Book-Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The usual drill of importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 6452: expected 8 fields, saw 9\\nSkipping line 43667: expected 8 fields, saw 10\\nSkipping line 51751: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 92038: expected 8 fields, saw 9\\nSkipping line 104319: expected 8 fields, saw 9\\nSkipping line 121768: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 144058: expected 8 fields, saw 9\\nSkipping line 150789: expected 8 fields, saw 9\\nSkipping line 157128: expected 8 fields, saw 9\\nSkipping line 180189: expected 8 fields, saw 9\\nSkipping line 185738: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 209388: expected 8 fields, saw 9\\nSkipping line 220626: expected 8 fields, saw 9\\nSkipping line 227933: expected 8 fields, saw 11\\nSkipping line 228957: expected 8 fields, saw 10\\nSkipping line 245933: expected 8 fields, saw 9\\nSkipping line 251296: expected 8 fields, saw 9\\nSkipping line 259941: expected 8 fields, saw 9\\nSkipping line 261529: expected 8 fields, saw 9\\n'\n"
     ]
    }
   ],
   "source": [
    "books = pd.read_csv('BX-Books.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
    "users = pd.read_csv('BX-Users.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
    "ratings = pd.read_csv('BX-Book-Ratings.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study The Data"
   ]
  },
  {
   "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>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Image-URL-S</th>\n",
       "      <th>Image-URL-M</th>\n",
       "      <th>Image-URL-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "      <td>2001</td>\n",
       "      <td>HarperFlamingo Canada</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperPerennial</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0374157065</td>\n",
       "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0393045218</td>\n",
       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ISBN                                         Book-Title  \\\n",
       "0  0195153448                                Classical Mythology   \n",
       "1  0002005018                                       Clara Callan   \n",
       "2  0060973129                               Decision in Normandy   \n",
       "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4  0393045218                             The Mummies of Urumchi   \n",
       "\n",
       "            Book-Author Year-Of-Publication                   Publisher  \\\n",
       "0    Mark P. O. Morford                2002     Oxford University Press   \n",
       "1  Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
       "2          Carlo D'Este                1991             HarperPerennial   \n",
       "3      Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
       "4       E. J. W. Barber                1999  W. W. Norton &amp; Company   \n",
       "\n",
       "                                         Image-URL-S  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-M  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-L  \n",
       "0  http://images.amazon.com/images/P/0195153448.0...  \n",
       "1  http://images.amazon.com/images/P/0002005018.0...  \n",
       "2  http://images.amazon.com/images/P/0060973129.0...  \n",
       "3  http://images.amazon.com/images/P/0374157065.0...  \n",
       "4  http://images.amazon.com/images/P/0393045218.0...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271360, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>nyc, new york, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>moscow, yukon territory, russia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>porto, v.n.gaia, portugal</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>farnborough, hants, united kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID                            Location   Age\n",
       "0        1                  nyc, new york, usa   NaN\n",
       "1        2           stockton, california, usa  18.0\n",
       "2        3     moscow, yukon territory, russia   NaN\n",
       "3        4           porto, v.n.gaia, portugal  17.0\n",
       "4        5  farnborough, hants, united kingdom   NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(278858, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,  18.,  17.,  61.,  26.,  14.,  25.,  19.,  46.,  55.,  32.,\n",
       "        24.,  20.,  34.,  23.,  51.,  31.,  21.,  44.,  30.,  57.,  43.,\n",
       "        37.,  41.,  54.,  42.,  50.,  39.,  53.,  47.,  36.,  28.,  35.,\n",
       "        13.,  58.,  49.,  38.,  45.,  62.,  63.,  27.,  33.,  29.,  66.,\n",
       "        40.,  15.,  60.,   0.,  79.,  22.,  16.,  65.,  59.,  48.,  72.,\n",
       "        56.,  67.,   1.,  80.,  52.,  69.,  71.,  73.,  78.,   9.,  64.,\n",
       "       103., 104.,  12.,  74.,  75., 231.,   3.,  76.,  83.,  68., 119.,\n",
       "        11.,  77.,   2.,  70.,  93.,   8.,   7.,   4.,  81., 114., 230.,\n",
       "       239.,  10.,   5., 148., 151.,   6., 101., 201.,  96.,  84.,  82.,\n",
       "        90., 123., 244., 133.,  91., 128.,  94.,  85., 141., 110.,  97.,\n",
       "       219.,  86., 124.,  92., 175., 172., 209., 212., 237.,  87., 162.,\n",
       "       100., 156., 136.,  95.,  89., 106.,  99., 108., 210.,  88., 199.,\n",
       "       147., 168., 132., 159., 186., 152., 102., 116., 200., 115., 226.,\n",
       "       137., 207., 229., 138., 109., 105., 228., 183., 204.,  98., 223.,\n",
       "       113., 208., 107., 157., 111., 146., 118., 220., 143., 140., 189.,\n",
       "       127.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.Age.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "sns.boxplot(x='Age',data=users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.0\n"
     ]
    }
   ],
   "source": [
    "q3=users.Age.quantile(0.75)\n",
    "print(1.5*q3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User-ID     3542\n",
       "Location    3542\n",
       "Age         3542\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['Age']>66.0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>User-ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>221</td>\n",
       "      <td>palm beach, florida, usa</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>483</td>\n",
       "      <td>shelton, washington, usa</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>540</td>\n",
       "      <td>watertown, south dakota, usa</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>690</td>\n",
       "      <td>lake oswego, oregon, usa</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>albuquerque, new mexico,</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     User-ID                      Location   Age\n",
       "220      221      palm beach, florida, usa  79.0\n",
       "482      483      shelton, washington, usa  72.0\n",
       "539      540  watertown, south dakota, usa  67.0\n",
       "689      690      lake oswego, oregon, usa  80.0\n",
       "729      730      albuquerque, new mexico,  69.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['Age']>66.0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Should I remove the outliers ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically there are users who specify thier ages higher than it actually is(don't know why like greater than 100) , it is better to remove the data of users of Age greater than 100 , because it would not be genuine Data , but since i am going for a basic approach towards explaining Collaborative Filtering i am going to consider only **User Similarity** on the basis of thier average rating. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Should I be concerned with the NULL values in Age Column?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this specific problem , the answer would be **yes** because you pobably want to match the users of same age group.But as i already mentioned i am going after matching users with only rating based criteria , i am skipping that part. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "users.Age.hist(bins=[10,20,30,40,50,60,70,80,90,100])"
   ]
  },
  {
   "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></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276725</td>\n",
       "      <td>034545104X</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>276726</td>\n",
       "      <td>0155061224</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>276727</td>\n",
       "      <td>0446520802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>276729</td>\n",
       "      <td>052165615X</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>276729</td>\n",
       "      <td>0521795028</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID        ISBN  Book-Rating\n",
       "0   276725  034545104X            0\n",
       "1   276726  0155061224            5\n",
       "2   276727  0446520802            0\n",
       "3   276729  052165615X            3\n",
       "4   276729  0521795028            6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1149780, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105283"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['User-ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings.rename(columns={'Book-Rating':'rating'},inplace=True)"
   ]
  },
  {
   "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>User-ID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>276725</td>\n",
       "      <td>034545104X</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>276726</td>\n",
       "      <td>0155061224</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>276727</td>\n",
       "      <td>0446520802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>276729</td>\n",
       "      <td>052165615X</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>276729</td>\n",
       "      <td>0521795028</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID        ISBN  rating\n",
       "0   276725  034545104X       0\n",
       "1   276726  0155061224       5\n",
       "2   276727  0446520802       0\n",
       "3   276729  052165615X       3\n",
       "4   276729  0521795028       6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings.rating.hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=ratings['User-ID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11676     13602\n",
       "198711     7550\n",
       "153662     6109\n",
       "98391      5891\n",
       "35859      5850\n",
       "          ...  \n",
       "158698        1\n",
       "17920         1\n",
       "277135        1\n",
       "275086        1\n",
       "187812        1\n",
       "Name: User-ID, Length: 105283, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    105283.000000\n",
       "mean         10.920851\n",
       "std          90.562825\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           1.000000\n",
       "75%           4.000000\n",
       "max       13602.000000\n",
       "Name: User-ID, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105283"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_count = pd.DataFrame(ratings.groupby('User-ID')['rating'].count())\n",
    "user_count.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13602"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_count.head()\n",
    "user_count['rating'].max()"
   ]
  },
  {
   "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></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User-ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3363</th>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3757</th>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6251</th>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6575</th>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7346</th>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274004</th>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274061</th>\n",
       "      <td>1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274308</th>\n",
       "      <td>1493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275970</th>\n",
       "      <td>1376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278418</th>\n",
       "      <td>4533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>287 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating\n",
       "User-ID        \n",
       "3363        901\n",
       "3757        605\n",
       "6251        892\n",
       "6575        814\n",
       "7346       1007\n",
       "...         ...\n",
       "274004      528\n",
       "274061     1038\n",
       "274308     1493\n",
       "275970     1376\n",
       "278418     4533\n",
       "\n",
       "[287 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_count[user_count['rating']>500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "k1=user_count[(user_count['rating']>1) & (user_count['rating']<100)].rating.count()\n",
    "k2=user_count[(user_count['rating']>100) & (user_count['rating']<200)].rating.count()\n",
    "k3=user_count[(user_count['rating']>200) & (user_count['rating']<500)].rating.count()\n",
    "k4=user_count[(user_count['rating']>500)].rating.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([k1,k2,k3,k4])\n",
    "y=np.array(['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44270 920 612 287\n"
     ]
    }
   ],
   "source": [
    "print(k1,k2,k3,k4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD7CAYAAACFfIhNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUoklEQVR4nO3dX0zdd/3H8dcp53BmCoYQOYWQpprNWdOTrZHTOXZxSIzyx8PJzHHEWiK7UOsabftbXM0JRbAXTapB6IyBXmiW2BgjqwwoHk6nM2011LWisR6t6aKlpmWBg9QBZ+NwzuH8Lvbr51fKBgcO+O1pn48bdr58v/D+fNLxPF9OobZ0Op0WAACSNlk9AADg3kEUAAAGUQAAGEQBAGAQBQCAYbd6gLVaWFhQLBaTw+GQzWazehwAyAnpdFqJREKbN2/Wpk1L7wtyNgqxWExXr161egwAyEmPPvqoCgsLlxzP2Sg4HA5J7y4sPz/f4mnWJhKJyO12Wz1GzmL/ssP+ZSdX929+fl5Xr141X0PvlrNRuP0to/z8fDmdTounWbtcnv1ewP5lh/3LTi7v3/t9250XmgEABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGA80FGYT6Qs/fwVFRWWfn6r1w/g3pOzP7y2HvIdefJ/s9/qMSxz+vtPWz0CgHvMA32nAABYjCgAAAyiAAAwiAIAwCAKAACDKAAADKIAADCIAgDAIAoAAIMoAAAMogAAMIgCAMAgCgAAgygAAAyiAAAwMo7Cd7/7XQWDQUnS8PCw/H6/qqur1dnZac65cuWKAoGAampqdPjwYSWTSUnS2NiYGhsbVVtbq3379ikWi0mSpqentXfvXtXV1amxsVHRaHQ91wYAWKWMonDhwgW98sorkqS5uTk1Nzerq6tLoVBIkUhE586dkyQdOnRIra2tOnPmjNLptHp6eiRJR44c0Z49exQOh+V2u9XV1SVJOn78uDwej4aGhtTQ0KCjR49uxBoBABlaMQr/+c9/1NnZqeeee06SdPnyZW3btk1bt26V3W6X3+9XOBzWzZs3NTc3p507d0qSAoGAwuGwEomELl26pJqamkXHJens2bPy+/2SpPr6ep0/f16JRGIj1gkAyMCK/xxna2urnn/+eb355puSpImJCZWUlJj3u1wujY+PLzleUlKi8fFx3bp1SwUFBbLb7YuO3/2x7Ha7CgoKNDU1pS1btmS8gEgkkvG5d7P630i+F4yMjFg9QlZyfX6rsX/ZuR/3b9kovPzyyyorK1NlZaV6e3slSQsLC7LZbOacdDotm832vsdvv73T3Y/vvGbTptW99u12u+V0Old1Df5fLodxZGQkp+e3GvuXnVzdv3g8vuyT6WWjEAqFFI1G9fTTT+utt97S22+/rZs3byovL8+cE41G5XK5VFpauuiF4snJSblcLhUXF2tmZkapVEp5eXnmfOndu4zJyUmVlpYqmUwqFoupqKgoyyUDANZq2aflL730kgYHB9Xf368DBw7oU5/6lH70ox/p2rVrun79ulKplAYHB+X1elVeXi6n02lup/r7++X1euVwOOTxeBQKhSRJfX198nq9kqSqqir19fVJejdAHo9HDodjA5cLAFjOiq8p3M3pdOrYsWPav3+/4vG4qqqqVFtbK0lqb29XS0uLZmdntWPHDjU1NUmS2traFAwG1d3drbKyMnV0dEiSDh48qGAwKJ/Pp8LCQrW3t6/j0gAAq5VxFAKBgAKBgCSpsrJSAwMDS87Zvn27Tp06teR4eXm5Tp48ueR4UVGRTpw4sZp5AQAbiJ9oBgAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYGUXhxRdf1Gc/+1n5fD699NJLkqTh4WH5/X5VV1ers7PTnHvlyhUFAgHV1NTo8OHDSiaTkqSxsTE1NjaqtrZW+/btUywWkyRNT09r7969qqurU2Njo6LR6HqvEQCQoRWjcPHiRf3+97/XwMCAfvGLX+jkyZP6+9//rubmZnV1dSkUCikSiejcuXOSpEOHDqm1tVVnzpxROp1WT0+PJOnIkSPas2ePwuGw3G63urq6JEnHjx+Xx+PR0NCQGhoadPTo0Q1cLgBgOStG4YknntBPfvIT2e12/fvf/1YqldL09LS2bdumrVu3ym63y+/3KxwO6+bNm5qbm9POnTslSYFAQOFwWIlEQpcuXVJNTc2i45J09uxZ+f1+SVJ9fb3Onz+vRCKxQcsFACwno28fORwO/eAHP5DP51NlZaUmJiZUUlJi3u9yuTQ+Pr7keElJicbHx3Xr1i0VFBTIbrcvOi5p0TV2u10FBQWamppatwUCADJnz/TEAwcO6Ktf/aqee+45jY6Oymazmfel02nZbDYtLCy85/Hbb+909+M7r9m0KfPXvyORSMbn3q2iomLN194vRkZGrB4hK7k+v9XYv+zcj/u3YhT+8Y9/aH5+Xh//+Mf1gQ98QNXV1QqHw8rLyzPnRKNRuVwulZaWLnqheHJyUi6XS8XFxZqZmVEqlVJeXp45X3r3LmNyclKlpaVKJpOKxWIqKirKeAFut1tOp3MVS8adcjmMIyMjOT2/1di/7OTq/sXj8WWfTK/4lPzGjRtqaWnR/Py85ufn9dprr2n37t26du2arl+/rlQqpcHBQXm9XpWXl8vpdJp69vf3y+v1yuFwyOPxKBQKSZL6+vrk9XolSVVVVerr65MkhUIheTweORyObNcNAFiDFe8UqqqqdPnyZX3uc59TXl6eqqur5fP5VFxcrP379ysej6uqqkq1tbWSpPb2drW0tGh2dlY7duxQU1OTJKmtrU3BYFDd3d0qKytTR0eHJOngwYMKBoPy+XwqLCxUe3v7Bi4XALAcWzqdTls9xFrcvgXK9ttH/m/2r+NUueX095+2eoSs5Ort+72C/ctOru7fSl87+YlmAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAARkZR+OEPfyifzyefz6fvfe97kqTh4WH5/X5VV1ers7PTnHvlyhUFAgHV1NTo8OHDSiaTkqSxsTE1NjaqtrZW+/btUywWkyRNT09r7969qqurU2Njo6LR6HqvEQCQoRWjMDw8rN/97nd65ZVX1NfXp7/+9a8aHBxUc3Ozurq6FAqFFIlEdO7cOUnSoUOH1NraqjNnziidTqunp0eSdOTIEe3Zs0fhcFhut1tdXV2SpOPHj8vj8WhoaEgNDQ06evToBi4XALCcFaNQUlKiYDCo/Px8ORwOPfzwwxodHdW2bdu0detW2e12+f1+hcNh3bx5U3Nzc9q5c6ckKRAIKBwOK5FI6NKlS6qpqVl0XJLOnj0rv98vSaqvr9f58+eVSCQ2aLkAgOWsGIWPfvSj5ov86OiohoaGZLPZVFJSYs5xuVwaHx/XxMTEouMlJSUaHx/XrVu3VFBQILvdvui4pEXX2O12FRQUaGpqat0WCADInD3TE9944w197Wtf07e+9S3l5eVpdHTUvC+dTstms2lhYUE2m23J8dtv73T34zuv2bQp89e/I5FIxuferaKiYs3X3i9GRkasHiEruT6/1di/7NyP+5dRFEZGRnTgwAE1NzfL5/Pp4sWLi14QjkajcrlcKi0tXXR8cnJSLpdLxcXFmpmZUSqVUl5enjlfevcuY3JyUqWlpUomk4rFYioqKsp4AW63W06nM+PzsVguh3FkZCSn57ca+5edXN2/eDy+7JPpFZ+Sv/nmm/r617+u9vZ2+Xw+SdLjjz+ua9eu6fr160qlUhocHJTX61V5ebmcTqepZ39/v7xerxwOhzwej0KhkCSpr69PXq9XklRVVaW+vj5JUigUksfjkcPhyGrRAIC1WfFO4cc//rHi8biOHTtmju3evVvHjh3T/v37FY/HVVVVpdraWklSe3u7WlpaNDs7qx07dqipqUmS1NbWpmAwqO7ubpWVlamjo0OSdPDgQQWDQfl8PhUWFqq9vX0j1gkAyMCKUWhpaVFLS8t7vm9gYGDJse3bt+vUqVNLjpeXl+vkyZNLjhcVFenEiROZzAoA2GD8RDMAwCAKAACDKAAADKIAADCIAgDAIAoAAIMoAAAMogAAMIgCAMAgCgAAgygAAAyiAAAwiAIAwCAKAACDKAAADKIAADCIAgDAIAoAAIMoAAAMogAAMIgCAMAgCgAAgygAAAyiAAAwiAIAwCAKAACDKAAADKIAADCIAgDAIAoAAIMoAAAMogAAMIgCAMAgCgAAgygAAAyiAAAwiAIAwCAKAAAjoyjMzs6qvr5eN27ckCQNDw/L7/erurpanZ2d5rwrV64oEAiopqZGhw8fVjKZlCSNjY2psbFRtbW12rdvn2KxmCRpenpae/fuVV1dnRobGxWNRtd7fQCAVVgxCn/+85/1xS9+UaOjo5Kkubk5NTc3q6urS6FQSJFIROfOnZMkHTp0SK2trTpz5ozS6bR6enokSUeOHNGePXsUDofldrvV1dUlSTp+/Lg8Ho+GhobU0NCgo0ePbtAyAQCZWDEKPT09amtrk8vlkiRdvnxZ27Zt09atW2W32+X3+xUOh3Xz5k3Nzc1p586dkqRAIKBwOKxEIqFLly6ppqZm0XFJOnv2rPx+vySpvr5e58+fVyKR2Ih1AgAyYF/phLufvU9MTKikpMQ8drlcGh8fX3K8pKRE4+PjunXrlgoKCmS32xcdv/tj2e12FRQUaGpqSlu2bMl+ZQCAVVsxCndbWFiQzWYzj9PptGw22/sev/32Tnc/vvOaTZtW99p3JBJZ1fl3qqioWPO194uRkRGrR8hKrs9vNfYvO/fj/q06CqWlpYteEI5Go3K5XEuOT05OyuVyqbi4WDMzM0qlUsrLyzPnS+/eZUxOTqq0tFTJZFKxWExFRUWrmsftdsvpdK52Gfg/uRzGkZGRnJ7fauxfdnJ1/+Lx+LJPplf9V1Iff/xxXbt2TdevX1cqldLg4KC8Xq/Ky8vldDpNOfv7++X1euVwOOTxeBQKhSRJfX198nq9kqSqqir19fVJkkKhkDwejxwOx2pHAgCsk1XfKTidTh07dkz79+9XPB5XVVWVamtrJUnt7e1qaWnR7OysduzYoaamJklSW1ubgsGguru7VVZWpo6ODknSwYMHFQwG5fP5VFhYqPb29nVcGgBgtTKOwm9+8xvz35WVlRoYGFhyzvbt23Xq1Kklx8vLy3Xy5Mklx4uKinTixIlMRwAAbDB+ohkAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFAIBBFAAABlEAABhEAQBgEAUAgEEUAAAGUQAAGEQBAGAQBQCAQRQAAAZRAAAYRAEAYBAFrNl8ImXp56+oqLD081u9fmAj2K0eQJJOnz6t7u5uJZNJPfvss2psbLR6JGQg35En/zf7rR7DMqe//3RW188nUsp35K3TNKt3L0TVyvXjvVkehfHxcXV2dqq3t1f5+fnavXu3PvnJT+qRRx6xejRgQxHV7KKKjWF5FIaHh/Xkk0+qqKhIklRTU6NwOKxvfOMby16XTqclSfPz81l9/qLND+4zlXg8nvXHYP+yw/6tXSKZksNu3f653e51+TOwVmtd/+2vmbe/ht7N8ihMTEyopKTEPHa5XLp8+fKK1yUSCUnS1atXs/r8//N0WVbX57JIJJL1x2D/ssP+wSqJREIPPfTQkuOWR2FhYUE2m808TqfTix6/n82bN+vRRx+Vw+HI6HwAwLtfYxOJhDZv3vye77c8CqWlpfrDH/5gHkejUblcrhWv27RpkwoLCzdyNAC4L73XHcJtlv+V1KeeekoXLlzQ1NSU3nnnHb366qvyer1WjwUADyTL7xS2bNmi559/Xk1NTUokEnrmmWf02GOPWT0WADyQbOn3ewkaAPDAsfzbRwCAewdRAAAYRAEAYBAFAIBBFJBTXn/9dX3pS1+yegxAkhQMBtXb22v1GOuKKAAADMt/TuFBk0wm9Z3vfEdvvPGGJicn9bGPfUwdHR3L/oQhFrt165a+/OUva2JiQo899pja2tqUn59v9Vg5IZ1Oq729Xb/+9a+Vl5enL3zhC3r22WetHitnpNNpHTt2TGfPnpXL5VIqldITTzxh9VjrijuF/7I//elPcjgc+vnPf65f/epXmpmZ0blz56weK6fcuHFD3/72tzUwMKBYLKaf/exnVo+UM8LhsP74xz/q9OnTevnll9Xb26toNGr1WDnjzJkz+tvf/qbBwUG9+OKL+te//mX1SOuOO4X/sl27dqmoqEg//elP9c9//lOjo6N6++23rR4rp3g8Hn34wx+WJPn9fvX29vJsN0OXLl1SXV2d8vPzlZ+fr/7+B/ffc1iLixcvqrq6Wg6HQ8XFxfflr+ThTuG/7LXXXtMLL7yghx56SIFAQLt27Xrf32uO92a3//9zmXQ6vegxlme32xf9VuEbN27wpGQVbDbbov9f78c/e0Thv+zChQuqq6vT5z//eX3wgx/U66+/rlSKf+t3NUZGRjQ2NqaFhQX19fXpqaeesnqknLFr1y69+uqrSiQSeuedd/SVr3xF4+PjVo+VMyorKzU0NKT5+Xm99dZb+u1vf2v1SOvu/svcPa6hoUEvvPCCfvnLX8rhcOgTn/iEbty4YfVYOeWRRx5Rc3OzotGonnzyST3zzDNWj5QzPvOZzygSiSgQCGhhYUFNTU36yEc+YvVYOePTn/60/vKXv6i+vl4f+tCH9PDDD1s90rrjF+IBAAy+fQQAMIgCAMAgCgAAgygAAAyiAAAwiAIAwCAKAACDKAAAjP8F9wKHZsJVQ94AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(y,x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Don't train the whole Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For statistical relevance i churned out users with less than 200 ratings and books with less than 100 ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts1=ratings['User-ID'].value_counts()\n",
    "ratings = ratings[ratings['User-ID'].isin(counts1[counts1 >= 200].index)]\n",
    "counts = ratings['rating'].value_counts()\n",
    "ratings = ratings[ratings['rating'].isin(counts[counts >= 100].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "622224 Datapoints has been churned out\n"
     ]
    }
   ],
   "source": [
    "ratings.shape\n",
    "print(1149780-ratings.shape[0],\"Datapoints has been churned out\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative Filtering using KNN  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to process further,merge or **book** and **rating** dataframe together on isbn and the useless columns are dropped. "
   ]
  },
  {
   "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></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>rating</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Image-URL-S</th>\n",
       "      <th>Image-URL-M</th>\n",
       "      <th>Image-URL-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277427</td>\n",
       "      <td>002542730X</td>\n",
       "      <td>10</td>\n",
       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>James Finn Garner</td>\n",
       "      <td>1994</td>\n",
       "      <td>John Wiley &amp;amp; Sons Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3363</td>\n",
       "      <td>002542730X</td>\n",
       "      <td>0</td>\n",
       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>James Finn Garner</td>\n",
       "      <td>1994</td>\n",
       "      <td>John Wiley &amp;amp; Sons Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11676</td>\n",
       "      <td>002542730X</td>\n",
       "      <td>6</td>\n",
       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>James Finn Garner</td>\n",
       "      <td>1994</td>\n",
       "      <td>John Wiley &amp;amp; Sons Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12538</td>\n",
       "      <td>002542730X</td>\n",
       "      <td>10</td>\n",
       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>James Finn Garner</td>\n",
       "      <td>1994</td>\n",
       "      <td>John Wiley &amp;amp; Sons Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13552</td>\n",
       "      <td>002542730X</td>\n",
       "      <td>0</td>\n",
       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>James Finn Garner</td>\n",
       "      <td>1994</td>\n",
       "      <td>John Wiley &amp;amp; Sons Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/002542730X.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID        ISBN  rating  \\\n",
       "0   277427  002542730X      10   \n",
       "1     3363  002542730X       0   \n",
       "2    11676  002542730X       6   \n",
       "3    12538  002542730X      10   \n",
       "4    13552  002542730X       0   \n",
       "\n",
       "                                          Book-Title        Book-Author  \\\n",
       "0  Politically Correct Bedtime Stories: Modern Ta...  James Finn Garner   \n",
       "1  Politically Correct Bedtime Stories: Modern Ta...  James Finn Garner   \n",
       "2  Politically Correct Bedtime Stories: Modern Ta...  James Finn Garner   \n",
       "3  Politically Correct Bedtime Stories: Modern Ta...  James Finn Garner   \n",
       "4  Politically Correct Bedtime Stories: Modern Ta...  James Finn Garner   \n",
       "\n",
       "  Year-Of-Publication                  Publisher  \\\n",
       "0                1994  John Wiley &amp; Sons Inc   \n",
       "1                1994  John Wiley &amp; Sons Inc   \n",
       "2                1994  John Wiley &amp; Sons Inc   \n",
       "3                1994  John Wiley &amp; Sons Inc   \n",
       "4                1994  John Wiley &amp; Sons Inc   \n",
       "\n",
       "                                         Image-URL-S  \\\n",
       "0  http://images.amazon.com/images/P/002542730X.0...   \n",
       "1  http://images.amazon.com/images/P/002542730X.0...   \n",
       "2  http://images.amazon.com/images/P/002542730X.0...   \n",
       "3  http://images.amazon.com/images/P/002542730X.0...   \n",
       "4  http://images.amazon.com/images/P/002542730X.0...   \n",
       "\n",
       "                                         Image-URL-M  \\\n",
       "0  http://images.amazon.com/images/P/002542730X.0...   \n",
       "1  http://images.amazon.com/images/P/002542730X.0...   \n",
       "2  http://images.amazon.com/images/P/002542730X.0...   \n",
       "3  http://images.amazon.com/images/P/002542730X.0...   \n",
       "4  http://images.amazon.com/images/P/002542730X.0...   \n",
       "\n",
       "                                         Image-URL-L  \n",
       "0  http://images.amazon.com/images/P/002542730X.0...  \n",
       "1  http://images.amazon.com/images/P/002542730X.0...  \n",
       "2  http://images.amazon.com/images/P/002542730X.0...  \n",
       "3  http://images.amazon.com/images/P/002542730X.0...  \n",
       "4  http://images.amazon.com/images/P/002542730X.0...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_book_rating = pd.merge(ratings, books, on='ISBN')\n",
    "columns = ['yearOfPublication', 'publisher', 'bookAuthor', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n",
    "combine_book_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
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      "text/plain": [
       "   User-ID        ISBN  rating  \\\n",
       "0   277427  002542730X      10   \n",
       "1     3363  002542730X       0   \n",
       "2    11676  002542730X       6   \n",
       "3    12538  002542730X      10   \n",
       "4    13552  002542730X       0   \n",
       "\n",
       "                                          Book-Title  \n",
       "0  Politically Correct Bedtime Stories: Modern Ta...  \n",
       "1  Politically Correct Bedtime Stories: Modern Ta...  \n",
       "2  Politically Correct Bedtime Stories: Modern Ta...  \n",
       "3  Politically Correct Bedtime Stories: Modern Ta...  \n",
       "4  Politically Correct Bedtime Stories: Modern Ta...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Year-Of-Publication', 'Publisher', 'Book-Author', 'Image-URL-S', 'Image-URL-M', 'Image-URL-L']\n",
    "combine_book_rating = combine_book_rating.drop(columns, axis=1)\n",
    "combine_book_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_counts=combine_book_rating.groupby(by=['Book-Title'])['rating'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_with_rating_count=combine_book_rating.merge(rating_counts,on='Book-Title',how='left')#merge on left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ISBN</th>\n",
       "      <th>rating_x</th>\n",
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       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13552</td>\n",
       "      <td>002542730X</td>\n",
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       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID        ISBN  rating_x  \\\n",
       "0   277427  002542730X        10   \n",
       "1     3363  002542730X         0   \n",
       "2    11676  002542730X         6   \n",
       "3    12538  002542730X        10   \n",
       "4    13552  002542730X         0   \n",
       "\n",
       "                                          Book-Title  rating_y  \n",
       "0  Politically Correct Bedtime Stories: Modern Ta...        82  \n",
       "1  Politically Correct Bedtime Stories: Modern Ta...        82  \n",
       "2  Politically Correct Bedtime Stories: Modern Ta...        82  \n",
       "3  Politically Correct Bedtime Stories: Modern Ta...        82  \n",
       "4  Politically Correct Bedtime Stories: Modern Ta...        82  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_rating_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_with_rating_count.rename(columns={'rating_x':'Book-Rating','rating_y':'Rating-count'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13552</td>\n",
       "      <td>002542730X</td>\n",
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       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User-ID        ISBN  Book-Rating  \\\n",
       "0   277427  002542730X           10   \n",
       "1     3363  002542730X            0   \n",
       "2    11676  002542730X            6   \n",
       "3    12538  002542730X           10   \n",
       "4    13552  002542730X            0   \n",
       "\n",
       "                                          Book-Title  Rating-count  \n",
       "0  Politically Correct Bedtime Stories: Modern Ta...            82  \n",
       "1  Politically Correct Bedtime Stories: Modern Ta...            82  \n",
       "2  Politically Correct Bedtime Stories: Modern Ta...            82  \n",
       "3  Politically Correct Bedtime Stories: Modern Ta...            82  \n",
       "4  Politically Correct Bedtime Stories: Modern Ta...            82  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_rating_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce Data with declaring popularity threshold , check different quantile distributions to get an idea about popularity threshold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_with_rating_count.rename(columns={'Rating-count':'RatingCount'},inplace=True)\n",
    "popularity_threshold = 50\n",
    "rating_with_rating_count.query('RatingCount >= @popularity_threshold',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_with_rating_count.drop_duplicates(['User-ID','Book-Title'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a pivot Table so as to calculate **Similarity** scores between different Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>User-ID</th>\n",
       "      <th>254</th>\n",
       "      <th>2276</th>\n",
       "      <th>2766</th>\n",
       "      <th>2977</th>\n",
       "      <th>3363</th>\n",
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       "      <th>277478</th>\n",
       "      <th>277639</th>\n",
       "      <th>278418</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Book-Title</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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1st to Die: A Novel</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>2nd Chance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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 Blondes</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>84 Charing Cross Road</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>10.0</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 × 894 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "User-ID                254     2276    2766    2977    3363    3757    4017    \\\n",
       "Book-Title                                                                      \n",
       "1984                      9.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "1st to Die: A Novel       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2nd Chance                0.0    10.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4 Blondes                 0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "84 Charing Cross Road     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "User-ID                4385    6242    6251    ...  274004  274061  274301  \\\n",
       "Book-Title                                     ...                           \n",
       "1984                      0.0     0.0     0.0  ...     0.0     0.0     0.0   \n",
       "1st to Die: A Novel       0.0     0.0     0.0  ...     0.0     0.0     0.0   \n",
       "2nd Chance                0.0     0.0     0.0  ...     0.0     0.0     0.0   \n",
       "4 Blondes                 0.0     0.0     0.0  ...     0.0     0.0     0.0   \n",
       "84 Charing Cross Road     0.0     0.0     0.0  ...     0.0     0.0     0.0   \n",
       "\n",
       "User-ID                274308  274808  275970  277427  277478  277639  278418  \n",
       "Book-Title                                                                     \n",
       "1984                      0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "1st to Die: A Novel       0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "2nd Chance                0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "4 Blondes                 0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "84 Charing Cross Road     0.0     0.0    10.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 894 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_rating_count_pivot=rating_with_rating_count.pivot(index='Book-Title',columns='User-ID',values='Book-Rating').fillna(0)\n",
    "rating_with_rating_count_pivot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the pivot table that is in DataFrame form to a sparse Matrix so as to feed it to **KNN classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "csr_matrix_rating=csr_matrix(rating_with_rating_count_pivot.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<746x894 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 15040 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr_matrix_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cosine Similarity scores are calculated using **dot products** between several given columns to match the users. "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n",
    "model_knn.fit(csr_matrix_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "358\n"
     ]
    }
   ],
   "source": [
    "query_index = np.random.choice(rating_with_rating_count_pivot.shape[0])\n",
    "print(query_index)\n",
    "distances, indices = model_knn.kneighbors(rating_with_rating_count_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Naked'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_rating_count_pivot.index[query_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a **function** to get recommendation using book name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getrec(book_name):\n",
    "    distances,indices=model_knn.kneighbors(rating_with_rating_count_pivot[rating_with_rating_count_pivot.index==book_name].values.reshape(1,-1),n_neighbors=6)\n",
    "    for i in range(0, len(distances.flatten())):\n",
    "        if i == 0:\n",
    "            print('Recommendations for {0}:\\n'.format(book_name))\n",
    "        else:\n",
    "            print('{0}: {1}, with distance of {2}:'.format(i, rating_with_rating_count_pivot.index[indices.flatten()[i]], distances.flatten()[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Naked:\n",
      "\n",
      "1: Me Talk Pretty One Day, with distance of 0.6545588833986331:\n",
      "2: 1984, with distance of 0.8039283491773567:\n",
      "3: Big Trouble, with distance of 0.806687213041357:\n",
      "4: Bastard Out of Carolina, with distance of 0.8135603168032868:\n",
      "5: Turtle Moon, with distance of 0.8207052172096709:\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(distances.flatten())):\n",
    "    if i == 0:\n",
    "        print('Recommendations for {0}:\\n'.format(rating_with_rating_count_pivot.index[query_index]))\n",
    "    else:\n",
    "        print('{0}: {1}, with distance of {2}:'.format(i, rating_with_rating_count_pivot.index[indices.flatten()[i]], distances.flatten()[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for 2nd Chance:\n",
      "\n",
      "1: Four Blind Mice, with distance of 0.6188065351158174:\n",
      "2: The Next Accident, with distance of 0.6403748967888808:\n",
      "3: Violets Are Blue, with distance of 0.6837233366146325:\n",
      "4: The Murder Book, with distance of 0.7178008597223355:\n",
      "5: The Blue Nowhere : A Novel, with distance of 0.7238502444117916:\n"
     ]
    }
   ],
   "source": [
    "getrec('2nd Chance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for 1st to Die: A Novel:\n",
      "\n",
      "1: Along Came a Spider (Alex Cross Novels), with distance of 0.6735718989875843:\n",
      "2: Pop Goes the Weasel, with distance of 0.7136513768524853:\n",
      "3: Violets Are Blue, with distance of 0.7177558215443278:\n",
      "4: 2nd Chance, with distance of 0.7257825109812934:\n",
      "5: Roses Are Red (Alex Cross Novels), with distance of 0.7336101642020221:\n"
     ]
    }
   ],
   "source": [
    "getrec('1st to Die: A Novel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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