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Niraj1608
The Anime Recommendation System is designed to help users discover new anime based on their preferences, watch history, and ratings. The system uses a combination of content-based and collaborative filtering techniques to provide personalized recommendations. This project can be useful for anime enthusiasts looking for tailored suggestions and for developers interested in building recommendation engines
Notebook Structure
Data Loading and Preprocessing: Load and clean the anime dataset, handling missing values, text cleaning, and any necessary transformations.
Exploratory Data Analysis (EDA): Visualize key insights about the dataset, such as popular genres, ratings distribution, and trends.
Content-Based Filtering:
Uses TF-IDF to represent anime descriptions and genres. Applies Cosine Similarity to recommend similar anime based on text features. Collaborative Filtering:
Implements SVD (Singular Value Decomposition) using the surprise library to predict user ratings for unrated anime. Suggests anime based on similar user ratings.