Enhance user experience and drive engagement with personalized recommendation systems. I specialize in developing advanced recommendation engines that deliver tailored suggestions to users, whether for e-commerce, streaming services, or any other application. By leveraging cutting-edge data science and machine learning techniques, I can create solutions that understand user preferences and predict their needs, boosting satisfaction and retention.

Services Offered:

• Development of collaborative filtering and content-based recommendation systems.
• Hybrid recommendation systems combining multiple techniques for improved accuracy.
• Personalized product, content, or service recommendations.
• Real-time recommendation engines for dynamic user interaction.
• Integration with existing platforms and databases.
• Analysis of user behavior and preferences to enhance recommendation accuracy.
• Continuous monitoring and updating of recommendation algorithms to maintain relevance.
• Custom reporting and visualization of recommendation performance metrics.

Skills and Tools:

• Data Analysis: Data cleaning, data preprocessing, exploratory data analysis (EDA)
• Machine Learning: Collaborative filtering, content-based filtering, matrix factorization, deep learning
• Algorithms: K-Nearest Neighbors (KNN), Singular Value Decomposition (SVD), Neural Collaborative Filtering (NCF)
• NLP for Recommendations: Word embeddings, semantic similarity
• Data Visualization: Matplotlib, Seaborn, Tableau, Power BI
• Programming Languages: Python, R
• Libraries and Frameworks: Scikit-learn, TensorFlow, PyTorch, Surprise
• Database Management: MySQL, PostgreSQL, MongoDB
• Version Control: Git, GitHub
• Project Management: Agile methodologies, JIRA, Trello