Content_Based_Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Content Based Recommendation System </h2>\n",
    "\n",
    "Dataset source: https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%205/data/moviedataset.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34208, 3)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesds = pd.read_csv('movies.csv')\n",
    "moviesds.shape"
   ]
  },
  {
   "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",
       "    </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",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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",
       "                                        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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>movieId</th>\n",
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       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
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       "      <td>1995</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1995</td>\n",
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       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
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       "      <td>[Comedy]</td>\n",
       "      <td>1995</td>\n",
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      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  year  \n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  1995  \n",
       "1                     [Adventure, Children, Fantasy]  1995  \n",
       "2                                  [Comedy, Romance]  1995  \n",
       "3                           [Comedy, Drama, Romance]  1995  \n",
       "4                                           [Comedy]  1995  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Formatting the data types \n",
    "moviesds['year'] = moviesds['title'].str.extract('(\\(\\d\\d\\d\\d\\))', expand=False)\n",
    "moviesds['year'] = moviesds['year'].str.extract('(\\d\\d\\d\\d)', expand=False)\n",
    "moviesds['title'] = moviesds['title'].str.extract(r'(.*)\\s\\(\\d{4}\\)')\n",
    "moviesds['genres'] = moviesds['genres'].str.split('|')\n",
    "\n",
    "moviesds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "<p>5 rows × 23 columns</p>\n",
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      "text/plain": [
       "   movieId                        title  year  Adventure  Animation  Children  \\\n",
       "0        1                    Toy Story  1995        1.0        1.0       1.0   \n",
       "1        2                      Jumanji  1995        1.0        0.0       1.0   \n",
       "2        3             Grumpier Old Men  1995        0.0        0.0       0.0   \n",
       "3        4            Waiting to Exhale  1995        0.0        0.0       0.0   \n",
       "4        5  Father of the Bride Part II  1995        0.0        0.0       0.0   \n",
       "\n",
       "   Comedy  Fantasy  Romance  Drama  ...  Horror  Mystery  Sci-Fi  IMAX  \\\n",
       "0     1.0      1.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "1     0.0      1.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "2     1.0      0.0      1.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "3     1.0      0.0      1.0    1.0  ...     0.0      0.0     0.0   0.0   \n",
       "4     1.0      0.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "\n",
       "   Documentary  War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "0          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "1          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "2          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "3          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "4          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the genres with One Hot Encoding, will be necessary for probability\n",
    "# computations with genre matrix.\n",
    "\n",
    "moviegenre = moviesds.copy()\n",
    "\n",
    "for index, row in moviesds.iterrows():\n",
    "    for i in row['genres']:\n",
    "        moviegenre.at[index, i] = 1\n",
    "\n",
    "moviegenre = moviegenre.fillna(0)\n",
    "moviegenre.drop('genres', axis=1, inplace=True)\n",
    "\n",
    "moviegenre.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>Toy Story</td>\n",
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       "      <td>Akira</td>\n",
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      "text/plain": [
       "                 title  rating\n",
       "0  Breakfast Club, The     5.0\n",
       "1            Toy Story     3.5\n",
       "2              Jumanji     2.0\n",
       "3         Pulp Fiction     5.0\n",
       "4                Akira     4.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a hypothetical user to recommend movies to. \n",
    "# The movies already viewed and rated by the user are as follows\n",
    "userinput = [\n",
    "    {'title': 'Breakfast Club, The', 'rating': 5},\n",
    "    {'title': 'Toy Story', 'rating': 3.5},\n",
    "    {'title': 'Jumanji', 'rating': 2},\n",
    "    {'title': 'Pulp Fiction', 'rating': 5},\n",
    "    {'title': 'Akira', 'rating': 4.5},\n",
    "]\n",
    "userinput = pd.DataFrame(userinput)\n",
    "userinput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sharmeen\\AppData\\Local\\Temp\\ipykernel_29032\\2187991048.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  inputid.drop(['genres', 'year'], axis=1, inplace=True)\n"
     ]
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      "text/plain": [
       "      movieId                title\n",
       "0           1            Toy Story\n",
       "1           2              Jumanji\n",
       "293       296         Pulp Fiction\n",
       "1246     1274                Akira\n",
       "1885     1968  Breakfast Club, The"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the IDs of the rated movies from the movies dataset\n",
    "inputid = moviesds[moviesds['title'].isin(userinput['title'].tolist())]\n",
    "inputid.drop(['genres', 'year'], axis=1, inplace=True)\n",
    "\n",
    "inputid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1274</td>\n",
       "      <td>Akira</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1968</td>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
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      ],
      "text/plain": [
       "   movieId                title  rating\n",
       "0        1            Toy Story     3.5\n",
       "1        2              Jumanji     2.0\n",
       "2      296         Pulp Fiction     5.0\n",
       "3     1274                Akira     4.5\n",
       "4     1968  Breakfast Club, The     5.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging to add movie IDs to the ratings dataset\n",
    "userinput = pd.merge(inputid, userinput)\n",
    "userinput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
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       "      <th>Drama</th>\n",
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       "      <th>Sci-Fi</th>\n",
       "      <th>IMAX</th>\n",
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       "      <th>War</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <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>293</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>1274</td>\n",
       "      <td>Akira</td>\n",
       "      <td>1988</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>1.0</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>1885</th>\n",
       "      <td>1968</td>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                title  year  Adventure  Animation  Children  \\\n",
       "0           1            Toy Story  1995        1.0        1.0       1.0   \n",
       "1           2              Jumanji  1995        1.0        0.0       1.0   \n",
       "293       296         Pulp Fiction  1994        0.0        0.0       0.0   \n",
       "1246     1274                Akira  1988        1.0        1.0       0.0   \n",
       "1885     1968  Breakfast Club, The  1985        0.0        0.0       0.0   \n",
       "\n",
       "      Comedy  Fantasy  Romance  Drama  ...  Horror  Mystery  Sci-Fi  IMAX  \\\n",
       "0        1.0      1.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "1        0.0      1.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "293      1.0      0.0      0.0    1.0  ...     0.0      0.0     0.0   0.0   \n",
       "1246     0.0      0.0      0.0    0.0  ...     0.0      0.0     1.0   0.0   \n",
       "1885     1.0      0.0      0.0    1.0  ...     0.0      0.0     0.0   0.0   \n",
       "\n",
       "      Documentary  War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "0             0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "1             0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "293           0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "1246          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "1885          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting information about the rated movies from the movies dataset\n",
    "usermovies = moviegenre[moviegenre['movieId'].isin(userinput['movieId'].tolist())]\n",
    "usermovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  Action  \\\n",
       "0        1.0        1.0       1.0     1.0      1.0      0.0    0.0     0.0   \n",
       "1        1.0        0.0       1.0     0.0      1.0      0.0    0.0     0.0   \n",
       "2        0.0        0.0       0.0     1.0      0.0      0.0    1.0     0.0   \n",
       "3        1.0        1.0       0.0     0.0      0.0      0.0    0.0     1.0   \n",
       "4        0.0        0.0       0.0     1.0      0.0      0.0    1.0     0.0   \n",
       "\n",
       "   Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  War  Musical  \\\n",
       "0    0.0       0.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "1    0.0       0.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "2    1.0       1.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "3    0.0       0.0     0.0      0.0     1.0   0.0          0.0  0.0      0.0   \n",
       "4    0.0       0.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "\n",
       "   Western  Film-Noir  (no genres listed)  \n",
       "0      0.0        0.0                 0.0  \n",
       "1      0.0        0.0                 0.0  \n",
       "2      0.0        0.0                 0.0  \n",
       "3      0.0        0.0                 0.0  \n",
       "4      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the genre matrix for the rated movies\n",
    "usermovies.reset_index(drop=True, inplace=True)\n",
    "genremtx = usermovies.drop(['movieId', 'title', 'year'], axis=1)\n",
    "genremtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.5\n",
       "1    2.0\n",
       "2    5.0\n",
       "3    4.5\n",
       "4    5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userinput['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adventure             10.0\n",
       "Animation              8.0\n",
       "Children               5.5\n",
       "Comedy                13.5\n",
       "Fantasy                5.5\n",
       "Romance                0.0\n",
       "Drama                 10.0\n",
       "Action                 4.5\n",
       "Crime                  5.0\n",
       "Thriller               5.0\n",
       "Horror                 0.0\n",
       "Mystery                0.0\n",
       "Sci-Fi                 4.5\n",
       "IMAX                   0.0\n",
       "Documentary            0.0\n",
       "War                    0.0\n",
       "Musical                0.0\n",
       "Western                0.0\n",
       "Film-Noir              0.0\n",
       "(no genres listed)     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating user profile based on rated movies and movie genres\n",
    "userprofile = genremtx.transpose().dot(userinput['rating'])\n",
    "userprofile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
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      "text/plain": [
       "         Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  \\\n",
       "movieId                                                                    \n",
       "1              1.0        1.0       1.0     1.0      1.0      0.0    0.0   \n",
       "2              1.0        0.0       1.0     0.0      1.0      0.0    0.0   \n",
       "3              0.0        0.0       0.0     1.0      0.0      1.0    0.0   \n",
       "4              0.0        0.0       0.0     1.0      0.0      1.0    1.0   \n",
       "5              0.0        0.0       0.0     1.0      0.0      0.0    0.0   \n",
       "\n",
       "         Action  Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  \\\n",
       "movieId                                                                        \n",
       "1           0.0    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     0.0   0.0          0.0   \n",
       "3           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     0.0   0.0          0.0   \n",
       "5           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "\n",
       "         War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "movieId                                                        \n",
       "1        0.0      0.0      0.0        0.0                 0.0  \n",
       "2        0.0      0.0      0.0        0.0                 0.0  \n",
       "3        0.0      0.0      0.0        0.0                 0.0  \n",
       "4        0.0      0.0      0.0        0.0                 0.0  \n",
       "5        0.0      0.0      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the genre matrix from the hot-encoded movie dataset\n",
    "genretable = moviegenre.set_index(moviegenre['movieId'])\n",
    "genretable.drop(['movieId', 'title', 'year'], axis=1, inplace=True)\n",
    "genretable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "5018      0.748252\n",
       "26093     0.734266\n",
       "27344     0.720280\n",
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating scores of movies to recommend based on user profile and movie genres\n",
    "recs = (genretable * userprofile).sum(axis=1) / userprofile.sum()\n",
    "\n",
    "# Sorting to find the top 20 movies to recommend\n",
    "recs = recs.sort_values(ascending=False).head(20)\n",
    "\n",
    "recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Motorama', '1991'],\n",
       " ['Wonderful World of the Brothers Grimm, The', '1962'],\n",
       " ['Revolutionary Girl Utena: Adolescence of Utena (a.k.a. Revolutionary Girl Utena the Movie) (Shoujo kakumei Utena: Adolescence mokushiroku)',\n",
       "  '1999'],\n",
       " ['Wizards of Waverly Place: The Movie', '2009'],\n",
       " ['Interstate 60', '2002'],\n",
       " ['Dragonheart 2: A New Beginning', '2000'],\n",
       " ['The 39 Steps', '1959'],\n",
       " ['The Wrecking Crew', '1968'],\n",
       " ['Rubber', '2010'],\n",
       " ['Who Framed Roger Rabbit?', '1988'],\n",
       " ['Gruffalo, The', '2009'],\n",
       " ['Space Jam', '1996'],\n",
       " [\"Atlantis: Milo's Return\", '2003'],\n",
       " ['TMNT (Teenage Mutant Ninja Turtles)', '2007'],\n",
       " ['Robots', '2005'],\n",
       " [\"Twelve Tasks of Asterix, The (Les douze travaux d'Astérix)\", '1976'],\n",
       " ['Mulan', '1998'],\n",
       " ['The Lego Movie', '2014'],\n",
       " ['Ernest & Célestine (Ernest et Célestine)', '2012'],\n",
       " ['Princes and Princesses', '2000']]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the movie information from recommended IDs\n",
    "movierecs = []\n",
    "\n",
    "for rec_id in recs.keys():\n",
    "    name = moviesds[moviesds['movieId'] == rec_id]\n",
    "    name = [name['title'].tolist()[0], name['year'].tolist()[0]]\n",
    "    movierecs.append(name)\n",
    "\n",
    "movierecs"
   ]
  }
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