In this project, I explore the process of building and fine-tuning a recommender system using the Surprise library in Python. My dataset consists of Goodreads book ratings, and my goal is to create a collaborative filtering-based recommender system that can predict user ratings for books. I will follow these key steps:
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Data Loading and Exploration: I begin by loading the dataset and conducting some initial data exploration. This step helps me understand the structure of the data, its size, and the distribution of ratings.
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Data Preprocessing: I identify and handle any data issues, such as zero ratings, which are outside the expected rating range.
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Data Preparation for Surprise: I prepare the data to be used with the Surprise library by creating a reader object and a Surprise dataset.
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Building the Recommender System: I use the K-nearest neighbors (KNN) collaborative filtering algorithm (KNNBasic) from the Surprise library to build my recommender system.
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Model Evaluation: I evaluate the performance of my recommender system using root-mean-square error (RMSE) as the evaluation metric.
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Hyperparameter Tuning: I perform hyperparameter tuning using grid search to find the best hyperparameters for my model. This step allows me to further improve the model's performance.
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Refitting the Model: After finding the best hyperparameters, I refit the KNNBasic model with these optimal settings.
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Making Predictions: I make predictions using the refitted model to estimate how a user would rate a particular book.
Through these steps, I aim to build an accurate and efficient recommender system that can provide meaningful book recommendations to users based on their past ratings and preferences. Additionally, I demonstrate the impact of hyperparameter tuning on the model's performance, showcasing the importance of fine-tuning in building effective recommendation systems.