  (4802, 4608)\t0.24002350969074696\n",
      "  (4802, 6417)\t0.21753405888348784\n",
      "  (4802, 4371)\t0.1538239182675544\n",
      "  (4802, 12989)\t0.1696476532191718\n",
      "  (4802, 1316)\t0.1960747079005741\n",
      "  (4802, 4528)\t0.19504460807622875\n",
      "  (4802, 3436)\t0.21753405888348784\n",
      "  (4802, 6155)\t0.18056463596934083\n",
      "  (4802, 4980)\t0.16078053641367315\n",
      "  (4802, 2129)\t0.3099656128577656\n",
      "  (4802, 4518)\t0.16784466610624255\n",
      "  (4802, 11161)\t0.17867407682173203\n"
     ]
    }
   ],
   "source": [
    "#convert text data into numerical characters/feature vectors\n",
    "vectorizer = TfidfVectorizer()\n",
    "feature_vectors = vectorizer.fit_transform(combined_features)\n",
    "print(feature_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "011d1da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.07219487, 0.037733  , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.07219487, 1.        , 0.03281499, ..., 0.03575545, 0.        ,\n",
       "        0.        ],\n",
       "       [0.037733  , 0.03281499, 1.        , ..., 0.        , 0.05389661,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.03575545, 0.        , ..., 1.        , 0.        ,\n",
       "        0.02651502],\n",
       "       [0.        , 0.        , 0.05389661, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.02651502, 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cosine Similarity Score\n",
    "similarity = cosine_similarity(feature_vectors)\n",
    "similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "223030eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 4803)"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "efa20092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the movie name: iron man\n"
     ]
    }
   ],
   "source": [
    "# getting movie name from the user\n",
    "movie_name = input('Enter the movie name: ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "f97d554b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Avatar',\n",
       " \"Pirates of the Caribbean: At World's End\",\n",
       " 'Spectre',\n",
       " 'The Dark Knight Rises',\n",
       " 'John Carter',\n",
       " 'Spider-Man 3',\n",
       " 'Tangled',\n",
       " 'Avengers: Age of Ultron',\n",
       " 'Harry Potter and the Half-Blood Prince',\n",
       " 'Batman v Superman: Dawn of Justice',\n",
       " 'Superman Returns',\n",
       " 'Quantum of Solace',\n",
       " \"Pirates of the Caribbean: Dead Man's Chest\",\n",
       " 'The Lone Ranger',\n",
       " 'Man of Steel',\n",
       " 'The Chronicles of Narnia: Prince Caspian',\n",
       " 'The Avengers',\n",
       " 'Pirates of the Caribbean: On Stranger Tides',\n",
       " 'Men in Black 3',\n",
       " 'The Hobbit: The Battle of the Five Armies',\n",
       " 'The Amazing Spider-Man',\n",
       " 'Robin Hood',\n",
       " 'The Hobbit: The Desolation of Smaug',\n",
       " 'The Golden Compass',\n",
       " 'King Kong',\n",
       " 'Titanic',\n",
       " 'Captain America: Civil War',\n",
       " 'Battleship',\n",
       " 'Jurassic World',\n",
       " 'Skyfall',\n",
       " 'Spider-Man 2',\n",
       " 'Iron Man 3',\n",
       " 'Alice in Wonderland',\n",
       " 'X-Men: The Last Stand',\n",
       " 'Monsters University',\n",
       " 'Transformers: Revenge of the Fallen',\n",
       " 'Transformers: Age of Extinction',\n",
       " 'Oz: The Great and Powerful',\n",
       " 'The Amazing Spider-Man 2',\n",
       " 'TRON: Legacy',\n",
       " 'Cars 2',\n",
       " 'Green Lantern',\n",
       " 'Toy Story 3',\n",
       " 'Terminator Salvation',\n",
       " 'Furious 7',\n",
       " 'World War Z',\n",
       " 'X-Men: Days of Future Past',\n",
       " 'Star Trek Into Darkness',\n",
       " 'Jack the Giant Slayer',\n",
       " 'The Great Gatsby',\n",
       " 'Prince of Persia: The Sands of Time',\n",
       " 'Pacific Rim',\n",
       " 'Transformers: Dark of the Moon',\n",
       " 'Indiana Jones and the Kingdom of the Crystal Skull',\n",
       " 'The Good Dinosaur',\n",
       " 'Brave',\n",
       " 'Star Trek Beyond',\n",
       " 'WALL·E',\n",
       " 'Rush Hour 3',\n",
       " '2012',\n",
       " 'A Christmas Carol',\n",
       " 'Jupiter Ascending',\n",
       " 'The Legend of Tarzan',\n",
       " 'The Chronicles of Narnia: The Lion, the Witch and the Wardrobe',\n",
       " 'X-Men: Apocalypse',\n",
       " 'The Dark Knight',\n",
       " 'Up',\n",
       " 'Monsters vs Aliens',\n",
       " 'Iron Man',\n",
       " 'Hugo',\n",
       " 'Wild Wild West',\n",
       " 'The Mummy: Tomb of the Dragon Emperor',\n",
       " 'Suicide Squad',\n",
       " 'Evan Almighty',\n",
       " 'Edge of Tomorrow',\n",
       " 'Waterworld',\n",
       " 'G.I. Joe: The Rise of Cobra',\n",
       " 'Inside Out',\n",
       " 'The Jungle Book',\n",
       " 'Iron Man 2',\n",
       " 'Snow White and the Huntsman',\n",
       " 'Maleficent',\n",
       " 'Dawn of the Planet of the Apes',\n",
       " 'The Lovers',\n",
       " '47 Ronin',\n",
       " 'Captain America: The Winter Soldier',\n",
       " 'Shrek Forever After',\n",
       " 'Tomorrowland',\n",
       " 'Big Hero 6',\n",
       " 'Wreck-It Ralph',\n",
       " 'The Polar Express',\n",
       " 'Independence Day: Resurgence',\n",
       " 'How to Train Your Dragon',\n",
       " 'Terminator 3: Rise of the Machines',\n",
       " 'Guardians of the Galaxy',\n",
       " 'Interstellar',\n",
       " 'Inception',\n",
       " 'Shin Godzilla',\n",
       " 'The Hobbit: An Unexpected Journey',\n",
       " 'The Fast and the Furious',\n",
       " 'The Curious Case of Benjamin Button',\n",
       " 'X-Men: First Class',\n",
       " 'The Hunger Games: Mockingjay - Part 2',\n",
       " \"The Sorcerer's Apprentice\",\n",
       " 'Poseidon',\n",
       " 'Alice Through the Looking Glass',\n",
       " 'Shrek the Third',\n",
       " 'Warcraft',\n",
       " 'Terminator Genisys',\n",
       " 'The Chronicles of Narnia: The Voyage of the Dawn Treader',\n",
       " 'Pearl Harbor',\n",
       " 'Transformers',\n",
       " 'Alexander',\n",
       " 'Harry Potter and the Order of the Phoenix',\n",
       " 'Harry Potter and the Goblet of Fire',\n",
       " 'Hancock',\n",
       " 'I Am Legend',\n",
       " 'Charlie and the Chocolate Factory',\n",
       " 'Ratatouille',\n",
       " 'Batman Begins',\n",
       " 'Madagascar: Escape 2 Africa',\n",
       " 'Night at the Museum: Battle of the Smithsonian',\n",
       " 'X-Men Origins: Wolverine',\n",
       " 'The Matrix Revolutions',\n",
       " 'Frozen',\n",
       " 'The Matrix Reloaded',\n",
       " 'Thor: The Dark World',\n",
       " 'Mad Max: Fury Road',\n",
       " 'Angels & Demons',\n",
       " 'Thor',\n",
       " 'Bolt',\n",
       " 'G-Force',\n",
       " 'Wrath of the Titans',\n",
       " 'Dark Shadows',\n",
       " 'Mission: Impossible - Rogue Nation',\n",
       " 'The Wolfman',\n",
       " 'Bee Movie',\n",
       " 'Kung Fu Panda 2',\n",
       " 'The Last Airbender',\n",
       " 'Mission: Impossible III',\n",
       " 'White House Down',\n",
       " 'Mars Needs Moms',\n",
       " 'Flushed Away',\n",
       " 'Pan',\n",
       " 'Mr. Peabody & Sherman',\n",
       " 'Troy',\n",
       " \"Madagascar 3: Europe's Most Wanted\",\n",
       " 'Die Another Day',\n",
       " 'Ghostbusters',\n",
       " 'Armageddon',\n",
       " 'Men in Black II',\n",
       " 'Beowulf',\n",
       " 'Kung Fu Panda 3',\n",
       " 'Mission: Impossible - Ghost Protocol',\n",
       " 'Rise of the Guardians',\n",
       " 'Fun with Dick and Jane',\n",
       " 'The Last Samurai',\n",
       " 'Exodus: Gods and Kings',\n",
       " 'Star Trek',\n",
       " 'Spider-Man',\n",
       " 'How to Train Your Dragon 2',\n",
       " 'Gods of Egypt',\n",
       " 'Stealth',\n",
       " 'Watchmen',\n",
       " 'Lethal Weapon 4',\n",
       " 'Hulk',\n",
       " 'G.I. Joe: Retaliation',\n",
       " 'Sahara',\n",
       " 'Final Fantasy: The Spirits Within',\n",
       " 'Captain America: The First Avenger',\n",
       " 'The World Is Not Enough',\n",
       " 'Master and Commander: The Far Side of the World',\n",
       " 'The Twilight Saga: Breaking Dawn - Part 2',\n",
       " 'Happy Feet Two',\n",
       " 'The Incredible Hulk',\n",
       " 'The BFG',\n",
       " 'The Revenant',\n",
       " 'Turbo',\n",
       " 'Rango',\n",
       " 'Penguins of Madagascar',\n",
       " 'The Bourne Ultimatum',\n",
       " 'Kung Fu Panda',\n",
       " 'Ant-Man',\n",
       " 'The Hunger Games: Catching Fire',\n",
       " 'Home',\n",
       " 'War of the Worlds',\n",
       " 'Bad Boys II',\n",
       " 'Puss in Boots',\n",
       " 'Salt',\n",
       " 'Noah',\n",
       " 'The Adventures of Tintin',\n",
       " 'Harry Potter and the Prisoner of Azkaban',\n",
       " 'Australia',\n",
       " 'After Earth',\n",
       " 'Dinosaur',\n",
       " 'Night at the Museum: Secret of the Tomb',\n",
       " 'Megamind',\n",
       " \"Harry Potter and the Philosopher's Stone\",\n",
       " 'R.I.P.D.',\n",
       " 'Pirates of the Caribbean: The Curse of the Black Pearl',\n",
       " 'The Hunger Games: Mockingjay - Part 1',\n",
       " 'The Da Vinci Code',\n",
       " 'Rio 2',\n",
       " 'X2',\n",
       " 'Fast Five',\n",
       " 'Sherlock Holmes: A Game of Shadows',\n",
       " 'Clash of the Titans',\n",
       " 'Total Recall',\n",
       " 'The 13th Warrior',\n",
       " 'The Bourne Legacy',\n",
       " 'Batman & Robin',\n",
       " 'How the Grinch Stole Christmas',\n",
       " 'The Day After Tomorrow',\n",
       " 'Mission: Impossible II',\n",
       " 'The Perfect Storm',\n",
       " 'Fantastic 4: Rise of the Silver Surfer',\n",
       " 'Life of Pi',\n",
       " 'Ghost Rider',\n",
       " 'Jason Bourne',\n",
       " \"Charlie's Angels: Full Throttle\",\n",
       " 'Prometheus',\n",
       " 'Stuart Little 2',\n",
       " 'Elysium',\n",
       " 'The Chronicles of Riddick',\n",
       " 'RoboCop',\n",
       " 'Speed Racer',\n",
       " 'How Do You Know',\n",
       " 'Knight and Day',\n",
       " 'Oblivion',\n",
       " 'Star Wars: Episode III - Revenge of the Sith',\n",
       " 'Star Wars: Episode II - Attack of the Clones',\n",
       " 'Monsters, Inc.',\n",
       " 'The Wolverine',\n",
       " 'Star Wars: Episode I - The Phantom Menace',\n",
       " 'The Croods',\n",
       " 'Asterix at the Olympic Games',\n",
       " 'Windtalkers',\n",
       " \"The Huntsman: Winter's War\",\n",
       " 'Teenage Mutant Ninja Turtles',\n",
       " 'Gravity',\n",
       " \"Dante's Peak\",\n",
       " 'Teenage Mutant Ninja Turtles: Out of the Shadows',\n",
       " 'Fantastic Four',\n",
       " 'Night at the Museum',\n",
       " 'San Andreas',\n",
       " 'Tomorrow Never Dies',\n",
       " 'The Patriot',\n",
       " \"Ocean's Twelve\",\n",
       " 'Mr. & Mrs. Smith',\n",
       " 'Insurgent',\n",
       " 'The Aviator',\n",
       " \"Gulliver's Travels\",\n",
       " 'The Green Hornet',\n",
       " '300: Rise of an Empire',\n",
       " 'The Smurfs',\n",
       " 'Home on the Range',\n",
       " 'Allegiant',\n",
       " 'Real Steel',\n",
       " 'The Smurfs 2',\n",
       " 'Speed 2: Cruise Control',\n",
       " \"Ender's Game\",\n",
       " 'Live Free or Die Hard',\n",
       " 'The Lord of the Rings: The Fellowship of the Ring',\n",
       " 'Around the World in 80 Days',\n",
       " 'Ali',\n",
       " 'The Cat in the Hat',\n",
       " 'I, Robot',\n",
       " 'Kingdom of Heaven',\n",
       " 'Stuart Little',\n",
       " 'The Princess and the Frog',\n",
       " 'The Martian',\n",
       " 'The Island',\n",
       " 'Town & Country',\n",
       " 'Gone in Sixty Seconds',\n",
       " 'Gladiator',\n",
       " 'Minority Report',\n",
       " 'Harry Potter and the Chamber of Secrets',\n",
       " 'Casino Royale',\n",
       " 'Planet of the Apes',\n",
       " 'Terminator 2: Judgment Day',\n",
       " 'Public Enemies',\n",
       " 'American Gangster',\n",
       " 'True Lies',\n",
       " 'The Taking of Pelham 1 2 3',\n",
       " 'Little Fockers',\n",
       " 'The Other Guys',\n",
       " 'Eraser',\n",
       " 'Django Unchained',\n",
       " 'The Hunchback of Notre Dame',\n",
       " \"The Emperor's New Groove\",\n",
       " 'The Expendables 2',\n",
       " 'National Treasure',\n",
       " 'Eragon',\n",
       " 'Where the Wild Things Are',\n",
       " 'Epic',\n",
       " 'The Tourist',\n",
       " 'End of Days',\n",
       " 'Blood Diamond',\n",
       " 'The Wolf of Wall Street',\n",
       " 'Batman Forever',\n",
       " 'Starship Troopers',\n",
       " 'Cloud Atlas',\n",
       " \"Legend of the Guardians: The Owls of Ga'Hoole\",\n",
       " 'Catwoman',\n",
       " 'Hercules',\n",
       " 'Treasure Planet',\n",
       " 'Land of the Lost',\n",
       " 'The Expendables 3',\n",
       " 'Point Break',\n",
       " 'Son of the Mask',\n",
       " 'In the Heart of the Sea',\n",
       " 'The Adventures of Pluto Nash',\n",
       " 'Green Zone',\n",
       " 'The Peanuts Movie',\n",
       " 'The Spanish Prisoner',\n",
       " 'The Mummy Returns',\n",
       " 'Gangs of New York',\n",
       " 'The Flowers of War',\n",
       " \"Surf's Up\",\n",
       " 'The Stepford Wives',\n",
       " 'Black Hawk Down',\n",
       " 'The Campaign',\n",
       " 'The Fifth Element',\n",
       " 'Sex and the City 2',\n",
       " 'The Road to El Dorado',\n",
       " 'Ice Age: Continental Drift',\n",
       " 'Cinderella',\n",
       " 'The Lovely Bones',\n",
       " 'Finding Nemo',\n",
       " 'The Lord of the Rings: The Return of the King',\n",
       " 'The Lord of the Rings: The Two Towers',\n",
       " 'Seventh Son',\n",
       " 'Lara Croft: Tomb Raider',\n",
       " 'Transcendence',\n",
       " 'Jurassic Park III',\n",
       " 'Rise of the Planet of the Apes',\n",
       " 'The Spiderwick Chronicles',\n",
       " 'A Good Day to Die Hard',\n",
       " 'The Alamo',\n",
       " 'The Incredibles',\n",
       " 'Cutthroat Island',\n",
       " 'Percy Jackson & the Olympians: The Lightning Thief',\n",
       " 'Men in Black',\n",
       " 'Toy Story 2',\n",
       " 'Unstoppable',\n",
       " 'Rush Hour 2',\n",
       " 'What Lies Beneath',\n",
       " 'Cloudy with a Chance of Meatballs',\n",
       " 'Ice Age: Dawn of the Dinosaurs',\n",
       " 'The Secret Life of Walter Mitty',\n",
       " \"Charlie's Angels\",\n",
       " 'The Departed',\n",
       " 'Mulan',\n",
       " 'Tropic Thunder',\n",
       " 'The Girl with the Dragon Tattoo',\n",
       " 'Die Hard: With a Vengeance',\n",
       " 'Sherlock Holmes',\n",
       " 'Ben-Hur',\n",
       " 'Atlantis: The Lost Empire',\n",
       " 'Alvin and the Chipmunks: The Road Chip',\n",
       " 'Valkyrie',\n",
       " \"You Don't Mess with the Zohan\",\n",
       " 'Pixels',\n",
       " 'A.I. Artificial Intelligence',\n",
       " 'The Haunted Mansion',\n",
       " 'Contact',\n",
       " 'Hollow Man',\n",
       " 'The Interpreter',\n",
       " 'Percy Jackson: Sea of Monsters',\n",
       " 'Lara Croft Tomb Raider: The Cradle of Life',\n",
       " 'Now You See Me 2',\n",
       " 'The Saint',\n",
       " 'Spy Game',\n",
       " 'Mission to Mars',\n",
       " 'Rio',\n",
       " 'Bicentennial Man',\n",
       " 'Volcano',\n",
       " \"The Devil's Own\",\n",
       " 'K-19: The Widowmaker',\n",
       " 'Conan the Barbarian',\n",
       " 'Cinderella Man',\n",
       " 'The Nutcracker: The Untold Story',\n",
       " 'Seabiscuit',\n",
       " 'Twister',\n",
       " 'Cast Away',\n",
       " 'Happy Feet',\n",
       " 'The Bourne Supremacy',\n",
       " 'Air Force One',\n",
       " \"Ocean's Eleven\",\n",
       " 'The Three Musketeers',\n",
       " 'Hotel Transylvania',\n",
       " 'Enchanted',\n",
       " 'Safe House',\n",
       " '102 Dalmatians',\n",
       " 'Tower Heist',\n",
       " 'The Holiday',\n",
       " 'Enemy of the State',\n",
       " \"It's Complicated\",\n",
       " \"Ocean's Thirteen\",\n",
       " 'Open Season',\n",
       " 'Divergent',\n",
       " 'Enemy at the Gates',\n",
       " 'The Rundown',\n",
       " 'Last Action Hero',\n",
       " 'Memoirs of a Geisha',\n",
       " 'The Fast and the Furious: Tokyo Drift',\n",
       " 'Arthur Christmas',\n",
       " 'Meet Joe Black',\n",
       " 'Collateral Damage',\n",
       " 'All That Jazz',\n",
       " 'Mirror Mirror',\n",
       " 'Scott Pilgrim vs. the World',\n",
       " 'The Core',\n",
       " 'Nutty Professor II: The Klumps',\n",
       " 'Scooby-Doo',\n",
       " 'Dredd',\n",
       " 'Click',\n",
       " 'Creepshow',\n",
       " 'Cats & Dogs 2 : The Revenge of Kitty Galore',\n",
       " 'Jumper',\n",
       " 'Hellboy II: The Golden Army',\n",
       " 'Zodiac',\n",
       " 'The 6th Day',\n",
       " 'Bruce Almighty',\n",
       " 'The Expendables',\n",
       " 'Mission: Impossible',\n",
       " 'The Hunger Games',\n",
       " 'The Hangover Part II',\n",
       " 'Batman Returns',\n",
       " 'Over the Hedge',\n",
       " 'Lilo & Stitch',\n",
       " \"Charlotte's Web\",\n",
       " 'Deep Impact',\n",
       " 'RED 2',\n",
       " 'The Longest Yard',\n",
       " 'Alvin and the Chipmunks: Chipwrecked',\n",
       " 'Grown Ups 2',\n",
       " 'Get Smart',\n",
       " \"Something's Gotta Give\",\n",
       " 'Shutter Island',\n",
       " 'Four Christmases',\n",
       " 'Robots',\n",
       " 'Face/Off',\n",
       " 'Bedtime Stories',\n",
       " 'Road to Perdition',\n",
       " 'Just Go with It',\n",
       " 'Con Air',\n",
       " 'Eagle Eye',\n",
       " 'Cold Mountain',\n",
       " 'The Book of Eli',\n",
       " 'Flubber',\n",
       " 'The Haunting',\n",
       " 'Space Jam',\n",
       " 'The Pink Panther',\n",
       " 'The Day the Earth Stood Still',\n",
       " 'Conspiracy Theory',\n",
       " 'Fury',\n",
       " 'Six Days Seven Nights',\n",
       " 'Yogi Bear',\n",
       " 'Spirit: Stallion of the Cimarron',\n",
       " 'Zookeeper',\n",
       " 'Lost in Space',\n",
       " 'The Manchurian Candidate',\n",
       " 'Déjà Vu',\n",
       " 'Hotel Transylvania 2',\n",
       " 'Fantasia 2000',\n",
       " 'The Time Machine',\n",
       " 'Mighty Joe Young',\n",
       " 'Swordfish',\n",
       " 'The Legend of Zorro',\n",
       " 'What Dreams May Come',\n",
       " 'Little Nicky',\n",
       " 'The Brothers Grimm',\n",
       " 'Mars Attacks!',\n",
       " 'Evolution',\n",
       " 'The Edge',\n",
       " 'Surrogates',\n",
       " 'Thirteen Days',\n",
       " 'Daylight',\n",
       " 'Walking With Dinosaurs',\n",
       " 'Battlefield Earth',\n",
       " 'Looney Tunes: Back in Action',\n",
       " 'Nine',\n",
       " 'Timeline',\n",
       " 'The Postman',\n",
       " 'Babe: Pig in the City',\n",
       " 'The Last Witch Hunter',\n",
       " 'Red Planet',\n",
       " 'Arthur and the Invisibles',\n",
       " 'Oceans',\n",
       " 'A Sound of Thunder',\n",
       " 'Pompeii',\n",
       " 'Top Cat Begins',\n",
       " 'A Beautiful Mind',\n",
       " 'The Lion King',\n",
       " 'Journey 2: The Mysterious Island',\n",
       " 'Cloudy with a Chance of Meatballs 2',\n",
       " 'Red Dragon',\n",
       " 'Hidalgo',\n",
       " 'Jack and Jill',\n",
       " '2 Fast 2 Furious',\n",
       " 'The Little Prince',\n",
       " 'The Invasion',\n",
       " 'The Adventures of Rocky & Bullwinkle',\n",
       " 'The Secret Life of Pets',\n",
       " 'The League of Extraordinary Gentlemen',\n",
       " 'Despicable Me 2',\n",
       " 'Independence Day',\n",
       " 'The Lost World: Jurassic Park',\n",
       " 'Madagascar',\n",
       " 'Children of Men',\n",
       " 'X-Men',\n",
       " 'Wanted',\n",
       " 'The Rock',\n",
       " 'Ice Age: The Meltdown',\n",
       " '50 First Dates',\n",
       " 'Hairspray',\n",
       " 'Exorcist: The Beginning',\n",
       " 'Inspector Gadget',\n",
       " 'Now You See Me',\n",
       " 'Grown Ups',\n",
       " 'The Terminal',\n",
       " 'Hotel for Dogs',\n",
       " 'Vertical Limit',\n",
       " \"Charlie Wilson's War\",\n",
       " 'Shark Tale',\n",
       " 'Dreamgirls',\n",
       " 'Be Cool',\n",
       " 'Munich',\n",
       " 'Tears of the Sun',\n",
       " 'Killers',\n",
       " 'The Man from U.N.C.L.E.',\n",
       " 'Spanglish',\n",
       " 'Monster House',\n",
       " 'Bandits',\n",
       " 'First Knight',\n",
       " 'Anna and the King',\n",
       " 'Immortals',\n",
       " 'Hostage',\n",
       " 'Titan A.E.',\n",
       " 'Hollywood Homicide',\n",
       " 'Soldier',\n",
       " 'Carriers',\n",
       " 'Monkeybone',\n",
       " 'Flight of the Phoenix',\n",
       " 'Unbreakable',\n",
       " 'Minions',\n",
       " 'Sucker Punch',\n",
       " 'Snake Eyes',\n",
       " 'Sphere',\n",
       " 'The Angry Birds Movie',\n",
       " \"Fool's Gold\",\n",
       " 'Funny People',\n",
       " 'The Kingdom',\n",
       " 'Talladega Nights: The Ballad of Ricky Bobby',\n",
       " 'Dr. Dolittle 2',\n",
       " 'Braveheart',\n",
       " 'Jarhead',\n",
       " 'The Simpsons Movie',\n",
       " 'The Majestic',\n",
       " 'Driven',\n",
       " 'Two Brothers',\n",
       " 'The Village',\n",
       " 'Doctor Dolittle',\n",
       " 'Signs',\n",
       " 'Shrek 2',\n",
       " 'Cars',\n",
       " 'Runaway Bride',\n",
       " 'xXx',\n",
       " 'The SpongeBob Movie: Sponge Out of Water',\n",
       " 'Ransom',\n",
       " 'Inglourious Basterds',\n",
       " 'Hook',\n",
       " 'Die Hard 2',\n",
       " 'S.W.A.T.',\n",
       " 'Vanilla Sky',\n",
       " 'Lady in the Water',\n",
       " 'AVP: Alien vs. Predator',\n",
       " 'Alvin and the Chipmunks: The Squeakquel',\n",
       " 'We Were Soldiers',\n",
       " 'Olympus Has Fallen',\n",
       " 'Star Trek: Insurrection',\n",
       " 'Battle: Los Angeles',\n",
       " 'Big Fish',\n",
       " 'Wolf',\n",
       " 'War Horse',\n",
       " 'The Monuments Men',\n",
       " 'The Abyss',\n",
       " 'Wall Street: Money Never Sleeps',\n",
       " 'Dracula Untold',\n",
       " 'The Siege',\n",
       " 'Stardust',\n",
       " 'Seven Years in Tibet',\n",
       " 'The Dilemma',\n",
       " 'Bad Company',\n",
       " 'Doom',\n",
       " 'I Spy',\n",
       " 'Underworld: Awakening',\n",
       " 'Rock of Ages',\n",
       " \"Hart's War\",\n",
       " 'Killer Elite',\n",
       " 'Rollerball',\n",
       " 'Ballistic: Ecks vs. Sever',\n",
       " 'Hard Rain',\n",
       " 'Osmosis Jones',\n",
       " \"Legends of Oz: Dorothy's Return\",\n",
       " 'Blackhat',\n",
       " 'Sky Captain and the World of Tomorrow',\n",
       " 'Basic Instinct 2',\n",
       " 'Escape Plan',\n",
       " 'The Legend of Hercules',\n",
       " 'The Sum of All Fears',\n",
       " 'The Twilight Saga: Eclipse',\n",
       " 'The Score',\n",
       " 'Despicable Me',\n",
       " 'Money Train',\n",
       " 'Ted 2',\n",
       " 'Agora',\n",
       " 'Mystery Men',\n",
       " 'Hall Pass',\n",
       " 'The Insider',\n",
       " 'The Finest Hours',\n",
       " 'Body of Lies',\n",
       " 'Dinner for Schmucks',\n",
       " 'Abraham Lincoln: Vampire Hunter',\n",
       " 'Entrapment',\n",
       " 'The X Files',\n",
       " 'The Last Legion',\n",
       " 'Saving Private Ryan',\n",
       " 'Need for Speed',\n",
       " 'What Women Want',\n",
       " 'Ice Age',\n",
       " 'Dreamcatcher',\n",
       " 'Lincoln',\n",
       " 'The Matrix',\n",
       " 'Apollo 13',\n",
       " 'The Santa Clause 2',\n",
       " 'Les Misérables',\n",
       " \"You've Got Mail\",\n",
       " 'Step Brothers',\n",
       " 'The Mask of Zorro',\n",
       " 'Due Date',\n",
       " 'Unbroken',\n",
       " 'Space Cowboys',\n",
       " 'Cliffhanger',\n",
       " 'Broken Arrow',\n",
       " 'The Kid',\n",
       " 'World Trade Center',\n",
       " 'Mona Lisa Smile',\n",
       " 'The Dictator',\n",
       " 'Eyes Wide Shut',\n",
       " 'Annie',\n",
       " 'Focus',\n",
       " 'This Means War',\n",
       " 'Blade: Trinity',\n",
       " 'Red Dawn',\n",
       " 'Primary Colors',\n",
       " 'Resident Evil: Retribution',\n",
       " 'Death Race',\n",
       " 'The Long Kiss Goodnight',\n",
       " 'Proof of Life',\n",
       " 'Zathura: A Space Adventure',\n",
       " 'Fight Club',\n",
       " 'We Are Marshall',\n",
       " 'Hudson Hawk',\n",
       " 'Lucky Numbers',\n",
       " 'I, Frankenstein',\n",
       " 'Oliver Twist',\n",
       " 'Elektra',\n",
       " 'Sin City: A Dame to Kill For',\n",
       " 'Random Hearts',\n",
       " 'Everest',\n",
       " 'Perfume: The Story of a Murderer',\n",
       " 'Austin Powers in Goldmember',\n",
       " 'Astro Boy',\n",
       " 'Jurassic Park',\n",
       " 'Wyatt Earp',\n",
       " 'Clear and Present Danger',\n",
       " 'Dragon Blade',\n",
       " 'Little Man',\n",
       " 'U-571',\n",
       " 'The American President',\n",
       " 'The Love Guru',\n",
       " '3000 Miles to Graceland',\n",
       " 'The Hateful Eight',\n",
       " 'Blades of Glory',\n",
       " 'Hop',\n",
       " '300',\n",
       " 'Meet the Fockers',\n",
       " 'Marley & Me',\n",
       " 'The Green Mile',\n",
       " 'Wild Hogs',\n",
       " 'Chicken Little',\n",
       " 'Gone Girl',\n",
       " 'The Bourne Identity',\n",
       " 'GoldenEye',\n",
       " \"The General's Daughter\",\n",
       " 'The Truman Show',\n",
       " 'The Prince of Egypt',\n",
       " 'Daddy Day Care',\n",
       " '2 Guns',\n",
       " 'Cats & Dogs',\n",
       " 'The Italian Job',\n",
       " 'Two Weeks Notice',\n",
       " 'Antz',\n",
       " 'Couples Retreat',\n",
       " 'Days of Thunder',\n",
       " 'Cheaper by the Dozen 2',\n",
       " 'Maze Runner: The Scorch Trials',\n",
       " 'Eat Pray Love',\n",
       " 'The Family Man',\n",
       " 'RED',\n",
       " 'Any Given Sunday',\n",
       " 'The Horse Whisperer',\n",
       " 'Collateral',\n",
       " 'The Scorpion King',\n",
       " 'Ladder 49',\n",
       " 'Jack Reacher',\n",
       " 'Deep Blue Sea',\n",
       " 'This Is It',\n",
       " 'Contagion',\n",
       " 'Kangaroo Jack',\n",
       " 'Coraline',\n",
       " 'The Happening',\n",
       " 'Man on Fire',\n",
       " 'The Shaggy Dog',\n",
       " 'Starsky & Hutch',\n",
       " 'Jingle All the Way',\n",
       " 'Hellboy',\n",
       " 'A Civil Action',\n",
       " 'ParaNorman',\n",
       " 'The Jackal',\n",
       " 'Paycheck',\n",
       " 'Up Close & Personal',\n",
       " 'The Tale of Despereaux',\n",
       " 'The Tuxedo',\n",
       " 'Under Siege 2: Dark Territory',\n",
       " 'Jack Ryan: Shadow Recruit',\n",
       " 'Joy',\n",
       " 'London Has Fallen',\n",
       " 'Alien: Resurrection',\n",
       " 'Shooter',\n",
       " 'The Boxtrolls',\n",
       " 'Practical Magic',\n",
       " 'The Lego Movie',\n",
       " 'Miss Congeniality 2: Armed and Fabulous',\n",
       " 'Reign of Fire',\n",
       " 'Gangster Squad',\n",
       " 'Year One',\n",
       " 'Invictus',\n",
       " 'State of Play',\n",
       " 'Duplicity',\n",
       " 'My Favorite Martian',\n",
       " 'The Sentinel',\n",
       " 'Planet 51',\n",
       " 'Star Trek: Nemesis',\n",
       " 'Intolerable Cruelty',\n",
       " 'Trouble with the Curve',\n",
       " 'Edge of Darkness',\n",
       " 'The Relic',\n",
       " 'Analyze That',\n",
       " 'Righteous Kill',\n",
       " 'Mercury Rising',\n",
       " 'The Soloist',\n",
       " 'The Legend of Bagger Vance',\n",
       " 'Almost Famous',\n",
       " 'Garfield: A Tail of Two Kitties',\n",
       " 'xXx: State of the Union',\n",
       " 'Priest',\n",
       " 'Sinbad: Legend of the Seven Seas',\n",
       " 'Event Horizon',\n",
       " 'Dragonfly',\n",
       " 'The Black Dahlia',\n",
       " 'Flyboys',\n",
       " 'The Last Castle',\n",
       " 'Supernova',\n",
       " \"Winter's Tale\",\n",
       " 'The Mortal Instruments: City of Bones',\n",
       " 'Meet Dave',\n",
       " 'Dark Water',\n",
       " 'Edtv',\n",
       " 'Inkheart',\n",
       " 'The Spirit',\n",
       " 'Mortdecai',\n",
       " 'In the Name of the King: A Dungeon Siege Tale',\n",
       " 'Beyond Borders',\n",
       " 'The Monkey King 2',\n",
       " 'The Great Raid',\n",
       " 'Deadpool',\n",
       " 'Holy Man',\n",
       " 'American Sniper',\n",
       " 'Goosebumps',\n",
       " 'Just Like Heaven',\n",
       " 'The Flintstones in Viva Rock Vegas',\n",
       " 'Rambo III',\n",
       " 'Leatherheads',\n",
       " 'The Ridiculous 6',\n",
       " 'Did You Hear About the Morgans?',\n",
       " 'The Internship',\n",
       " 'Resident Evil: Afterlife',\n",
       " 'Red Tails',\n",
       " \"The Devil's Advocate\",\n",
       " \"That's My Boy\",\n",
       " 'DragonHeart',\n",
       " 'After the Sunset',\n",
       " 'Ghost Rider: Spirit of Vengeance',\n",
       " \"Captain Corelli's Mandolin\",\n",
       " 'The Pacifier',\n",
       " 'Walking Tall',\n",
       " 'Forrest Gump',\n",
       " 'Alvin and the Chipmunks',\n",
       " 'Meet the Parents',\n",
       " 'Pocahontas',\n",
       " 'Superman',\n",
       " 'The Nutty Professor',\n",
       " 'Hitch',\n",
       " 'George of the Jungle',\n",
       " 'American Wedding',\n",
       " 'Captain Phillips',\n",
       " 'Date Night',\n",
       " 'Casper',\n",
       " 'The Equalizer',\n",
       " 'Maid in Manhattan',\n",
       " 'Crimson Tide',\n",
       " 'The Pursuit of Happyness',\n",
       " 'Flightplan',\n",
       " 'Disclosure',\n",
       " 'City of Angels',\n",
       " 'Kill Bill: Vol. 1',\n",
       " 'Bowfinger',\n",
       " 'Kill Bill: Vol. 2',\n",
       " 'Tango & Cash',\n",
       " 'Death Becomes Her',\n",
       " 'Shanghai Noon',\n",
       " 'Executive Decision',\n",
       " \"Mr. Popper's Penguins\",\n",
       " 'The Forbidden Kingdom',\n",
       " 'Free Birds',\n",
       " 'Alien³',\n",
       " 'Evita',\n",
       " 'Ronin',\n",
       " 'The Ghost and the Darkness',\n",
       " 'Paddington',\n",
       " 'The Watch',\n",
       " 'The Hunted',\n",
       " 'Instinct',\n",
       " 'Stuck on You',\n",
       " 'Semi-Pro',\n",
       " 'The Pirates! In an Adventure with Scientists!',\n",
       " 'Changeling',\n",
       " 'Chain Reaction',\n",
       " 'The Fan',\n",
       " 'The Phantom of the Opera',\n",
       " 'Elizabeth: The Golden Age',\n",
       " 'Æon Flux',\n",
       " 'Gods and Generals',\n",
       " 'Turbulence',\n",
       " 'Imagine That',\n",
       " 'Muppets Most Wanted',\n",
       " 'Thunderbirds',\n",
       " 'Burlesque',\n",
       " 'A Very Long Engagement',\n",
       " 'Lolita',\n",
       " 'D-Tox',\n",
       " 'Blade II',\n",
       " 'Seven Pounds',\n",
       " 'Bullet to the Head',\n",
       " 'The Godfather: Part III',\n",
       " 'Elizabethtown',\n",
       " 'You, Me and Dupree',\n",
       " 'Superman II',\n",
       " 'Gigli',\n",
       " \"All the King's Men\",\n",
       " 'Shaft',\n",
       " 'Anastasia',\n",
       " 'Moulin Rouge!',\n",
       " 'Domestic Disturbance',\n",
       " 'Black Mass',\n",
       " 'Flags of Our Fathers',\n",
       " 'Law Abiding Citizen',\n",
       " 'Grindhouse',\n",
       " 'Beloved',\n",
       " 'Lucky You',\n",
       " 'Catch Me If You Can',\n",
       " 'Zero Dark Thirty',\n",
       " 'The Break-Up',\n",
       " 'Mamma Mia!',\n",
       " \"Valentine's Day\",\n",
       " 'The Dukes of Hazzard',\n",
       " 'The Thin Red Line',\n",
       " 'The Change-Up',\n",
       " 'Man on the Moon',\n",
       " 'Casino',\n",
       " 'From Paris with Love',\n",
       " 'Bulletproof Monk',\n",
       " 'Me, Myself & Irene',\n",
       " 'Barnyard',\n",
       " 'Deck the Halls',\n",
       " 'The Twilight Saga: New Moon',\n",
       " 'Shrek',\n",
       " 'The Adjustment Bureau',\n",
       " 'Robin Hood: Prince of Thieves',\n",
       " 'Jerry Maguire',\n",
       " 'Ted',\n",
       " 'As Good as It Gets',\n",
       " 'Patch Adams',\n",
       " 'Anchorman 2: The Legend Continues',\n",
       " 'Mr. Deeds',\n",
       " 'Super 8',\n",
       " 'Erin Brockovich',\n",
       " 'How to Lose a Guy in 10 Days',\n",
       " '22 Jump Street',\n",
       " 'Interview with the Vampire',\n",
       " 'Yes Man',\n",
       " 'Central Intelligence',\n",
       " 'Stepmom',\n",
       " \"Daddy's Home\",\n",
       " 'Into the Woods',\n",
       " 'Inside Man',\n",
       " 'Payback',\n",
       " 'Congo',\n",
       " 'We Bought a Zoo',\n",
       " 'Knowing',\n",
       " 'Failure to Launch',\n",
       " 'The Ring Two',\n",
       " 'Crazy, Stupid, Love.',\n",
       " 'Garfield',\n",
       " 'Christmas with the Kranks',\n",
       " 'Moneyball',\n",
       " 'Outbreak',\n",
       " 'Non-Stop',\n",
       " 'Race to Witch Mountain',\n",
       " 'V for Vendetta',\n",
       " 'Shanghai Knights',\n",
       " 'Curious George',\n",
       " 'Herbie Fully Loaded',\n",
       " \"Don't Say a Word\",\n",
       " 'Hansel & Gretel: Witch Hunters',\n",
       " 'Unfaithful',\n",
       " 'I Am Number Four',\n",
       " 'Syriana',\n",
       " '13 Hours: The Secret Soldiers of Benghazi',\n",
       " 'The Book of Life',\n",
       " 'Firewall',\n",
       " 'Absolute Power',\n",
       " 'G.I. Jane',\n",
       " 'The Game',\n",
       " 'Silent Hill',\n",
       " 'The Replacements',\n",
       " 'American Reunion',\n",
       " 'The Negotiator',\n",
       " 'Into the Storm',\n",
       " 'Beverly Hills Cop III',\n",
       " 'Gremlins 2: The New Batch',\n",
       " 'The Judge',\n",
       " 'The Peacemaker',\n",
       " 'Resident Evil: Apocalypse',\n",
       " 'Bridget Jones: The Edge of Reason',\n",
       " 'Out of Time',\n",
       " 'On Deadly Ground',\n",
       " 'The Adventures of Sharkboy and Lavagirl',\n",
       " 'The Beach',\n",
       " 'Raising Helen',\n",
       " 'Ninja Assassin',\n",
       " 'For Love of the Game',\n",
       " 'Striptease',\n",
       " 'Marmaduke',\n",
       " 'Hereafter',\n",
       " 'Murder by Numbers',\n",
       " 'Assassins',\n",
       " 'Hannibal Rising',\n",
       " 'The Story of Us',\n",
       " 'The Host',\n",
       " 'Basic',\n",
       " 'Blood Work',\n",
       " 'The International',\n",
       " 'Escape from L.A.',\n",
       " 'The Iron Giant',\n",
       " 'The Life Aquatic with Steve Zissou',\n",
       " 'Free State of Jones',\n",
       " 'The Life of David Gale',\n",
       " 'Man of the House',\n",
       " 'Run All Night',\n",
       " 'Eastern Promises',\n",
       " 'Into the Blue',\n",
       " 'The Messenger: The Story of Joan of Arc',\n",
       " 'Your Highness',\n",
       " 'Dream House',\n",
       " 'Mad City',\n",
       " \"Baby's Day Out\",\n",
       " 'The Scarlet Letter',\n",
       " 'Fair Game',\n",
       " 'Domino',\n",
       " 'Jade',\n",
       " 'Gamer',\n",
       " 'Beautiful Creatures',\n",
       " 'Death to Smoochy',\n",
       " 'Zoolander 2',\n",
       " 'The Big Bounce',\n",
       " 'What Planet Are You From?',\n",
       " ...]"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a list with all the movie titles present in the dataset\n",
    "title_list = df_movies['title'].tolist()\n",
    "title_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "9341f933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Iron Man', 'Iron Man 3', 'Iron Man 2']"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the close match of the movie name which is given by the user\n",
    "find_close_match = difflib.get_close_matches(movie_name, title_list)\n",
    "find_close_match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "d8cae00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Iron Man'"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_match = find_close_match[0]\n",
    "close_match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "10230e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the index of the movie with title\n",
    "movie_index = df_movies[df_movies.title == close_match]['index'].values[0]\n",
    "movie_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "d8f077ca",
   "metadata": {},
   "outputs": [
    {
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       " (842, 0.008579526252826037),\n",
       " (843, 0.0),\n",
       " (844, 0.005681781411921748),\n",
       " (845, 0.05051888852390088),\n",
       " (846, 0.0),\n",
       " (847, 0.0),\n",
       " (848, 0.008125457201045575),\n",
       " (849, 0.0),\n",
       " (850, 0.025281720420590577),\n",
       " (851, 0.01080080345536633),\n",
       " (852, 0.0),\n",
       " (853, 0.0),\n",
       " (854, 0.022985123576253916),\n",
       " (855, 0.03378697436406514),\n",
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       " (857, 0.0),\n",
       " (858, 0.00791244537827933),\n",
       " (859, 0.011516857999652522),\n",
       " (860, 0.0),\n",
       " (861, 0.0),\n",
       " (862, 0.016330945599710902),\n",
       " (863, 0.005935686222933201),\n",
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       " (865, 0.0),\n",
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       " (868, 0.0),\n",
       " (869, 0.0),\n",
       " (870, 0.09574351274416697),\n",
       " (871, 0.0),\n",
       " (872, 0.0),\n",
       " (873, 0.04973721665258645),\n",
       " (874, 0.00691070424128861),\n",
       " (875, 0.0),\n",
       " (876, 0.0),\n",
       " (877, 0.0),\n",
       " (878, 0.029916995295350024),\n",
       " (879, 0.0),\n",
       " (880, 0.017823930922964717),\n",
       " (881, 0.0),\n",
       " (882, 0.08853815053442779),\n",
       " (883, 0.0),\n",
       " (884, 0.0),\n",
       " (885, 0.07127713298596235),\n",
       " (886, 0.0),\n",
       " (887, 0.0),\n",
       " (888, 0.012878509897401861),\n",
       " (889, 0.03786402108262418),\n",
       " (890, 0.0),\n",
       " (891, 0.0),\n",
       " (892, 0.013239017469059035),\n",
       " (893, 0.005367943615150826),\n",
       " (894, 0.005067770618611982),\n",
       " (895, 0.014704260918041844),\n",
       " (896, 0.0),\n",
       " (897, 0.0),\n",
       " (898, 0.02143172754595199),\n",
       " (899, 0.007790845567337891),\n",
       " (900, 0.021561086103725723),\n",
       " (901, 0.006437488983856181),\n",
       " (902, 0.02095095434446304),\n",
       " (903, 0.0),\n",
       " (904, 0.021809702100360512),\n",
       " (905, 0.0),\n",
       " (906, 0.0),\n",
       " (907, 0.0),\n",
       " (908, 0.021712919094558747),\n",
       " (909, 0.0),\n",
       " (910, 0.0),\n",
       " (911, 0.04751544760285254),\n",
       " (912, 0.0),\n",
       " (913, 0.0),\n",
       " (914, 0.005099427210943033),\n",
       " (915, 0.0),\n",
       " (916, 0.0),\n",
       " (917, 0.0),\n",
       " (918, 0.0),\n",
       " (919, 0.006094178240441095),\n",
       " (920, 0.0311792452957307),\n",
       " (921, 0.0),\n",
       " (922, 0.03271895346827846),\n",
       " (923, 0.0),\n",
       " (924, 0.0),\n",
       " (925, 0.0),\n",
       " (926, 0.0),\n",
       " (927, 0.0),\n",
       " (928, 0.0),\n",
       " (929, 0.04170744779575931),\n",
       " (930, 0.005202293188738005),\n",
       " (931, 0.02845676257575145),\n",
       " (932, 0.004170932018430365),\n",
       " (933, 0.013082655484141375),\n",
       " (934, 0.006335966231410641),\n",
       " (935, 0.00771296774034122),\n",
       " (936, 0.0),\n",
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       " (938, 0.0),\n",
       " (939, 0.0274286269728791),\n",
       " (940, 0.0875800937999186),\n",
       " (941, 0.004986872337716621),\n",
       " (942, 0.007872311595830392),\n",
       " (943, 0.0),\n",
       " (944, 0.0),\n",
       " (945, 0.08346370644008311),\n",
       " (946, 0.0),\n",
       " (947, 0.0),\n",
       " (948, 0.02545239795208989),\n",
       " (949, 0.022228977010921528),\n",
       " (950, 0.011668214950350168),\n",
       " (951, 0.005581683539618901),\n",
       " (952, 0.006249052683161279),\n",
       " (953, 0.044641883333497764),\n",
       " (954, 0.0986387254713941),\n",
       " (955, 0.005217551867953665),\n",
       " (956, 0.04937542698440879),\n",
       " (957, 0.0),\n",
       " (958, 0.0),\n",
       " (959, 0.0049588174673148265),\n",
       " (960, 0.08227394174342709),\n",
       " (961, 0.006650011466193838),\n",
       " (962, 0.0),\n",
       " (963, 0.005107152214046513),\n",
       " (964, 0.0),\n",
       " (965, 0.02857826964363057),\n",
       " (966, 0.0),\n",
       " (967, 0.02749066096114915),\n",
       " (968, 0.0),\n",
       " (969, 0.012282682736021022),\n",
       " (970, 0.0),\n",
       " (971, 0.0),\n",
       " (972, 0.03408568528429526),\n",
       " (973, 0.0050857303713312246),\n",
       " (974, 0.021733329175255178),\n",
       " (975, 0.0),\n",
       " (976, 0.03552989221799278),\n",
       " (977, 0.045549138020087365),\n",
       " (978, 0.007395600581911993),\n",
       " (979, 0.0058113885465888235),\n",
       " (980, 0.0),\n",
       " (981, 0.005792146668768498),\n",
       " (982, 0.005772596594420832),\n",
       " (983, 0.0),\n",
       " (984, 0.014517895617346838),\n",
       " (985, 0.015050744845155263),\n",
       " (986, 0.0),\n",
       " (987, 0.0),\n",
       " (988, 0.018142110370525988),\n",
       " (989, 0.012085579929807806),\n",
       " (990, 0.021813855154124904),\n",
       " (991, 0.0),\n",
       " (992, 0.00551625688349404),\n",
       " (993, 0.00591592279706204),\n",
       " (994, 0.021161324009381345),\n",
       " (995, 0.0),\n",
       " (996, 0.021779072193357778),\n",
       " (997, 0.0),\n",
       " (998, 0.0),\n",
       " (999, 0.015235598070902703),\n",
       " ...]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting a list of similar movies based on similarity score\n",
    "similarity_score = list(enumerate(similarity[movie_index]))\n",
    "similarity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "031d5464",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.0), (4771, 0.0), (4774, 0.0), (4775, 0.0), (4776, 0.0), (4777, 0.0), (4779, 0.0), (4780, 0.0), (4781, 0.0), (4782, 0.0), (4783, 0.0), (4784, 0.0), (4785, 0.0), (4786, 0.0), (4789, 0.0), (4790, 0.0), (4791, 0.0), (4792, 0.0), (4793, 0.0), (4794, 0.0), (4795, 0.0), (4797, 0.0), (4799, 0.0), (4800, 0.0), (4801, 0.0), (4802, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "#to find highest similarity score sorting the movies based on their similarity score\n",
    "sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True) \n",
    "print(sorted_similar_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "ce7ae412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0000000000000002,\n",
       " 0.40890433998005965,\n",
       " 0.31467052449477506,\n",
       " 0.23944423963486405,\n",
       " 0.22704403782296803,\n",
       " 0.21566241096831154,\n",
       " 0.20615862984665329,\n",
       " 0.19573956139611606,\n",
       " 0.16702973947860686,\n",
       " 0.1609246088135586,\n",
       " 0.15299924139445145,\n",
       " 0.14818667948665118,\n",
       " 0.1471993120942043,\n",
       " 0.14505971470107848,\n",
       " 0.14401677581826294,\n",
       " 0.14216268867232237,\n",
       " 0.1380947013224906,\n",
       " 0.13624382641690763,\n",
       " 0.1361681957902901,\n",
       " 0.1330589507422922,\n",
       " 0.13263982780511066,\n",
       " 0.13137698586006535,\n",
       " 0.13089810941050173,\n",
       " 0.12309731939910507,\n",
       " 0.12162995562040377,\n",
       " 0.11846458075866884,\n",
       " 0.11725512335483321,\n",
       " 0.11719294096248463,\n",
       " 0.11387063493435637,\n",
       " 0.1126182690487113,\n",
       " 0.1121878787373205,\n",
       " 0.1116846512704428,\n",
       " 0.10850296033661253,\n",
       " 0.1079782217151326,\n",
       " 0.107849394974707,\n",
       " 0.10630339022327012,\n",
       " 0.1025469263536857,\n",
       " 0.10087565815879387,\n",
       " 0.10006436988307142,\n",
       " 0.09911415072466837,\n",
       " 0.09877044853778177,\n",
       " 0.0986387254713941,\n",
       " 0.09829572149918514,\n",
       " 0.09760018407279153,\n",
       " 0.09657127116284188,\n",
       " 0.09574351274416697,\n",
       " 0.09571953277826747,\n",
       " 0.0957179952078477,\n",
       " 0.09551596914906027,\n",
       " 0.09503280362598002,\n",
       " 0.09403221247985363,\n",
       " 0.09363711287421697,\n",
       " 0.09330019170580414,\n",
       " 0.09304312287733228,\n",
       " 0.09247435261735147,\n",
       " 0.09147088765725242,\n",
       " 0.09092162527012912,\n",
       " 0.0897400597380081,\n",
       " 0.08926961894498348,\n",
       " 0.08902766481306311,\n",
       " 0.08896119950952218,\n",
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       " 0.02097961026417998,\n",
       " ...]"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = [lis[1] for lis in sorted_similar_movies]\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "72043d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Scores\n",
      "0     1.000000\n",
      "1     0.408904\n",
      "2     0.314671\n",
      "3     0.239444\n",
      "4     0.227044\n",
      "...        ...\n",
      "4798  0.000000\n",
      "4799  0.000000\n",
      "4800  0.000000\n",
      "4801  0.000000\n",
      "4802  0.000000\n",
      "\n",
      "[4803 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "df_score = pd.DataFrame({'Scores':res})\n",
    "print (df_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "29a32cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.000000\n",
       "1       0.408904\n",
       "2       0.314671\n",
       "3       0.239444\n",
       "4       0.227044\n",
       "          ...   \n",
       "4798    0.000000\n",
       "4799    0.000000\n",
       "4800    0.000000\n",
       "4801    0.000000\n",
       "4802    0.000000\n",
       "Name: Scores, Length: 4803, dtype: float64"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score['Scores']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "e78d3089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies suggested for you : \n",
      "\n",
      "0 . Iron Man\n",
      "1 . Iron Man 2\n",
      "2 . Iron Man 3\n",
      "3 . Avengers: Age of Ultron\n",
      "4 . The Avengers\n",
      "5 . Captain America: Civil War\n",
      "6 . Captain America: The Winter Soldier\n",
      "7 . Ant-Man\n",
      "8 . X-Men\n",
      "9 . Made\n",
      "10 . X-Men: Apocalypse\n",
      "11 . X2\n",
      "12 . The Incredible Hulk\n",
      "13 . The Helix... Loaded\n",
      "14 . X-Men: First Class\n",
      "15 . X-Men: Days of Future Past\n",
      "16 . Captain America: The First Avenger\n",
      "17 . Kick-Ass 2\n",
      "18 . Guardians of the Galaxy\n",
      "19 . Deadpool\n"
     ]
    }
   ],
   "source": [
    "# suggest the name of similar movies based on the index\n",
    "print('Movies suggested for you : \\n')\n",
    "\n",
    "i = 0\n",
    "\n",
    "for movie in sorted_similar_movies:\n",
    "  index = movie[0]\n",
    "  title_from_index = df_movies[df_movies.index==index]['title'].values[0]\n",
    "  if (i<20):\n",
    "    print(i, '.',title_from_index)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9400a0",
   "metadata": {},
   "source": [
    "## Movie Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "4c01c90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Enter the movie name : iron man\n",
      "Recommended Movies : \n",
      "\n",
      "1 . Iron Man\n",
      "2 . Iron Man 2\n",
      "3 . Iron Man 3\n",
      "4 . Avengers: Age of Ultron\n",
      "5 . The Avengers\n",
      "6 . Captain America: Civil War\n",
      "7 . Captain America: The Winter Soldier\n",
      "8 . Ant-Man\n",
      "9 . X-Men\n",
      "10 . Made\n",
      "11 . X-Men: Apocalypse\n",
      "12 . X2\n",
      "13 . The Incredible Hulk\n",
      "14 . The Helix... Loaded\n",
      "15 . X-Men: First Class\n",
      "16 . X-Men: Days of Future Past\n",
      "17 . Captain America: The First Avenger\n",
      "18 . Kick-Ass 2\n",
      "19 . Guardians of the Galaxy\n"
     ]
    }
   ],
   "source": [
    "movie_name = input(' Enter the movie name : ')\n",
    "\n",
    "title_list = df_movies['title'].tolist()\n",
    "\n",
    "find_close_match = difflib.get_close_matches(movie_name, title_list)\n",
    "\n",
    "close_match = find_close_match[0]\n",
    "\n",
    "movie_index = df_movies[df_movies.title == close_match]['index'].values[0]\n",
    "\n",
    "similarity_score = list(enumerate(similarity[movie_index]))\n",
    "\n",
    "sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True) \n",
    "\n",
    "print('Recommended Movies : \\n')\n",
    "\n",
    "i = 1\n",
    "\n",
    "for movie in sorted_similar_movies:\n",
    "  index = movie[0]\n",
    "  title_from_index = df_movies[df_movies.index==index]['title'].values[0]\n",
    "  if (i<20):\n",
    "    print(i, '.',title_from_index)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d771651",
   "metadata": {},
   "source": [
    "# Popularity Based Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "f2e473f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "fcf04853",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('movies.csv', index_col=None, usecols=['movieId','title'], dtype={'movieId':'int32','title':'str'})\n",
    "ratings = pd.read_csv('ratings.csv', index_col=None, usecols=['userId','movieId','rating'],dtype={'userId':'int32','movieId':'int32','rating':'float32'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "0a017576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title\n",
       "0        1                    Toy Story (1995)\n",
       "1        2                      Jumanji (1995)\n",
       "2        3             Grumpier Old Men (1995)\n",
       "3        4            Waiting to Exhale (1995)\n",
       "4        5  Father of the Bride Part II (1995)"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "a4519dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9742, 2)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "d98b2d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9742 entries, 0 to 9741\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   movieId  9742 non-null   int32 \n",
      " 1   title    9742 non-null   object\n",
      "dtypes: int32(1), object(1)\n",
      "memory usage: 114.3+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "5b842566",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1        1     4.0\n",
       "1       1        3     4.0\n",
       "2       1        6     4.0\n",
       "3       1       47     5.0\n",
       "4       1       50     5.0"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "4d1385d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100836, 3)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "ce6f44dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100836 entries, 0 to 100835\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   userId   100836 non-null  int32  \n",
      " 1   movieId  100836 non-null  int32  \n",
      " 2   rating   100836 non-null  float32\n",
      "dtypes: float32(1), int32(2)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "b095e778",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.merge(movies, ratings, how='left', on='movieId')\n",
    "dataset = dataset.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "105e2d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>userId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>502</td>\n",
       "      <td>Next Karate Kid, The (1994)</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>50</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>237.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32975</th>\n",
       "      <td>1580</td>\n",
       "      <td>Men in Black (a.k.a. MIB) (1997)</td>\n",
       "      <td>187.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47049</th>\n",
       "      <td>2710</td>\n",
       "      <td>Blair Witch Project, The (1999)</td>\n",
       "      <td>542.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82912</th>\n",
       "      <td>46723</td>\n",
       "      <td>Babel (2006)</td>\n",
       "      <td>414.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60235</th>\n",
       "      <td>4105</td>\n",
       "      <td>Evil Dead, The (1981)</td>\n",
       "      <td>274.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                             title  userId  rating\n",
       "13721      502       Next Karate Kid, The (1994)   146.0     2.5\n",
       "2454        50        Usual Suspects, The (1995)   237.0     2.5\n",
       "32975     1580  Men in Black (a.k.a. MIB) (1997)   187.0     2.0\n",
       "47049     2710   Blair Witch Project, The (1999)   542.0     0.5\n",
       "82912    46723                      Babel (2006)   414.0     4.0\n",
       "60235     4105             Evil Dead, The (1981)   274.0     4.0"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "c73c519a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100854, 4)"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "0535fe2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100854 entries, 0 to 100853\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   movieId  100854 non-null  int32  \n",
      " 1   title    100854 non-null  object \n",
      " 2   userId   100854 non-null  float64\n",
      " 3   rating   100854 non-null  float32\n",
      "dtypes: float32(1), float64(1), int32(1), object(1)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "1ff7fa15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    0\n",
       "title      0\n",
       "userId     0\n",
       "rating     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "697e38c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "611"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total unique users in the dataset\n",
    "dataset['userId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "021c4d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9742"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total unique movies in the dataset\n",
    "dataset['movieId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "6870586e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(dataset, test_size =.30, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "7dfd518c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70597, 4)"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 70% train data\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "90c25014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30257, 4)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30% test data\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b229a1",
   "metadata": {},
   "source": [
    "## Movie Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "bb54d682",
   "metadata": {},
   "outputs": [],
   "source": [
    "class popularity_model():\n",
    "    def __init__(self, train_data, test_data, user_id, title):\n",
    "        self.train_data = train_data\n",
    "        self.test_data = test_data\n",
    "        self.user_id = user_id\n",
    "        self.title = title\n",
    "        self.popularity_recommendation = None\n",
    "        \n",
    "    def fit(self):\n",
    "        # counting movies with more ratings from users\n",
    "        train_data_grouped = train_data.groupby([self.title]).agg(self.user_id).count().reset_index()\n",
    "        train_data_grouped.rename(columns = {'userId': 'score'},inplace=True)\n",
    "    \n",
    "        # sorting the score column of train_data_grouped in ascending order\n",
    "        train_data_sort = train_data_grouped.sort_values(['score', self.title], ascending = [0,1])\n",
    "        \n",
    "    \n",
    "        # recommendation rank based on score\n",
    "        # ranking the highest score with 1 \n",
    "        train_data_sort['Rank'] = train_data_sort['score'].rank(ascending=0, method='first')\n",
    "        \n",
    "    \n",
    "        self.popularity_recommendation = train_data_sort\n",
    "        \n",
    "    def recommendation(self, user_id, n=20):    \n",
    "        user_recommendations = self.popularity_recommendation\n",
    "        \n",
    "        #getting movies that are rated by the given userId \n",
    "        movies_already_rated_by_user = self.train_data.loc[self.train_data[self.user_id] == user_id][self.title]  \n",
    "       \n",
    "        # recommending the movies with the highest ratings that havent been rated by the given user\n",
    "        # `~` refers to all the title that is not in the variable `movies_already_rated_by_user`\n",
    "        user_recommendations = user_recommendations[~user_recommendations[self.title].isin(movies_already_rated_by_user)]\n",
    "        \n",
    "        \n",
    "        #Add user_id column for which the recommendations are being generated\n",
    "        user_recommendations['user_id'] = user_id\n",
    "    \n",
    "        #Bringing the user_id column to the front\n",
    "        cols = user_recommendations.columns.tolist()\n",
    "        user_recommendations = user_recommendations[cols]\n",
    "        return user_recommendations.head(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "a3550e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "hr = popularity_model(train_data=train_data, test_data=test_data, user_id='userId', title='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "dd49090f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the variable hr has all the highest rated movies\n",
    "hr.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "232b521f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter UserID : 520\n"
     ]
    },
    {
     "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>title</th>\n",
       "      <th>score</th>\n",
       "      <th>Rank</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6701</th>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>228</td>\n",
       "      <td>1.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>226</td>\n",
       "      <td>2.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>213</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4871</th>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>192</td>\n",
       "      <td>4.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6780</th>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>188</td>\n",
       "      <td>5.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>176</td>\n",
       "      <td>6.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6549</th>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>172</td>\n",
       "      <td>7.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>169</td>\n",
       "      <td>8.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7068</th>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>160</td>\n",
       "      <td>9.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2661</th>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>150</td>\n",
       "      <td>10.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6638</th>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "      <td>150</td>\n",
       "      <td>11.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7384</th>\n",
       "      <td>Terminator 2: Judgment Day (1991)</td>\n",
       "      <td>150</td>\n",
       "      <td>12.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7822</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>150</td>\n",
       "      <td>13.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8043</th>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>144</td>\n",
       "      <td>14.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>142</td>\n",
       "      <td>15.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7069</th>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "      <td>140</td>\n",
       "      <td>16.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>Batman (1989)</td>\n",
       "      <td>139</td>\n",
       "      <td>17.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6521</th>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>139</td>\n",
       "      <td>18.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903</th>\n",
       "      <td>Fugitive, The (1993)</td>\n",
       "      <td>137</td>\n",
       "      <td>19.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>Independence Day (a.k.a. ID4) (1996)</td>\n",
       "      <td>137</td>\n",
       "      <td>20.0</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  score  Rank user_id\n",
       "6701                   Shawshank Redemption, The (1994)    228   1.0     520\n",
       "2796                                Forrest Gump (1994)    226   2.0     520\n",
       "6075                                Pulp Fiction (1994)    213   3.0     520\n",
       "4871                                 Matrix, The (1999)    192   4.0     520\n",
       "6780                   Silence of the Lambs, The (1991)    188   5.0     520\n",
       "4114                               Jurassic Park (1993)    176   6.0     520\n",
       "6549                            Schindler's List (1993)    172   7.0     520\n",
       "1177                                  Braveheart (1995)    169   8.0     520\n",
       "7068          Star Wars: Episode IV - A New Hope (1977)    160   9.0     520\n",
       "2661                                  Fight Club (1999)    150  10.0     520\n",
       "6638                        Seven (a.k.a. Se7en) (1995)    150  11.0     520\n",
       "7384                  Terminator 2: Judgment Day (1991)    150  12.0     520\n",
       "7822                                   Toy Story (1995)    150  13.0     520\n",
       "8043                         Usual Suspects, The (1995)    144  14.0     520\n",
       "504                                    Apollo 13 (1995)    142  15.0     520\n",
       "7069  Star Wars: Episode V - The Empire Strikes Back...    140  16.0     520\n",
       "739                                       Batman (1989)    139  17.0     520\n",
       "6521                         Saving Private Ryan (1998)    139  18.0     520\n",
       "2903                               Fugitive, The (1993)    137  19.0     520\n",
       "3830               Independence Day (a.k.a. ID4) (1996)    137  20.0     520"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_suggestion = hr.recommendation(input('Enter UserID : '))\n",
    "movie_suggestion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "48c9bd5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.bar(movie_suggestion['title'],movie_suggestion['score'],align=\"center\",color='green')\n",
    "plt.xlabel('Movies')\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.title('Movie-Score Bar Plot')\n",
    "plt.xticks(rotation= 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "509538fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended Movies for Particular User : \n",
      "\n",
      "                                                  title user_id\n",
      "6701                   Shawshank Redemption, The (1994)     520\n",
      "2796                                Forrest Gump (1994)     520\n",
      "6075                                Pulp Fiction (1994)     520\n",
      "4871                                 Matrix, The (1999)     520\n",
      "6780                   Silence of the Lambs, The (1991)     520\n",
      "4114                               Jurassic Park (1993)     520\n",
      "6549                            Schindler's List (1993)     520\n",
      "1177                                  Braveheart (1995)     520\n",
      "7068          Star Wars: Episode IV - A New Hope (1977)     520\n",
      "2661                                  Fight Club (1999)     520\n",
      "6638                        Seven (a.k.a. Se7en) (1995)     520\n",
      "7384                  Terminator 2: Judgment Day (1991)     520\n",
      "7822                                   Toy Story (1995)     520\n",
      "8043                         Usual Suspects, The (1995)     520\n",
      "504                                    Apollo 13 (1995)     520\n",
      "7069  Star Wars: Episode V - The Empire Strikes Back...     520\n",
      "739                                       Batman (1989)     520\n",
      "6521                         Saving Private Ryan (1998)     520\n",
      "2903                               Fugitive, The (1993)     520\n",
      "3830               Independence Day (a.k.a. ID4) (1996)     520\n"
     ]
    }
   ],
   "source": [
    "print(f'Recommended Movies for Particular User : \\n')\n",
    "print(movie_suggestion [['title', 'user_id']])"
   ]
  }
 ],
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