Retail Store Data Analysis and Predictive Modeling

Conducted an end-to-end data analysis project on retail store performance using Python. The project involved data cleaning, exploratory data analysis (EDA), handling missing and duplicated values, and applying machine learning techniques including linear regression, K-means clustering, and PCA (Principal Component Analysis). Utilized libraries such as Pandas, Seaborn, Matplotlib, and Scikit-learn to derive insights and build predictive models.