Movie Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data\n",
    "movies = pd.read_csv('movies.csv')\n",
    "ratings = pd.read_csv('ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9742, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Movies Dataset\n",
    "print(movies.shape)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100836, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\n",
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       "<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",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ratings Dataset\n",
    "print(ratings.shape)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unqiue movies 9724\n",
      "Number of unique users 610\n",
      "Sparsity :  98.30003169443864\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f54881b4c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis - In Statistics, EDA is an approach to analyzing datasets to summarize their main characteristics\n",
    "\n",
    "# Number of movies and users\n",
    "n_movies = ratings['movieId'].nunique()\n",
    "n_users = ratings['userId'].nunique()\n",
    "print('Number of unqiue movies', n_movies)\n",
    "print('Number of unique users', n_users)\n",
    "\n",
    "# Sparsity of Data - Sparsity (%) = (No. of missing values / (Total values )) * 100\n",
    "available_ratings = ratings['rating'].count()\n",
    "total_ratings = n_movies * n_users\n",
    "missing_ratings = total_ratings - available_ratings\n",
    "sparsity = (missing_ratings / total_ratings) * 100\n",
    "print('Sparsity : ', sparsity)\n",
    "\n",
    "# Ratings Distribution\n",
    "ratings['rating'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things easier to understand and work with, make a new dataframe where each column would represent each unique userId and each row represents each unique movieId."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  NaN  NaN  NaN  4.0  NaN  4.5  NaN  NaN  NaN  ...  4.0  NaN  4.0   \n",
       "2        NaN  NaN  NaN  NaN  NaN  4.0  NaN  4.0  NaN  NaN  ...  NaN  4.0  NaN   \n",
       "3        4.0  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
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       "5        NaN  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
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       "\n",
       "[5 rows x 610 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset = ratings.pivot(index='movieId',columns='userId',values='rating')\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  0.0  4.0  0.0  4.5  0.0  0.0  0.0  ...  4.0  0.0  4.0   \n",
       "2        0.0  0.0  0.0  0.0  0.0  4.0  0.0  4.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "3        4.0  0.0  0.0  0.0  0.0  5.0  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  3.0  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  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
       "1        3.0  4.0  2.5  4.0  2.5  3.0  5.0  \n",
       "2        5.0  3.5  0.0  0.0  2.0  0.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  2.0  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 610 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute NaN with 0\n",
    "final_dataset.fillna(0,inplace=True)\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Noise from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a real world, ratings are very sparse and data points are mostly collected from very popular movies and highly engaged users. So we will reduce the noise by adding some filters and qualify the movies for the final dataset.\n",
    "\n",
    "- To qualify a movie, minimum 10 users should have voted a movie.\n",
    "- To qualify a user, minimum 50 movies should have voted by the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_user_voted = ratings.groupby('movieId')['rating'].agg('count')\n",
    "no_movies_voted = ratings.groupby('userId')['rating'].agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s visualize the number of users who voted with our threshold of 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_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,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = final_dataset.loc[no_user_voted[no_user_voted > 10].index,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s visualize the number of votes by each user with our threshold of 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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,
   "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>4</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>...</th>\n",
       "      <th>600</th>\n",
       "      <th>601</th>\n",
       "      <th>602</th>\n",
       "      <th>603</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>610</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",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</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>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</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>2.5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</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>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174055</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>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176371</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>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177765</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179819</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>...</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",
       "      <td>187593</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>...</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>2121 rows × 378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    4    6    7    10   11   15   16   17   18   ...  600  601  602  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  4.5  0.0  0.0  2.5  0.0  4.5  3.5  ...  2.5  4.0  0.0   \n",
       "2        0.0  0.0  4.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0  ...  4.0  0.0  4.0   \n",
       "3        4.0  0.0  5.0  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  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  2.5  0.0  0.0   \n",
       "6        4.0  0.0  4.0  0.0  0.0  5.0  0.0  0.0  0.0  4.0  ...  0.0  0.0  3.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.5  0.0   \n",
       "179819   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",
       "187593   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   603  604  605  606  607  608  610  \n",
       "movieId                                     \n",
       "1        4.0  3.0  4.0  2.5  4.0  2.5  5.0  \n",
       "2        0.0  5.0  3.5  0.0  0.0  2.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  0.0  2.0  0.0  \n",
       "5        0.0  3.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6        4.0  3.0  0.0  0.0  0.0  0.0  5.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2121 rows x 378 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset=final_dataset.loc[:,no_movies_voted[no_movies_voted > 50].index]\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CSR - Compressed Sparse Row (Yale Format)\n",
    "csr_data = csr_matrix(final_dataset.values)\n",
    "final_dataset.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movie recommendation system model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
       "                 metric_params=None, n_jobs=-1, n_neighbors=20, p=2,\n",
       "                 radius=1.0)"
      ]
     },
     "execution_count": 15,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### Recommendation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_movie_recommendation(movie_name):\n",
    "    n_movies_to_reccomend = 10\n",
    "    movie_list = movies[movies['title'].str.contains(movie_name)]  \n",
    "    if len(movie_list):        \n",
    "        movie_idx= movie_list.iloc[0]['movieId']\n",
    "        movie_idx = final_dataset[final_dataset['movieId'] == movie_idx].index[0]\n",
    "        distances , indices = knn.kneighbors(csr_data[movie_idx],n_neighbors=n_movies_to_reccomend+1)    \n",
    "        rec_movie_indices = sorted(list(zip(indices.squeeze().tolist(),distances.squeeze().tolist())),key=lambda x: x[1])[:0:-1]\n",
    "        recommend_frame = []\n",
    "        for val in rec_movie_indices:\n",
    "            movie_idx = final_dataset.iloc[val[0]]['movieId']\n",
    "            idx = movies[movies['movieId'] == movie_idx].index\n",
    "            recommend_frame.append({'Title':movies.iloc[idx]['title'].values[0],'Distance':val[1]})\n",
    "        df = pd.DataFrame(recommend_frame,index=range(1,n_movies_to_reccomend+1))\n",
    "        return df\n",
    "    else:\n",
    "        return \"No movies found. Please check your input\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "      <td>1</td>\n",
       "      <td>Up (2009)</td>\n",
       "      <td>0.368857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Guardians of the Galaxy (2014)</td>\n",
       "      <td>0.368758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Watchmen (2009)</td>\n",
       "      <td>0.368558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Star Trek (2009)</td>\n",
       "      <td>0.366029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Batman Begins (2005)</td>\n",
       "      <td>0.362759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Avatar (2009)</td>\n",
       "      <td>0.310893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Iron Man 2 (2010)</td>\n",
       "      <td>0.307492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>WALL·E (2008)</td>\n",
       "      <td>0.298138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Dark Knight, The (2008)</td>\n",
       "      <td>0.285835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Avengers, The (2012)</td>\n",
       "      <td>0.285319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  Distance\n",
       "1                        Up (2009)  0.368857\n",
       "2   Guardians of the Galaxy (2014)  0.368758\n",
       "3                  Watchmen (2009)  0.368558\n",
       "4                 Star Trek (2009)  0.366029\n",
       "5             Batman Begins (2005)  0.362759\n",
       "6                    Avatar (2009)  0.310893\n",
       "7                Iron Man 2 (2010)  0.307492\n",
       "8                    WALL·E (2008)  0.298138\n",
       "9          Dark Knight, The (2008)  0.285835\n",
       "10            Avengers, The (2012)  0.285319"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_movie_recommendation('Iron Man')"
   ]
  }
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