David Matamoros

Data Science Enthusiast with an MIT DSML course-based experience driven by an insatiable curiosity to explore the complexities of data and its applications in solving real-world problems and committed to expanding my knowledge and skills through hands-on projects and continuous learning.

Foundations for Data Science: FoodHub Data Analysis

• Conducted an in-depth analysis of customer transaction data to identify restaurant demand trends and enhance customer experience for a food aggregator platform.
• Processed and analyzed historical order data from FoodHub’s online portal using Python libraries like NumPy, Pandas, Matplotlib.pyplot, and Seaborn.
• Performed exploratory data analysis (EDA), focusing on customer behavior, order frequency, and ratings.
• Delivered actionable insights on top-performing restaurants, peak demand times, and popular cuisines, contributing to strategic decision-making for resource allocation and customer engagement.

ExtraaLearn - Lead Conversion Analysis

• Conducted an in-depth analysis to identify behavioral patterns and features driving lead conversion rates for an EdTech platform.
• Analyzed lead data, including demographics, engagement metrics, and interaction behaviors, using Python libraries like Pandas, NumPy, and Seaborn.
• Performed data cleaning and preprocessing, exploratory data analysis (EDA), and feature engineering to prepare the dataset for model development.
• Developed a classification model to predict lead conversion and improved prediction accuracy by 30%, enabling targeted marketing strategies.

Recommendation Systems: Amazon Product Recommendation System

• Designed and implemented a recommendation system to enhance user engagement by suggesting relevant products tailored to individual preferences and behaviors.
• Processed a dataset containing user-product interactions, including ratings, reviews, and purchase history, to derive actionable insights for personalization.
• Conducted exploratory data analysis (EDA) and developed recommendation models using collaborative filtering (user-user, item-item), rank-based methods, and matrix factorization.
• Utilized the Surprise library with hyperparameter tuning to optimize model performance and measured recommendation quality using precision@k and recall@k.
• Achieved significant improvements in recommendation precision and recall, enabling data-driven personalization strategies and increasing user satisfaction and engagement.

One of my main goals is to further advance my professional career and expertise in Data Science by either pursuing either a Bachelor's or a Master's degree in the field.