Developed a machine learning project to predict customer churn using Python, Pandas, NumPy, and Scikit-learn. Performed data cleaning, exploratory data analysis, preprocessing, feature encoding, and model evaluation. Implemented Logistic Regression and Random Forest models and created a Streamlit application to demonstrate churn prediction.

Analyzed Superstore sales data to understand sales, profit, customer, product, and regional performance. Cleaned and transformed the dataset and performed exploratory data analysis to identify business trends and patterns. Used SQL to extract meaningful insights and analyzed key performance indicators such as sales and profit by region, category, and product. Created visual reports and dashboards to present business insights and support data-driven decision-making. This project strengthened my skills in SQL, Excel, data analysis, visualization, and business reporting.