ReadRadar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7147381d",
   "metadata": {},
   "source": [
    "# ReadRadar: Book Recommendation using Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ef246f",
   "metadata": {},
   "source": [
    "Machine Learning powered book recommendations through collaborative filtering. Personalized suggestions based on user profiles and similarity analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d617d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ojasvi Doye\\AppData\\Local\\Temp\\ipykernel_11928\\2258892480.py:6: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  books = pd.read_csv('BX-Books.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
      "Skipping line 6452: expected 8 fields, saw 9\n",
      "Skipping line 43667: expected 8 fields, saw 10\n",
      "Skipping line 51751: expected 8 fields, saw 9\n",
      "\n",
      "Skipping line 92038: expected 8 fields, saw 9\n",
      "Skipping line 104319: expected 8 fields, saw 9\n",
      "Skipping line 121768: expected 8 fields, saw 9\n",
      "\n",
      "Skipping line 144058: expected 8 fields, saw 9\n",
      "Skipping line 150789: expected 8 fields, saw 9\n",
      "Skipping line 157128: expected 8 fields, saw 9\n",
      "Skipping line 180189: expected 8 fields, saw 9\n",
      "Skipping line 185738: expected 8 fields, saw 9\n",
      "\n",
      "Skipping line 209388: expected 8 fields, saw 9\n",
      "Skipping line 220626: expected 8 fields, saw 9\n",
      "Skipping line 227933: expected 8 fields, saw 11\n",
      "Skipping line 228957: expected 8 fields, saw 10\n",
      "Skipping line 245933: expected 8 fields, saw 9\n",
      "Skipping line 251296: expected 8 fields, saw 9\n",
      "Skipping line 259941: expected 8 fields, saw 9\n",
      "Skipping line 261529: expected 8 fields, saw 9\n",
      "\n",
      "C:\\Users\\Ojasvi Doye\\AppData\\Local\\Temp\\ipykernel_11928\\2258892480.py:6: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  books = pd.read_csv('BX-Books.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
      "C:\\Users\\Ojasvi Doye\\AppData\\Local\\Temp\\ipykernel_11928\\2258892480.py:10: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  users = pd.read_csv('BX-Users.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
      "C:\\Users\\Ojasvi Doye\\AppData\\Local\\Temp\\ipykernel_11928\\2258892480.py:14: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  ratings = pd.read_csv('BX-Book-Ratings.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load books data\n",
    "books = pd.read_csv('BX-Books.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
    "books.columns = ['ISBN', 'bookTitle', 'bookAuthor', 'yearOfPublication', 'publisher', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n",
    "\n",
    "# Load users data\n",
    "users = pd.read_csv('BX-Users.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
    "users.columns = ['userID', 'Location', 'Age']\n",
    "\n",
    "# Load ratings data\n",
    "ratings = pd.read_csv('BX-Book-Ratings.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
    "ratings.columns = ['userID', 'ISBN', 'bookRating']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de8313a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1149780, 3)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2bb78942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['userID', 'ISBN', 'bookRating']\n"
     ]
    }
   ],
   "source": [
    "print(list(ratings.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ec6b6f",
   "metadata": {},
   "source": [
    "## Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac35f8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc(\"font\", size=15)\n",
    "ratings.bookRating.value_counts(sort=False).plot(kind='bar')\n",
    "plt.title('Rating Distribution\\n')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('system1.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "525e5a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(271360, 8)\n",
      "['ISBN', 'bookTitle', 'bookAuthor', 'yearOfPublication', 'publisher', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n"
     ]
    }
   ],
   "source": [
    "print(books.shape)\n",
    "print(list(books.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "736821fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(278858, 3)\n",
      "['userID', 'Location', 'Age']\n"
     ]
    }
   ],
   "source": [
    "print(users.shape)\n",
    "print(list(users.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26e602d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "users.Age.hist(bins=[0, 10, 20, 30, 40, 50, 100])\n",
    "plt.title('Age Distribution\\n')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('system2.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e8cae3",
   "metadata": {},
   "source": [
    "### To maintain statistical significance, we exclude users who have rated fewer than 200 books and books that have received fewer than 100 ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7ed2d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts1 = ratings['userID'].value_counts()\n",
    "\n",
    "# Filter the ratings DataFrame to keep only users with at least 200 ratings\n",
    "ratings = ratings[ratings['userID'].isin(counts1[counts1 >= 200].index)]\n",
    "\n",
    "counts = ratings['bookRating'].value_counts()\n",
    "\n",
    "# Filter the ratings DataFrame to keep only books with at least 100 ratings\n",
    "ratings = ratings[ratings['bookRating'].isin(counts[counts >= 100].index)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6e54bd",
   "metadata": {},
   "source": [
    "## Collaborative Filtering Using k-Nearest Neighbors (kNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "288365c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>bookRating</th>\n",
       "      <th>bookTitle</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID        ISBN  bookRating  \\\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",
       "                                           bookTitle  \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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the ratings and books DataFrames based on the 'ISBN' column\n",
    "combine_book_rating = pd.merge(ratings, books, on='ISBN')\n",
    "\n",
    "columns = ['yearOfPublication', 'publisher', 'bookAuthor', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n",
    "\n",
    "# Drop the specified columns from the combined DataFrame\n",
    "combine_book_rating = combine_book_rating.drop(columns, axis=1)\n",
    "\n",
    "combine_book_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f76c5abd",
   "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>bookTitle</th>\n",
       "      <th>totalRatingCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Light in the Storm: The Civil War Diary of ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Always Have Popsicles</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple Magic (The Collector's series)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beyond IBM: Leadership Marketing and Finance ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clifford Visita El Hospital (Clifford El Gran...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           bookTitle  totalRatingCount\n",
       "0   A Light in the Storm: The Civil War Diary of ...                 2\n",
       "1                              Always Have Popsicles                 1\n",
       "2               Apple Magic (The Collector's series)                 1\n",
       "3   Beyond IBM: Leadership Marketing and Finance ...                 1\n",
       "4   Clifford Visita El Hospital (Clifford El Gran...                 1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with missing values in the 'bookTitle' column\n",
    "combine_book_rating = combine_book_rating.dropna(axis=0, subset=['bookTitle'])\n",
    "\n",
    "# Group by 'bookTitle' and calculate the count of book ratings\n",
    "book_ratingCount = (combine_book_rating\n",
    "                    .groupby(by=['bookTitle'])['bookRating']\n",
    "                    .count()\n",
    "                    .reset_index()\n",
    "                    .rename(columns={'bookRating': 'totalRatingCount'})\n",
    "                    [['bookTitle', 'totalRatingCount']]\n",
    "                   )\n",
    "\n",
    "book_ratingCount.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91593356",
   "metadata": {},
   "source": [
    "By combining the rating data with the total rating count data, we obtain the necessary information to identify popular books and filter out lesser-known ones. This allows us to determine the level of popularity for each book based on the count of ratings it has received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d746b0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>bookRating</th>\n",
       "      <th>bookTitle</th>\n",
       "      <th>totalRatingCount</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>82</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>82</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>82</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>82</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>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID        ISBN  bookRating  \\\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",
       "                                           bookTitle  totalRatingCount  \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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_totalRatingCount = combine_book_rating.merge(book_ratingCount, left_on = 'bookTitle', right_on = 'bookTitle', how = 'left')\n",
    "rating_with_totalRatingCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65c07302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count   160576.000\n",
      "mean         3.044\n",
      "std          7.428\n",
      "min          1.000\n",
      "25%          1.000\n",
      "50%          1.000\n",
      "75%          2.000\n",
      "max        365.000\n",
      "Name: totalRatingCount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "print(book_ratingCount['totalRatingCount'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d08a172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.900    5.000\n",
      "0.910    6.000\n",
      "0.920    7.000\n",
      "0.930    7.000\n",
      "0.940    8.000\n",
      "0.950   10.000\n",
      "0.960   11.000\n",
      "0.970   14.000\n",
      "0.980   19.000\n",
      "0.990   31.000\n",
      "Name: totalRatingCount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(book_ratingCount['totalRatingCount'].quantile(np.arange(.9, 1, .01)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cbf21534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>bookRating</th>\n",
       "      <th>bookTitle</th>\n",
       "      <th>totalRatingCount</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>82</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>82</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>82</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>82</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>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID        ISBN  bookRating  \\\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",
       "                                           bookTitle  totalRatingCount  \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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity_threshold = 50\n",
    "rating_popular_book = rating_with_totalRatingCount.query('totalRatingCount >= @popularity_threshold')\n",
    "rating_popular_book.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7f2d8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>bookRating</th>\n",
       "      <th>bookTitle</th>\n",
       "      <th>totalRatingCount</th>\n",
       "      <th>Location</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>82</td>\n",
       "      <td>gilbert, arizona, usa</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>82</td>\n",
       "      <td>knoxville, tennessee, usa</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>82</td>\n",
       "      <td>byron, minnesota, usa</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>82</td>\n",
       "      <td>cordova, tennessee, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16795</td>\n",
       "      <td>002542730X</td>\n",
       "      <td>0</td>\n",
       "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
       "      <td>82</td>\n",
       "      <td>mechanicsville, maryland, usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID        ISBN  bookRating  \\\n",
       "0  277427  002542730X          10   \n",
       "1    3363  002542730X           0   \n",
       "3   12538  002542730X          10   \n",
       "4   13552  002542730X           0   \n",
       "5   16795  002542730X           0   \n",
       "\n",
       "                                           bookTitle  totalRatingCount  \\\n",
       "0  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
       "1  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   \n",
       "5  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
       "\n",
       "                        Location  \n",
       "0          gilbert, arizona, usa  \n",
       "1      knoxville, tennessee, usa  \n",
       "3          byron, minnesota, usa  \n",
       "4        cordova, tennessee, usa  \n",
       "5  mechanicsville, maryland, usa  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter to users in US and Canada only\n",
    "combined = rating_popular_book.merge(users, left_on = 'userID', right_on = 'userID', how = 'left')\n",
    "\n",
    "us_canada_user_rating = combined[combined['Location'].str.contains(\"usa|canada\")]\n",
    "us_canada_user_rating=us_canada_user_rating.drop('Age', axis=1)\n",
    "us_canada_user_rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d4799e",
   "metadata": {},
   "source": [
    "## Implementing kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b36450dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "import numpy as np\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "us_canada_user_rating = us_canada_user_rating.drop_duplicates(['userID', 'bookTitle'])\n",
    "\n",
    "# Pivot the user ratings DataFrame to create a matrix\n",
    "us_canada_user_rating_pivot = us_canada_user_rating.pivot(index='bookTitle', columns='userID', values='bookRating').fillna(0)\n",
    "\n",
    "# Convert the pivot table to a sparse matrix\n",
    "us_canada_user_rating_matrix = csr_matrix(us_canada_user_rating_pivot.values)\n",
    "\n",
    "model_knn = NearestNeighbors(metric='cosine', algorithm='brute')\n",
    "model_knn.fit(us_canada_user_rating_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f5ec9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "583\n"
     ]
    }
   ],
   "source": [
    "query_index = np.random.choice(us_canada_user_rating_pivot.shape[0])\n",
    "print(query_index)\n",
    "\n",
    "# Find the distances and indices of the nearest neighbors for the query index\n",
    "distances, indices = model_knn.kneighbors(us_canada_user_rating_pivot.iloc[query_index, :].values.reshape(1, -1), n_neighbors=6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37530f48",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_canada_user_rating_pivot.iloc[query_index,:].values.reshape(1,-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33ad804d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Hot Zone'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_canada_user_rating_pivot.index[query_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a07c259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for \"The Hot Zone\":\n",
      "\n",
      "1: \"The Sweet Potato Queens' Book of Love\", with a distance of 0.7802834391806333\n",
      "2: \"Chromosome 6\", with a distance of 0.7837372588304705\n",
      "3: \"The Shipping News : A Novel\", with a distance of 0.7963469735058204\n",
      "4: \"Jurassic Park\", with a distance of 0.7981743613793414\n",
      "5: \"The Alienist\", with a distance of 0.7996422531916683\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(distances.flatten())):\n",
    "    if i == 0:\n",
    "        print('Recommendations for \"{0}\":\\n'.format(us_canada_user_rating_pivot.index[query_index]))\n",
    "    else:\n",
    "        # Retrieve the book title and distance for the recommendation\n",
    "        book_title = us_canada_user_rating_pivot.index[indices.flatten()[i]]\n",
    "        distance = distances.flatten()[i]\n",
    "        \n",
    "        print('{0}: \"{1}\", with a distance of {2}'.format(i, book_title, distance))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69c4825e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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