IMDB-rec.ipynb

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   "source": [
    "# Movie Rec Sys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d27261c",
   "metadata": {},
   "source": [
    "## Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0b72a5",
   "metadata": {
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    }
   },
   "source": [
    "Importing required modules "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e31f348",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from math import sqrt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bba0b7a",
   "metadata": {},
   "source": [
    "## Acquiring the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a09089be",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -O moviedataset.zip https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%205/data/moviedataset.zip\n",
    "print('unziping ...')\n",
    "!unzip -o -j moviedataset.zip "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de170a2d",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "loading the csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78032ee0",
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   },
   "outputs": [],
   "source": [
    "movies_df = pd.read_csv('movies.csv')\n",
    "ratings_df = pd.read_csv('ratings_sample.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaee4af",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Removing year from movie names and add it to a seprate column  using regex \\d\\d\\d\\d => match 4 digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f5a34b2",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6426/1075324330.py:3: 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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       "<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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "</div>"
      ],
      "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": [
    "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": "markdown",
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   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Removing genres columns because its not needed in collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bf4a35c",
   "metadata": {
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     "text": [
      "/tmp/ipykernel_6426/2711619272.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  movies_df = movies_df.drop('genres', 1)\n"
     ]
    },
    {
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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",
       "    </tr>\n",
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       "      <td>Toy Story</td>\n",
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       "      <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>1995</td>\n",
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       "      <td>5</td>\n",
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       "   movieId                        title  year\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"
      ]
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   "source": [
    "movies_df = movies_df.drop('genres', 1)\n",
    "movies_df.head() "
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>169</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1204927694</td>\n",
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       "   userId  movieId  rating   timestamp\n",
       "0       1      169     2.5  1204927694\n",
       "1       1     2471     3.0  1204927438\n",
       "2       1    48516     5.0  1204927435\n",
       "3       2     2571     3.5  1436165433\n",
       "4       2   109487     4.0  1436165496"
      ]
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   "source": [
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b13d78",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Removing timestamp because its not needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89b2449a",
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6426/3809009477.py:1: 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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1      169     2.5\n",
       "1       1     2471     3.0\n",
       "2       1    48516     5.0\n",
       "3       2     2571     3.5\n",
       "4       2   109487     4.0"
      ]
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "ratings_df = ratings_df.drop('timestamp', 1)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e35e7e6",
   "metadata": {},
   "source": [
    "making a df out of userinputs data of the watched movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f46efd0b",
   "metadata": {
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   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akira</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\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": 7,
     "metadata": {},
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   ],
   "source": [
    "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",
    "inputMovies = pd.DataFrame(userInput)\n",
    "inputMovies"
   ]
  },
  {
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   "id": "e65d47c0",
   "metadata": {
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Adding movie Id to input user\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ad98024",
   "metadata": {
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    "scrolled": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6426/406558113.py:3: 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('year', 1) # removing year because its not needed\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
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       "      <td>Toy Story</td>\n",
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       "      <td>Jumanji</td>\n",
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       "    </tr>\n",
       "    <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",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1968</td>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())] # matching movies\n",
    "inputMovies = pd.merge(inputId, inputMovies) # merging inputId and input movies\n",
    "inputMovies = inputMovies.drop('year', 1) # removing year because its not needed\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbafec7a",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Finding users who has seen the same movies (same as inputMovies = the movies that main user has watched)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "415d3eec",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>296</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>12</td>\n",
       "      <td>1968</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>13</td>\n",
       "      <td>1274</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>14</td>\n",
       "      <td>296</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating\n",
       "19        4      296     4.0\n",
       "441      12     1968     3.0\n",
       "479      13        2     2.0\n",
       "531      13     1274     5.0\n",
       "681      14      296     2.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userSubset = ratings_df[ratings_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
    "userSubset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191f52a2",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "and group them by id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6937f88e",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "userSubsetGroup = userSubset.groupby(['userId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792af95b",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets look at a random numebr for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "492263ae",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>13</td>\n",
       "      <td>1274</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating\n",
       "479      13        2     2.0\n",
       "531      13     1274     5.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userSubsetGroup.get_group(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625e0303",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Sorting users with more same movies as input movies therefore the top rows are the most relevant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "095b6710",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(75,\n",
       "        userId  movieId  rating\n",
       "  7507      75        1     5.0\n",
       "  7508      75        2     3.5\n",
       "  7540      75      296     5.0\n",
       "  7633      75     1274     4.5\n",
       "  7673      75     1968     5.0),\n",
       " (106,\n",
       "        userId  movieId  rating\n",
       "  9083     106        1     2.5\n",
       "  9084     106        2     3.0\n",
       "  9115     106      296     3.5\n",
       "  9198     106     1274     3.0\n",
       "  9238     106     1968     3.5),\n",
       " (686,\n",
       "         userId  movieId  rating\n",
       "  61336     686        1     4.0\n",
       "  61337     686        2     3.0\n",
       "  61377     686      296     4.0\n",
       "  61478     686     1274     4.0\n",
       "  61569     686     1968     5.0)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userSubsetGroup = sorted(userSubsetGroup,  key=lambda x: len(x[1]), reverse=True)\n",
    "userSubsetGroup[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e648b5b",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "grabbing top 100 users with the same movies to loop through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c2ad43d",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "userSubsetGroup = userSubsetGroup[0:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525acd50",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "calculating user rating using Pearson Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79f491f4",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name:  75\n",
      "name:  106\n",
      "name:  686\n",
      "name:  815\n",
      "name:  1040\n",
      "name:  1130\n",
      "name:  1502\n",
      "name:  1599\n",
      "name:  1625\n",
      "name:  1950\n",
      "name:  2065\n",
      "name:  2128\n",
      "name:  2432\n",
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      "name:  4292\n",
      "name:  4415\n",
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      "name:  10368\n",
      "name:  10607\n",
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      "name:  18509\n"
     ]
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   ],
   "source": [
    "pearsonCorrelationDict = {} ## tmp storage\n",
    "\n",
    "for name, group in userSubsetGroup:\n",
    "    print('name: ',name) # name == userId\n",
    "    group = group.sort_values(by='movieId')\n",
    "    inputMovies = inputMovies.sort_values(by='movieId')\n",
    "    nRatings = len(group) # amount of ratings\n",
    "    \n",
    "    ## get to movies that the person in loop and the input user have in common\n",
    "    temp_df = inputMovies[inputMovies['movieId'].isin(group['movieId'].tolist())] \n",
    "    tempRatingList = temp_df['rating'].tolist()\n",
    "    tempGroupList = group['rating'].tolist()\n",
    "    \n",
    "    # calculating pearson alg between 2 users\n",
    "    Sxx = sum([i**2 for i in tempRatingList]) - pow(sum(tempRatingList),2)/float(nRatings)\n",
    "    Syy = sum([i**2 for i in tempGroupList]) - pow(sum(tempGroupList),2)/float(nRatings)\n",
    "    Sxy = sum( i*j for i, j in zip(tempRatingList, tempGroupList)) - sum(tempRatingList)*sum(tempGroupList)/float(nRatings)\n",
    "    \n",
    "    # check that the value is not zero because division by 0 would be infinite \n",
    "    if Sxx != 0 and Syy != 0:\n",
    "        pearsonCorrelationDict[name] = Sxy/sqrt(Sxx*Syy)\n",
    "    else:\n",
    "        pearsonCorrelationDict[name] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94ccbab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "{75: 0.8272781516947562,\n",
       " 106: 0.5860090386731182,\n",
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       " 17501: 0.8168748513121271,\n",
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       " 17666: 0.7689238340176859,\n",
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       " 17757: 0.64657575013984,\n",
       " 17854: 0.537086155529574,\n",
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       " 17944: 0.2713848825944774,\n",
       " 18301: 0.29838119751643016,\n",
       " 18509: 0.1322214713369862}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonCorrelationDict # dictionary of userIds and ratings (similary ratings to the input user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63808232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>similarityIndex</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.827278</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.586009</td>\n",
       "      <td>106</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.832050</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.576557</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.943456</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   similarityIndex  userId\n",
       "0         0.827278      75\n",
       "1         0.586009     106\n",
       "2         0.832050     686\n",
       "3         0.576557     815\n",
       "4         0.943456    1040"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making df from results\n",
    "pearsonDF = pd.DataFrame.from_dict(pearsonCorrelationDict, orient='index')\n",
    "pearsonDF.columns = ['similarityIndex']\n",
    "pearsonDF['userId'] = pearsonDF.index\n",
    "pearsonDF.index = range(len(pearsonDF))\n",
    "pearsonDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544f5d14",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "find the 50 with highest similarity or perason value to the input user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b08866e",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>similarityIndex</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.961538</td>\n",
       "      <td>6207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.961538</td>\n",
       "      <td>10707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.960769</td>\n",
       "      <td>13053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.943456</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    similarityIndex  userId\n",
       "64         0.961678   12325\n",
       "34         0.961538    6207\n",
       "55         0.961538   10707\n",
       "67         0.960769   13053\n",
       "4          0.943456    1040"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topUsers=pearsonDF.sort_values(by='similarityIndex', ascending=False)[0:50]\n",
    "topUsers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675089a7",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "weighted rating from the top users which is calculated by multiplying similarityindex (pearson alg val) and users rating\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4302425",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>similarityIndex</th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>weightedRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.365874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.442517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.885035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.480839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.404196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   similarityIndex  userId  movieId  rating  weightedRating\n",
       "0         0.961678   12325        1     3.5        3.365874\n",
       "1         0.961678   12325        2     1.5        1.442517\n",
       "2         0.961678   12325        3     3.0        2.885035\n",
       "3         0.961678   12325        5     0.5        0.480839\n",
       "4         0.961678   12325        6     2.5        2.404196"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topUsersRating=topUsers.merge(ratings_df, left_on='userId', right_on='userId', how='inner')\n",
    "topUsersRating['weightedRating'] = topUsersRating['similarityIndex']*topUsersRating['rating']\n",
    "topUsersRating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca81b93",
   "metadata": {},
   "source": [
    "sum of the simlarity index (simlarity rating between user and the input user) and the weighted ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f13b2aab",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_similarityIndex</th>\n",
       "      <th>sum_weightedRating</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>38.376281</td>\n",
       "      <td>140.800834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.376281</td>\n",
       "      <td>96.656745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.253981</td>\n",
       "      <td>27.254477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.929294</td>\n",
       "      <td>2.787882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.723262</td>\n",
       "      <td>27.151751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sum_similarityIndex  sum_weightedRating\n",
       "movieId                                         \n",
       "1                  38.376281          140.800834\n",
       "2                  38.376281           96.656745\n",
       "3                  10.253981           27.254477\n",
       "4                   0.929294            2.787882\n",
       "5                  11.723262           27.151751"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempTopUsersRating = topUsersRating.groupby('movieId').sum()[['similarityIndex','weightedRating']]\n",
    "tempTopUsersRating.columns = ['sum_similarityIndex','sum_weightedRating']\n",
    "tempTopUsersRating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa404c9",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "get the mean reconmmendation score by dividing weighted rating by simlarity index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e5eb826",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>weighted average recommendation score</th>\n",
       "      <th>movieId</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>3.668955</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.518658</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.657941</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.316058</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weighted average recommendation score  movieId\n",
       "movieId                                                \n",
       "1                                     3.668955        1\n",
       "2                                     2.518658        2\n",
       "3                                     2.657941        3\n",
       "4                                     3.000000        4\n",
       "5                                     2.316058        5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation_df = pd.DataFrame()\n",
    "recommendation_df['weighted average recommendation score'] = tempTopUsersRating['sum_weightedRating']/tempTopUsersRating['sum_similarityIndex']\n",
    "recommendation_df['movieId'] = tempTopUsersRating.index\n",
    "recommendation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb29f415",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "and get the top 10 movies that fits best for the input uesr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ef306c2",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weighted average recommendation score</th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>5073</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5073</td>\n",
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       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3329</td>\n",
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       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2284</td>\n",
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       "    <tr>\n",
       "      <th>26801</th>\n",
       "      <td>5.0</td>\n",
       "      <td>26801</td>\n",
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       "    <tr>\n",
       "      <th>6776</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6776</td>\n",
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       "    <tr>\n",
       "      <th>6672</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3759</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3769</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3775</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90531</th>\n",
       "      <td>5.0</td>\n",
       "      <td>90531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weighted average recommendation score  movieId\n",
       "movieId                                                \n",
       "5073                                       5.0     5073\n",
       "3329                                       5.0     3329\n",
       "2284                                       5.0     2284\n",
       "26801                                      5.0    26801\n",
       "6776                                       5.0     6776\n",
       "6672                                       5.0     6672\n",
       "3759                                       5.0     3759\n",
       "3769                                       5.0     3769\n",
       "3775                                       5.0     3775\n",
       "90531                                      5.0    90531"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation_df = recommendation_df.sort_values(by='weighted average recommendation score', ascending=False)\n",
    "recommendation_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7562d5",
   "metadata": {},
   "source": [
    "find the movie location to get movie details by movie id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f96bf15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>2284</td>\n",
       "      <td>Bandit Queen</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3243</th>\n",
       "      <td>3329</td>\n",
       "      <td>Year My Voice Broke, The</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3669</th>\n",
       "      <td>3759</td>\n",
       "      <td>Fun and Fancy Free</td>\n",
       "      <td>1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>3769</td>\n",
       "      <td>Thunderbolt and Lightfoot</td>\n",
       "      <td>1974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3685</th>\n",
       "      <td>3775</td>\n",
       "      <td>Make Mine Music</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>5073</td>\n",
       "      <td>Son's Room, The (Stanza del figlio, La)</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>6672</td>\n",
       "      <td>War Photographer</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6667</th>\n",
       "      <td>6776</td>\n",
       "      <td>Lagaan: Once Upon a Time in India</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9064</th>\n",
       "      <td>26801</td>\n",
       "      <td>Dragon Inn (Sun lung moon hak chan)</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18106</th>\n",
       "      <td>90531</td>\n",
       "      <td>Shame</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                    title  year\n",
       "2200      2284                             Bandit Queen  1994\n",
       "3243      3329                 Year My Voice Broke, The  1987\n",
       "3669      3759                       Fun and Fancy Free  1947\n",
       "3679      3769                Thunderbolt and Lightfoot  1974\n",
       "3685      3775                          Make Mine Music  1946\n",
       "4978      5073  Son's Room, The (Stanza del figlio, La)  2001\n",
       "6563      6672                         War Photographer  2001\n",
       "6667      6776        Lagaan: Once Upon a Time in India  2001\n",
       "9064     26801      Dragon Inn (Sun lung moon hak chan)  1992\n",
       "18106    90531                                    Shame  2011"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.loc[movies_df['movieId'].isin(recommendation_df.head(10)['movieId'].tolist())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196cc29c",
   "metadata": {},
   "source": [
    "# Content Based"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db0bed98",
   "metadata": {},
   "source": [
    "reading the data again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "258d3b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>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",
       "</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",
       "                                        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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df = pd.read_csv('movies.csv')\n",
    "ratings_df = pd.read_csv('ratings_sample.csv')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7c8705",
   "metadata": {},
   "source": [
    "and remove the year from title and add it to seprate columns again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c96880c8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6426/298990096.py:3: 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"
     ]
    },
    {
     "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",
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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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "</div>"
      ],
      "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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()) # removing extra spaces arount it\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfab9aec",
   "metadata": {},
   "source": [
    "split the genre list by pipe line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d540cec9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "</div>"
      ],
      "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['genres'] = movies_df.genres.str.split('|')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff23617",
   "metadata": {},
   "source": [
    "and add seprate column for each genre and add corresponding value as binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "61a1372f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>year</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>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Documentary</th>\n",
       "      <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>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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>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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1995</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>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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>1995</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>...</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 × 24 columns</p>\n",
       "</div>"
      ],
      "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  IMAX  Documentary  War  Musical  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",
       "4                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesWithGenres_df = movies_df.copy()\n",
    "\n",
    "for index, row in movies_df.iterrows():\n",
    "    for genre in row['genres']:\n",
    "        moviesWithGenres_df.at[index, genre] = 1\n",
    "moviesWithGenres_df = moviesWithGenres_df.fillna(0)\n",
    "moviesWithGenres_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "19e8fcc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      169     2.5  1204927694\n",
       "1       1     2471     3.0  1204927438\n",
       "2       1    48516     5.0  1204927435\n",
       "3       2     2571     3.5  1436165433\n",
       "4       2   109487     4.0  1436165496"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7228de9",
   "metadata": {},
   "source": [
    "removing the timestamp from the df again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fba8cbdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6426/3809009477.py:1: 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      169     2.5\n",
       "1       1     2471     3.0\n",
       "2       1    48516     5.0\n",
       "3       2     2571     3.5\n",
       "4       2   109487     4.0"
      ]
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     "metadata": {},
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   "source": [
    "ratings_df = ratings_df.drop('timestamp', 1)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8795a180",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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"
      ]
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "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",
    "inputMovies = pd.DataFrame(userInput)\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05189a7",
   "metadata": {},
   "source": [
    "add movie id from movie list to userinput list and dropping genre array and year added from movies data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2772bf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]\n",
    "inputMovies = pd.merge(inputId, inputMovies)\n",
    "inputMovies = inputMovies.drop('genres', 1).drop('year', 1)\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d08e91",
   "metadata": {},
   "source": [
    "and add corresponding genre to the userinput movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "383e7d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "userMovies = moviesWithGenres_df[moviesWithGenres_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
    "userMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c9c678",
   "metadata": {},
   "source": [
    "cleaning up the data by reseting the index and removing movieId title genre array and year because divided genre columns are the only thing that will be using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ff16d8ac",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'userMovies' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_6426/638483878.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muserMovies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muserMovies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0muserGenreTable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muserMovies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'movieId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'title'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'genres'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0muserGenreTable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'userMovies' is not defined"
     ]
    }
   ],
   "source": [
    "userMovies = userMovies.reset_index(drop=True)\n",
    "userGenreTable = userMovies.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
    "userGenreTable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a64d214",
   "metadata": {},
   "source": [
    "multiplying user rating to the genre matrix to get weigted ratings or user profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d4610a91",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'userGenreTable' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_6426/2043095634.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muserProfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muserGenreTable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputMovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rating'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0muserProfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'userGenreTable' is not defined"
     ]
    }
   ],
   "source": [
    "userProfile = userGenreTable.transpose().dot(inputMovies['rating'])\n",
    "userProfile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8577c791",
   "metadata": {},
   "source": [
    "and now we have the user profile we can start matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66dc6cd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/tmp/ipykernel_6426/4266602852.py:2: 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) # removing the unnecessary information\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genreTable = moviesWithGenres_df.set_index(moviesWithGenres_df['movieId']) # set movieId as index\n",
    "genreTable = genreTable.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1) # removing the unnecessary information\n",
    "genreTable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373b056b",
   "metadata": {},
   "source": [
    "multiply the genres by the weights and then take the weighted average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "397954a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'userProfile' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_6426/1156059108.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrecommendationTable_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenreTable\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0muserProfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muserProfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mrecommendationTable_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'userProfile' is not defined"
     ]
    }
   ],
   "source": [
    "recommendationTable_df = ((genreTable*userProfile).sum(axis=1))/(userProfile.sum()) \n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3529f5c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommendationTable_df = recommendationTable_df.sort_values(ascending=False) # sorting the data \n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a9f235",
   "metadata": {},
   "source": [
    "and find the movielocation to get details based on movieId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ca642e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "movies_df.loc[movies_df['movieId'].isin(recommendationTable_df.head(20).keys())]"
   ]
  }
 ],
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