Book Recommendation using KNN

Book recommendation system is to predict buyer’s interest and recommend books to them accordingly. A book recommendation system can take into account many parameters like ratings count and language code by filtering user reviews. various attributes were analyzed before choosing the parameters using seaborn and various python libraries.

install Python, jupyter notebook to run the project

The project is using k - Nearest neighbor algorithm and min max scaler to transform the data. Used numpy and pandas library for statistics and preprocessing of data

image 1

Import the relative path of the dataset clone the project and install all the required libraries and run the ipynb file in jupyter notebook.

Source Code: book.py

import numpy as np 
import pandas as pd
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn import neighbors
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import streamlit as st
data = pd.read_csv('books.csv',error_bad_lines = False)

rating = data[data["ratings_count"] > 1000000] #1cr
df = rating.sort_values(by='average_rating', ascending=False)

author = data.groupby(by="authors")
author = author["title"].count().reset_index().sort_values(by="title", ascending=False).set_index("authors")
author = author.head(10)

rated = data.sort_values(by='ratings_count', ascending=False).head(10)
rated = rated.set_index("title")
# rated

df2 = data.copy()
df2.loc[ (df2['average_rating'] >= 0) & (df2['average_rating'] <= 1), 'rating_between'] = "between 0 and 1"
df2.loc[ (df2['average_rating'] > 1) & (df2['average_rating'] <= 2), 'rating_between'] = "between 1 and 2"
df2.loc[ (df2['average_rating'] > 2) & (df2['average_rating'] <= 3), 'rating_between'] = "between 2 and 3"
df2.loc[ (df2['average_rating'] > 3) & (df2['average_rating'] <= 4), 'rating_between'] = "between 3 and 4"
df2.loc[ (df2['average_rating'] > 4) & (df2['average_rating'] <= 5), 'rating_between'] = "between 4 and 5"

rating_df = pd.get_dummies(df2['rating_between'])
language_df = pd.get_dummies(df2['language_code'])
features = pd.concat([rating_df, language_df, df2['average_rating'],  df2['ratings_count']], axis=1)

from sklearn.preprocessing import MinMaxScaler
min_max_scaler = MinMaxScaler()
features = min_max_scaler.fit_transform(features)
model = neighbors.NearestNeighbors(n_neighbors=6, algorithm='ball_tree')
model.fit(features)
dist, idlist = model.kneighbors(features)

def book_recommend(bookname):
    books=[]
    book_id = df2[df2["title"] == bookname].index
    #print(book_id)
    book_id = book_id[0]
    for newid in idlist[book_id]:
        books.append(df2.loc[newid].title)
    return books
# BookNames = book_recommend('Harry Potter and the Half-Blood Prince (Harry Potter  #6)')
# BookNames


def book():
    bookname = st.text_input("Enter book")
    bookr = book_recommend(bookname)
    st.table(bookr)
book()