Movie Recommendation - TASK 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading Movie Dataset using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 3 columns</p>\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)   \n",
       "...        ...                                 ...   \n",
       "27273   131254        Kein Bund für's Leben (2007)   \n",
       "27274   131256       Feuer, Eis & Dosenbier (2002)   \n",
       "27275   131258                  The Pirates (2014)   \n",
       "27276   131260                 Rentun Ruusu (2001)   \n",
       "27277   131262                    Innocence (2014)   \n",
       "\n",
       "                                            genres  \n",
       "0      Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                       Adventure|Children|Fantasy  \n",
       "2                                   Comedy|Romance  \n",
       "3                             Comedy|Drama|Romance  \n",
       "4                                           Comedy  \n",
       "...                                            ...  \n",
       "27273                                       Comedy  \n",
       "27274                                       Comedy  \n",
       "27275                                    Adventure  \n",
       "27276                           (no genres listed)  \n",
       "27277                     Adventure|Fantasy|Horror  \n",
       "\n",
       "[27278 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "movie = pd.read_csv(\"movie.csv\")\n",
    "movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLEANING THE TITLES AND BUILDING A SEARCH ENGINE**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning Movie Titles using Regular Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Creating a function to clean titles\n",
    "def clean_title(title):\n",
    "    return re.sub(\"[^a-zA-Z0-9 ]\",\"\", title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>clean_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",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</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",
       "      <td>Adventure|Children|Fantasy</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",
       "      <td>Comedy|Romance</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",
       "      <td>Comedy|Drama|Romance</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",
       "      <td>Comedy</td>\n",
       "      <td>Father of the Bride Part II 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Kein Bund frs Leben 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Feuer Eis  Dosenbier 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>The Pirates 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>Rentun Ruusu 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "      <td>Innocence 2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 4 columns</p>\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)   \n",
       "...        ...                                 ...   \n",
       "27273   131254        Kein Bund für's Leben (2007)   \n",
       "27274   131256       Feuer, Eis & Dosenbier (2002)   \n",
       "27275   131258                  The Pirates (2014)   \n",
       "27276   131260                 Rentun Ruusu (2001)   \n",
       "27277   131262                    Innocence (2014)   \n",
       "\n",
       "                                            genres  \\\n",
       "0      Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1                       Adventure|Children|Fantasy   \n",
       "2                                   Comedy|Romance   \n",
       "3                             Comedy|Drama|Romance   \n",
       "4                                           Comedy   \n",
       "...                                            ...   \n",
       "27273                                       Comedy   \n",
       "27274                                       Comedy   \n",
       "27275                                    Adventure   \n",
       "27276                           (no genres listed)   \n",
       "27277                     Adventure|Fantasy|Horror   \n",
       "\n",
       "                            clean_title  \n",
       "0                        Toy Story 1995  \n",
       "1                          Jumanji 1995  \n",
       "2                 Grumpier Old Men 1995  \n",
       "3                Waiting to Exhale 1995  \n",
       "4      Father of the Bride Part II 1995  \n",
       "...                                 ...  \n",
       "27273          Kein Bund frs Leben 2007  \n",
       "27274         Feuer Eis  Dosenbier 2002  \n",
       "27275                  The Pirates 2014  \n",
       "27276                 Rentun Ruusu 2001  \n",
       "27277                    Innocence 2014  \n",
       "\n",
       "[27278 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating and adding a new column in movie dataframe\n",
    "movie[\"clean_title\"] = movie[\"title\"].apply(clean_title)\n",
    "movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating A TFIDF Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TFIDF -> Term Frequency, Inverse Document Frequency (tf*idf) - idf finds terms that are unique\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# This will make the search even more accurate\n",
    "# ngram - group of two words that are consecutive\n",
    "vectorizer = TfidfVectorizer(ngram_range = (1,2))\n",
    "\n",
    "# Using vectorizer to turn sets of titles into a matrix (a set of numbers)\n",
    "tfidf = vectorizer.fit_transform(movie[\"clean_title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating A Search Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np\n",
    "\n",
    "def search(title):\n",
    "    title = clean_title(title)\n",
    "\n",
    "    # Using vectorizer to turn the term entered into a set of numbers (matrix)\n",
    "    query_vec = vectorizer.transform([title])\n",
    "\n",
    "    # Computing similarity between the term entered by user and all of the movies listed in the dataset using cosine similarity\n",
    "    # flatten() -> returns a copy of a given array in such a way that it is collapsed into one dimension (flatten).\n",
    "    similarity = cosine_similarity(query_vec, tfidf).flatten()\n",
    "\n",
    "    # Finding 5 most similar titles to the search term\n",
    "    indices = np.argpartition(similarity,-5)[-5:]\n",
    "    # Reversing the result so that the most similar title is on top\n",
    "    results = movie.iloc[indices][::-1]\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building an Interactive Search Box with Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "454bb6cf61f840f3b1ca7a4978c3c048",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='Enter a movie name', description='Movie Title: ')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8b522d2673b490c94f4edf06688844e",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "# Creating an input text widget\n",
    "movie_input = widgets.Text( \n",
    "    value = \"Enter a movie name\", \n",
    "    description = \"Movie Title: \", \n",
    "    disabled = False\n",
    ")\n",
    "\n",
    "# Creating an input text widget to show results\n",
    "movie_list = widgets.Output()\n",
    "\n",
    "#Funtion that will be called whenever when one types something in the input widget\n",
    "def on_type(data):\n",
    "\n",
    "    # with output widget\n",
    "    with movie_list:\n",
    "        movie_list.clear_output()\n",
    "        title  = data[\"new\"]\n",
    "        if len(title) > 2:\n",
    "            display(search(title))\n",
    "\n",
    "# Hooking up the input widget with the on_type function\n",
    "movie_input.observe(on_type, names = 'value')\n",
    "# Displaying both the widgets\n",
    "display(movie_input, movie_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BUILDING IN THE ACTUAL RECOMMENDATION SYSTEM**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading In The Rating Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:53:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:31:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:33:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:32:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:29:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20000258</th>\n",
       "      <td>138493</td>\n",
       "      <td>68954</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-11-13 15:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000259</th>\n",
       "      <td>138493</td>\n",
       "      <td>69526</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-12-03 18:31:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000260</th>\n",
       "      <td>138493</td>\n",
       "      <td>69644</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2009-12-07 18:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000261</th>\n",
       "      <td>138493</td>\n",
       "      <td>70286</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2009-11-13 15:42:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000262</th>\n",
       "      <td>138493</td>\n",
       "      <td>71619</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2009-10-17 20:25:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000263 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating            timestamp\n",
       "0              1        2     3.5  2005-04-02 23:53:47\n",
       "1              1       29     3.5  2005-04-02 23:31:16\n",
       "2              1       32     3.5  2005-04-02 23:33:39\n",
       "3              1       47     3.5  2005-04-02 23:32:07\n",
       "4              1       50     3.5  2005-04-02 23:29:40\n",
       "...          ...      ...     ...                  ...\n",
       "20000258  138493    68954     4.5  2009-11-13 15:42:00\n",
       "20000259  138493    69526     4.5  2009-12-03 18:31:48\n",
       "20000260  138493    69644     3.0  2009-12-07 18:10:57\n",
       "20000261  138493    70286     5.0  2009-11-13 15:42:24\n",
       "20000262  138493    71619     2.5  2009-10-17 20:25:36\n",
       "\n",
       "[20000263 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating = pd.read_csv(\"rating.csv\")\n",
    "rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId         int64\n",
       "movieId        int64\n",
       "rating       float64\n",
       "timestamp     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding Users Who Liked The Same Movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To convert timestamps's data type from object to datetime64\n",
    "rating[\"timestamp\"] = pd.to_datetime(rating.timestamp, errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId                int64\n",
       "movieId               int64\n",
       "rating              float64\n",
       "timestamp    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     3,      6,      8, ..., 138481, 138483, 138486], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hardcoding the code to check proper working of the below code\n",
    "movie_id = 1\n",
    "\n",
    "# Finding user who also liked same movies as us, and then getting unique user IDs\n",
    "similar_users = rating[(rating[\"movieId\"] == movie_id) & (rating[\"rating\"] >= 4)][\"userId\"].unique()\n",
    "similar_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "236             1\n",
       "238            32\n",
       "239            50\n",
       "242           175\n",
       "244           223\n",
       "            ...  \n",
       "19999506    53953\n",
       "19999507    54771\n",
       "19999508    55232\n",
       "19999509    55282\n",
       "19999510    56174\n",
       "Name: movieId, Length: 4223798, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Other movies that similar_users liked\n",
    "similar_user_recommendations = rating[(rating[\"userId\"].isin(similar_users)) & (rating[\"rating\"] >= 4)][\"movieId\"]\n",
    "similar_user_recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1       1.000000\n",
       "318     0.527783\n",
       "260     0.522917\n",
       "296     0.492011\n",
       "356     0.490028\n",
       "          ...   \n",
       "3408    0.101159\n",
       "899     0.101129\n",
       "3421    0.100949\n",
       "708     0.100799\n",
       "910     0.100318\n",
       "Name: count, Length: 267, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding only movies that greater than 10% of similar_users also liked and dividing it by number of users to convert it into percentage\n",
    "similar_user_recs = similar_user_recommendations.value_counts() / len(similar_users)\n",
    "\n",
    "# Taking the ones that are greater than 10% liked\n",
    "similar_user_recs = similar_user_recs[ similar_user_recs > 0.1]\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding How Much All Users Like Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "318     0.307220\n",
       "296     0.269810\n",
       "593     0.221542\n",
       "527     0.212621\n",
       "356     0.210485\n",
       "          ...   \n",
       "2302    0.018750\n",
       "586     0.018478\n",
       "2321    0.017114\n",
       "2       0.016024\n",
       "708     0.013010\n",
       "Name: count, Length: 267, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding how much all users liked the movies present in our dataset\n",
    "all_users = rating[(rating[\"movieId\"].isin(similar_user_recs.index)) & (rating[\"rating\"] > 4)]\n",
    "\n",
    "# Finding what percentage of all users will recommend each of these movies that are there in similar_user_recs\n",
    "all_users_recommendations = all_users[\"movieId\"].value_counts() / len(all_users[\"userId\"].unique())\n",
    "all_users_recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating A Recommendation Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>similar</th>\n",
       "      <th>all</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.527783</td>\n",
       "      <td>0.307220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.522917</td>\n",
       "      <td>0.209221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0.492011</td>\n",
       "      <td>0.269810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0.490028</td>\n",
       "      <td>0.210485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3408</th>\n",
       "      <td>0.101159</td>\n",
       "      <td>0.020325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>0.101129</td>\n",
       "      <td>0.034486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421</th>\n",
       "      <td>0.100949</td>\n",
       "      <td>0.027436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>0.100799</td>\n",
       "      <td>0.013010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>0.100318</td>\n",
       "      <td>0.034532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          similar       all\n",
       "movieId                    \n",
       "1        1.000000  0.122361\n",
       "318      0.527783  0.307220\n",
       "260      0.522917  0.209221\n",
       "296      0.492011  0.269810\n",
       "356      0.490028  0.210485\n",
       "...           ...       ...\n",
       "3408     0.101159  0.020325\n",
       "899      0.101129  0.034486\n",
       "3421     0.100949  0.027436\n",
       "708      0.100799  0.013010\n",
       "910      0.100318  0.034532\n",
       "\n",
       "[267 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the percentages by combining both the series together\n",
    "# We want movies that have a big difference between these two numbers\n",
    "rec_percentages = pd.concat([similar_user_recs, all_users_recommendations], axis = 1)\n",
    "rec_percentages.columns = [\"similar\", \"all\"]\n",
    "rec_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a score by dividing one number by the other - finding ratio\n",
    "rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "\n",
    "# Sorting values on score - biggest value at beginning\n",
    "# Higher the score, better the recommendation is\n",
    "rec_percentages = rec_percentages.sort_values(\"score\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>similar</th>\n",
       "      <th>all</th>\n",
       "      <th>score</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>clean_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122361</td>\n",
       "      <td>8.172546</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>Toy Story 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>0.100799</td>\n",
       "      <td>0.013010</td>\n",
       "      <td>7.747789</td>\n",
       "      <td>708</td>\n",
       "      <td>Truth About Cats &amp; Dogs, The (1996)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Truth About Cats  Dogs The 1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>0.202919</td>\n",
       "      <td>0.026792</td>\n",
       "      <td>7.573747</td>\n",
       "      <td>2355</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy</td>\n",
       "      <td>Bugs Life A 1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>0.209798</td>\n",
       "      <td>0.031798</td>\n",
       "      <td>6.597806</td>\n",
       "      <td>648</td>\n",
       "      <td>Mission: Impossible (1996)</td>\n",
       "      <td>Action|Adventure|Mystery|Thriller</td>\n",
       "      <td>Mission Impossible 1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.103652</td>\n",
       "      <td>0.016024</td>\n",
       "      <td>6.468570</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>Jumanji 1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>0.107437</td>\n",
       "      <td>0.017114</td>\n",
       "      <td>6.277540</td>\n",
       "      <td>2321</td>\n",
       "      <td>Pleasantville (1998)</td>\n",
       "      <td>Comedy|Drama|Fantasy</td>\n",
       "      <td>Pleasantville 1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>0.127591</td>\n",
       "      <td>0.020606</td>\n",
       "      <td>6.192057</td>\n",
       "      <td>440</td>\n",
       "      <td>Dave (1993)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Dave 1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>0.232594</td>\n",
       "      <td>0.038394</td>\n",
       "      <td>6.058099</td>\n",
       "      <td>592</td>\n",
       "      <td>Batman (1989)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Batman 1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>0.167388</td>\n",
       "      <td>0.027663</td>\n",
       "      <td>6.050878</td>\n",
       "      <td>2797</td>\n",
       "      <td>Big (1988)</td>\n",
       "      <td>Comedy|Drama|Fantasy|Romance</td>\n",
       "      <td>Big 1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>0.165405</td>\n",
       "      <td>0.027527</td>\n",
       "      <td>6.008827</td>\n",
       "      <td>2987</td>\n",
       "      <td>Who Framed Roger Rabbit? (1988)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Crime|Fant...</td>\n",
       "      <td>Who Framed Roger Rabbit 1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       similar       all     score  movieId  \\\n",
       "0     1.000000  0.122361  8.172546        1   \n",
       "696   0.100799  0.013010  7.747789      708   \n",
       "2270  0.202919  0.026792  7.573747     2355   \n",
       "640   0.209798  0.031798  6.597806      648   \n",
       "1     0.103652  0.016024  6.468570        2   \n",
       "2236  0.107437  0.017114  6.277540     2321   \n",
       "436   0.127591  0.020606  6.192057      440   \n",
       "586   0.232594  0.038394  6.058099      592   \n",
       "2711  0.167388  0.027663  6.050878     2797   \n",
       "2901  0.165405  0.027527  6.008827     2987   \n",
       "\n",
       "                                    title  \\\n",
       "0                        Toy Story (1995)   \n",
       "696   Truth About Cats & Dogs, The (1996)   \n",
       "2270                 Bug's Life, A (1998)   \n",
       "640            Mission: Impossible (1996)   \n",
       "1                          Jumanji (1995)   \n",
       "2236                 Pleasantville (1998)   \n",
       "436                           Dave (1993)   \n",
       "586                         Batman (1989)   \n",
       "2711                           Big (1988)   \n",
       "2901      Who Framed Roger Rabbit? (1988)   \n",
       "\n",
       "                                                 genres  \\\n",
       "0           Adventure|Animation|Children|Comedy|Fantasy   \n",
       "696                                      Comedy|Romance   \n",
       "2270                Adventure|Animation|Children|Comedy   \n",
       "640                   Action|Adventure|Mystery|Thriller   \n",
       "1                            Adventure|Children|Fantasy   \n",
       "2236                               Comedy|Drama|Fantasy   \n",
       "436                                      Comedy|Romance   \n",
       "586                               Action|Crime|Thriller   \n",
       "2711                       Comedy|Drama|Fantasy|Romance   \n",
       "2901  Adventure|Animation|Children|Comedy|Crime|Fant...   \n",
       "\n",
       "                          clean_title  \n",
       "0                      Toy Story 1995  \n",
       "696   Truth About Cats  Dogs The 1996  \n",
       "2270                 Bugs Life A 1998  \n",
       "640           Mission Impossible 1996  \n",
       "1                        Jumanji 1995  \n",
       "2236               Pleasantville 1998  \n",
       "436                         Dave 1993  \n",
       "586                       Batman 1989  \n",
       "2711                         Big 1988  \n",
       "2901     Who Framed Roger Rabbit 1988  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking top 10 recommendations and merging it with the movie dataset\n",
    "rec_percentages.head(10).merge(movie, left_index = True, right_on = \"movieId\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building A Recommendation Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting it all together in a recommendation function\n",
    "\n",
    "# Output of this function will be a dataframe that you can display in the widget created in the final step\n",
    "def find_similar_movies(movie_id):\n",
    "    similar_users = rating[(rating[\"movieId\"] == movie_id) & (rating[\"rating\"] >= 4)][\"userId\"].unique()\n",
    "    similar_user_recs = rating[(rating[\"userId\"].isin(similar_users)) & (rating[\"rating\"] >= 4)][\"movieId\"]\n",
    "    \n",
    "    similar_user_recs = similar_user_recs.value_counts() / len(similar_users)\n",
    "    similar_user_recs = similar_user_recs[ similar_user_recs > 0.1]\n",
    "    \n",
    "    all_users = rating[(rating[\"movieId\"].isin(similar_user_recs.index)) & (rating[\"rating\"] > 4)]\n",
    "    all_users_recs = all_users[\"movieId\"].value_counts() / len(all_users[\"userId\"].unique())\n",
    "\n",
    "    rec_percentages = pd.concat([similar_user_recs, all_users_recs], axis = 1)\n",
    "    rec_percentages.columns = [\"similar\", \"all\"]\n",
    "\n",
    "    rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "\n",
    "    rec_percentages = rec_percentages.sort_values(\"score\", ascending = False)\n",
    "     \n",
    "    # Selecting only three columns to show - \"score\", \"title\", and \"genres\"\n",
    "    return rec_percentages.head(10).merge(movie, left_index = True, right_on = \"movieId\")[[\"score\", \"title\", \"genres\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Final Part - Creating An Interactive Recommendation Widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f259ff629ddb4dce9896ffd172ed758d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='Enter a movie name', description='Movie Title')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d6a75eef3e040c4b029d9d0cb6809d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an input widget\n",
    "movie_name_input = widgets.Text(\n",
    "    value = \"Enter a movie name\",\n",
    "    description = \"Movie Title\",\n",
    "    disabled = False\n",
    ")\n",
    "\n",
    "# Creating an output widget\n",
    "recommendation_list = widgets.Output()\n",
    "\n",
    "#creating an on_type function which is slightly different from the previous one\n",
    "def on_type(data):\n",
    "    with recommendation_list:\n",
    "        recommendation_list.clear_output()\n",
    "\n",
    "        # Grabbing title from the input widget\n",
    "        title = data[\"new\"]\n",
    "        if len(title) > 2:\n",
    "            results = search(title)\n",
    "\n",
    "            # Extract the movie ID - first row of result has the highest confidence, so take it's movie id  \n",
    "            movie_id = results.iloc[0][\"movieId\"]\n",
    "\n",
    "            # call find_similar_movies function and pass movie_id to that function, and then display the dataframe output obtained\n",
    "            display(find_similar_movies(movie_id))\n",
    "\n",
    "# Observe on_type on movie_input\n",
    "movie_name_input.observe(on_type, names = \"value\")\n",
    "\n",
    "# Displaying the widgets\n",
    "display( movie_name_input, recommendation_list)"
   ]
  }
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