Developed an automated web scraping solution using BeautifulSoup, Selenium, and Python scripts to track product prices. Implemented alert systems, scheduling automation, and data logging, reducing manual tracking efforts by 90% and enabling real-time monitoring of product pricing trends efficiently.

Built regression-based predictive models using XGBoost and feature engineering techniques to estimate housing prices. Conducted exploratory data analysis, encoding, scaling, and correlation analysis, improving model accuracy by 12% and enabling data-driven real estate valuation insights for better pricing strategies.

Developed a supervised machine learning model using logistic regression, KNN, and Naive Bayes to predict loan approvals. Performed feature selection, scaling, cross-validation, and evaluation, achieving 92% accuracy and improving decision-making efficiency through predictive analytics and classification modeling techniques.

Designed an unsupervised machine learning pipeline using K-Means and clustering techniques to segment customers into 4 distinct groups. Performed data preprocessing, feature engineering, normalization, and visualization, enabling targeted marketing strategies and improving business decision-making through actionable customer insights.