RecommendationSystem_Aadish.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                            RecommendationSystems Assignment - Aadish Agrawal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                            Importing Libraries and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aadishagrawal/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "#Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.externals import joblib\n",
    "import scipy.sparse\n",
    "from scipy.sparse import csr_matrix\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1334707200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1QGNMC6O1VW39</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1397433600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   productId  Rating   timestamp\n",
       "0  A2CX7LUOHB2NDG  0321732944     5.0  1341100800\n",
       "1  A2NWSAGRHCP8N5  0439886341     1.0  1367193600\n",
       "2  A2WNBOD3WNDNKT  0439886341     3.0  1374451200\n",
       "3  A1GI0U4ZRJA8WN  0439886341     1.0  1334707200\n",
       "4  A1QGNMC6O1VW39  0511189877     5.0  1397433600"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Import\n",
    "#Read and explore the dataset. ( Rename column, plot histograms, find data characteristics)\n",
    "#Import the data set\n",
    "df = pd.read_csv('ratings_Electronics.csv')\n",
    "df.columns = ['userId', 'productId', 'Rating','timestamp']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7824481 entries, 0 to 7824480\n",
      "Data columns (total 4 columns):\n",
      "userId       object\n",
      "productId    object\n",
      "Rating       float64\n",
      "timestamp    int64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 238.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking dataset datatypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " User ID-  User A5JLAU2ARJ0BO has rate most frequently with frequency as 520 | 4201696 unique user present in    data set\n",
    " \n",
    " productId - Product B0074BW614 has been rate most frequently with frequency 18244 | 476001 product present in data set\n",
    " \n",
    " In Rating distribution users has rated product from 1 to 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7824481</td>\n",
       "      <td>7824481</td>\n",
       "      <td>7.824481e+06</td>\n",
       "      <td>7.824481e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4201696</td>\n",
       "      <td>476001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>A5JLAU2ARJ0BO</td>\n",
       "      <td>B0074BW614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>520</td>\n",
       "      <td>18244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.012337e+00</td>\n",
       "      <td>1.338178e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.380910e+00</td>\n",
       "      <td>6.900426e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.127296e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.315354e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.361059e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.386115e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.406074e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               userId   productId        Rating     timestamp\n",
       "count         7824481     7824481  7.824481e+06  7.824481e+06\n",
       "unique        4201696      476001           NaN           NaN\n",
       "top     A5JLAU2ARJ0BO  B0074BW614           NaN           NaN\n",
       "freq              520       18244           NaN           NaN\n",
       "mean              NaN         NaN  4.012337e+00  1.338178e+09\n",
       "std               NaN         NaN  1.380910e+00  6.900426e+07\n",
       "min               NaN         NaN  1.000000e+00  9.127296e+08\n",
       "25%               NaN         NaN  3.000000e+00  1.315354e+09\n",
       "50%               NaN         NaN  5.000000e+00  1.361059e+09\n",
       "75%               NaN         NaN  5.000000e+00  1.386115e+09\n",
       "max               NaN         NaN  5.000000e+00  1.406074e+09"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows:  7824481\n",
      "No of columns:  4\n"
     ]
    }
   ],
   "source": [
    "# Check the number of rows and columns\n",
    "rows, columns = df.shape\n",
    "print(\"No of rows: \", rows) \n",
    "print(\"No of columns: \", columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values across columns-\n",
      " userId       0\n",
      "productId    0\n",
      "Rating       0\n",
      "timestamp    0\n",
      "dtype: int64\n",
      "Number of missing values across columns-\n",
      " userId       0\n",
      "productId    0\n",
      "Rating       0\n",
      "timestamp    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values present\n",
    "#There are no missing values with total records 7824481\n",
    "print('Number of missing values across columns-\\n', df.isnull().sum())\n",
    "print('Number of missing values across columns-\\n', df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a1dc7c4e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of ratings\n",
    "#Users has mostly rated 5 rating compare to other rating\n",
    "with sns.axes_style('white'):\n",
    "    g = sns.factorplot(\"Rating\", data=df, aspect=2.0,kind='count')\n",
    "    g.set_ylabels(\"Total number of ratings\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique USERS in Raw data =  4201696\n",
      "Number of unique ITEMS in Raw data =  476001\n"
     ]
    }
   ],
   "source": [
    "# Number of unique user id and product id in the data\n",
    "print('Number of unique USERS in Raw data = ', df['userId'].nunique())\n",
    "print('Number of unique ITEMS in Raw data = ', df['productId'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1QGNMC6O1VW39</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   productId  Rating\n",
       "0  A2CX7LUOHB2NDG  0321732944     5.0\n",
       "1  A2NWSAGRHCP8N5  0439886341     1.0\n",
       "2  A2WNBOD3WNDNKT  0439886341     3.0\n",
       "3  A1GI0U4ZRJA8WN  0439886341     1.0\n",
       "4  A1QGNMC6O1VW39  0511189877     5.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping timestamp column as it seems to be irrelevant\n",
    "df_new = df.drop(columns=['timestamp'])\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take subset of dataset to make it less sparse/more dense. (For example, keep the users only who has given 15 or more number of ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId\n",
       "A5JLAU2ARJ0BO     520\n",
       "ADLVFFE4VBT8      501\n",
       "A3OXHLG6DIBRW8    498\n",
       "A6FIAB28IS79      431\n",
       "A680RUE1FDO8B     406\n",
       "A1ODOGXEYECQQ8    380\n",
       "A36K2N527TXXJN    314\n",
       "A2AY4YUOX2N1BQ    311\n",
       "AWPODHOB4GFWL     308\n",
       "A25C2M3QF9G7OQ    296\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 users based on rating\n",
    "most_rated = df_new.groupby('userId').size().sort_values(ascending=False)[:10]\n",
    "most_rated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data model preparation as per requirement on number of minimum ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = df_new['userId'].value_counts()\n",
    "df_final = df_new[df_new['userId'].isin(counts[counts >= 50].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users who have rated 50 or more items = 125871\n",
      "Number of unique USERS in final data =  1540\n",
      "Number of unique ITEMS in final data =  48190\n"
     ]
    }
   ],
   "source": [
    "print('Number of users who have rated 50 or more items =', len(df_final))\n",
    "print('Number of unique USERS in final data = ', df_final['userId'].nunique())\n",
    "print('Number of unique ITEMS in final data = ', df_final['productId'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                Calculate the density of the rating matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of final_ratings_matrix:  (1540, 48190)\n",
      "given_num_of_ratings =  125871\n",
      "possible_num_of_ratings =  74212600\n",
      "density: 0.17%\n"
     ]
    }
   ],
   "source": [
    "final_ratings_matrix = df_final.pivot(index = 'userId', columns ='productId', values = 'Rating').fillna(0)\n",
    "print('Shape of final_ratings_matrix: ', final_ratings_matrix.shape)\n",
    "\n",
    "given_num_of_ratings = np.count_nonzero(final_ratings_matrix)\n",
    "print('given_num_of_ratings = ', given_num_of_ratings)\n",
    "possible_num_of_ratings = final_ratings_matrix.shape[0] * final_ratings_matrix.shape[1]\n",
    "print('possible_num_of_ratings = ', possible_num_of_ratings)\n",
    "density = (given_num_of_ratings/possible_num_of_ratings)\n",
    "density *= 100\n",
    "print ('density: {:4.2f}%'.format(density))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</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>A100UD67AHFODS</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>A100WO06OQR8BQ</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>A105S56ODHGJEK</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>A105TOJ6LTVMBG</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>A10AFVU66A79Y1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId       0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       ...  B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  \\\n",
       "userId          ...                                                   \n",
       "A100UD67AHFODS  ...         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ  ...         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK  ...         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG  ...         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LBZ1Z7K  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LKG1MC8  \n",
       "userId                      \n",
       "A100UD67AHFODS         0.0  \n",
       "A100WO06OQR8BQ         0.0  \n",
       "A105S56ODHGJEK         0.0  \n",
       "A105TOJ6LTVMBG         0.0  \n",
       "A10AFVU66A79Y1         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>A100UD67AHFODS</th>\n",
       "      <th>A100WO06OQR8BQ</th>\n",
       "      <th>A105S56ODHGJEK</th>\n",
       "      <th>A105TOJ6LTVMBG</th>\n",
       "      <th>A10AFVU66A79Y1</th>\n",
       "      <th>A10H24TDLK2VDP</th>\n",
       "      <th>A10NMELR4KX0J6</th>\n",
       "      <th>A10O7THJ2O20AG</th>\n",
       "      <th>A10PEXB6XAQ5XF</th>\n",
       "      <th>A10X9ME6R66JDX</th>\n",
       "      <th>...</th>\n",
       "      <th>AYOTEJ617O60K</th>\n",
       "      <th>AYP0YPLSP9ISM</th>\n",
       "      <th>AZ515FFZ7I2P7</th>\n",
       "      <th>AZ8XSDMIX04VJ</th>\n",
       "      <th>AZAC8O310IK4E</th>\n",
       "      <th>AZBXKUH4AIW3X</th>\n",
       "      <th>AZCE11PSTCH1L</th>\n",
       "      <th>AZMY6E8B52L2T</th>\n",
       "      <th>AZNUHQSHZHSUE</th>\n",
       "      <th>AZOK5STV85FBJ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>productId</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>0594451647</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>0594481813</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>0970407998</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>0972683275</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>1400501466</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1540 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId      A100UD67AHFODS  A100WO06OQR8BQ  A105S56ODHGJEK  A105TOJ6LTVMBG  \\\n",
       "productId                                                                    \n",
       "0594451647             0.0             0.0             0.0             0.0   \n",
       "0594481813             0.0             0.0             0.0             0.0   \n",
       "0970407998             0.0             0.0             0.0             0.0   \n",
       "0972683275             0.0             0.0             0.0             0.0   \n",
       "1400501466             0.0             0.0             0.0             0.0   \n",
       "\n",
       "userId      A10AFVU66A79Y1  A10H24TDLK2VDP  A10NMELR4KX0J6  A10O7THJ2O20AG  \\\n",
       "productId                                                                    \n",
       "0594451647             0.0             0.0             0.0             0.0   \n",
       "0594481813             0.0             0.0             0.0             0.0   \n",
       "0970407998             0.0             0.0             0.0             0.0   \n",
       "0972683275             0.0             0.0             0.0             0.0   \n",
       "1400501466             0.0             0.0             0.0             0.0   \n",
       "\n",
       "userId      A10PEXB6XAQ5XF  A10X9ME6R66JDX  ...  AYOTEJ617O60K  AYP0YPLSP9ISM  \\\n",
       "productId                                   ...                                 \n",
       "0594451647             0.0             0.0  ...            0.0            0.0   \n",
       "0594481813             0.0             0.0  ...            0.0            0.0   \n",
       "0970407998             0.0             0.0  ...            0.0            0.0   \n",
       "0972683275             0.0             0.0  ...            0.0            0.0   \n",
       "1400501466             0.0             0.0  ...            0.0            0.0   \n",
       "\n",
       "userId      AZ515FFZ7I2P7  AZ8XSDMIX04VJ  AZAC8O310IK4E  AZBXKUH4AIW3X  \\\n",
       "productId                                                                \n",
       "0594451647            0.0            0.0            0.0            0.0   \n",
       "0594481813            0.0            0.0            0.0            0.0   \n",
       "0970407998            0.0            0.0            0.0            0.0   \n",
       "0972683275            0.0            0.0            0.0            0.0   \n",
       "1400501466            0.0            0.0            0.0            0.0   \n",
       "\n",
       "userId      AZCE11PSTCH1L  AZMY6E8B52L2T  AZNUHQSHZHSUE  AZOK5STV85FBJ  \n",
       "productId                                                               \n",
       "0594451647            0.0            0.0            0.0            0.0  \n",
       "0594481813            0.0            0.0            0.0            0.0  \n",
       "0970407998            0.0            0.0            0.0            0.0  \n",
       "0972683275            0.0            0.0            0.0            0.0  \n",
       "1400501466            0.0            0.0            0.0            0.0  \n",
       "\n",
       "[5 rows x 1540 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matrix with one row per 'Product' and one column per 'user' for Item-based CF\n",
    "final_ratings_matrix_T = final_ratings_matrix.transpose()\n",
    "final_ratings_matrix_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6595852</th>\n",
       "      <td>A2BYV7S1QP2YIG</td>\n",
       "      <td>B009EAHVTA</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4738240</th>\n",
       "      <td>AB094YABX21WQ</td>\n",
       "      <td>B0056XCEAA</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175595</th>\n",
       "      <td>A3D0UM4ZD2CMAW</td>\n",
       "      <td>B004I763AW</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3753015</th>\n",
       "      <td>AATWFX0ZZSE6C</td>\n",
       "      <td>B0040NPHMO</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734766</th>\n",
       "      <td>A1NNMOD9H36Q8E</td>\n",
       "      <td>B0015VW3BM</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 userId   productId  Rating\n",
       "6595852  A2BYV7S1QP2YIG  B009EAHVTA     5.0\n",
       "4738240   AB094YABX21WQ  B0056XCEAA     1.0\n",
       "4175595  A3D0UM4ZD2CMAW  B004I763AW     5.0\n",
       "3753015   AATWFX0ZZSE6C  B0040NPHMO     3.0\n",
       "1734766  A1NNMOD9H36Q8E  B0015VW3BM     4.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split the data randomnly into test and train datasets\n",
    "#Split the training and test data in the ratio 70:30\n",
    "train_data, test_data = train_test_split(df_final, test_size = 0.3, random_state=1)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88109, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(37762, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                       Build Popularity Recommender model. (Non-personalised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>productId</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0594451647</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0594481813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0970407998</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0972683275</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1400501466</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    productId  score\n",
       "0  0594451647      1\n",
       "1  0594481813      1\n",
       "2  0970407998      1\n",
       "3  0972683275      3\n",
       "4  1400501466      4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_grouped_data = train_data.groupby('productId').agg({'userId' : 'count'}).reset_index()\n",
    "trained_grouped_data.rename(columns = {'userId' : 'score'}, inplace = True)\n",
    "trained_grouped_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>productId</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30847</th>\n",
       "      <td>B0088CJT4U</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30287</th>\n",
       "      <td>B007WTAJTO</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19647</th>\n",
       "      <td>B003ES5ZUU</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>B000N99BBC</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30555</th>\n",
       "      <td>B00829THK0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        productId  score\n",
       "30847  B0088CJT4U    133\n",
       "30287  B007WTAJTO    124\n",
       "19647  B003ES5ZUU    122\n",
       "8752   B000N99BBC    114\n",
       "30555  B00829THK0     97"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort the products on recommendation score \n",
    "train_data_sorted = trained_grouped_data.sort_values(['score', 'productId'], ascending = [0,1])\n",
    "train_data_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>productId</th>\n",
       "      <th>score</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30847</th>\n",
       "      <td>B0088CJT4U</td>\n",
       "      <td>133</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30287</th>\n",
       "      <td>B007WTAJTO</td>\n",
       "      <td>124</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19647</th>\n",
       "      <td>B003ES5ZUU</td>\n",
       "      <td>122</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>B000N99BBC</td>\n",
       "      <td>114</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30555</th>\n",
       "      <td>B00829THK0</td>\n",
       "      <td>97</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        productId  score  Rank\n",
       "30847  B0088CJT4U    133   1.0\n",
       "30287  B007WTAJTO    124   2.0\n",
       "19647  B003ES5ZUU    122   3.0\n",
       "8752   B000N99BBC    114   4.0\n",
       "30555  B00829THK0     97   5.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate a recommendation rank based upon score \n",
    "train_data_sorted['Rank'] = train_data_sorted['score'].rank(ascending=0, method='first') \n",
    "          \n",
    "#Get the top 5 recommendations \n",
    "popularity_recommendations = train_data_sorted.head(5) \n",
    "popularity_recommendations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use popularity based recommender model to make predictions\n",
    "def recommend(user_id):     \n",
    "    user_recommendations = popularity_recommendations \n",
    "          \n",
    "    #Add user_id column for which the recommendations are being generated \n",
    "    user_recommendations['userId'] = user_id \n",
    "      \n",
    "    #Bring user_id column to the front \n",
    "    cols = user_recommendations.columns.tolist() \n",
    "    cols = cols[-1:] + cols[:-1] \n",
    "    user_recommendations = user_recommendations[cols] \n",
    "          \n",
    "    return user_recommendations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the recommendation for the userId: 15\n",
      "\n",
      "       userId   productId  score  Rank\n",
      "30847      15  B0088CJT4U    133   1.0\n",
      "30287      15  B007WTAJTO    124   2.0\n",
      "19647      15  B003ES5ZUU    122   3.0\n",
      "8752       15  B000N99BBC    114   4.0\n",
      "30555      15  B00829THK0     97   5.0\n",
      "\n",
      "\n",
      "Here is the recommendation for the userId: 121\n",
      "\n",
      "       userId   productId  score  Rank\n",
      "30847     121  B0088CJT4U    133   1.0\n",
      "30287     121  B007WTAJTO    124   2.0\n",
      "19647     121  B003ES5ZUU    122   3.0\n",
      "8752      121  B000N99BBC    114   4.0\n",
      "30555     121  B00829THK0     97   5.0\n",
      "\n",
      "\n",
      "Here is the recommendation for the userId: 53\n",
      "\n",
      "       userId   productId  score  Rank\n",
      "30847      53  B0088CJT4U    133   1.0\n",
      "30287      53  B007WTAJTO    124   2.0\n",
      "19647      53  B003ES5ZUU    122   3.0\n",
      "8752       53  B000N99BBC    114   4.0\n",
      "30555      53  B00829THK0     97   5.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "find_recom = [15,121,53]   # This list is user choice.\n",
    "for i in find_recom:\n",
    "    print(\"Here is the recommendation for the userId: %d\\n\" %(i))\n",
    "    print(recommend(i))    \n",
    "    print(\"\\n\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is a popularity-based recommender model, recommendations remain the same for all users\n",
    "\n",
    "We predict the products based on the popularity. It is not personalized to particular user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                        Build Collaborative Filtering model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model-based Collaborative Filtering: Singular Value Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6595852</td>\n",
       "      <td>A2BYV7S1QP2YIG</td>\n",
       "      <td>B009EAHVTA</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4738240</td>\n",
       "      <td>AB094YABX21WQ</td>\n",
       "      <td>B0056XCEAA</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4175595</td>\n",
       "      <td>A3D0UM4ZD2CMAW</td>\n",
       "      <td>B004I763AW</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3753015</td>\n",
       "      <td>AATWFX0ZZSE6C</td>\n",
       "      <td>B0040NPHMO</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1734766</td>\n",
       "      <td>A1NNMOD9H36Q8E</td>\n",
       "      <td>B0015VW3BM</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index          userId   productId  Rating\n",
       "0  6595852  A2BYV7S1QP2YIG  B009EAHVTA     5.0\n",
       "1  4738240   AB094YABX21WQ  B0056XCEAA     1.0\n",
       "2  4175595  A3D0UM4ZD2CMAW  B004I763AW     5.0\n",
       "3  3753015   AATWFX0ZZSE6C  B0040NPHMO     3.0\n",
       "4  1734766  A1NNMOD9H36Q8E  B0015VW3BM     4.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CF = pd.concat([train_data, test_data]).reset_index()\n",
    "df_CF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1540, 48190)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</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>A100UD67AHFODS</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>A100WO06OQR8BQ</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>A105S56ODHGJEK</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>A105TOJ6LTVMBG</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>A10AFVU66A79Y1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId       0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       ...  B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  \\\n",
       "userId          ...                                                   \n",
       "A100UD67AHFODS  ...         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ  ...         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK  ...         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG  ...         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LBZ1Z7K  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LKG1MC8  \n",
       "userId                      \n",
       "A100UD67AHFODS         0.0  \n",
       "A100WO06OQR8BQ         0.0  \n",
       "A105S56ODHGJEK         0.0  \n",
       "A105TOJ6LTVMBG         0.0  \n",
       "A10AFVU66A79Y1         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#User-based Collaborative Filtering\n",
    "# Matrix with row per 'user' and column per 'item' \n",
    "pivot_df = df_CF.pivot(index = 'userId', columns ='productId', values = 'Rating').fillna(0)\n",
    "pivot_df.shape\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "      <th>user_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</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>A100UD67AHFODS</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A100WO06OQR8BQ</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A105S56ODHGJEK</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A105TOJ6LTVMBG</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10AFVU66A79Y1</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>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48191 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId       0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       ...  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  B00LBZ1Z7K  \\\n",
       "userId          ...                                                   \n",
       "A100UD67AHFODS  ...         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ  ...         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK  ...         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG  ...         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       user_index  \n",
       "userId                      \n",
       "A100UD67AHFODS           0  \n",
       "A100WO06OQR8BQ           1  \n",
       "A105S56ODHGJEK           2  \n",
       "A105TOJ6LTVMBG           3  \n",
       "A10AFVU66A79Y1           4  \n",
       "\n",
       "[5 rows x 48191 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df['user_index'] = np.arange(0, pivot_df.shape[0], 1)\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_index</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>0</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>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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId   0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "user_index                                                               \n",
       "0                  0.0         0.0         0.0         0.0         0.0   \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId   1400501520  1400501776  1400532620  1400532655  140053271X  ...  \\\n",
       "user_index                                                              ...   \n",
       "0                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "1                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "2                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "3                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "4                  0.0         0.0         0.0         0.0         0.0  ...   \n",
       "\n",
       "productId   B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  B00LBZ1Z7K  \\\n",
       "user_index                                                               \n",
       "0                  0.0         0.0         0.0         0.0         0.0   \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
       "2                  0.0         0.0         0.0         0.0         0.0   \n",
       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId   B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8  \n",
       "user_index                                                              \n",
       "0                  0.0         0.0         0.0         0.0         0.0  \n",
       "1                  0.0         0.0         0.0         0.0         0.0  \n",
       "2                  0.0         0.0         0.0         0.0         0.0  \n",
       "3                  0.0         0.0         0.0         0.0         0.0  \n",
       "4                  0.0         0.0         0.0         0.0         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df.set_index(['user_index'], inplace=True)\n",
    "\n",
    "# Actual ratings given by users\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                    SVD method - SVD is best to apply on a large sparse matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import svds\n",
    "# Singular Value Decomposition\n",
    "U, sigma, Vt = svds(pivot_df, k = 10)\n",
    "# Construct diagonal array in SVD\n",
    "sigma = np.diag(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002661</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.004050</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>0.003073</td>\n",
       "      <td>0.005782</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.014386</td>\n",
       "      <td>0.002708</td>\n",
       "      <td>...</td>\n",
       "      <td>6.108890e-04</td>\n",
       "      <td>0.044224</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.060347</td>\n",
       "      <td>-0.002137</td>\n",
       "      <td>0.006751</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>0.130951</td>\n",
       "      <td>0.059243</td>\n",
       "      <td>0.015014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002262</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>0.005136</td>\n",
       "      <td>0.016517</td>\n",
       "      <td>0.007120</td>\n",
       "      <td>0.001438</td>\n",
       "      <td>0.013258</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>0.001190</td>\n",
       "      <td>...</td>\n",
       "      <td>2.024793e-04</td>\n",
       "      <td>0.029213</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>-0.003111</td>\n",
       "      <td>-0.000621</td>\n",
       "      <td>0.004409</td>\n",
       "      <td>-0.039241</td>\n",
       "      <td>-0.006889</td>\n",
       "      <td>0.003696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.001600</td>\n",
       "      <td>-0.002502</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.016742</td>\n",
       "      <td>0.006716</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>0.005805</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>-0.005009</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.240446e-04</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>-0.002459</td>\n",
       "      <td>-0.016922</td>\n",
       "      <td>0.019936</td>\n",
       "      <td>-0.002483</td>\n",
       "      <td>-0.000155</td>\n",
       "      <td>-0.002889</td>\n",
       "      <td>-0.011522</td>\n",
       "      <td>-0.004525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.009395</td>\n",
       "      <td>0.004075</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.007983</td>\n",
       "      <td>0.002381</td>\n",
       "      <td>...</td>\n",
       "      <td>6.031462e-04</td>\n",
       "      <td>-0.003369</td>\n",
       "      <td>0.003433</td>\n",
       "      <td>-0.003428</td>\n",
       "      <td>-0.000750</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>-0.015107</td>\n",
       "      <td>-0.006740</td>\n",
       "      <td>0.003276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>0.009664</td>\n",
       "      <td>0.004438</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.005992</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>...</td>\n",
       "      <td>3.745108e-08</td>\n",
       "      <td>-0.001140</td>\n",
       "      <td>-0.000323</td>\n",
       "      <td>-0.025215</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>-0.002170</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>-0.048555</td>\n",
       "      <td>-0.016301</td>\n",
       "      <td>-0.003377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId  0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "0            0.002661    0.003576    0.004050    0.006906    0.003967   \n",
       "1            0.002262    0.002505    0.005136    0.016517    0.007120   \n",
       "2           -0.001600   -0.002502    0.002186    0.016742    0.006716   \n",
       "3            0.002732    0.003867    0.001799    0.009395    0.004075   \n",
       "4            0.000704    0.000085    0.002051    0.009664    0.004438   \n",
       "\n",
       "productId  1400501520  1400501776  1400532620  1400532655  140053271X  ...  \\\n",
       "0            0.003073    0.005782    0.000568    0.014386    0.002708  ...   \n",
       "1            0.001438    0.013258    0.000335   -0.003781    0.001190  ...   \n",
       "2           -0.002113    0.005805    0.003497   -0.005009   -0.001588  ...   \n",
       "3            0.002778    0.003507    0.000095    0.007983    0.002381  ...   \n",
       "4            0.000335    0.005992    0.001056   -0.000369    0.000373  ...   \n",
       "\n",
       "productId    B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  B00LBZ1Z7K  \\\n",
       "0          6.108890e-04    0.044224    0.002919    0.060347   -0.002137   \n",
       "1          2.024793e-04    0.029213    0.000010    0.000244   -0.003111   \n",
       "2         -3.240446e-04    0.009180   -0.002459   -0.016922    0.019936   \n",
       "3          6.031462e-04   -0.003369    0.003433   -0.003428   -0.000750   \n",
       "4          3.745108e-08   -0.001140   -0.000323   -0.025215    0.004700   \n",
       "\n",
       "productId  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8  \n",
       "0            0.006751    0.001525    0.130951    0.059243    0.015014  \n",
       "1           -0.000621    0.004409   -0.039241   -0.006889    0.003696  \n",
       "2           -0.002483   -0.000155   -0.002889   -0.011522   -0.004525  \n",
       "3            0.000119    0.002612   -0.015107   -0.006740    0.003276  \n",
       "4           -0.002170    0.001263   -0.048555   -0.016301   -0.003377  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) \n",
    "\n",
    "# Predicted ratings\n",
    "preds_df = pd.DataFrame(all_user_predicted_ratings, columns = pivot_df.columns)\n",
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recommend the items with the highest predicted ratings\n",
    "\n",
    "def recommend_items(userID, pivot_df, preds_df, num_recommendations):\n",
    "      \n",
    "    user_idx = userID-1 # index starts at 0\n",
    "    \n",
    "    # Get and sort the user's ratings\n",
    "    sorted_user_ratings = pivot_df.iloc[user_idx].sort_values(ascending=False)\n",
    "    #sorted_user_ratings\n",
    "    sorted_user_predictions = preds_df.iloc[user_idx].sort_values(ascending=False)\n",
    "    #sorted_user_predictions\n",
    "\n",
    "    temp = pd.concat([sorted_user_ratings, sorted_user_predictions], axis=1)\n",
    "    temp.index.name = 'Recommended Items'\n",
    "    temp.columns = ['user_ratings', 'user_predictions']\n",
    "    \n",
    "    temp = temp.loc[temp.user_ratings == 0]   \n",
    "    temp = temp.sort_values('user_predictions', ascending=False)\n",
    "    print('\\nBelow are the recommended items for user(user_id = {}):\\n'.format(userID))\n",
    "    print(temp.head(num_recommendations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 999):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B0088CJT4U                  0.0          1.069630\n",
      "B000N99BBC                  0.0          0.876860\n",
      "B00829TIEK                  0.0          0.748533\n",
      "B008DWCRQW                  0.0          0.704374\n",
      "B004CLYEFK                  0.0          0.667480\n"
     ]
    }
   ],
   "source": [
    "#Enter 'userID' and 'num_recommendations' for the user #\n",
    "userID = 999\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</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>A100UD67AHFODS</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>A100WO06OQR8BQ</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>A105S56ODHGJEK</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>A105TOJ6LTVMBG</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>A10AFVU66A79Y1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId       0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       1400501520  1400501776  1400532620  1400532655  140053271X  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       ...  B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  \\\n",
       "userId          ...                                                   \n",
       "A100UD67AHFODS  ...         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ  ...         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK  ...         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG  ...         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LBZ1Z7K  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  \\\n",
       "userId                                                                       \n",
       "A100UD67AHFODS         0.0         0.0         0.0         0.0         0.0   \n",
       "A100WO06OQR8BQ         0.0         0.0         0.0         0.0         0.0   \n",
       "A105S56ODHGJEK         0.0         0.0         0.0         0.0         0.0   \n",
       "A105TOJ6LTVMBG         0.0         0.0         0.0         0.0         0.0   \n",
       "A10AFVU66A79Y1         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00LKG1MC8  \n",
       "userId                      \n",
       "A100UD67AHFODS         0.0  \n",
       "A100WO06OQR8BQ         0.0  \n",
       "A105S56ODHGJEK         0.0  \n",
       "A105TOJ6LTVMBG         0.0  \n",
       "A10AFVU66A79Y1         0.0  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual ratings given by the users\n",
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "productId\n",
       "0594451647    0.003247\n",
       "0594481813    0.001948\n",
       "0970407998    0.003247\n",
       "0972683275    0.012338\n",
       "1400501466    0.012987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average ACTUAL rating for each item\n",
    "final_ratings_matrix.mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>productId</th>\n",
       "      <th>0594451647</th>\n",
       "      <th>0594481813</th>\n",
       "      <th>0970407998</th>\n",
       "      <th>0972683275</th>\n",
       "      <th>1400501466</th>\n",
       "      <th>1400501520</th>\n",
       "      <th>1400501776</th>\n",
       "      <th>1400532620</th>\n",
       "      <th>1400532655</th>\n",
       "      <th>140053271X</th>\n",
       "      <th>...</th>\n",
       "      <th>B00L5YZCCG</th>\n",
       "      <th>B00L8I6SFY</th>\n",
       "      <th>B00L8QCVL6</th>\n",
       "      <th>B00LA6T0LS</th>\n",
       "      <th>B00LBZ1Z7K</th>\n",
       "      <th>B00LED02VY</th>\n",
       "      <th>B00LGN7Y3G</th>\n",
       "      <th>B00LGQ6HL8</th>\n",
       "      <th>B00LI4ZZO8</th>\n",
       "      <th>B00LKG1MC8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002661</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.004050</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>0.003073</td>\n",
       "      <td>0.005782</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.014386</td>\n",
       "      <td>0.002708</td>\n",
       "      <td>...</td>\n",
       "      <td>6.108890e-04</td>\n",
       "      <td>0.044224</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.060347</td>\n",
       "      <td>-0.002137</td>\n",
       "      <td>0.006751</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>0.130951</td>\n",
       "      <td>0.059243</td>\n",
       "      <td>0.015014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002262</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>0.005136</td>\n",
       "      <td>0.016517</td>\n",
       "      <td>0.007120</td>\n",
       "      <td>0.001438</td>\n",
       "      <td>0.013258</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>0.001190</td>\n",
       "      <td>...</td>\n",
       "      <td>2.024793e-04</td>\n",
       "      <td>0.029213</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>-0.003111</td>\n",
       "      <td>-0.000621</td>\n",
       "      <td>0.004409</td>\n",
       "      <td>-0.039241</td>\n",
       "      <td>-0.006889</td>\n",
       "      <td>0.003696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.001600</td>\n",
       "      <td>-0.002502</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.016742</td>\n",
       "      <td>0.006716</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>0.005805</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>-0.005009</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.240446e-04</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>-0.002459</td>\n",
       "      <td>-0.016922</td>\n",
       "      <td>0.019936</td>\n",
       "      <td>-0.002483</td>\n",
       "      <td>-0.000155</td>\n",
       "      <td>-0.002889</td>\n",
       "      <td>-0.011522</td>\n",
       "      <td>-0.004525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.009395</td>\n",
       "      <td>0.004075</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.007983</td>\n",
       "      <td>0.002381</td>\n",
       "      <td>...</td>\n",
       "      <td>6.031462e-04</td>\n",
       "      <td>-0.003369</td>\n",
       "      <td>0.003433</td>\n",
       "      <td>-0.003428</td>\n",
       "      <td>-0.000750</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>-0.015107</td>\n",
       "      <td>-0.006740</td>\n",
       "      <td>0.003276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>0.009664</td>\n",
       "      <td>0.004438</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.005992</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>...</td>\n",
       "      <td>3.745108e-08</td>\n",
       "      <td>-0.001140</td>\n",
       "      <td>-0.000323</td>\n",
       "      <td>-0.025215</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>-0.002170</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>-0.048555</td>\n",
       "      <td>-0.016301</td>\n",
       "      <td>-0.003377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId  0594451647  0594481813  0970407998  0972683275  1400501466  \\\n",
       "0            0.002661    0.003576    0.004050    0.006906    0.003967   \n",
       "1            0.002262    0.002505    0.005136    0.016517    0.007120   \n",
       "2           -0.001600   -0.002502    0.002186    0.016742    0.006716   \n",
       "3            0.002732    0.003867    0.001799    0.009395    0.004075   \n",
       "4            0.000704    0.000085    0.002051    0.009664    0.004438   \n",
       "\n",
       "productId  1400501520  1400501776  1400532620  1400532655  140053271X  ...  \\\n",
       "0            0.003073    0.005782    0.000568    0.014386    0.002708  ...   \n",
       "1            0.001438    0.013258    0.000335   -0.003781    0.001190  ...   \n",
       "2           -0.002113    0.005805    0.003497   -0.005009   -0.001588  ...   \n",
       "3            0.002778    0.003507    0.000095    0.007983    0.002381  ...   \n",
       "4            0.000335    0.005992    0.001056   -0.000369    0.000373  ...   \n",
       "\n",
       "productId    B00L5YZCCG  B00L8I6SFY  B00L8QCVL6  B00LA6T0LS  B00LBZ1Z7K  \\\n",
       "0          6.108890e-04    0.044224    0.002919    0.060347   -0.002137   \n",
       "1          2.024793e-04    0.029213    0.000010    0.000244   -0.003111   \n",
       "2         -3.240446e-04    0.009180   -0.002459   -0.016922    0.019936   \n",
       "3          6.031462e-04   -0.003369    0.003433   -0.003428   -0.000750   \n",
       "4          3.745108e-08   -0.001140   -0.000323   -0.025215    0.004700   \n",
       "\n",
       "productId  B00LED02VY  B00LGN7Y3G  B00LGQ6HL8  B00LI4ZZO8  B00LKG1MC8  \n",
       "0            0.006751    0.001525    0.130951    0.059243    0.015014  \n",
       "1           -0.000621    0.004409   -0.039241   -0.006889    0.003696  \n",
       "2           -0.002483   -0.000155   -0.002889   -0.011522   -0.004525  \n",
       "3            0.000119    0.002612   -0.015107   -0.006740    0.003276  \n",
       "4           -0.002170    0.001263   -0.048555   -0.016301   -0.003377  \n",
       "\n",
       "[5 rows x 48190 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted ratings \n",
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "productId\n",
       "0594451647    0.001542\n",
       "0594481813    0.002341\n",
       "0970407998    0.002597\n",
       "0972683275    0.011807\n",
       "1400501466    0.004848\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average PREDICTED rating for each item\n",
    "preds_df.mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48190, 2)\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>Avg_actual_ratings</th>\n",
       "      <th>Avg_predicted_ratings</th>\n",
       "      <th>item_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>productId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0594451647</th>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.001542</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0594481813</th>\n",
       "      <td>0.001948</td>\n",
       "      <td>0.002341</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0970407998</th>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.002597</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0972683275</th>\n",
       "      <td>0.012338</td>\n",
       "      <td>0.011807</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400501466</th>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg_actual_ratings  Avg_predicted_ratings  item_index\n",
       "productId                                                        \n",
       "0594451647            0.003247               0.001542           0\n",
       "0594481813            0.001948               0.002341           1\n",
       "0970407998            0.003247               0.002597           2\n",
       "0972683275            0.012338               0.011807           3\n",
       "1400501466            0.012987               0.004848           4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_df = pd.concat([final_ratings_matrix.mean(), preds_df.mean()], axis=1)\n",
    "rmse_df.columns = ['Avg_actual_ratings', 'Avg_predicted_ratings']\n",
    "print(rmse_df.shape)\n",
    "rmse_df['item_index'] = np.arange(0, rmse_df.shape[0], 1)\n",
    "rmse_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE SVD Model = 0.0033 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "RMSE = round((((rmse_df.Avg_actual_ratings - rmse_df.Avg_predicted_ratings) ** 2).mean() ** 0.5), 5)\n",
    "print('\\nRMSE SVD Model = {} \\n'.format(RMSE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get top - K ( K = 5) recommendations. Since our goal is to recommend new products to each user based on his/her habits, we will \n",
    "recommend 5 new products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 15):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B007WTAJTO                  0.0          0.247331\n",
      "B003ES5ZUU                  0.0          0.196123\n",
      "B002V88HFE                  0.0          0.133789\n",
      "B002WE6D44                  0.0          0.112403\n",
      "B000QUUFRW                  0.0          0.111686\n"
     ]
    }
   ],
   "source": [
    "# Enter 'userID' and 'num_recommendations' for the user #\n",
    "userID = 15\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 121):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B007WTAJTO                  0.0          0.419325\n",
      "B003ES5ZUU                  0.0          0.372930\n",
      "B002V88HFE                  0.0          0.280845\n",
      "B001TH7GUU                  0.0          0.233768\n",
      "B002WE6D44                  0.0          0.216767\n"
     ]
    }
   ],
   "source": [
    "userID = 121\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarise your insights.\n",
    "Model-based Collaborative Filtering is a personalised recommender system, the recommendations are based on the past behavior of the user and it is not dependent on any additional information.\n",
    "The Popularity-based recommender system is non-personalised and the recommendations are based on frequecy counts, which may be not suitable to the user.You can see the differance above for the user id 121 & 15, The Popularity based model has recommended the same set of 5 products to both but Collaborative Filtering based model has recommended entire different list based on the user past purchase history"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}