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Recommendation Systems
Data Analysis and ML Development: Jupyter Notebook, Google Colab, Visual Studio Code
Web Development and Deployment: Streamlit, Gradio
Libraries and Frameworks: Sklearn, LightFM, Scikit-Surprise, Keras, Joblib
Data Processing and Visualization: Pandas, Numpy, Matplotlib, Seaborn
As e-commerce platforms grow, the vast array of products available can overwhelm consumers. This project develops a sophisticated recommender system to enhance user experience by providing personalized product recommendations. The system aims to:
Classify products into meaningful clusters.
Implement a content-based recommendation system that suggests similar products to what customers are currently viewing.
Develop a recommender system that utilizes product reviews for personalized suggestions based on past user interactions.
Deploy the recommendation models into a web application, making them accessible and user-friendly.
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