Data Scientist - Portfolio
Python Pandas NumPy Matplotlib Seaborn Scikit-Learn XGBoost Supervised Learning Feature Engineering Hyperparameter Tuning Statistics Probability Jupyter GitHub Markdown Data Cleaning Data Visualization
This project focuses on building a predictive model to estimate the daily demand for each product sold at a café that specializes in Indian snacks. By analyzing historical transaction data, we aim to forecast the quantity of each item expected to be sold on a given day. This can support inventory planning, staffing, and promotional strategies.
Python Pandas NumPy Matplotlib Seaborn Scikit-Learn XGBoost Supervised Learning Feature Engineering Hyperparameter Tuning Statistics Probability Jupyter GitHub Markdown Data Cleaning Data Visualization
This project aims to apply machine learning techniques to predict the focal depth of earthquakes based on geographic and magnitude-related features. We use the Quakes dataset, a real-world collection of seismic events, to build and evaluate regression models that help uncover patterns influencing earthquake depth.