book_recommendation_content_based.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Book Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import pickle5 as pickle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Overview\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Books data** : Giving the detail overview about the book information including the book title, publication year, as well as the author of the book\n",
    "2. **Users data** : Give the detailed overview about each user such as the user's location as well as the user's age\n",
    "3. **Ratings data** : Give the detailed overview about the rating that each user give to the book"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Books Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books1 = pd.read_csv('data/Books.csv', sep=',', error_bad_lines=False, usecols = [0,1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 271360 entries, 0 to 271359\n",
      "Data columns (total 5 columns):\n",
      " #   Column               Non-Null Count   Dtype \n",
      "---  ------               --------------   ----- \n",
      " 0   ISBN                 271360 non-null  object\n",
      " 1   Book-Title           271360 non-null  object\n",
      " 2   Book-Author          271359 non-null  object\n",
      " 3   Year-Of-Publication  271360 non-null  object\n",
      " 4   Publisher            271358 non-null  object\n",
      "dtypes: object(5)\n",
      "memory usage: 10.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_books1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid literal for int() with base 10: 'DK Publishing Inc'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df_books1['Year-Of-Publication']  = df_books1['Year-Of-Publication'].astype(int)\n",
    "except Exception as e:\n",
    "    print(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209538</th>\n",
       "      <td>078946697X</td>\n",
       "      <td>DK Readers: Creating the X-Men, How It All Beg...</td>\n",
       "      <td>2000</td>\n",
       "      <td>DK Publishing Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/078946697X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221678</th>\n",
       "      <td>0789466953</td>\n",
       "      <td>DK Readers: Creating the X-Men, How Comic Book...</td>\n",
       "      <td>2000</td>\n",
       "      <td>DK Publishing Inc</td>\n",
       "      <td>http://images.amazon.com/images/P/0789466953.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ISBN                                         Book-Title  \\\n",
       "209538  078946697X  DK Readers: Creating the X-Men, How It All Beg...   \n",
       "221678  0789466953  DK Readers: Creating the X-Men, How Comic Book...   \n",
       "\n",
       "       Book-Author Year-Of-Publication  \\\n",
       "209538        2000   DK Publishing Inc   \n",
       "221678        2000   DK Publishing Inc   \n",
       "\n",
       "                                                Publisher  \n",
       "209538  http://images.amazon.com/images/P/078946697X.0...  \n",
       "221678  http://images.amazon.com/images/P/0789466953.0...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books1[df_books1['Year-Of-Publication'] == 'DK Publishing Inc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books1['Year-Of-Publication'] = pd.to_numeric(df_books1['Year-Of-Publication'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books1 = df_books1.dropna()\n",
    "df_books1['Year-Of-Publication'] = df_books1['Year-Of-Publication'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books2 = pd.read_csv('data/BooksWithCategory.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books2.set_axis(['ASIN', 'Filename', 'Image Url', 'Book-Title', 'Author', 'Category ID', 'Category'],\n",
    "                    axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\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>ASIN</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Image Url</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>Category ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623439671</td>\n",
       "      <td>1623439671.jpg</td>\n",
       "      <td>http://ecx.images-amazon.com/images/I/61t-hrSw...</td>\n",
       "      <td>Doug the Pug 2016 Wall Calendar</td>\n",
       "      <td>Doug the Pug</td>\n",
       "      <td>3</td>\n",
       "      <td>Calendars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00O80WC6I</td>\n",
       "      <td>B00O80WC6I.jpg</td>\n",
       "      <td>http://ecx.images-amazon.com/images/I/41X-KQqs...</td>\n",
       "      <td>Moleskine 2016 Weekly Notebook, 12M, Large, Bl...</td>\n",
       "      <td>Moleskine</td>\n",
       "      <td>3</td>\n",
       "      <td>Calendars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>761182187</td>\n",
       "      <td>0761182187.jpg</td>\n",
       "      <td>http://ecx.images-amazon.com/images/I/61j-4gxJ...</td>\n",
       "      <td>365 Cats Color Page-A-Day Calendar 2016</td>\n",
       "      <td>Workman Publishing</td>\n",
       "      <td>3</td>\n",
       "      <td>Calendars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1578052084</td>\n",
       "      <td>1578052084.jpg</td>\n",
       "      <td>http://ecx.images-amazon.com/images/I/51Ry4Tsq...</td>\n",
       "      <td>Sierra Club Engagement Calendar 2016</td>\n",
       "      <td>Sierra Club</td>\n",
       "      <td>3</td>\n",
       "      <td>Calendars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1578052076</td>\n",
       "      <td>1578052076.jpg</td>\n",
       "      <td>http://ecx.images-amazon.com/images/I/619KxYEq...</td>\n",
       "      <td>Sierra Club Wilderness Calendar 2016</td>\n",
       "      <td>Sierra Club</td>\n",
       "      <td>3</td>\n",
       "      <td>Calendars</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ASIN        Filename  \\\n",
       "0  1623439671  1623439671.jpg   \n",
       "1  B00O80WC6I  B00O80WC6I.jpg   \n",
       "2   761182187  0761182187.jpg   \n",
       "3  1578052084  1578052084.jpg   \n",
       "4  1578052076  1578052076.jpg   \n",
       "\n",
       "                                           Image Url  \\\n",
       "0  http://ecx.images-amazon.com/images/I/61t-hrSw...   \n",
       "1  http://ecx.images-amazon.com/images/I/41X-KQqs...   \n",
       "2  http://ecx.images-amazon.com/images/I/61j-4gxJ...   \n",
       "3  http://ecx.images-amazon.com/images/I/51Ry4Tsq...   \n",
       "4  http://ecx.images-amazon.com/images/I/619KxYEq...   \n",
       "\n",
       "                                          Book-Title              Author  \\\n",
       "0                    Doug the Pug 2016 Wall Calendar        Doug the Pug   \n",
       "1  Moleskine 2016 Weekly Notebook, 12M, Large, Bl...           Moleskine   \n",
       "2            365 Cats Color Page-A-Day Calendar 2016  Workman Publishing   \n",
       "3               Sierra Club Engagement Calendar 2016         Sierra Club   \n",
       "4               Sierra Club Wilderness Calendar 2016         Sierra Club   \n",
       "\n",
       "   Category ID   Category  \n",
       "0            3  Calendars  \n",
       "1            3  Calendars  \n",
       "2            3  Calendars  \n",
       "3            3  Calendars  \n",
       "4            3  Calendars  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books2 = df_books2.drop(['ASIN', 'Filename', 'Image Url', 'Author'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 207571 entries, 0 to 207570\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count   Dtype \n",
      "---  ------       --------------   ----- \n",
      " 0   Book-Title   207571 non-null  object\n",
      " 1   Category ID  207571 non-null  int64 \n",
      " 2   Category     207571 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_books2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books = pd.merge(df_books1, df_books2, on='Book-Title')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Users Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_users = pd.read_csv('data/Users.csv', sep=',', error_bad_lines=False, usecols = [0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 278858 entries, 0 to 278857\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   User-ID   278858 non-null  int64  \n",
      " 1   Location  278858 non-null  object \n",
      " 2   Age       168096 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratings Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings = pd.read_csv('data/Ratings.csv', sep=',', error_bad_lines=False, usecols = [0,1,2])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1149780 entries, 0 to 1149779\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count    Dtype \n",
      "---  ------       --------------    ----- \n",
      " 0   User-ID      1149780 non-null  int64 \n",
      " 1   ISBN         1149780 non-null  object\n",
      " 2   Book-Rating  1149780 non-null  int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 26.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <td>9</td>\n",
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       "      <th>1149776</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1149777</th>\n",
       "      <td>276709</td>\n",
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       "      <td>10</td>\n",
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       "      <th>1149778</th>\n",
       "      <td>276721</td>\n",
       "      <td>0590442449</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1149779</th>\n",
       "      <td>276723</td>\n",
       "      <td>05162443314</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1149780 rows × 3 columns</p>\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\n",
       "...          ...          ...          ...\n",
       "1149775   276704   1563526298            9\n",
       "1149776   276706   0679447156            0\n",
       "1149777   276709   0515107662           10\n",
       "1149778   276721   0590442449           10\n",
       "1149779   276723  05162443314            8\n",
       "\n",
       "[1149780 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Category ID</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
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       "  <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>15</td>\n",
       "      <td>Literature &amp; Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0801319536</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>1998</td>\n",
       "      <td>John Wiley &amp;amp; Sons</td>\n",
       "      <td>15</td>\n",
       "      <td>Literature &amp; Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0399135782</td>\n",
       "      <td>The Kitchen God's Wife</td>\n",
       "      <td>Amy Tan</td>\n",
       "      <td>1991</td>\n",
       "      <td>Putnam Pub Group</td>\n",
       "      <td>15</td>\n",
       "      <td>Literature &amp; Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>080410753X</td>\n",
       "      <td>The Kitchen God's Wife</td>\n",
       "      <td>Amy Tan</td>\n",
       "      <td>1992</td>\n",
       "      <td>Ivy Books</td>\n",
       "      <td>15</td>\n",
       "      <td>Literature &amp; Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>080410753x</td>\n",
       "      <td>The Kitchen God's Wife</td>\n",
       "      <td>Amy Tan</td>\n",
       "      <td>1992</td>\n",
       "      <td>Ivy Books</td>\n",
       "      <td>15</td>\n",
       "      <td>Literature &amp; Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14620</th>\n",
       "      <td>0911647155</td>\n",
       "      <td>Cutting (Western Horseman Books)</td>\n",
       "      <td>Leon Harrel</td>\n",
       "      <td>2002</td>\n",
       "      <td>Western Horseman</td>\n",
       "      <td>26</td>\n",
       "      <td>Sports &amp; Outdoors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>0691027641</td>\n",
       "      <td>Makers of Modern Strategy from Machiavelli to ...</td>\n",
       "      <td>Peter Paret</td>\n",
       "      <td>1986</td>\n",
       "      <td>Princeton University Press</td>\n",
       "      <td>12</td>\n",
       "      <td>History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14622</th>\n",
       "      <td>0395957699</td>\n",
       "      <td>One Man's Garden</td>\n",
       "      <td>Henry Mitchell</td>\n",
       "      <td>1999</td>\n",
       "      <td>Mariner Books</td>\n",
       "      <td>8</td>\n",
       "      <td>Crafts, Hobbies &amp; Home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14623</th>\n",
       "      <td>0881924989</td>\n",
       "      <td>The Cactus Family</td>\n",
       "      <td>Edward F. Anderson</td>\n",
       "      <td>2001</td>\n",
       "      <td>Timber Press (OR)</td>\n",
       "      <td>8</td>\n",
       "      <td>Crafts, Hobbies &amp; Home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14624</th>\n",
       "      <td>0345298608</td>\n",
       "      <td>The Broken Sword</td>\n",
       "      <td>Northam Anderson</td>\n",
       "      <td>1981</td>\n",
       "      <td>Del Rey Books</td>\n",
       "      <td>24</td>\n",
       "      <td>Science Fiction &amp; Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14625 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ISBN                                         Book-Title  \\\n",
       "0      0195153448                                Classical Mythology   \n",
       "1      0801319536                                Classical Mythology   \n",
       "2      0399135782                             The Kitchen God's Wife   \n",
       "3      080410753X                             The Kitchen God's Wife   \n",
       "4      080410753x                             The Kitchen God's Wife   \n",
       "...           ...                                                ...   \n",
       "14620  0911647155                   Cutting (Western Horseman Books)   \n",
       "14621  0691027641  Makers of Modern Strategy from Machiavelli to ...   \n",
       "14622  0395957699                                   One Man's Garden   \n",
       "14623  0881924989                                  The Cactus Family   \n",
       "14624  0345298608                                   The Broken Sword   \n",
       "\n",
       "              Book-Author  Year-Of-Publication                   Publisher  \\\n",
       "0      Mark P. O. Morford                 2002     Oxford University Press   \n",
       "1      Mark P. O. Morford                 1998       John Wiley &amp; Sons   \n",
       "2                 Amy Tan                 1991            Putnam Pub Group   \n",
       "3                 Amy Tan                 1992                   Ivy Books   \n",
       "4                 Amy Tan                 1992                   Ivy Books   \n",
       "...                   ...                  ...                         ...   \n",
       "14620         Leon Harrel                 2002            Western Horseman   \n",
       "14621         Peter Paret                 1986  Princeton University Press   \n",
       "14622      Henry Mitchell                 1999               Mariner Books   \n",
       "14623  Edward F. Anderson                 2001           Timber Press (OR)   \n",
       "14624    Northam Anderson                 1981               Del Rey Books   \n",
       "\n",
       "       Category ID                   Category  \n",
       "0               15       Literature & Fiction  \n",
       "1               15       Literature & Fiction  \n",
       "2               15       Literature & Fiction  \n",
       "3               15       Literature & Fiction  \n",
       "4               15       Literature & Fiction  \n",
       "...            ...                        ...  \n",
       "14620           26          Sports & Outdoors  \n",
       "14621           12                    History  \n",
       "14622            8     Crafts, Hobbies & Home  \n",
       "14623            8     Crafts, Hobbies & Home  \n",
       "14624           24  Science Fiction & Fantasy  \n",
       "\n",
       "[14625 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data before cleaning : 1149780\n",
      "Number of data after cleaning : 96495\n"
     ]
    }
   ],
   "source": [
    "print('Number of data before cleaning : {}'.format(len(df_ratings)))\n",
    "df_ratings = df_ratings[df_ratings['ISBN'].isin(df_books['ISBN'])]\n",
    "print('Number of data after cleaning : {}'.format(len(df_ratings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1149751</th>\n",
       "      <td>276690</td>\n",
       "      <td>0440439884</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1149763</th>\n",
       "      <td>276704</td>\n",
       "      <td>0395404258</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1149768</th>\n",
       "      <td>276704</td>\n",
       "      <td>0446605409</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1149771</th>\n",
       "      <td>276704</td>\n",
       "      <td>0743211383</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>1149774</th>\n",
       "      <td>276704</td>\n",
       "      <td>0876044011</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96495 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User-ID        ISBN  Book-Rating\n",
       "2         276727  0446520802            0\n",
       "8         276744  038550120X            7\n",
       "10        276746  0425115801            0\n",
       "19        276747  0671537458            9\n",
       "24        276748  0747558167            6\n",
       "...          ...         ...          ...\n",
       "1149751   276690  0440439884            0\n",
       "1149763   276704  0395404258            0\n",
       "1149768   276704  0446605409            0\n",
       "1149771   276704  0743211383            7\n",
       "1149774   276704  0876044011            0\n",
       "\n",
       "[96495 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_official = pd.merge(df_ratings, df_books, on='ISBN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_official.drop_duplicates(subset=['User-ID', 'ISBN'], inplace=True, keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_official\n",
    "df_official.to_csv('data/Official.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Content Based Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_official"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>The Notebook</td>\n",
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       "      <td>10</td>\n",
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       "      <td>1996</td>\n",
       "      <td>Bantam</td>\n",
       "      <td>15</td>\n",
       "      <td>Literature &amp; Fiction</td>\n",
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       "      <td>276581</td>\n",
       "      <td>0312311362</td>\n",
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       "      <td>Radiance: A Novel</td>\n",
       "      <td>Carter Scholz</td>\n",
       "      <td>2003</td>\n",
       "      <td>Picador USA</td>\n",
       "      <td>24</td>\n",
       "      <td>Science Fiction &amp; Fantasy</td>\n",
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       "      <th>115570</th>\n",
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       "      <td>0</td>\n",
       "      <td>Bay of Tigers: An Odyssey through War-torn Angola</td>\n",
       "      <td>Pedro Rosa Mendes</td>\n",
       "      <td>2003</td>\n",
       "      <td>Harcourt</td>\n",
       "      <td>29</td>\n",
       "      <td>Travel</td>\n",
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       "    <tr>\n",
       "      <th>115571</th>\n",
       "      <td>276688</td>\n",
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       "      <td>Sacred Clowns</td>\n",
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       "      <td>Science Fiction &amp; Fantasy</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>96495 rows × 9 columns</p>\n",
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      "text/plain": [
       "        User-ID        ISBN  Book-Rating  \\\n",
       "0        276727  0446520802            0   \n",
       "1        278418  0446520802            0   \n",
       "2           638  0446520802            0   \n",
       "3          3363  0446520802            0   \n",
       "4          7158  0446520802           10   \n",
       "...         ...         ...          ...   \n",
       "115568   276556  055337849x           10   \n",
       "115569   276581  0312311362            0   \n",
       "115570   276641  0151006555            0   \n",
       "115571   276688  0060168307            0   \n",
       "115572   276688  1575660792            7   \n",
       "\n",
       "                                               Book-Title         Book-Author  \\\n",
       "0                                            The Notebook     Nicholas Sparks   \n",
       "1                                            The Notebook     Nicholas Sparks   \n",
       "2                                            The Notebook     Nicholas Sparks   \n",
       "3                                            The Notebook     Nicholas Sparks   \n",
       "4                                            The Notebook     Nicholas Sparks   \n",
       "...                                                   ...                 ...   \n",
       "115568                                     The Brothers K  David James Duncan   \n",
       "115569                                  Radiance: A Novel       Carter Scholz   \n",
       "115570  Bay of Tigers: An Odyssey through War-torn Angola   Pedro Rosa Mendes   \n",
       "115571                                      Sacred Clowns      Tony Hillerman   \n",
       "115572                                        Gray Matter     Shirley Kennett   \n",
       "\n",
       "        Year-Of-Publication                          Publisher  Category ID  \\\n",
       "0                      1996                       Warner Books           22   \n",
       "1                      1996                       Warner Books           22   \n",
       "2                      1996                       Warner Books           22   \n",
       "3                      1996                       Warner Books           22   \n",
       "4                      1996                       Warner Books           22   \n",
       "...                     ...                                ...          ...   \n",
       "115568                 1996                             Bantam           15   \n",
       "115569                 2003                        Picador USA           24   \n",
       "115570                 2003                           Harcourt           29   \n",
       "115571                 1994                      Harpercollins           15   \n",
       "115572                 1996  Kensington Publishing Corporation           24   \n",
       "\n",
       "                         Category  \n",
       "0                         Romance  \n",
       "1                         Romance  \n",
       "2                         Romance  \n",
       "3                         Romance  \n",
       "4                         Romance  \n",
       "...                           ...  \n",
       "115568       Literature & Fiction  \n",
       "115569  Science Fiction & Fantasy  \n",
       "115570                     Travel  \n",
       "115571       Literature & Fiction  \n",
       "115572  Science Fiction & Fantasy  \n",
       "\n",
       "[96495 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg = df1.groupby('Book-Title')['Book-Rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 =  df1.join(avg, on='Book-Title', rsuffix='_Avg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        User-ID        ISBN  Book-Rating  \\\n",
       "0        276727  0446520802            0   \n",
       "1        278418  0446520802            0   \n",
       "2           638  0446520802            0   \n",
       "3          3363  0446520802            0   \n",
       "4          7158  0446520802           10   \n",
       "...         ...         ...          ...   \n",
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       "115570   276641  0151006555            0   \n",
       "115571   276688  0060168307            0   \n",
       "115572   276688  1575660792            7   \n",
       "\n",
       "                                               Book-Title         Book-Author  \\\n",
       "0                                            The Notebook     Nicholas Sparks   \n",
       "1                                            The Notebook     Nicholas Sparks   \n",
       "2                                            The Notebook     Nicholas Sparks   \n",
       "3                                            The Notebook     Nicholas Sparks   \n",
       "4                                            The Notebook     Nicholas Sparks   \n",
       "...                                                   ...                 ...   \n",
       "115568                                     The Brothers K  David James Duncan   \n",
       "115569                                  Radiance: A Novel       Carter Scholz   \n",
       "115570  Bay of Tigers: An Odyssey through War-torn Angola   Pedro Rosa Mendes   \n",
       "115571                                      Sacred Clowns      Tony Hillerman   \n",
       "115572                                        Gray Matter     Shirley Kennett   \n",
       "\n",
       "        Year-Of-Publication                          Publisher  Category ID  \\\n",
       "0                      1996                       Warner Books           22   \n",
       "1                      1996                       Warner Books           22   \n",
       "2                      1996                       Warner Books           22   \n",
       "3                      1996                       Warner Books           22   \n",
       "4                      1996                       Warner Books           22   \n",
       "...                     ...                                ...          ...   \n",
       "115568                 1996                             Bantam           15   \n",
       "115569                 2003                        Picador USA           24   \n",
       "115570                 2003                           Harcourt           29   \n",
       "115571                 1994                      Harpercollins           15   \n",
       "115572                 1996  Kensington Publishing Corporation           24   \n",
       "\n",
       "                         Category  Book-Rating_Avg  \n",
       "0                         Romance         3.560000  \n",
       "1                         Romance         3.560000  \n",
       "2                         Romance         3.560000  \n",
       "3                         Romance         3.560000  \n",
       "4                         Romance         3.560000  \n",
       "...                           ...              ...  \n",
       "115568       Literature & Fiction         5.000000  \n",
       "115569  Science Fiction & Fantasy         0.000000  \n",
       "115570                     Travel         0.000000  \n",
       "115571       Literature & Fiction         1.611111  \n",
       "115572  Science Fiction & Fantasy         2.619048  \n",
       "\n",
       "[96495 rows x 10 columns]"
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['Publisher', 'Category ID', 'ISBN', 'Year-Of-Publication', 'User-ID', 'Book-Rating'], axis=1, inplace=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                               Book-Title         Book-Author  \\\n",
       "0                                            The Notebook     Nicholas Sparks   \n",
       "1                                            The Notebook     Nicholas Sparks   \n",
       "2                                            The Notebook     Nicholas Sparks   \n",
       "3                                            The Notebook     Nicholas Sparks   \n",
       "4                                            The Notebook     Nicholas Sparks   \n",
       "...                                                   ...                 ...   \n",
       "115568                                     The Brothers K  David James Duncan   \n",
       "115569                                  Radiance: A Novel       Carter Scholz   \n",
       "115570  Bay of Tigers: An Odyssey through War-torn Angola   Pedro Rosa Mendes   \n",
       "115571                                      Sacred Clowns      Tony Hillerman   \n",
       "115572                                        Gray Matter     Shirley Kennett   \n",
       "\n",
       "                         Category  Book-Rating_Avg  \n",
       "0                         Romance         3.560000  \n",
       "1                         Romance         3.560000  \n",
       "2                         Romance         3.560000  \n",
       "3                         Romance         3.560000  \n",
       "4                         Romance         3.560000  \n",
       "...                           ...              ...  \n",
       "115568       Literature & Fiction         5.000000  \n",
       "115569  Science Fiction & Fantasy         0.000000  \n",
       "115570                     Travel         0.000000  \n",
       "115571       Literature & Fiction         1.611111  \n",
       "115572  Science Fiction & Fantasy         2.619048  \n",
       "\n",
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "df1.drop_duplicates(subset=\"Book-Title\",inplace=True, keep=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Category</th>\n",
       "      <th>Book-Rating_Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28510</th>\n",
       "      <td>The Guardian</td>\n",
       "      <td>Nicholas Sparks</td>\n",
       "      <td>Romance</td>\n",
       "      <td>3.076503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Book-Title      Book-Author Category  Book-Rating_Avg\n",
       "28510  The Guardian  Nicholas Sparks  Romance         3.076503"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df1[df1[\"Book-Title\"] == \"The Guardian\"]\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fillnabooks= df1.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(x):\n",
    "        return str.lower(x.replace(\" \", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['Book-Title','Book-Author', 'Category','Book-Rating_Avg']\n",
    "fillednabooks=fillnabooks[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Book-Title         object\n",
       "Book-Author        object\n",
       "Category           object\n",
       "Book-Rating_Avg    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillednabooks = fillednabooks.astype(str)\n",
    "fillednabooks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Category</th>\n",
       "      <th>Book-Rating_Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>thenotebook</td>\n",
       "      <td>nicholassparks</td>\n",
       "      <td>romance</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>apaintedhouse</td>\n",
       "      <td>johngrisham</td>\n",
       "      <td>mystery,thriller&amp;suspense</td>\n",
       "      <td>3.231503579952267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Book-Title     Book-Author                   Category  \\\n",
       "0      thenotebook  nicholassparks                    romance   \n",
       "116  apaintedhouse     johngrisham  mystery,thriller&suspense   \n",
       "\n",
       "       Book-Rating_Avg  \n",
       "0                 3.56  \n",
       "116  3.231503579952267  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for feature in features:\n",
    "    fillednabooks[feature] = fillednabooks[feature].apply(clean_data)\n",
    "    \n",
    "fillednabooks.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_soup(x):\n",
    "    return x['Book-Title']+ ' ' + x['Book-Author'] + ' ' + x['Category']  + ' ' + x['Book-Rating_Avg'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "fillednabooks['soup'] = fillednabooks.apply(create_soup, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = CountVectorizer(stop_words='english')\n",
    "count_matrix = count.fit_transform(fillednabooks['soup'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_sim2 = cosine_similarity(count_matrix, count_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(cosine_sim2, open('model/book_c-bd_cos.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "fillednabooks=fillednabooks.reset_index()\n",
    "indices = pd.Series(fillednabooks.index, index=fillednabooks['Book-Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(indices, open('model/indices.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommendations_new(title, cosine_sim=cosine_sim2):\n",
    "    title=title.replace(' ','').lower()\n",
    "    idx = indices[title]\n",
    "\n",
    "    \n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "\n",
    "  \n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "  \n",
    "    sim_scores = sim_scores[1:11]\n",
    "\n",
    "  \n",
    "    book_indices = [i[0] for i in sim_scores]\n",
    "\n",
    " \n",
    "    return list(df1['Book-Title'].iloc[book_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The Guardian',\n",
       " 'She',\n",
       " 'Viking Passion',\n",
       " 'Consequences',\n",
       " 'Slow Burn',\n",
       " 'Sins of Omission',\n",
       " 'Always',\n",
       " 'In the Cut',\n",
       " 'The Next',\n",
       " 'Secret']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=get_recommendations_new('The Notebook', cosine_sim2)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(count, open('model/book_c-bd.pkl', 'wb'))"
   ]
  }
 ],
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