Book recommendation systems have become increasingly popular with the exponential growth of digital libraries and online bookstores. These systems aim to personalize the reading experience for users by suggesting books tailored to their interests and preferences.

K-Nearest Neighbors (KNN) is a simple yet effective algorithm used in recommendation systems. It operates on the principle that similar items are likely to be liked by similar users. In the context of book recommendations, if two users have similar reading histories or preferences, they are likely to enjoy similar books.

This repository provides a simple implementation of a book recommendation engine using the KNN algorithm. It allows users to input their reading history or preferences and receive personalized book recommendations.

The recommendation engine follows these basic steps: Data Collection: Gather data on books and users' reading histories or preferences. This dataset typically includes information such as book titles, authors, genres, and user ratings.

Preprocessing: Clean and preprocess the data to remove noise and irrelevant information. This step may involve tasks such as handling missing values, normalizing data, and feature engineering.

Feature Extraction: Transform the data into a format suitable for KNN. This often involves representing books and users as vectors in a multi-dimensional space, where each dimension corresponds to a feature (e.g., genre, author, rating).

Model Training: Train the KNN model using the preprocessed data. The model learns to identify similar users or items based on their feature vectors.

Recommendation Generation: Given a user's reading history or preferences, the model identifies the K nearest neighbors (users with similar tastes) and recommends books that they have enjoyed but the user has not yet read.

Evaluation: Evaluate the performance of the recommendation engine using metrics such as accuracy, precision, recall, and F1-score. This step helps assess the effectiveness of the model and identify areas for improvement.

This project is licensed under the MIT License. Feel free to use, modify, and distribute this code for any purpose.