Preparing Data for a Book Recommendation System
In the age of digital reading, having a personalised book recommendation system can greatly enhance the reader's experience. Recently, I worked on preparing datasets for a machine learning model designed to recommend books to users based on their ratings. Here’s how I did it and why it’s important.
Overview
The goal was to create three CSV files containing user information, book details, and book ratings. These files would be used by a machine learning model to suggest books that users might enjoy. Here's a breakdown of the steps I took to prepare the data.
Steps Taken
1. Combining Datasets:
- Merging Data: I used Python to combine the user information, book details, and ratings datasets. This was done using unique identifiers like User-ID for users and ISBN for books to ensure accuracy.
2. Data Preprocessing and Cleaning:
- Location Splitting: The 'location' column had information in the 'city, state, country' format. I separated this data to keep only the 'country' part, making the data simpler.
- Standardising Text: To keep things consistent, I converted all text to lowercase. This helps in avoiding confusion due to different text formats.
- Title and Author Corrections: Sometimes, book titles and author names were entered differently. I corrected these to remove duplicates and ensure each book and author was listed correctly.
- Adding Genre: I introduced a new 'Genre' column and identified the genres for about 70% of the books. This helps the recommendation system understand what type of books users like.
- Handling Missing Values: I removed any rows that had missing information to ensure the data was complete and reliable.
- Language Filtering: To focus on English-language recommendations, I excluded books that were not in English.
- Rating Filtering: I kept only the books that had a rating of 8 or higher. This helps the recommendation system suggest highly rated books to users.
Outcome
The cleaned and processed datasets provided a solid foundation for developing an effective book recommendation system. This project highlights my ability to handle complex data preparation tasks, ensuring that the data is accurate and relevant for machine learning applications.
Tools Used
- Python: A powerful programming language used for data manipulation.
- Pandas: A Python library that makes data cleaning and preparation easy.
- Numpy: A Python library used for numerical operations.
Why This Matters
Having a well-prepared dataset is crucial for any machine learning model. By ensuring the data is clean and organised, the recommendation system can make accurate and relevant suggestions, enhancing the user's reading experience.
If you need help preparing your data for any project, feel free to get in touch. Click on 'Services' to learn more or contact me to discuss your needs.