Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the reqired libraries\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import json\n",
    "import time\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",
    "from scipy.sparse.linalg import svds\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Load the dataset and add headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dataset and give the column names\n",
    "columns=['userId', 'productId', 'ratings','timestamp']\n",
    "electronics_df=pd.read_csv('ratings_Electronics.csv',names=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AKM1MP6P0OYPR</td>\n",
       "      <td>0132793040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1365811200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1334707200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   productId  ratings   timestamp\n",
       "0   AKM1MP6P0OYPR  0132793040      5.0  1365811200\n",
       "1  A2CX7LUOHB2NDG  0321732944      5.0  1341100800\n",
       "2  A2NWSAGRHCP8N5  0439886341      1.0  1367193600\n",
       "3  A2WNBOD3WNDNKT  0439886341      3.0  1374451200\n",
       "4  A1GI0U4ZRJA8WN  0439886341      1.0  1334707200"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electronics_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping the timestamp column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "electronics_df.drop('timestamp',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7824482 entries, 0 to 7824481\n",
      "Data columns (total 3 columns):\n",
      "userId       object\n",
      "productId    object\n",
      "ratings      float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 179.1+ MB\n"
     ]
    }
   ],
   "source": [
    "electronics_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  7824482\n",
      "Number of columns:  3\n"
     ]
    }
   ],
   "source": [
    "#Check the number of rows and columns\n",
    "rows,columns=electronics_df.shape\n",
    "print('Number of rows: ',rows)\n",
    "print('Number of columns: ',columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId        object\n",
       "productId     object\n",
       "ratings      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the datatypes\n",
    "electronics_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking subset of the dataset\n",
    "electronics_df1=electronics_df.iloc[:50000,0:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since the data is very big. Consider electronics_df1 named dataframe with  first 50000 rows and all columns from 0 of dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 3 columns):\n",
      "userId       50000 non-null object\n",
      "productId    50000 non-null object\n",
      "ratings      50000 non-null float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "electronics_df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50000.00000\n",
       "mean         4.03524\n",
       "std          1.35555\n",
       "min          1.00000\n",
       "25%          4.00000\n",
       "50%          5.00000\n",
       "75%          5.00000\n",
       "max          5.00000\n",
       "Name: ratings, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary statistics of rating variable\n",
    "electronics_df1['ratings'].describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum rating is: 1\n",
      "Maximum rating is: 5\n"
     ]
    }
   ],
   "source": [
    "#Find the minimum and maximum ratings\n",
    "print('Minimum rating is: %d' %(electronics_df1.ratings.min()))\n",
    "print('Maximum rating is: %d' %(electronics_df1.ratings.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Rating are on the scale 1 to 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values across columns: \n",
      " userId       0\n",
      "productId    0\n",
      "ratings      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Check for missing values\n",
    "print('Number of missing values across columns: \\n',electronics_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are no missing records in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x247bbfdbc18>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the rating\n",
    "with sns.axes_style('white'):\n",
    "    g = sns.factorplot(\"ratings\", data=electronics_df1, aspect=2.0,kind='count')\n",
    "    g.set_ylabels(\"Total number of ratings\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see that more number of users have given the rating of 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Users and products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique users in Raw data =  46554\n",
      "Number of unique product in Raw data =  3446\n"
     ]
    }
   ],
   "source": [
    "# Number of unique user id  in the data\n",
    "print('Number of unique users in Raw data = ', electronics_df1['userId'].nunique())\n",
    "# Number of unique product id  in the data\n",
    "print('Number of unique product in Raw data = ', electronics_df1['productId'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Taking the subset of dataset to make it less sparse/ denser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 users based on ratings: \n",
      " userId\n",
      "A231WM2Z2JL0U3    37\n",
      "AY8Q1X7G96HV5     31\n",
      "ALUNVOQRXOZIA     20\n",
      "A1NVD0TKNS1GT5    19\n",
      "A243HY69GIAHFI    18\n",
      "A1RPTVW5VEOSI     17\n",
      "A1ISUNUWG0K02V    16\n",
      "A1MJMYLRTZ76ZX    16\n",
      "A23ZO1BVFFLGHO    15\n",
      "A7Y6AVS576M03     15\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Check the top 10 users based on ratings\n",
    "most_rated=electronics_df1.groupby('userId').size().sort_values(ascending=False)[:10]\n",
    "print('Top 10 users based on ratings: \\n',most_rated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users who have rated 25 or more items = 219\n",
      "Number of unique users in the final data =  11\n",
      "Number of unique products in the final data =  11\n"
     ]
    }
   ],
   "source": [
    "counts=electronics_df1.userId.value_counts()\n",
    "electronics_df1_final=electronics_df1[electronics_df1.userId.isin(counts[counts>=15].index)]\n",
    "print('Number of users who have rated 25 or more items =', len(electronics_df1_final))\n",
    "print('Number of unique users in the final data = ', electronics_df1_final['userId'].nunique())\n",
    "print('Number of unique products in the final data = ', electronics_df1_final['userId'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* electronics_df1_final has the users who have rated 25 or more items."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ratings analysis in final dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</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>A1MJMYLRTZ76ZX</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>A1NVD0TKNS1GT5</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>A1RPTVW5VEOSI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A231WM2Z2JL0U3</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>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 186 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId       1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         0.0         0.0         0.0         0.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
       "A1NVD0TKNS1GT5         0.0         0.0         0.0         0.0         0.0   \n",
       "A1RPTVW5VEOSI          0.0         0.0         5.0         0.0         0.0   \n",
       "A231WM2Z2JL0U3         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "productId       B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         3.0         0.0         5.0         5.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
       "A1NVD0TKNS1GT5         0.0         0.0         0.0         0.0         0.0   \n",
       "A1RPTVW5VEOSI          0.0         0.0         0.0         0.0         0.0   \n",
       "A231WM2Z2JL0U3         0.0         0.0         5.0         0.0         0.0   \n",
       "\n",
       "productId          ...      B00004TDWY  B00004TE75  B00004TEN2  B00004TH2W  \\\n",
       "userId             ...                                                       \n",
       "A1ISUNUWG0K02V     ...             0.0         0.0         0.0         0.0   \n",
       "A1MJMYLRTZ76ZX     ...             0.0         0.0         0.0         0.0   \n",
       "A1NVD0TKNS1GT5     ...             0.0         0.0         0.0         0.0   \n",
       "A1RPTVW5VEOSI      ...             0.0         0.0         0.0         5.0   \n",
       "A231WM2Z2JL0U3     ...             0.0         5.0         5.0         0.0   \n",
       "\n",
       "productId       B00004TH2Y  B00004THCX  B00004THCY  B00004THCZ  B00004THDE  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         0.0         0.0         0.0         0.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
       "A1NVD0TKNS1GT5         0.0         0.0         0.0         0.0         0.0   \n",
       "A1RPTVW5VEOSI          0.0         0.0         0.0         0.0         0.0   \n",
       "A231WM2Z2JL0U3         4.0         0.0         0.0         0.0         5.0   \n",
       "\n",
       "productId       B00004THM6  \n",
       "userId                      \n",
       "A1ISUNUWG0K02V         0.0  \n",
       "A1MJMYLRTZ76ZX         0.0  \n",
       "A1NVD0TKNS1GT5         0.0  \n",
       "A1RPTVW5VEOSI          0.0  \n",
       "A231WM2Z2JL0U3         0.0  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#constructing the pivot table\n",
    "final_ratings_matrix = electronics_df1_final.pivot(index = 'userId', columns ='productId', values = 'ratings').fillna(0)\n",
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It shows that it is a sparse matrix. So, many cells are filled with 0 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of final_ratings_matrix:  (11, 186)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of final_ratings_matrix: ', final_ratings_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see that there are 7 products and 236 users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "given_num_of_ratings =  219\n",
      "possible_num_of_ratings =  2046\n",
      "density: 10.70%\n"
     ]
    }
   ],
   "source": [
    "#Calucating the density of the rating marix\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": "markdown",
   "metadata": {},
   "source": [
    "* The density value of the matrix also shows that it is a sparse matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17509</th>\n",
       "      <td>AY8Q1X7G96HV5</td>\n",
       "      <td>B00000JSES</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11968</th>\n",
       "      <td>A243HY69GIAHFI</td>\n",
       "      <td>B00000J3Q7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35533</th>\n",
       "      <td>A1RPTVW5VEOSI</td>\n",
       "      <td>B00003WGP5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31480</th>\n",
       "      <td>A1NVD0TKNS1GT5</td>\n",
       "      <td>B00002JXFH</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13526</th>\n",
       "      <td>A23ZO1BVFFLGHO</td>\n",
       "      <td>B00000J570</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               userId   productId  ratings\n",
       "17509   AY8Q1X7G96HV5  B00000JSES      4.0\n",
       "11968  A243HY69GIAHFI  B00000J3Q7      3.0\n",
       "35533   A1RPTVW5VEOSI  B00003WGP5      5.0\n",
       "31480  A1NVD0TKNS1GT5  B00002JXFH      4.0\n",
       "13526  A23ZO1BVFFLGHO  B00000J570      5.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split the data randomnly into train and test datasets into 70:30 ratio\n",
    "train_data, test_data = train_test_split(electronics_df1_final, test_size = 0.3, random_state=0)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data:  (153, 3)\n",
      "Shape of testing data:  (66, 3)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of training data: ',train_data.shape)\n",
    "print('Shape of testing data: ',test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Building Popularity Recommder model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>productId</th>\n",
       "      <th>score</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>B00000J3HB</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00000J3II</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B00000J3Q7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00000J3T1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B00000J47A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B00000J4ER</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B00000J4FS</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B00000J4O2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B00000J570</td>\n",
       "      <td>2</td>\n",
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       "      <th>14</th>\n",
       "      <td>B00000J579</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B00000JBIA</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B00000JBJQ</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>B00000JBK6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>B00000JBUI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>B00000JDEI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>B00000JFDW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>B00000JFJA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>B00000JFMW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>B00000JHVP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>B00000JI2C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>B00000JSES</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>B00000JSEW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>B00000JSGF</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>B00000K1SD</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>B00000K390</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>B00000K3RI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>B00000K3RO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>B00000K4KH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>B00001O2YP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>B00001OPK8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>B00001OWYM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>B00001P4ZH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>B00001QEMF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>B00001QHP5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>B00001RMCY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     productId  score\n",
       "0   1400599997      1\n",
       "1   B00000DM9M      1\n",
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       "38  B00001QHP5      1\n",
       "39  B00001RMCY      1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count of user_id for each unique product as recommendation score \n",
    "train_data_grouped = train_data.groupby('productId').agg({'userId': 'count'}).reset_index()\n",
    "train_data_grouped.rename(columns = {'userId': 'score'},inplace=True)\n",
    "train_data_grouped.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      productId  score  rank\n",
       "87   B00004RC2D      4   1.0\n",
       "57   B00002SWHH      3   2.0\n",
       "99   B00004SC3Y      3   3.0\n",
       "100  B00004SCKA      3   4.0\n",
       "6    B00000J3II      2   5.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort the products on recommendation score \n",
    "train_data_sort = train_data_grouped.sort_values(['score', 'productId'], ascending = [0,1]) \n",
    "      \n",
    "#Generate a recommendation rank based upon score \n",
    "train_data_sort['rank'] = train_data_sort['score'].rank(ascending=0, method='first') \n",
    "          \n",
    "#Get the top 5 recommendations \n",
    "popularity_recommendations = train_data_sort.head(5) \n",
    "popularity_recommendations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The list of recommendations for the userId: 10\n",
      "\n",
      "     userId   productId  score  rank\n",
      "87       10  B00004RC2D      4   1.0\n",
      "57       10  B00002SWHH      3   2.0\n",
      "99       10  B00004SC3Y      3   3.0\n",
      "100      10  B00004SCKA      3   4.0\n",
      "6        10  B00000J3II      2   5.0\n",
      "\n",
      "\n",
      "The list of recommendations for the userId: 100\n",
      "\n",
      "     userId   productId  score  rank\n",
      "87      100  B00004RC2D      4   1.0\n",
      "57      100  B00002SWHH      3   2.0\n",
      "99      100  B00004SC3Y      3   3.0\n",
      "100     100  B00004SCKA      3   4.0\n",
      "6       100  B00000J3II      2   5.0\n",
      "\n",
      "\n",
      "The list of recommendations for the userId: 150\n",
      "\n",
      "     userId   productId  score  rank\n",
      "87      150  B00004RC2D      4   1.0\n",
      "57      150  B00002SWHH      3   2.0\n",
      "99      150  B00004SC3Y      3   3.0\n",
      "100     150  B00004SCKA      3   4.0\n",
      "6       150  B00000J3II      2   5.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "find_recom = [10,100,150]   # This list is user choice.\n",
    "for i in find_recom:\n",
    "    print(\"The list of recommendations for the userId: %d\\n\" %(i))\n",
    "    print(recommend(i))    \n",
    "    print(\"\\n\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since, it is a Popularity recommender model, so, all the three users are given the same recommendations. Here, we predict the products based on the popularity. It is not personalized to particular user. It is a non-personalized recommender system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Building Collaborative Filtering recommender model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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      "text/plain": [
       "   index          userId   productId  ratings\n",
       "0  17509   AY8Q1X7G96HV5  B00000JSES      4.0\n",
       "1  11968  A243HY69GIAHFI  B00000J3Q7      3.0\n",
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       "3  31480  A1NVD0TKNS1GT5  B00002JXFH      4.0\n",
       "4  13526  A23ZO1BVFFLGHO  B00000J570      5.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electronics_df_CF = pd.concat([train_data, test_data]).reset_index()\n",
    "electronics_df_CF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Based Collaborative Filtering model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "productId       1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         0.0         0.0         0.0         0.0   \n",
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       "productId       B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "userId                                                                       \n",
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       "userId             ...                                                       \n",
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       "userId                                                                       \n",
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       "A1RPTVW5VEOSI          0.0         0.0         0.0         0.0         0.0   \n",
       "A231WM2Z2JL0U3         4.0         0.0         0.0         0.0         5.0   \n",
       "\n",
       "productId       B00004THM6  \n",
       "userId                      \n",
       "A1ISUNUWG0K02V         0.0  \n",
       "A1MJMYLRTZ76ZX         0.0  \n",
       "A1NVD0TKNS1GT5         0.0  \n",
       "A1RPTVW5VEOSI          0.0  \n",
       "A231WM2Z2JL0U3         0.0  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matrix with row per 'user' and column per 'item' \n",
    "pivot_df = electronics_df_CF.pivot(index = 'userId', columns ='productId', values = 'ratings').fillna(0)\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the pivot table:  (11, 186)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of the pivot table: ', pivot_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "productId       1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId       B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         3.0         0.0         5.0         5.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId          ...      B00004TE75  B00004TEN2  B00004TH2W  B00004TH2Y  \\\n",
       "userId             ...                                                       \n",
       "A1ISUNUWG0K02V     ...             0.0         0.0         0.0         0.0   \n",
       "A1MJMYLRTZ76ZX     ...             0.0         0.0         0.0         0.0   \n",
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       "\n",
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       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         0.0         0.0         0.0         0.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
       "A1NVD0TKNS1GT5         0.0         0.0         0.0         0.0         0.0   \n",
       "A1RPTVW5VEOSI          0.0         0.0         0.0         0.0         0.0   \n",
       "A231WM2Z2JL0U3         0.0         0.0         0.0         5.0         0.0   \n",
       "\n",
       "productId       user_index  \n",
       "userId                      \n",
       "A1ISUNUWG0K02V           0  \n",
       "A1MJMYLRTZ76ZX           1  \n",
       "A1NVD0TKNS1GT5           2  \n",
       "A1RPTVW5VEOSI            3  \n",
       "A231WM2Z2JL0U3           4  \n",
       "\n",
       "[5 rows x 187 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define user index from 0 to 10\n",
    "pivot_df['user_index'] = np.arange(0, pivot_df.shape[0], 1)\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "productId   1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\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",
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       "\n",
       "productId   B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "user_index                                                               \n",
       "0                  0.0         3.0         0.0         5.0         5.0   \n",
       "1                  0.0         0.0         0.0         0.0         0.0   \n",
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       "3                  0.0         0.0         0.0         0.0         0.0   \n",
       "4                  0.0         0.0         5.0         0.0         0.0   \n",
       "\n",
       "productId      ...      B00004TDWY  B00004TE75  B00004TEN2  B00004TH2W  \\\n",
       "user_index     ...                                                       \n",
       "0              ...             0.0         0.0         0.0         0.0   \n",
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       "productId   B00004TH2Y  B00004THCX  B00004THCY  B00004THCZ  B00004THDE  \\\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                  4.0         0.0         0.0         0.0         5.0   \n",
       "\n",
       "productId   B00004THM6  \n",
       "user_index              \n",
       "0                  0.0  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df.set_index(['user_index'], inplace=True)\n",
    "# Actual ratings given by users\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As this is a sparse matrix we will use SVD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Singular Value Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Singular Value Decomposition\n",
    "U, sigma, Vt = svds(pivot_df, k = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left singular matrix: \n",
      " [[ 2.65871898e-01  8.28788249e-01  1.14160900e-01 -2.15685919e-01\n",
      "  -2.65928896e-04 -4.93636653e-02  1.66297884e-01 -3.12732051e-01\n",
      "  -1.53122256e-01  1.38465143e-01]\n",
      " [ 5.53711304e-02  1.21204397e-01 -8.17836609e-02  3.29682359e-01\n",
      "  -1.47955175e-01 -8.18019500e-01 -2.13209931e-01  2.76257471e-01\n",
      "  -2.09061119e-01  1.01498819e-01]\n",
      " [ 5.46679471e-01 -1.37512688e-01  2.79596983e-02  1.45212122e-01\n",
      "  -3.57798248e-02  1.89642626e-01 -6.60636300e-01 -2.07100247e-01\n",
      "   3.59790632e-02  1.23044532e-01]\n",
      " [ 4.63168744e-02  5.39880164e-02 -8.32935752e-01  1.36292972e-01\n",
      "   1.12974956e-02  9.74799278e-02 -5.43326589e-02 -2.99913518e-01\n",
      "  -1.00398248e-01  1.16155900e-01]\n",
      " [-4.82857184e-02 -5.06969281e-03  6.10478781e-02  7.63553438e-02\n",
      "   8.79528911e-02  1.33412578e-01  9.24837886e-02  2.48586367e-01\n",
      "   1.79064339e-01  9.26201600e-01]\n",
      " [-1.33072419e-01 -3.30520700e-01 -8.58433108e-03 -6.82184355e-01\n",
      "  -3.10981905e-02 -4.06414738e-01 -1.22417282e-01 -4.14339191e-01\n",
      "   4.04459226e-03  2.33218097e-01]\n",
      " [-5.85058830e-01  2.82858362e-01  2.67444902e-01  1.03926107e-01\n",
      "  -7.39796949e-02  1.25896947e-01 -5.86529180e-01 -1.64067216e-01\n",
      "   9.37707797e-03  1.79755959e-02]\n",
      " [-5.04793124e-01  1.13702295e-01 -3.57486565e-01  6.92659013e-02\n",
      "   2.00732743e-02  4.29965080e-02  5.56583151e-04 -2.51788542e-02\n",
      "   9.00894193e-04  3.04631617e-02]\n",
      " [ 1.37401523e-02  1.68164402e-03 -5.23514746e-02 -1.04867805e-01\n",
      "  -9.72327480e-01  1.54469490e-01  6.89319218e-02  9.60100045e-02\n",
      "   2.28758644e-02  4.64399549e-02]\n",
      " [-7.77524293e-02 -2.36994718e-01  2.76662643e-01  5.39661989e-01\n",
      "  -1.21856053e-01 -7.39304558e-02  3.36795340e-01 -6.29018917e-01\n",
      "  -1.54119324e-01  1.21495612e-01]\n",
      " [-4.31106769e-02 -1.34978010e-01  5.75166098e-02 -1.26848095e-01\n",
      "   4.30464817e-02  2.30094616e-01 -4.18979575e-02  1.64726898e-01\n",
      "  -9.30068553e-01  1.07164263e-01]]\n"
     ]
    }
   ],
   "source": [
    "print('Left singular matrix: \\n',U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigma: \n",
      " [15.65710264 16.05091576 17.43121071 17.74252629 18.18622003 18.83061496\n",
      " 19.14527912 19.94315139 22.99328895 28.83750492]\n"
     ]
    }
   ],
   "source": [
    "print('Sigma: \\n',sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As sigma is not a diagonal matrix we have to convert it into diagonal matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagonal matrix: \n",
      " [[15.65710264  0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.        ]\n",
      " [ 0.         16.05091576  0.          0.          0.          0.\n",
      "   0.          0.          0.          0.        ]\n",
      " [ 0.          0.         17.43121071  0.          0.          0.\n",
      "   0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.         17.74252629  0.          0.\n",
      "   0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.         18.18622003  0.\n",
      "   0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.         18.83061496\n",
      "   0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "  19.14527912  0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.         19.94315139  0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.         22.99328895  0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.         28.83750492]]\n"
     ]
    }
   ],
   "source": [
    "# Construct diagonal array in SVD\n",
    "sigma = np.diag(sigma)\n",
    "print('Diagonal matrix: \\n',sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right singular matrix: \n",
      " [[-0.00496595 -0.01376713  0.01479101 ... -0.0110137  -0.01541975\n",
      "  -0.0110137 ]\n",
      " [-0.01476518 -0.04204683  0.01681774 ... -0.03363746 -0.00157925\n",
      "  -0.03363746]\n",
      " [ 0.01587168  0.01649817 -0.2389208  ...  0.01319853  0.01751108\n",
      "   0.01319853]\n",
      " ...\n",
      " [-0.0315406   0.04129911 -0.07519211 ...  0.03303929  0.06232374\n",
      "   0.03303929]\n",
      " [-0.0067028  -0.20224783 -0.02183208 ... -0.16179826  0.03893839\n",
      "  -0.16179826]\n",
      " [ 0.00421311  0.01858071  0.02013973 ...  0.01486457  0.16058976\n",
      "   0.01486457]]\n"
     ]
    }
   ],
   "source": [
    "print('Right singular matrix: \\n',Vt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>productId</th>\n",
       "      <th>1400599997</th>\n",
       "      <th>B00000DM9M</th>\n",
       "      <th>B00000J061</th>\n",
       "      <th>B00000J08C</th>\n",
       "      <th>B00000J0A2</th>\n",
       "      <th>B00000J0E8</th>\n",
       "      <th>B00000J1QZ</th>\n",
       "      <th>B00000J1US</th>\n",
       "      <th>B00000J3H5</th>\n",
       "      <th>B00000J3HB</th>\n",
       "      <th>...</th>\n",
       "      <th>B00004TDWY</th>\n",
       "      <th>B00004TE75</th>\n",
       "      <th>B00004TEN2</th>\n",
       "      <th>B00004TH2W</th>\n",
       "      <th>B00004TH2Y</th>\n",
       "      <th>B00004THCX</th>\n",
       "      <th>B00004THCY</th>\n",
       "      <th>B00004THCZ</th>\n",
       "      <th>B00004THDE</th>\n",
       "      <th>B00004THM6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.005077</td>\n",
       "      <td>-0.010023</td>\n",
       "      <td>0.219864</td>\n",
       "      <td>-0.010023</td>\n",
       "      <td>-0.020310</td>\n",
       "      <td>-0.006562</td>\n",
       "      <td>2.962929</td>\n",
       "      <td>0.026594</td>\n",
       "      <td>4.938216</td>\n",
       "      <td>4.938216</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025387</td>\n",
       "      <td>0.026594</td>\n",
       "      <td>0.026594</td>\n",
       "      <td>0.219864</td>\n",
       "      <td>0.021275</td>\n",
       "      <td>-0.008018</td>\n",
       "      <td>-0.006014</td>\n",
       "      <td>-0.008018</td>\n",
       "      <td>0.026594</td>\n",
       "      <td>-0.008018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000261</td>\n",
       "      <td>-0.000515</td>\n",
       "      <td>0.011300</td>\n",
       "      <td>-0.000515</td>\n",
       "      <td>-0.001044</td>\n",
       "      <td>-0.000337</td>\n",
       "      <td>-0.001905</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>-0.003176</td>\n",
       "      <td>-0.003176</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001305</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.011300</td>\n",
       "      <td>0.001093</td>\n",
       "      <td>-0.000412</td>\n",
       "      <td>-0.000309</td>\n",
       "      <td>-0.000412</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>-0.000412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.016300</td>\n",
       "      <td>-0.032177</td>\n",
       "      <td>0.705839</td>\n",
       "      <td>-0.032177</td>\n",
       "      <td>-0.065201</td>\n",
       "      <td>-0.021066</td>\n",
       "      <td>-0.119009</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>-0.198349</td>\n",
       "      <td>-0.198349</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081502</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>0.705839</td>\n",
       "      <td>0.068299</td>\n",
       "      <td>-0.025742</td>\n",
       "      <td>-0.019306</td>\n",
       "      <td>-0.025742</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>-0.025742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.018068</td>\n",
       "      <td>0.035667</td>\n",
       "      <td>4.217599</td>\n",
       "      <td>0.035667</td>\n",
       "      <td>0.072274</td>\n",
       "      <td>0.023351</td>\n",
       "      <td>0.131918</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>0.219864</td>\n",
       "      <td>0.219864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090342</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>4.217599</td>\n",
       "      <td>-0.075708</td>\n",
       "      <td>0.028534</td>\n",
       "      <td>0.021400</td>\n",
       "      <td>0.028534</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>0.028534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002185</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.008742</td>\n",
       "      <td>0.002824</td>\n",
       "      <td>0.015956</td>\n",
       "      <td>4.988553</td>\n",
       "      <td>0.026594</td>\n",
       "      <td>0.026594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010927</td>\n",
       "      <td>4.988553</td>\n",
       "      <td>4.988553</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>3.990843</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>4.988553</td>\n",
       "      <td>0.003451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 186 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "productId  1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\n",
       "0           -0.005077   -0.010023    0.219864   -0.010023   -0.020310   \n",
       "1           -0.000261   -0.000515    0.011300   -0.000515   -0.001044   \n",
       "2           -0.016300   -0.032177    0.705839   -0.032177   -0.065201   \n",
       "3            0.018068    0.035667    4.217599    0.035667    0.072274   \n",
       "4            0.002185    0.004314   -0.094635    0.004314    0.008742   \n",
       "\n",
       "productId  B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "0           -0.006562    2.962929    0.026594    4.938216    4.938216   \n",
       "1           -0.000337   -0.001905    0.001367   -0.003176   -0.003176   \n",
       "2           -0.021066   -0.119009    0.085374   -0.198349   -0.198349   \n",
       "3            0.023351    0.131918   -0.094635    0.219864    0.219864   \n",
       "4            0.002824    0.015956    4.988553    0.026594    0.026594   \n",
       "\n",
       "productId     ...      B00004TDWY  B00004TE75  B00004TEN2  B00004TH2W  \\\n",
       "0             ...       -0.025387    0.026594    0.026594    0.219864   \n",
       "1             ...       -0.001305    0.001367    0.001367    0.011300   \n",
       "2             ...       -0.081502    0.085374    0.085374    0.705839   \n",
       "3             ...        0.090342   -0.094635   -0.094635    4.217599   \n",
       "4             ...        0.010927    4.988553    4.988553   -0.094635   \n",
       "\n",
       "productId  B00004TH2Y  B00004THCX  B00004THCY  B00004THCZ  B00004THDE  \\\n",
       "0            0.021275   -0.008018   -0.006014   -0.008018    0.026594   \n",
       "1            0.001093   -0.000412   -0.000309   -0.000412    0.001367   \n",
       "2            0.068299   -0.025742   -0.019306   -0.025742    0.085374   \n",
       "3           -0.075708    0.028534    0.021400    0.028534   -0.094635   \n",
       "4            3.990843    0.003451    0.002588    0.003451    4.988553   \n",
       "\n",
       "productId  B00004THM6  \n",
       "0           -0.008018  \n",
       "1           -0.000412  \n",
       "2           -0.025742  \n",
       "3            0.028534  \n",
       "4            0.003451  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicted ratings\n",
    "all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) \n",
    "# Convert predicted ratings to dataframe\n",
    "preds_df = pd.DataFrame(all_user_predicted_ratings, columns = pivot_df.columns)\n",
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recommend the items with the highest predicted ratings\n",
    "\n",
    "def recommend_items(userID, pivot_df, preds_df, num_recommendations):\n",
    "    # index starts at 0  \n",
    "    user_idx = userID-1 \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",
    "    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",
    "    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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 4):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B00001P4XA                  0.0          1.526746\n",
      "B00000JYLO                  0.0          1.526746\n",
      "B00004T1WX                  0.0          1.526746\n",
      "B00004T1WZ                  0.0          1.526746\n",
      "B00000J4ER                  0.0          1.526746\n"
     ]
    }
   ],
   "source": [
    "userID = 4\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 6):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B00004RERZ                  0.0          0.038989\n",
      "B00000J061                  0.0          0.038989\n",
      "B00003WGP5                  0.0          0.038989\n",
      "B00004TH2W                  0.0          0.038989\n",
      "B00004S9WQ                  0.0          0.038989\n"
     ]
    }
   ],
   "source": [
    "userID = 6\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 8):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B00000J061                  0.0          1.526746\n",
      "B00003WGP5                  0.0          1.526746\n",
      "B00004TH2W                  0.0          1.526746\n",
      "B00004RERZ                  0.0          1.526746\n",
      "B00004RIPE                  0.0          1.526746\n"
     ]
    }
   ],
   "source": [
    "userID = 8\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since, it is a Collaborative recommender model, so, all the three users are given different recommendations based on users past behaviour."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Evaluation of Collabrative recommendation model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
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       "productId       1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\n",
       "userId                                                                       \n",
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       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId       B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         3.0         0.0         5.0         5.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
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       "A231WM2Z2JL0U3         0.0         0.0         5.0         0.0         0.0   \n",
       "\n",
       "productId          ...      B00004TDWY  B00004TE75  B00004TEN2  B00004TH2W  \\\n",
       "userId             ...                                                       \n",
       "A1ISUNUWG0K02V     ...             0.0         0.0         0.0         0.0   \n",
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       "\n",
       "productId       B00004TH2Y  B00004THCX  B00004THCY  B00004THCZ  B00004THDE  \\\n",
       "userId                                                                       \n",
       "A1ISUNUWG0K02V         0.0         0.0         0.0         0.0         0.0   \n",
       "A1MJMYLRTZ76ZX         0.0         0.0         0.0         0.0         0.0   \n",
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       "A231WM2Z2JL0U3         4.0         0.0         0.0         0.0         5.0   \n",
       "\n",
       "productId       B00004THM6  \n",
       "userId                      \n",
       "A1ISUNUWG0K02V         0.0  \n",
       "A1MJMYLRTZ76ZX         0.0  \n",
       "A1NVD0TKNS1GT5         0.0  \n",
       "A1RPTVW5VEOSI          0.0  \n",
       "A231WM2Z2JL0U3         0.0  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual ratings given by the users\n",
    "final_ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "productId\n",
       "1400599997    0.090909\n",
       "B00000DM9M    0.454545\n",
       "B00000J061    0.454545\n",
       "B00000J08C    0.454545\n",
       "B00000J0A2    0.363636\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average ACTUAL rating for each item\n",
    "final_ratings_matrix.mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 186 columns</p>\n",
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       "productId  1400599997  B00000DM9M  B00000J061  B00000J08C  B00000J0A2  \\\n",
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       "4            0.002185    0.004314   -0.094635    0.004314    0.008742   \n",
       "\n",
       "productId  B00000J0E8  B00000J1QZ  B00000J1US  B00000J3H5  B00000J3HB  \\\n",
       "0           -0.006562    2.962929    0.026594    4.938216    4.938216   \n",
       "1           -0.000337   -0.001905    0.001367   -0.003176   -0.003176   \n",
       "2           -0.021066   -0.119009    0.085374   -0.198349   -0.198349   \n",
       "3            0.023351    0.131918   -0.094635    0.219864    0.219864   \n",
       "4            0.002824    0.015956    4.988553    0.026594    0.026594   \n",
       "\n",
       "productId     ...      B00004TDWY  B00004TE75  B00004TEN2  B00004TH2W  \\\n",
       "0             ...       -0.025387    0.026594    0.026594    0.219864   \n",
       "1             ...       -0.001305    0.001367    0.001367    0.011300   \n",
       "2             ...       -0.081502    0.085374    0.085374    0.705839   \n",
       "3             ...        0.090342   -0.094635   -0.094635    4.217599   \n",
       "4             ...        0.010927    4.988553    4.988553   -0.094635   \n",
       "\n",
       "productId  B00004TH2Y  B00004THCX  B00004THCY  B00004THCZ  B00004THDE  \\\n",
       "0            0.021275   -0.008018   -0.006014   -0.008018    0.026594   \n",
       "1            0.001093   -0.000412   -0.000309   -0.000412    0.001367   \n",
       "2            0.068299   -0.025742   -0.019306   -0.025742    0.085374   \n",
       "3           -0.075708    0.028534    0.021400    0.028534   -0.094635   \n",
       "4            3.990843    0.003451    0.002588    0.003451    4.988553   \n",
       "\n",
       "productId  B00004THM6  \n",
       "0           -0.008018  \n",
       "1           -0.000412  \n",
       "2           -0.025742  \n",
       "3            0.028534  \n",
       "4            0.003451  \n",
       "\n",
       "[5 rows x 186 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted ratings \n",
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "productId\n",
       "1400599997    0.088513\n",
       "B00000DM9M    0.449816\n",
       "B00000J061    0.558292\n",
       "B00000J08C    0.449816\n",
       "B00000J0A2    0.354053\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average PREDICTED rating for each item\n",
    "preds_df.mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(186, 2)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</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",
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       "      <th>1400599997</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.088513</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>B00000DM9M</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.449816</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00000J061</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.558292</td>\n",
       "      <td>2</td>\n",
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       "      <th>B00000J08C</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.449816</td>\n",
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       "      <th>B00000J0A2</th>\n",
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       "      <td>0.354053</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",
       "1400599997            0.090909               0.088513           0\n",
       "B00000DM9M            0.454545               0.449816           1\n",
       "B00000J061            0.454545               0.558292           2\n",
       "B00000J08C            0.454545               0.449816           3\n",
       "B00000J0A2            0.363636               0.354053           4"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE SVD Model = 0.05854 \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": [
    "# 8. Getting top - K ( K = 5) recommendations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Below are the recommended items for user(user_id = 9):\n",
      "\n",
      "                   user_ratings  user_predictions\n",
      "Recommended Items                                \n",
      "B00003WGP5                  0.0          0.023351\n",
      "B00000J061                  0.0          0.023351\n",
      "B00004TH2W                  0.0          0.023351\n",
      "B00001QHP5                  0.0          0.023351\n",
      "B00004S9WQ                  0.0          0.023351\n"
     ]
    }
   ],
   "source": [
    "# Enter 'userID' and 'num_recommendations' for the user #\n",
    "userID = 9\n",
    "num_recommendations = 5\n",
    "recommend_items(userID, pivot_df, preds_df, num_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarising insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Popularity-based recommender system is a non-personalised recommender system and these are based on frequecy counts, which may be not suitable to the user.We can see the differance above for the user id 4, 6 & 8, 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.\n",
    "\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."
   ]
  }
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