item_based_collaborative.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fabde546",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ast import literal_eval\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8376002",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>862</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8844</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15602</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31357</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11862</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  movieId                        title  \\\n",
       "0     862                    Toy Story   \n",
       "1    8844                      Jumanji   \n",
       "2   15602             Grumpier Old Men   \n",
       "3   31357            Waiting to Exhale   \n",
       "4   11862  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  \n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...  \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...  \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(\"./dataset/movies_metadata.csv\")\n",
    "movies = movies[[\"id\", \"title\", \"genres\"]]\n",
    "movies = movies.rename(columns={\"id\": \"movieId\"})\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29117265",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>862</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8844</td>\n",
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       "      <th>2</th>\n",
       "      <td>15602</td>\n",
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       "      <td>[Romance, Comedy]</td>\n",
       "    </tr>\n",
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       "      <td>31357</td>\n",
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      ],
      "text/plain": [
       "  movieId                        title                        genres\n",
       "0     862                    Toy Story   [Animation, Comedy, Family]\n",
       "1    8844                      Jumanji  [Adventure, Fantasy, Family]\n",
       "2   15602             Grumpier Old Men             [Romance, Comedy]\n",
       "3   31357            Waiting to Exhale      [Comedy, Drama, Romance]\n",
       "4   11862  Father of the Bride Part II                      [Comedy]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[\"genres\"] = (\n",
    "    movies[\"genres\"]\n",
    "    .fillna(\"[]\")\n",
    "    .apply(literal_eval)\n",
    "    .apply(lambda x: [i[\"name\"] for i in x] if isinstance(x, list) else [])\n",
    ")\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c17b723",
   "metadata": {},
   "outputs": [
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       "      <th>userId</th>\n",
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       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(\"./dataset/ratings_small.csv\")\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89fa851a",
   "metadata": {},
   "outputs": [
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       "<p>5 rows × 671 columns</p>\n",
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      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  662  663  664  \\\n",
       "movieId                                                    ...                  \n",
       "1        NaN  NaN  NaN  NaN  NaN  NaN  3.0  NaN  4.0  NaN  ...  NaN  4.0  3.5   \n",
       "2        NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  5.0  NaN  NaN   \n",
       "3        NaN  NaN  NaN  NaN  4.0  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "4        NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "5        NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "\n",
       "userId   665  666  667  668  669  670  671  \n",
       "movieId                                     \n",
       "1        NaN  NaN  NaN  NaN  NaN  4.0  5.0  \n",
       "2        3.0  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3        3.0  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4        NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "5        3.0  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 671 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_pivot = ratings.pivot(index=\"movieId\", columns=\"userId\", values=\"rating\")\n",
    "ratings_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2ff5534",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>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>...</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>5</th>\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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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 × 671 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  662  663  664  \\\n",
       "movieId                                                    ...                  \n",
       "1        0.0  0.0  0.0  0.0  0.0  0.0  3.0  0.0  4.0  0.0  ...  0.0  4.0  3.5   \n",
       "2        0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  5.0  0.0  0.0   \n",
       "3        0.0  0.0  0.0  0.0  4.0  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  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  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   665  666  667  668  669  670  671  \n",
       "movieId                                     \n",
       "1        0.0  0.0  0.0  0.0  0.0  4.0  5.0  \n",
       "2        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3        3.0  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",
       "5        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 671 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_pivot.fillna(0, inplace=True)\n",
    "ratings_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f2c1068",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_user_voted = ratings.groupby(\"movieId\")[\"rating\"].agg(\"count\")\n",
    "no_movies_voted = ratings.groupby(\"userId\")[\"rating\"].agg(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72482b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 1, figsize=(16, 4))\n",
    "# ratings['rating'].plot(kind='hist')\n",
    "plt.scatter(no_user_voted.index, no_user_voted, color=\"mediumseagreen\")\n",
    "plt.axhline(y=10, color=\"r\")\n",
    "plt.xlabel(\"MovieId\")\n",
    "plt.ylabel(\"No. of users voted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d803be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ratings = ratings_pivot.loc[no_user_voted[no_user_voted > 0].index, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34d768fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 1, figsize=(16, 4))\n",
    "plt.scatter(no_movies_voted.index, no_movies_voted, color=\"mediumseagreen\")\n",
    "plt.axhline(y=50, color=\"r\")\n",
    "plt.xlabel(\"UserId\")\n",
    "plt.ylabel(\"No. of votes by user\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e336eadd",
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>662</th>\n",
       "      <th>663</th>\n",
       "      <th>664</th>\n",
       "      <th>665</th>\n",
       "      <th>666</th>\n",
       "      <th>667</th>\n",
       "      <th>668</th>\n",
       "      <th>669</th>\n",
       "      <th>670</th>\n",
       "      <th>671</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</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>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>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>...</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>5</th>\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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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 × 671 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  662  663  664  \\\n",
       "movieId                                                    ...                  \n",
       "1        0.0  0.0  0.0  0.0  0.0  0.0  3.0  0.0  4.0  0.0  ...  0.0  4.0  3.5   \n",
       "2        0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  5.0  0.0  0.0   \n",
       "3        0.0  0.0  0.0  0.0  4.0  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  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  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   665  666  667  668  669  670  671  \n",
       "movieId                                     \n",
       "1        0.0  0.0  0.0  0.0  0.0  4.0  5.0  \n",
       "2        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3        3.0  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",
       "5        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 671 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final_ratings = final_ratings.loc[:, no_movies_voted[no_movies_voted > 50].index]\n",
    "final_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c479be75",
   "metadata": {},
   "outputs": [],
   "source": [
    "csr_data = csr_matrix(final_ratings.values)\n",
    "final_ratings.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa752d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine', n_jobs=-1, n_neighbors=20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = NearestNeighbors(metric=\"cosine\", algorithm=\"brute\", n_neighbors=20, n_jobs=-1)\n",
    "knn.fit(csr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cb12b10",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def get_movie_recommendation(movie_name):\n",
    "    n_movies_to_reccomend = 10\n",
    "    movie_list = movies[movies[\"title\"].str.contains(movie_name, na=False)]\n",
    "    if len(movie_list):\n",
    "        movie_idx = movie_list.iloc[0][\"movieId\"]\n",
    "        movie_idx = final_ratings[final_ratings[\"movieId\"] == int(movie_idx)].index[0]\n",
    "        distances, indices = knn.kneighbors(\n",
    "            csr_data[movie_idx], n_neighbors=n_movies_to_reccomend + 1\n",
    "        )\n",
    "        rec_movie_indices = sorted(\n",
    "            list(zip(indices.squeeze().tolist(), distances.squeeze().tolist())),\n",
    "            key=lambda x: x[1],\n",
    "        )[:0:-1]\n",
    "        recommend_frame = []\n",
    "        for val in rec_movie_indices:\n",
    "            movie_idx = final_ratings.iloc[val[0]][\"movieId\"]\n",
    "            idx = movies[movies[\"movieId\"] == str(int(movie_idx))].index\n",
    "            if movies.iloc[idx][\"title\"].values:\n",
    "                recommend_frame.append(\n",
    "                    {\"Title\": movies.iloc[idx][\"title\"], \"Distance\": val[1]}\n",
    "                )\n",
    "        df = pd.DataFrame(recommend_frame, index=range(1, min(len(recommend_frame), n_movies_to_reccomend) + 1))\n",
    "        return df\n",
    "    else:\n",
    "        return \"No movies found. Please check your input\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "771c8a80",
   "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>Title</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12953    In Bed\n",
       "Name: title, dtype: object</td>\n",
       "      <td>0.292893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3859    Diamonds Are Forever\n",
       "Name: title, dtyp...</td>\n",
       "      <td>0.292893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3900    Miss Congeniality\n",
       "Name: title, dtype: ...</td>\n",
       "      <td>0.292893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2253    King Kong\n",
       "Name: title, dtype: object</td>\n",
       "      <td>0.292893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2289    Rambo: First Blood Part II\n",
       "Name: title...</td>\n",
       "      <td>0.019419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6556    Party Monster\n",
       "Name: title, dtype: object</td>\n",
       "      <td>0.010051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title  Distance\n",
       "1         12953    In Bed\n",
       "Name: title, dtype: object  0.292893\n",
       "2  3859    Diamonds Are Forever\n",
       "Name: title, dtyp...  0.292893\n",
       "3  3900    Miss Congeniality\n",
       "Name: title, dtype: ...  0.292893\n",
       "4       2253    King Kong\n",
       "Name: title, dtype: object  0.292893\n",
       "5  2289    Rambo: First Blood Part II\n",
       "Name: title...  0.019419\n",
       "6   6556    Party Monster\n",
       "Name: title, dtype: object  0.010051"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_movie_recommendation(\"Memento\")"
   ]
  }
 ],
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