Movie Recomendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "scenic-software",
   "metadata": {},
   "source": [
    "## KNN Item-Based Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "extraordinary-brief",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-translation",
   "metadata": {},
   "source": [
    "To build a movie recommender, I choose MovieLens Datasets.\n",
    "URL - https://grouplens.org/datasets/movielens/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "armed-uncle",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_data = pd.read_csv('./Datasets/ml-25m/movies.csv', usecols=['movieId', 'title'])\n",
    "rating_data = pd.read_csv('./Datasets/ml-25m/ratings.csv', usecols=['userId', 'movieId', 'rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nonprofit-myrtle",
   "metadata": {},
   "source": [
    "First, we need to transform the dataframe of ratings into a proper format that can be consumed by a KNN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "multiple-cartoon",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(movies_data, rating_data)\n",
    "user_movie_table = data.iloc[:10000000,:].pivot(\n",
    "    index = 'title',\n",
    "    columns = 'userId',\n",
    "    values = 'rating'\n",
    ").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "rubber-chosen",
   "metadata": {},
   "outputs": [
    {
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       "<p>1993 rows × 161577 columns</p>\n",
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      ],
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       "userId                                      1       2       3       4       \\\n",
       "title                                                                        \n",
       "'Til There Was You (1997)                      0.0     0.0     0.0     0.0   \n",
       "'burbs, The (1989)                             0.0     0.0     0.0     0.0   \n",
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       "12 Angry Men (1957)                            0.0     0.0     0.0     0.0   \n",
       "...                                            ...     ...     ...     ...   \n",
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       "Zeus and Roxanne (1997)                        0.0     0.0     0.0     0.0   \n",
       "\n",
       "userId                                      5       6       7       8       \\\n",
       "title                                                                        \n",
       "'Til There Was You (1997)                      0.0     0.0     0.0     0.0   \n",
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       "...                                            ...     ...     ...     ...   \n",
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       "userId                                      9       10      ...  162532  \\\n",
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       "userId                                      162533  162534  162535  162536  \\\n",
       "title                                                                        \n",
       "'Til There Was You (1997)                      0.0     0.0     0.0     0.0   \n",
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       "12 Angry Men (1957)                            0.0     4.5     0.0     0.0   \n",
       "...                                            ...     ...     ...     ...   \n",
       "Young Guns II (1990)                           0.0     0.0     0.0     0.0   \n",
       "Young Poisoner's Handbook, The (1995)          0.0     0.0     0.0     0.0   \n",
       "Zero Effect (1998)                             0.0     0.0     0.0     0.0   \n",
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       "userId                                      162537  162538  162539  162540  \\\n",
       "title                                                                        \n",
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       "...                                            ...     ...     ...     ...   \n",
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       "\n",
       "userId                                      162541  \n",
       "title                                               \n",
       "'Til There Was You (1997)                      0.0  \n",
       "'burbs, The (1989)                             1.5  \n",
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       "Zeus and Roxanne (1997)                        0.0  \n",
       "\n",
       "[1993 rows x 161577 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charitable-external",
   "metadata": {},
   "source": [
    "## Let’s Make Some Movie Recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "temporal-jesus",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "thick-monitor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='brute', leaf_size=30, metric='cosine',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=11, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommender = KNeighborsClassifier(n_neighbors = 11, algorithm = 'brute', metric = 'cosine')\n",
    "recommender.fit(user_movie_table.to_numpy(), user_movie_table.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "engaged-minnesota",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'Jumanji (1995)'\n",
    "distances, indices = recommender.kneighbors(user_movie_table.loc[query].values.reshape(1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mathematical-application",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recommendations for Jumanji (1995) are-\n",
      "\n",
      "Mask, The (1994)\n",
      "Lion King, The (1994)\n",
      "Mrs. Doubtfire (1993)\n",
      "Home Alone (1990)\n",
      "Jurassic Park (1993)\n",
      "Aladdin (1992)\n",
      "Speed (1994)\n",
      "Beauty and the Beast (1991)\n",
      "Santa Clause, The (1994)\n",
      "Waterworld (1995)\n"
     ]
    }
   ],
   "source": [
    "print('recommendations for', query, 'are-', end = '\\n\\n')\n",
    "\n",
    "recommendions = []\n",
    "for i in indices[0]:\n",
    "    movie = user_movie_table.index[i]\n",
    "    if movie != query:\n",
    "        recommendions.append(movie)\n",
    "\n",
    "for i in recommendions:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "united-software",
   "metadata": {},
   "source": [
    "### Our recommender system actually works!!\n",
    "## Now, we have our own movie recommender."
   ]
  }
 ],
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