recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb245810",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb02dc8c",
   "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",
       "      <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>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 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",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \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",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9742 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the csv files (Movies and Ratings)\n",
    "movies_df = pd.read_csv('movies.csv')\n",
    "ratings_df = pd.read_csv('ratings.csv')\n",
    "movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4c46bd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MT\\AppData\\Local\\Temp/ipykernel_7824/522903812.py:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n"
     ]
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       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate the year of each movies from title column and ctrate a new column for years (just numbers without any Braces)\n",
    "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))', expand=False)\n",
    "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)', expand=False)\n",
    "movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
    "movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efb16856",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1995</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</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": [
    "# Split each genre with \"|\" and make sth like a list of genres\n",
    "movies_df['genres'] = movies_df.genres.str.split('|')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8c14a1f",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>Adventure</th>\n",
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       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
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       "      <th>IMAX</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
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       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1995</td>\n",
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       "<p>5 rows × 24 columns</p>\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  Adventure  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  1995        1.0   \n",
       "1                     [Adventure, Children, Fantasy]  1995        1.0   \n",
       "2                                  [Comedy, Romance]  1995        0.0   \n",
       "3                           [Comedy, Drama, Romance]  1995        0.0   \n",
       "4                                           [Comedy]  1995        0.0   \n",
       "\n",
       "   Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0        1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1        0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "2        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "3        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "4        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "   Sci-Fi  War  Musical  Documentary  IMAX  Western  Film-Noir  \\\n",
       "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   \n",
       "2     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   \n",
       "4     0.0  0.0      0.0          0.0   0.0      0.0        0.0   \n",
       "\n",
       "   (no genres listed)  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
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       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_with_genres_df = movies_df.copy() # Copy the movies DataFrame and create a new one like that\n",
    "\n",
    "for index, row in movies_with_genres_df.iterrows(): # for every row in Movie DataFrame , if the genres are in the movie replace it to 1 \n",
    "    for genre in row['genres']:\n",
    "        movies_with_genres_df.at[index, genre] = 1\n",
    "        \n",
    "        \n",
    "# At the end write 0 for that genres which are not for that movie.(It is a little hard to explain it in english :) )   \n",
    "movies_with_genres_df = movies_with_genres_df.fillna(0) \n",
    "movies_with_genres_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01fbaac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MT\\AppData\\Local\\Temp/ipykernel_7824/1107861065.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  ratings_df = ratings_df.drop('timestamp', 1)\n"
     ]
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       "   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"
      ]
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   "source": [
    "# In rating csv , remove timestamp column\n",
    "ratings_df = ratings_df.drop('timestamp', 1)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b1fb201",
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   "outputs": [
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       "                      title  rating\n",
       "0              12 Angry Men       5\n",
       "1       There Will Be Blood       4\n",
       "2                  Papillon       4\n",
       "3  The Silence of the Lambs       3\n",
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   "source": [
    "# Create a user profile of watched movies that contains the name and rate of each movies\n",
    "userInput = [\n",
    "    {'title':'12 Angry Men', 'rating':5},\n",
    "    {'title':'There Will Be Blood', 'rating':4},\n",
    "    {'title':'Papillon', 'rating':4},\n",
    "    {'title':'The Silence of the Lambs', 'rating':3},\n",
    "    {'title':'Enemy of the State', 'rating':3}\n",
    "    \n",
    "]\n",
    "\n",
    "# Create a DataFrame of userInput (That contains the name and ratings of movies that have watched)\n",
    "inputMovies = pd.DataFrame(userInput)\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f6bcff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MT\\AppData\\Local\\Temp/ipykernel_7824/3051924602.py:6: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  inputMovies = inputMovies.drop('genres',1).drop('year', 1) # drop genres and year columns\n"
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       "   movieId                     title  rating\n",
       "0      593  The Silence of the Lambs       3\n",
       "1     1203              12 Angry Men       5\n",
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   "source": [
    "# If the title of movies is existes in inputMovies , create a list of them\n",
    "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]\n",
    "\n",
    "inputMovies = pd.merge(inputId, inputMovies) # merge the InputId and InputMovies together\n",
    "\n",
    "inputMovies = inputMovies.drop('genres',1).drop('year', 1) # drop genres and year columns\n",
    "\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "code",
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       "      movieId                     title                     genres  year  \\\n",
       "510       593  The Silence of the Lambs  [Crime, Horror, Thriller]  1991   \n",
       "905      1203              12 Angry Men                    [Drama]  1957   \n",
       "1755     2353        Enemy of the State         [Action, Thriller]  1998   \n",
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       "6648    56782       There Will Be Blood           [Drama, Western]  2007   \n",
       "7332    77846              12 Angry Men             [Crime, Drama]  1997   \n",
       "\n",
       "      Adventure  Animation  Children  Comedy  Fantasy  Romance  ...  Horror  \\\n",
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       "\n",
       "      Mystery  Sci-Fi  War  Musical  Documentary  IMAX  Western  Film-Noir  \\\n",
       "510       0.0     0.0  0.0      0.0          0.0   0.0      0.0        0.0   \n",
       "905       0.0     0.0  0.0      0.0          0.0   0.0      0.0        0.0   \n",
       "1755      0.0     0.0  0.0      0.0          0.0   0.0      0.0        0.0   \n",
       "2409      0.0     0.0  0.0      0.0          0.0   0.0      0.0        0.0   \n",
       "6648      0.0     0.0  0.0      0.0          0.0   0.0      1.0        0.0   \n",
       "7332      0.0     0.0  0.0      0.0          0.0   0.0      0.0        0.0   \n",
       "\n",
       "      (no genres listed)  \n",
       "510                  0.0  \n",
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       "1755                 0.0  \n",
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       "6648                 0.0  \n",
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       "\n",
       "[6 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "# If the movieId existes in InputMovies , Then create a list of them (A kind of filtering)\n",
    "userMovies = movies_with_genres_df[movies_with_genres_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
    "userMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9021204",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\MT\\AppData\\Local\\Temp/ipykernel_7824/2113410367.py:4: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  userGenreTable = userMovies.drop('movieId',1).drop('title',1).drop('genres',1).drop('year',1)\n"
     ]
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       "      <th></th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Action</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Thriller</th>\n",
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       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>War</th>\n",
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       "      <th>IMAX</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
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      ],
      "text/plain": [
       "   Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  Action  \\\n",
       "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    1.0     0.0   \n",
       "2        0.0        0.0       0.0     0.0      0.0      0.0    0.0     1.0   \n",
       "3        0.0        0.0       0.0     0.0      0.0      0.0    1.0     0.0   \n",
       "4        0.0        0.0       0.0     0.0      0.0      0.0    1.0     0.0   \n",
       "5        0.0        0.0       0.0     0.0      0.0      0.0    1.0     0.0   \n",
       "\n",
       "   Crime  Thriller  Horror  Mystery  Sci-Fi  War  Musical  Documentary  IMAX  \\\n",
       "0    1.0       1.0     1.0      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    0.0       1.0     0.0      0.0     0.0  0.0      0.0          0.0   0.0   \n",
       "3    1.0       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  0.0      0.0          0.0   0.0   \n",
       "5    1.0       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      1.0        0.0                 0.0  \n",
       "5      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userMovies = userMovies.reset_index(drop=True) # reset the number of index and start at the first\n",
    "\n",
    "# remove movieId, genres, title, and year columns\n",
    "userGenreTable = userMovies.drop('movieId',1).drop('title',1).drop('genres',1).drop('year',1)\n",
    "userGenreTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ccce6e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adventure              0.0\n",
       "Animation              0.0\n",
       "Children               0.0\n",
       "Comedy                 0.0\n",
       "Fantasy                0.0\n",
       "Romance                0.0\n",
       "Drama                 16.0\n",
       "Action                 5.0\n",
       "Crime                 10.0\n",
       "Thriller               8.0\n",
       "Horror                 3.0\n",
       "Mystery                0.0\n",
       "Sci-Fi                 0.0\n",
       "War                    0.0\n",
       "Musical                0.0\n",
       "Documentary            0.0\n",
       "IMAX                   0.0\n",
       "Western                4.0\n",
       "Film-Noir              0.0\n",
       "(no genres listed)     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get user profile we have to DOt usetGenreTable to inputMovies :)\n",
    "userProfile = userGenreTable.transpose().dot(inputMovies['rating'])\n",
    "userProfile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "909b447d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MT\\AppData\\Local\\Temp/ipykernel_7824/3557667000.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  genreTable = genreTable.drop('movieId',1).drop('title',1).drop('genres', 1).drop('year',1)\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  War  Musical  \\\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",
       "         Documentary  IMAX  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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from Movies with genres just get the genres of movies and write them to genreTable\n",
    "genreTable = movies_with_genres_df.set_index(movies_with_genres_df['movieId'])\n",
    "\n",
    "# Remove the unnecessary columns\n",
    "genreTable = genreTable.drop('movieId',1).drop('title',1).drop('genres', 1).drop('year',1)\n",
    "genreTable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d88a2db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1         0.000000\n",
       "2         0.000000\n",
       "3         0.000000\n",
       "4         0.347826\n",
       "5         0.000000\n",
       "            ...   \n",
       "193581    0.108696\n",
       "193583    0.000000\n",
       "193585    0.347826\n",
       "193587    0.108696\n",
       "193609    0.000000\n",
       "Length: 9742, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to calculate the weighted average we have to multiply the genres with weights\n",
    "recommendationTable_df = ((genreTable*userProfile).sum(axis=1))/(userProfile.sum())\n",
    "recommendationTable_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e236a5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "81132    1.000000\n",
       "4210     0.913043\n",
       "7235     0.913043\n",
       "55721    0.847826\n",
       "79132    0.847826\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the values and at the first show the best values...\n",
    "recommendationTable_df = recommendationTable_df.sort_values(ascending=False)\n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95756469",
   "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>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>78</td>\n",
       "      <td>Crossing Guard, The</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>519</td>\n",
       "      <td>RoboCop 3</td>\n",
       "      <td>[Action, Crime, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>3682</td>\n",
       "      <td>Magnum Force</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3128</th>\n",
       "      <td>4210</td>\n",
       "      <td>Manhunter</td>\n",
       "      <td>[Action, Crime, Drama, Horror, Thriller]</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657</th>\n",
       "      <td>5027</td>\n",
       "      <td>Another 48 Hrs.</td>\n",
       "      <td>[Action, Comedy, Crime, Drama, Thriller]</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4249</th>\n",
       "      <td>6196</td>\n",
       "      <td>Cradle 2 the Grave</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4843</th>\n",
       "      <td>7235</td>\n",
       "      <td>Ichi the Killer (Koroshiya 1)</td>\n",
       "      <td>[Action, Comedy, Crime, Drama, Horror, Thriller]</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5088</th>\n",
       "      <td>8016</td>\n",
       "      <td>Getaway, The</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5305</th>\n",
       "      <td>8798</td>\n",
       "      <td>Collateral</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5327</th>\n",
       "      <td>8860</td>\n",
       "      <td>Cellular</td>\n",
       "      <td>[Action, Crime, Drama, Mystery, Thriller]</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5491</th>\n",
       "      <td>26344</td>\n",
       "      <td>French Connection II</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5895</th>\n",
       "      <td>33437</td>\n",
       "      <td>Unleashed (Danny the Dog)</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6358</th>\n",
       "      <td>49530</td>\n",
       "      <td>Blood Diamond</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller, War]</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>50794</td>\n",
       "      <td>Smokin' Aces</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>55721</td>\n",
       "      <td>Elite Squad (Tropa de Elite)</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6864</th>\n",
       "      <td>62394</td>\n",
       "      <td>Max Payne</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7372</th>\n",
       "      <td>79132</td>\n",
       "      <td>Inception</td>\n",
       "      <td>[Action, Crime, Drama, Mystery, Sci-Fi, Thrill...</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7441</th>\n",
       "      <td>81132</td>\n",
       "      <td>Rubber</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Film...</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8797</th>\n",
       "      <td>130083</td>\n",
       "      <td>Kidnapping Mr. Heineken</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9696</th>\n",
       "      <td>184931</td>\n",
       "      <td>Death Wish</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                          title  \\\n",
       "70         78            Crossing Guard, The   \n",
       "454       519                      RoboCop 3   \n",
       "2744     3682                   Magnum Force   \n",
       "3128     4210                      Manhunter   \n",
       "3657     5027                Another 48 Hrs.   \n",
       "4249     6196             Cradle 2 the Grave   \n",
       "4843     7235  Ichi the Killer (Koroshiya 1)   \n",
       "5088     8016                   Getaway, The   \n",
       "5305     8798                     Collateral   \n",
       "5327     8860                       Cellular   \n",
       "5491    26344           French Connection II   \n",
       "5895    33437      Unleashed (Danny the Dog)   \n",
       "6358    49530                  Blood Diamond   \n",
       "6396    50794                   Smokin' Aces   \n",
       "6607    55721   Elite Squad (Tropa de Elite)   \n",
       "6864    62394                      Max Payne   \n",
       "7372    79132                      Inception   \n",
       "7441    81132                         Rubber   \n",
       "8797   130083        Kidnapping Mr. Heineken   \n",
       "9696   184931                     Death Wish   \n",
       "\n",
       "                                                 genres  year  \n",
       "70                     [Action, Crime, Drama, Thriller]  1995  \n",
       "454            [Action, Crime, Drama, Sci-Fi, Thriller]  1993  \n",
       "2744                   [Action, Crime, Drama, Thriller]  1973  \n",
       "3128           [Action, Crime, Drama, Horror, Thriller]  1986  \n",
       "3657           [Action, Comedy, Crime, Drama, Thriller]  1990  \n",
       "4249                   [Action, Crime, Drama, Thriller]  2003  \n",
       "4843   [Action, Comedy, Crime, Drama, Horror, Thriller]  2001  \n",
       "5088                   [Action, Crime, Drama, Thriller]  1972  \n",
       "5305                   [Action, Crime, Drama, Thriller]  2004  \n",
       "5327          [Action, Crime, Drama, Mystery, Thriller]  2004  \n",
       "5491                   [Action, Crime, Drama, Thriller]  1975  \n",
       "5895                   [Action, Crime, Drama, Thriller]  2005  \n",
       "6358   [Action, Adventure, Crime, Drama, Thriller, War]  2006  \n",
       "6396                   [Action, Crime, Drama, Thriller]  2006  \n",
       "6607                   [Action, Crime, Drama, Thriller]  2007  \n",
       "6864                   [Action, Crime, Drama, Thriller]  2008  \n",
       "7372  [Action, Crime, Drama, Mystery, Sci-Fi, Thrill...  2010  \n",
       "7441  [Action, Adventure, Comedy, Crime, Drama, Film...  2010  \n",
       "8797                   [Action, Crime, Drama, Thriller]  2015  \n",
       "9696                   [Action, Crime, Drama, Thriller]  2018  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if movieId is in recommendation table the show 20 of the (20 movies for recommend to user)\n",
    "movies_df.loc[movies_df['movieId'].isin(recommendationTable_df.head(20).keys())]"
   ]
  }
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