Recommending Movies using SVD .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Recommendation System for Netflix Prize Dataset using SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To load the 'combined_data_1' dataset after uploading it to Jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading dataset file\n",
    "dataset = pd.read_csv('combined_data_1.txt',header = None, names = ['Cust_Id', 'Rating'], usecols = [0,1])\n",
    "\n",
    "# Convert Ratings column to a float\n",
    "dataset['Rating'] = dataset['Rating'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cust_Id     object\n",
       "Rating     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To print the datatype of columns\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24058263, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To inspect the shape of the datset\n",
    "dataset.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1488844</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822109</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>885013</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30878</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating\n",
       "0       1:     NaN\n",
       "1  1488844     3.0\n",
       "2   822109     5.0\n",
       "3   885013     4.0\n",
       "4    30878     4.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To print the head of dataset\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1118186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2439073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>6904181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>8085741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>5506583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count\n",
       "Rating         \n",
       "1.0     1118186\n",
       "2.0     2439073\n",
       "3.0     6904181\n",
       "4.0     8085741\n",
       "5.0     5506583"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To find the distribution of different ratings in the datset\n",
    "p = dataset.groupby('Rating')['Rating'].agg(['count'])\n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4499"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get movie count by counting nan values\n",
    "\n",
    "movie_count = dataset.isnull().sum()[1]\n",
    "\n",
    "movie_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "470758"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get customer count\n",
    "cust_count = dataset['Cust_Id'].nunique()-movie_count\n",
    "\n",
    "cust_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24053764"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get rating count\n",
    "\n",
    "rating_count = dataset['Cust_Id'].count() - movie_count\n",
    "\n",
    "rating_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To plot the distribution of the ratings in as a bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = p.plot(kind = 'barh', legend = False, figsize = (15,10))\n",
    "plt.title(f'Total pool: {movie_count} Movies, {cust_count} customers, {rating_count} ratings given', fontsize=20)\n",
    "plt.axis('off')\n",
    "\n",
    "for i in range(1,6):\n",
    "    ax.text(p.iloc[i-1][0]/4, i-1, 'Rating {}: {:.0f}%'.format(i, p.iloc[i-1][0]*100 / p.sum()[0]), color = 'white', weight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To create a numpy array containing movie ids corresponding to the rows in the 'ratings' dataset"
   ]
  },
  {
   "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></th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating\n",
       "0    True\n",
       "1   False\n",
       "2   False\n",
       "3   False\n",
       "4   False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To count all the 'nan' values in the Ratings column in the 'ratings' dataset\n",
    "df_nan = pd.DataFrame(pd.isnull(dataset.Rating), )\n",
    "\n",
    "df_nan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4499, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To store the index of all the rows containing 'nan' values\n",
    "df_nan = df_nan[df_nan['Rating'] == True]\n",
    "\n",
    "\n",
    "df_nan.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>index</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>548</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>694</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2707</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2850</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Rating\n",
       "0      0    True\n",
       "1    548    True\n",
       "2    694    True\n",
       "3   2707    True\n",
       "4   2850    True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To reset the index of the dataframe\n",
    "df_nan = df_nan.reset_index()\n",
    "\n",
    "\n",
    "\n",
    "df_nan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie numpy: [1.000e+00 1.000e+00 1.000e+00 ... 4.499e+03 4.499e+03 4.499e+03]\n",
      "Length: 24053764\n"
     ]
    }
   ],
   "source": [
    "#To create a numpy array containing movie ids according the 'ratings' dataset\n",
    "\n",
    "movie_np = []\n",
    "movie_id = 1\n",
    "\n",
    "for i,j in zip(df_nan['index'][1:],df_nan['index'][:-1]):\n",
    "    # numpy approach\n",
    "    temp = np.full((1,i-j-1), movie_id)\n",
    "    movie_np = np.append(movie_np, temp)\n",
    "    movie_id += 1\n",
    "\n",
    "# Account for last record and corresponding length\n",
    "# numpy approach\n",
    "last_record = np.full((1,len(dataset) - df_nan.iloc[-1, 0] - 1),movie_id)\n",
    "movie_np = np.append(movie_np, last_record)\n",
    "\n",
    "print(f'Movie numpy: {movie_np}')\n",
    "print(f'Length: {len(movie_np)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x =zip(df_nan['index'][1:],df_nan['index'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "#temp = np.full((1,547), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tuple(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-Dataset examples-\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",
       "        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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1488844</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822109</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>885013</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30878</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>823519</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id  Rating  Movie_Id\n",
       "1  1488844     3.0         1\n",
       "2   822109     5.0         1\n",
       "3   885013     4.0         1\n",
       "4    30878     4.0         1\n",
       "5   823519     3.0         1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To append the above created array to the datset after removing the 'nan' rows\n",
    "dataset = dataset[pd.notnull(dataset['Rating'])]\n",
    "\n",
    "dataset['Movie_Id'] = movie_np.astype(int)\n",
    "dataset['Cust_Id'] =dataset['Cust_Id'].astype(int)\n",
    "print('-Dataset examples-')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24053764, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = ['count','mean']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie minimum times of review: 1799.0\n"
     ]
    }
   ],
   "source": [
    "#To create a list of all the movies rated less often(only include top 30% rated movies)\n",
    "dataset_movie_summary = dataset.groupby('Movie_Id')['Rating'].agg(f)\n",
    "\n",
    "dataset_movie_summary.index = dataset_movie_summary.index.map(int)\n",
    "\n",
    "movie_benchmark = round(dataset_movie_summary['count'].quantile(0.7),0)\n",
    "\n",
    "drop_movie_list = dataset_movie_summary[dataset_movie_summary['count'] < movie_benchmark].index\n",
    "\n",
    "print('Movie minimum times of review: {}'.format(movie_benchmark))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer minimum times of review: 52.0\n"
     ]
    }
   ],
   "source": [
    "#To create a list of all the inactive users(users who rate less often)\n",
    "dataset_cust_summary = dataset.groupby('Cust_Id')['Rating'].agg(f)\n",
    "dataset_cust_summary.index = dataset_cust_summary.index.map(int)\n",
    "cust_benchmark = round(dataset_cust_summary['count'].quantile(0.7),0)\n",
    "drop_cust_list = dataset_cust_summary[dataset_cust_summary['count'] < cust_benchmark].index\n",
    "\n",
    "print(f'Customer minimum times of review: {cust_benchmark}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Shape: (24053764, 3)\n"
     ]
    }
   ],
   "source": [
    "print(f'Original Shape: {dataset.shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Trim Shape: (17337458, 3)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset[~dataset['Movie_Id'].isin(drop_movie_list)]\n",
    "dataset = dataset[~dataset['Cust_Id'].isin(drop_cust_list)]\n",
    "print('After Trim Shape: {}'.format(dataset.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-Data Examples-\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",
       "        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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>712664</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1331154</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2632461</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>44937</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>656399</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cust_Id  Rating  Movie_Id\n",
       "696   712664     5.0         3\n",
       "697  1331154     4.0         3\n",
       "698  2632461     3.0         3\n",
       "699    44937     5.0         3\n",
       "700   656399     4.0         3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print('-Data Examples-')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create ratings matrix for 'ratings' matrix with Rows = userId, Columns = movieId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(143458, 1350)\n"
     ]
    }
   ],
   "source": [
    "df_p = pd.pivot_table(dataset,values='Rating',index='Cust_Id',columns='Movie_Id')\n",
    "\n",
    "print(df_p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Movie_Id</th>\n",
       "      <th>3</th>\n",
       "      <th>8</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>26</th>\n",
       "      <th>28</th>\n",
       "      <th>30</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>...</th>\n",
       "      <th>4472</th>\n",
       "      <th>4474</th>\n",
       "      <th>4478</th>\n",
       "      <th>4479</th>\n",
       "      <th>4485</th>\n",
       "      <th>4488</th>\n",
       "      <th>4490</th>\n",
       "      <th>4492</th>\n",
       "      <th>4493</th>\n",
       "      <th>4496</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cust_Id</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>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1350 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Movie_Id  3     8     16    17    18    26    28    30    32    33    ...  \\\n",
       "Cust_Id                                                               ...   \n",
       "6          NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...   \n",
       "7          NaN   5.0   NaN   NaN   NaN   NaN   4.0   5.0   NaN   NaN  ...   \n",
       "79         NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...   \n",
       "97         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "134        NaN   NaN   NaN   NaN   NaN   NaN   5.0   NaN   NaN   NaN  ...   \n",
       "\n",
       "Movie_Id  4472  4474  4478  4479  4485  4488  4490  4492  4493  4496  \n",
       "Cust_Id                                                               \n",
       "6          3.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "7          3.0   NaN   NaN   5.0   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "79         4.0   NaN   NaN   NaN   NaN   NaN   4.0   NaN   NaN   NaN  \n",
       "97         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "134        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 1350 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To load the movie_titles dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Year                          Name\n",
      "Movie_Id                                      \n",
      "1         2003.0               Dinosaur Planet\n",
      "2         2004.0    Isle of Man TT 2004 Review\n",
      "3         1997.0                     Character\n",
      "4         1994.0  Paula Abdul's Get Up & Dance\n",
      "5         2004.0      The Rise and Fall of ECW\n",
      "6         1997.0                          Sick\n",
      "7         1992.0                         8 Man\n",
      "8         2004.0    What the #$*! Do We Know!?\n",
      "9         1991.0      Class of Nuke 'Em High 2\n",
      "10        2001.0                       Fighter\n"
     ]
    }
   ],
   "source": [
    "df_title = pd.read_csv('movie_titles.csv', encoding = \"ISO-8859-1\", header = None, names = ['Movie_Id', 'Year', 'Name'])\n",
    "\n",
    "df_title.set_index('Movie_Id', inplace = True)\n",
    "\n",
    "print (df_title.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To install the scikit-surprise library for implementing SVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the following command in the Anaconda Prompt to install surprise package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conda install -c conda-forge scikit-surprise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import math\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from surprise import Reader, Dataset, SVD\n",
    "from surprise.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm SVD on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    1.0037  0.9920  0.9975  0.9977  0.0048  \n",
      "MAE (testset)     0.8083  0.7966  0.8039  0.8029  0.0048  \n",
      "Fit time          3.79    3.64    3.71    3.72    0.06    \n",
      "Test time         0.17    0.25    0.17    0.20    0.04    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([1.0036749 , 0.99200842, 0.99751972]),\n",
       " 'test_mae': array([0.80832566, 0.79661763, 0.80385189]),\n",
       " 'fit_time': (3.7909035682678223, 3.641662836074829, 3.714344024658203),\n",
       " 'test_time': (0.1735367774963379, 0.24933338165283203, 0.17453360557556152)}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Reader library\n",
    "reader = Reader()\n",
    "\n",
    "# get just top 100K rows for faster run time\n",
    "data = Dataset.load_from_df(dataset[['Cust_Id', 'Movie_Id', 'Rating']][:100000], reader)\n",
    "\n",
    "# Use the SVD algorithm.\n",
    "svd = SVD()\n",
    "\n",
    "# Compute the RMSE of the SVD algorithm\n",
    "cross_validate(svd, data, measures=['RMSE', 'MAE'], cv=3, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Cust_Id</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Movie_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>712664</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1331154</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2632461</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>44937</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>656399</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cust_Id  Rating  Movie_Id\n",
       "696   712664     5.0         3\n",
       "697  1331154     4.0         3\n",
       "698  2632461     3.0         3\n",
       "699    44937     5.0         3\n",
       "700   656399     4.0         3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To find all the movies rated as 5 stars by user with userId = 712664"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie_Id\n",
       "3                              Character\n",
       "79                           The Killing\n",
       "175                       Reservoir Dogs\n",
       "199                      The Deer Hunter\n",
       "241                   North by Northwest\n",
       "256    Ghost Dog: The Way of the Samurai\n",
       "348        The Last Temptation of Christ\n",
       "357                House of Sand and Fog\n",
       "416                             Elephant\n",
       "442                  Mississippi Burning\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_712664 = dataset[(dataset['Cust_Id'] == 712664) & (dataset['Rating'] == 5)]\n",
    "dataset_712664 = dataset_712664.set_index('Movie_Id')\n",
    "dataset_712664 = dataset_712664.join(df_title)['Name']\n",
    "dataset_712664.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train an SVD to predict ratings for user with userId = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Year                                            Name  Estimate_Score\n",
      "4352  2002.0                  Curb Your Enthusiasm: Season 3        5.000000\n",
      "2018  2004.0                                Samurai Champloo        5.000000\n",
      "721   2003.0                              The Wire: Season 1        5.000000\n",
      "241   1995.0  Neon Genesis Evangelion: The End of Evangelion        5.000000\n",
      "871   1954.0                                   Seven Samurai        5.000000\n",
      "174   1992.0                                  Reservoir Dogs        5.000000\n",
      "2101  1994.0                          The Simpsons: Season 6        4.994354\n",
      "4114  1999.0                         The Simpsons: Bart Wars        4.972430\n",
      "2429  1979.0                      Alien: Collector's Edition        4.970744\n",
      "404   1987.0                                 Wings of Desire        4.952919\n"
     ]
    }
   ],
   "source": [
    "# Create a shallow copy for the movies dataset\n",
    "user_712664 = df_title.copy()\n",
    "\n",
    "user_712664 = user_712664.reset_index()\n",
    "\n",
    "#To remove all the movies rated less often \n",
    "user_712664 = user_712664[~user_712664['Movie_Id'].isin(drop_movie_list)]\n",
    "\n",
    "\n",
    "# getting full dataset\n",
    "data = Dataset.load_from_df(dataset[['Cust_Id', 'Movie_Id', 'Rating']], reader)\n",
    "\n",
    "#create a training set for svd\n",
    "trainset = data.build_full_trainset()\n",
    "svd.fit(trainset)\n",
    "\n",
    "#Predict the ratings for user_712664\n",
    "user_712664['Estimate_Score'] = user_712664['Movie_Id'].apply(lambda x: svd.predict(712664, x).est)\n",
    "\n",
    "#Drop extra columns from the user_712664 data frame\n",
    "user_712664 = user_712664.drop('Movie_Id', axis = 1)\n",
    "\n",
    "# Sort predicted ratings for user_712664 in descending order\n",
    "user_712664 = user_712664.sort_values('Estimate_Score', ascending=False)\n",
    "\n",
    "#Print top 10 recommendations\n",
    "print(user_712664.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}