Books Recommendation System

This project demonstrates an unsupervised machine learning approach to building a book recommendation system. The system uses frequent pattern mining algorithms to analyze and identify relationships in the dataset and generate recommendations. A web interface is provided for users to input a book and receive recommendations based on it.

Table of Contents

1. Overview
2. Dataset Description
3. Approach and Algorithms
4. Project Workflow
5. Results
6. Technologies Used
7. Future Work

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Overview

This project is a books recommendation system that suggests books to users based on patterns derived from a dataset of user ratings. It leverages frequent pattern mining algorithms, specifically Eclat, Apriori, and FP-Growth, to uncover associations between books.

A web interface allows users to input a book and receive recommendations generated by the FP-Growth model. The recommendations are based on the most frequent patterns discovered in the dataset.

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Dataset Description

The dataset used in this project consists of two files:

1. Books: Contains metadata about books, including:
   • ISBN
   • Book title
   • Author
   • Year of publication
   • Publisher
   • Image URL (Small, mediam, large)
2. Ratings: Contains user interaction data:
   • User ID
   • ISBN of the book
   • Book rating (0–10 scale)

After cleaning the data:

• The final dataset includes the following columns:
  • User ID
  • ISBN
  • Book rating
  • Book title

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Approach and Algorithms

The recommendation system explores frequent patterns in user ratings using:

1. Eclat Algorithm
2. Apriori Algorithm
3. FP-Growth Algorithm

The FP-Growth Algorithm produced the best results, as it efficiently handled large datasets and provided meaningful book association rules.

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Project Workflow

1. Data Cleaning:

   • Removed duplicate and missing entries.
   • Merged Books and Ratings datasets into a single CSV file.
   • Ensured consistent formatting.

2. Frequent Pattern Mining:

   • Tested Eclat, Apriori, and FP-Growth on the dataset.
   • Evaluated each algorithm based on computational efficiency and meaningful recommendations.

3. Evaluation:

   • Selected the FP-Growth Algorithm for its superior performance.

4. Web Interface:

   • Developed a simple web interface where users can input a book title.
   • The system returns book recommendations based on the input.

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Results

• Frequent itemsets: Discovered frequent combinations of books rated by users.
• Recommendations: Generated book suggestions based on association rules derived using the FP-Growth Algorithm. Web Interface: Functional interface to provide recommendations interactively.

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Technologies Used

The following technologies were used to develop the project:

• Python: The core programming language for data processing and algorithm implementation.
• Pandas: For data cleaning, preprocessing, and manipulation.
• Mlxtend: Library providing implementation of the FP-Growth algorithm and association rules.
• Flask: Web framework used to create the user interface for inputting books and displaying recommendations.
• HTML/CSS: To design and style the web interface.
• Jupyter Notebook: Used during the exploratory data analysis (EDA) phase for visualizing and understanding the data.

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Future Work

Here are the potential future improvements and extensions to the project:

1. Transition to Streamlit:

   • Replace Flask with Streamlit to provide a more interactive and visually appealing user interface.
   • Integrate Streamlit’s features to improve user experience and interactivity.

2. MLOps Integration:

   • Develop the project as an MLOps pipeline.
   • Automate data ingestion, model training, deployment, and monitoring.