Ca4 Book recommendation system.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae358b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>language_code</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.57</td>\n",
       "      <td>0439785960</td>\n",
       "      <td>9780439785969</td>\n",
       "      <td>eng</td>\n",
       "      <td>652</td>\n",
       "      <td>2095690</td>\n",
       "      <td>27591</td>\n",
       "      <td>9/16/2006</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.49</td>\n",
       "      <td>0439358078</td>\n",
       "      <td>9780439358071</td>\n",
       "      <td>eng</td>\n",
       "      <td>870</td>\n",
       "      <td>2153167</td>\n",
       "      <td>29221</td>\n",
       "      <td>9/1/2004</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (Harry...</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0439554896</td>\n",
       "      <td>9780439554893</td>\n",
       "      <td>eng</td>\n",
       "      <td>352</td>\n",
       "      <td>6333</td>\n",
       "      <td>244</td>\n",
       "      <td>11/1/2003</td>\n",
       "      <td>Scholastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.56</td>\n",
       "      <td>043965548X</td>\n",
       "      <td>9780439655484</td>\n",
       "      <td>eng</td>\n",
       "      <td>435</td>\n",
       "      <td>2339585</td>\n",
       "      <td>36325</td>\n",
       "      <td>5/1/2004</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Harry Potter Boxed Set  Books 1-5 (Harry Potte...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.78</td>\n",
       "      <td>0439682584</td>\n",
       "      <td>9780439682589</td>\n",
       "      <td>eng</td>\n",
       "      <td>2690</td>\n",
       "      <td>41428</td>\n",
       "      <td>164</td>\n",
       "      <td>9/13/2004</td>\n",
       "      <td>Scholastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bookID                                              title  \\\n",
       "0       1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1       2  Harry Potter and the Order of the Phoenix (Har...   \n",
       "2       4  Harry Potter and the Chamber of Secrets (Harry...   \n",
       "3       5  Harry Potter and the Prisoner of Azkaban (Harr...   \n",
       "4       8  Harry Potter Boxed Set  Books 1-5 (Harry Potte...   \n",
       "\n",
       "                      authors  average_rating        isbn         isbn13  \\\n",
       "0  J.K. Rowling/Mary GrandPré            4.57  0439785960  9780439785969   \n",
       "1  J.K. Rowling/Mary GrandPré            4.49  0439358078  9780439358071   \n",
       "2                J.K. Rowling            4.42  0439554896  9780439554893   \n",
       "3  J.K. Rowling/Mary GrandPré            4.56  043965548X  9780439655484   \n",
       "4  J.K. Rowling/Mary GrandPré            4.78  0439682584  9780439682589   \n",
       "\n",
       "  language_code    num_pages  ratings_count  text_reviews_count  \\\n",
       "0           eng          652        2095690               27591   \n",
       "1           eng          870        2153167               29221   \n",
       "2           eng          352           6333                 244   \n",
       "3           eng          435        2339585               36325   \n",
       "4           eng         2690          41428                 164   \n",
       "\n",
       "  publication_date        publisher  \n",
       "0        9/16/2006  Scholastic Inc.  \n",
       "1         9/1/2004  Scholastic Inc.  \n",
       "2        11/1/2003       Scholastic  \n",
       "3         5/1/2004  Scholastic Inc.  \n",
       "4        9/13/2004       Scholastic  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import neighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "df = pd.read_csv('books.csv',on_bad_lines = 'skip')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6893a1f1-2148-46c5-b63b-0c399da6c87e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11123 entries, 0 to 11122\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   bookID              11123 non-null  int64  \n",
      " 1   title               11123 non-null  object \n",
      " 2   authors             11123 non-null  object \n",
      " 3   average_rating      11123 non-null  float64\n",
      " 4   isbn                11123 non-null  object \n",
      " 5   isbn13              11123 non-null  int64  \n",
      " 6   language_code       11123 non-null  object \n",
      " 7     num_pages         11123 non-null  int64  \n",
      " 8   ratings_count       11123 non-null  int64  \n",
      " 9   text_reviews_count  11123 non-null  int64  \n",
      " 10  publication_date    11123 non-null  object \n",
      " 11  publisher           11123 non-null  object \n",
      "dtypes: float64(1), int64(5), object(6)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a846871c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
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       "      <th>count</th>\n",
       "      <td>11123.000000</td>\n",
       "      <td>11123.000000</td>\n",
       "      <td>1.112300e+04</td>\n",
       "      <td>11123.000000</td>\n",
       "      <td>1.112300e+04</td>\n",
       "      <td>11123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21310.856963</td>\n",
       "      <td>3.934075</td>\n",
       "      <td>9.759880e+12</td>\n",
       "      <td>336.405556</td>\n",
       "      <td>1.794285e+04</td>\n",
       "      <td>542.048099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13094.727252</td>\n",
       "      <td>0.350485</td>\n",
       "      <td>4.429758e+11</td>\n",
       "      <td>241.152626</td>\n",
       "      <td>1.124992e+05</td>\n",
       "      <td>2576.619589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.987060e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10277.500000</td>\n",
       "      <td>3.770000</td>\n",
       "      <td>9.780345e+12</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1.040000e+02</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20287.000000</td>\n",
       "      <td>3.960000</td>\n",
       "      <td>9.780582e+12</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>7.450000e+02</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32104.500000</td>\n",
       "      <td>4.140000</td>\n",
       "      <td>9.780872e+12</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>5.000500e+03</td>\n",
       "      <td>238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45641.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.790008e+12</td>\n",
       "      <td>6576.000000</td>\n",
       "      <td>4.597666e+06</td>\n",
       "      <td>94265.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             bookID  average_rating        isbn13     num_pages  \\\n",
       "count  11123.000000    11123.000000  1.112300e+04  11123.000000   \n",
       "mean   21310.856963        3.934075  9.759880e+12    336.405556   \n",
       "std    13094.727252        0.350485  4.429758e+11    241.152626   \n",
       "min        1.000000        0.000000  8.987060e+09      0.000000   \n",
       "25%    10277.500000        3.770000  9.780345e+12    192.000000   \n",
       "50%    20287.000000        3.960000  9.780582e+12    299.000000   \n",
       "75%    32104.500000        4.140000  9.780872e+12    416.000000   \n",
       "max    45641.000000        5.000000  9.790008e+12   6576.000000   \n",
       "\n",
       "       ratings_count  text_reviews_count  \n",
       "count   1.112300e+04        11123.000000  \n",
       "mean    1.794285e+04          542.048099  \n",
       "std     1.124992e+05         2576.619589  \n",
       "min     0.000000e+00            0.000000  \n",
       "25%     1.040000e+02            9.000000  \n",
       "50%     7.450000e+02           47.000000  \n",
       "75%     5.000500e+03          238.000000  \n",
       "max     4.597666e+06        94265.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74d2d811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bookID                0\n",
       "title                 0\n",
       "authors               0\n",
       "average_rating        0\n",
       "isbn                  0\n",
       "isbn13                0\n",
       "language_code         0\n",
       "  num_pages           0\n",
       "ratings_count         0\n",
       "text_reviews_count    0\n",
       "publication_date      0\n",
       "publisher             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding null values:\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6bb60aff",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11123.000000</td>\n",
       "      <td>11123.000000</td>\n",
       "      <td>1.112300e+04</td>\n",
       "      <td>11123.000000</td>\n",
       "      <td>1.112300e+04</td>\n",
       "      <td>11123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21310.856963</td>\n",
       "      <td>3.934075</td>\n",
       "      <td>9.759880e+12</td>\n",
       "      <td>336.405556</td>\n",
       "      <td>1.794285e+04</td>\n",
       "      <td>542.048099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13094.727252</td>\n",
       "      <td>0.350485</td>\n",
       "      <td>4.429758e+11</td>\n",
       "      <td>241.152626</td>\n",
       "      <td>1.124992e+05</td>\n",
       "      <td>2576.619589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.987060e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10277.500000</td>\n",
       "      <td>3.770000</td>\n",
       "      <td>9.780345e+12</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1.040000e+02</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20287.000000</td>\n",
       "      <td>3.960000</td>\n",
       "      <td>9.780582e+12</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>7.450000e+02</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32104.500000</td>\n",
       "      <td>4.140000</td>\n",
       "      <td>9.780872e+12</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>5.000500e+03</td>\n",
       "      <td>238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45641.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.790008e+12</td>\n",
       "      <td>6576.000000</td>\n",
       "      <td>4.597666e+06</td>\n",
       "      <td>94265.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             bookID  average_rating        isbn13     num_pages  \\\n",
       "count  11123.000000    11123.000000  1.112300e+04  11123.000000   \n",
       "mean   21310.856963        3.934075  9.759880e+12    336.405556   \n",
       "std    13094.727252        0.350485  4.429758e+11    241.152626   \n",
       "min        1.000000        0.000000  8.987060e+09      0.000000   \n",
       "25%    10277.500000        3.770000  9.780345e+12    192.000000   \n",
       "50%    20287.000000        3.960000  9.780582e+12    299.000000   \n",
       "75%    32104.500000        4.140000  9.780872e+12    416.000000   \n",
       "max    45641.000000        5.000000  9.790008e+12   6576.000000   \n",
       "\n",
       "       ratings_count  text_reviews_count  \n",
       "count   1.112300e+04        11123.000000  \n",
       "mean    1.794285e+04          542.048099  \n",
       "std     1.124992e+05         2576.619589  \n",
       "min     0.000000e+00            0.000000  \n",
       "25%     1.040000e+02            9.000000  \n",
       "50%     7.450000e+02           47.000000  \n",
       "75%     5.000500e+03          238.000000  \n",
       "max     4.597666e+06        94265.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f855e544",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select rows from the DataFrame where 'ratings_count' is greater than 1000000\n",
    "top_ten = df[df['ratings_count'] > 1000000]\n",
    "\n",
    "# Sort the selected rows based on 'average_rating' in descending orde5r\n",
    "top_ten_sorted = top_ten.sort_values(by='average_rating', ascending=False)\n",
    "\n",
    "# Create a new figure with a size of 10x10 inches\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "# Select the top 10 rows from the sorted DataFrame based on 'average_rating'\n",
    "data = top_ten_sorted.head(10)\n",
    "\n",
    "sns.barplot(x=\"average_rating\", y=\"title\", data=data, palette='pastel')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69c627bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 10 authors\n",
    "\n",
    "# Group the DataFrame by 'authors' and count the number of books for each author\n",
    "most_books = df.groupby('authors')['title'].count().reset_index()\n",
    "\n",
    "# Sort the grouped data by the count of books in descending order and select the top 10\n",
    "most_books_sorted = most_books.sort_values('title', ascending=False).head(10)\n",
    "\n",
    "# Set the 'authors' column as the index for better readability in the plot\n",
    "most_books_sorted = most_books_sorted.set_index('authors')\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "ax = sns.barplot(x=most_books_sorted['title'], y=most_books_sorted.index, palette='plasma')\n",
    "ax.set_title(\"Top 10 authors with most books\")\n",
    "ax.set_xlabel(\"Total number of books\")\n",
    "\n",
    "# Add annotations (total number of books) to each bar in the plot\n",
    "totals = []\n",
    "for i in ax.patches:\n",
    "    totals.append(i.get_width())\n",
    "\n",
    "total = sum(totals)\n",
    "\n",
    "for i in ax.patches:\n",
    "    # Display the count of books next to each bar, with a slight offset for better visibility\n",
    "    ax.text(i.get_width() + 0.2, i.get_y() + 0.2, str(round(i.get_width())), fontsize=15, color='black')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08690201",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tenco\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average rating distribution:\n",
    "\n",
    "# Convert 'average_rating' to float\n",
    "df['average_rating'] = df['average_rating'].astype(float)\n",
    "\n",
    "# Create a figure-level distribution plot\n",
    "fig = plt.figure(figsize=[15, 10])\n",
    "sns.displot(df['average_rating'], kde=True)\n",
    "plt.title('Average rating distribution for all books', fontsize=20)\n",
    "plt.xlabel('Average rating', fontsize=13)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8252320a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x258332f5ad0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.relplot(data=df, x=\"average_rating\", y=\"ratings_count\", color = 'blue', sizes=(100, 200), height=7, marker='o')\n",
    "plt.title(\"Relation between Rating counts and Average Ratings\",fontsize = 15)\n",
    "ax.set_axis_labels(\"Average Rating\", \"Ratings Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f541a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x258356d2c50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "ax = sns.relplot(x=\"average_rating\", y=\"  num_pages\", data = df, color = 'red',sizes=(100, 200), height=7, marker='o')\n",
    "ax.set_axis_labels(\"Average Rating\", \"Number of Pages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d510fe7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "#splitting\n",
    "\n",
    "df2.loc[ (df2['average_rating'] >= 0) & (df2['average_rating'] <= 1), 'rating_between'] = \"between 0 and 1\"\n",
    "df2.loc[ (df2['average_rating'] > 1) & (df2['average_rating'] <= 2), 'rating_between'] = \"between 1 and 2\"\n",
    "df2.loc[ (df2['average_rating'] > 2) & (df2['average_rating'] <= 3), 'rating_between'] = \"between 2 and 3\"\n",
    "df2.loc[ (df2['average_rating'] > 3) & (df2['average_rating'] <= 4), 'rating_between'] = \"between 3 and 4\"\n",
    "df2.loc[ (df2['average_rating'] > 4) & (df2['average_rating'] <= 5), 'rating_between'] = \"between 4 and 5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7d3b1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#binary conversion using dummy method\n",
    "rating_df = pd.get_dummies(df2['rating_between'])\n",
    "language_df = pd.get_dummies(df2['language_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5dba302f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         ale    ara  en-CA  en-GB  en-US    eng    enm    fre    ger    gla  \\\n",
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       "11122  False  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "       ...     nl    nor    por    rus    spa    srp    swe    tur    wel  \\\n",
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       "11122  ...  False  False  False  False   True  False  False  False  False   \n",
       "\n",
       "         zho  \n",
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       "...      ...  \n",
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       "\n",
       "[11123 rows x 27 columns]"
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       "       between 0 and 1  between 1 and 2  between 2 and 3  between 3 and 4  \\\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.72</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11122</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.91</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11123 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       between 0 and 1  between 1 and 2  between 2 and 3  between 3 and 4  \\\n",
       "0                False            False            False            False   \n",
       "1                False            False            False            False   \n",
       "2                False            False            False            False   \n",
       "3                False            False            False            False   \n",
       "4                False            False            False            False   \n",
       "...                ...              ...              ...              ...   \n",
       "11118            False            False            False            False   \n",
       "11119            False            False            False            False   \n",
       "11120            False            False            False             True   \n",
       "11121            False            False            False             True   \n",
       "11122            False            False            False             True   \n",
       "\n",
       "       between 4 and 5    ale    ara  en-CA  en-GB  en-US  ...    por    rus  \\\n",
       "0                 True  False  False  False  False  False  ...  False  False   \n",
       "1                 True  False  False  False  False  False  ...  False  False   \n",
       "2                 True  False  False  False  False  False  ...  False  False   \n",
       "3                 True  False  False  False  False  False  ...  False  False   \n",
       "4                 True  False  False  False  False  False  ...  False  False   \n",
       "...                ...    ...    ...    ...    ...    ...  ...    ...    ...   \n",
       "11118             True  False  False  False  False  False  ...  False  False   \n",
       "11119             True  False  False  False  False  False  ...  False  False   \n",
       "11120            False  False  False  False  False  False  ...  False  False   \n",
       "11121            False  False  False  False  False  False  ...  False  False   \n",
       "11122            False  False  False  False  False  False  ...  False  False   \n",
       "\n",
       "         spa    srp    swe    tur    wel    zho  average_rating  ratings_count  \n",
       "0      False  False  False  False  False  False            4.57        2095690  \n",
       "1      False  False  False  False  False  False            4.49        2153167  \n",
       "2      False  False  False  False  False  False            4.42           6333  \n",
       "3      False  False  False  False  False  False            4.56        2339585  \n",
       "4      False  False  False  False  False  False            4.78          41428  \n",
       "...      ...    ...    ...    ...    ...    ...             ...            ...  \n",
       "11118  False  False  False  False  False  False            4.06            156  \n",
       "11119  False  False  False  False  False  False            4.08            783  \n",
       "11120  False  False  False  False  False  False            3.96            820  \n",
       "11121  False  False  False  False  False  False            3.72            769  \n",
       "11122   True  False  False  False  False  False            3.91            113  \n",
       "\n",
       "[11123 rows x 34 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenating \n",
    "features = pd.concat([rating_df, \n",
    "                      language_df, \n",
    "                      df2['average_rating'], \n",
    "                      df2['ratings_count']], axis=1)\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4fbc50a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.0\n",
      "Mean Absolute Error: 0.0\n",
      "Silhouette Score: 0.02822979412029129\n",
      "Davies-Bouldin Index: 3.9361525148540354e-05\n"
     ]
    }
   ],
   "source": [
    "# Required Libraries\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, silhouette_score, davies_bouldin_score\n",
    "\n",
    "# Data Preprocessing\n",
    "min_max_scaler = MinMaxScaler()\n",
    "features = min_max_scaler.fit_transform(df[['average_rating', 'ratings_count', 'text_reviews_count']])\n",
    "\n",
    "# Model Building\n",
    "model = NearestNeighbors(n_neighbors=6, algorithm='auto')\n",
    "model.fit(features)\n",
    "dist, idlist = model.kneighbors(features)\n",
    "\n",
    "# Performance Evaluation\n",
    "# Mean Squared Error (for distances)\n",
    "mse = mean_squared_error(features, model._fit_X)\n",
    "\n",
    "# Mean Absolute Error (for distances)\n",
    "mae = mean_absolute_error(features, model._fit_X)\n",
    "\n",
    "# Silhouette Score (requires predicted labels)\n",
    "silhouette = silhouette_score(features, idlist[:, 0])  # Assuming first id in idlist as prediction\n",
    "\n",
    "# Davies-Bouldin Index (requires predicted labels)\n",
    "dbi = davies_bouldin_score(features, idlist[:, 0])  # Assuming first id in idlist as prediction\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "print(f\"Silhouette Score: {silhouette}\")\n",
    "print(f\"Davies-Bouldin Index: {dbi}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a8370e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Women in Love',\n",
       " 'Red Rabbit (Jack Ryan  #2)',\n",
       " \"Flaubert's Parrot\",\n",
       " 'Trump: The Art of the Deal',\n",
       " 'Finnegans Wake']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def BookRecommender(book_name):\n",
    "    # Initialize an empty list to store recommended book names\n",
    "    book_list_name = []\n",
    "    \n",
    "    # Find the index of the input book_name in the DataFrame\n",
    "    book_id = df2[df2['title'] == book_name].index\n",
    "    \n",
    "    # Extract the first (and only) element from the index array\n",
    "    book_id = book_id[0]\n",
    "    \n",
    "    # Iterate through the indices of the k-nearest neighbors for the input book_id\n",
    "    for newid in idlist[book_id]:\n",
    "        # Append the title of each recommended book to the book_list_name\n",
    "        book_list_name.append(df2.loc[newid].title)\n",
    "    \n",
    "    # Return the list of recommended book names\n",
    "    return book_list_name\n",
    "\n",
    "# Call the BookRecommender function with a specific book name\n",
    "BookNames = BookRecommender('Are You Afraid of the Dark?')\n",
    "\n",
    "# Display the list of recommended book names\n",
    "BookNames[1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "77333e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Call the BookRecommender function with a specific book name\n",
    "BookNames = BookRecommender('A Brief History of Time')\n",
    "\n",
    "# Display the list of recommended book names (excluding the queried book itself)\n",
    "recommended_books = BookNames[1:]\n",
    "\n",
    "# Extract relevant information for the recommended books\n",
    "recommended_books_info = df2[df2['title'].isin(recommended_books)][['title', 'average_rating', 'ratings_count']]\n",
    "\n",
    "# Set up the plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create a bar plot for average ratings\n",
    "sns.barplot(x='average_rating', y='title', data=recommended_books_info, palette='viridis')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Recommended Books and Their Ratings', fontsize=16)\n",
    "plt.xlabel('Average Rating', fontsize=14)\n",
    "plt.ylabel('Book Title', fontsize=14)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7979a830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style='display:flex; flex-wrap: wrap;'><img src='http://books.google.com/books/content?id=5qolPyJ8pHgC&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api' style='margin: 5px; max-width: 150px; max-height: 200px;'><img src='http://books.google.com/books/content?id=H5Pqjj4f9kEC&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api' style='margin: 5px; max-width: 150px; max-height: 200px;'><img src='http://books.google.com/books/content?id=5i53jAz6vwAC&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api' style='margin: 5px; max-width: 150px; max-height: 200px;'><img src='http://books.google.com/books/content?id=CTB8DQAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api' style='margin: 5px; max-width: 150px; max-height: 200px;'><img src='http://books.google.com/books/content?id=5kOVEAAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&source=gbs_api' style='margin: 5px; max-width: 150px; max-height: 200px;'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import IPython.display as display\n",
    "from IPython.display import Image, HTML\n",
    "\n",
    "# Function to get book cover image from Google Books API\n",
    "def get_book_cover(book_title):\n",
    "    base_url = \"https://www.googleapis.com/books/v1/volumes\"\n",
    "    params = {\"q\": f\"intitle:{book_title}\"}\n",
    "    response = requests.get(base_url, params=params)\n",
    "    data = response.json()\n",
    "\n",
    "    if \"items\" in data and data[\"items\"]:\n",
    "        image_link = data[\"items\"][0][\"volumeInfo\"].get(\"imageLinks\", {}).get(\"thumbnail\", \"\")\n",
    "        return image_link\n",
    "\n",
    "    return None\n",
    "\n",
    "# Call the BookRecommender function with a specific book name\n",
    "BookNames = BookRecommender(\"Are You Afraid of the Dark?\")\n",
    "\n",
    "# Display the list of recommended book names (excluding the queried book itself)\n",
    "recommended_books = BookNames[1:]\n",
    "\n",
    "# Generate HTML to display book covers in a row\n",
    "html_content = \"<div style='display:flex; flex-wrap: wrap;'>\"\n",
    "for book_title in recommended_books:\n",
    "    cover_url = get_book_cover(book_title)\n",
    "    if cover_url:\n",
    "        html_content += f\"<img src='{cover_url}' style='margin: 5px; max-width: 150px; max-height: 200px;'>\"\n",
    "\n",
    "html_content += \"</div>\"\n",
    "\n",
    "# Display the HTML content\n",
    "display.display(HTML(html_content))\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0b939289-2b80-4d15-b7eb-1291bd9ecf6d",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('books.csv', on_bad_lines='skip')\n",
    "\n",
    "# ... (previous code for data exploration and preprocessing)\n",
    "\n",
    "# Concatenating \n",
    "features = pd.concat([rating_df, \n",
    "                      language_df, \n",
    "                      df2['average_rating'], \n",
    "                      df2['ratings_count']], axis=1)\n",
    "\n",
    "# Split the features into X and y\n",
    "X = features.drop('ratings_count', axis=1)  # Features\n",
    "y = features['ratings_count']  # Target variable\n",
    "\n",
    "# Binarize the target variable for classification (example: based on some threshold)\n",
    "y_class = np.where(y > 4, 1, 0)\n",
    "\n",
    "# Scaling the values\n",
    "min_max_scaler = MinMaxScaler()\n",
    "X_scaled = min_max_scaler.fit_transform(X)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_class, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize and train the KNN classifier\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=3)\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = knn_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the performance using accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b632b13a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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