I recently completed a project where I built a machine learning pipeline to predict delivery times for e-commerce orders. The goal was to improve logistics efficiency and enhance customer satisfaction by accurately estimating delivery durations based on factors like product category, distance, traffic, weather, and agent performance. What I did: - Cleaned and preprocessed a detailed delivery dataset - Performed Exploratory Data Analysis (EDA) to uncover insights on delivery trends, agent performance, and external conditions - Engineered features like geospatial distances and time-based variables - Developed regression models: Linear Regression, Random Forest, Gradient Boosting - Tracked and compared models using MLflow - Built a Streamlit app for real-time delivery time predictions Business Impact: - Optimize delivery schedules - Adjust delivery estimates dynamically for traffic & weather - Evaluate delivery agent performance - Enhance customer satisfaction Tech Stack: Python | scikit-learn | LightGBM | Streamlit This project strengthened my skills in data cleaning, EDA, feature engineering, regression modeling, and end-to-end deployment. 💡 Excited to apply these skills to real-world logistics and e-commerce challenges!