A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

🎨 SeabornMasterPro is a comprehensive, modular project to master Seaborn for data visualization. Includes themed utilities, advanced plotting notebooks, dashboards, time series, Streamlit app, and Docker support — perfect for learners, analysts, and open-source enthusiasts.

End-to-end ML workflow on the UCI Cleveland Heart Disease dataset: missing-value imputation (median/mode), Min-Max scaling, EDA with describe() and a correlation heatmap, training Logistic Regression and Random Forest, evaluating with accuracy, and selecting the better model. All work is in Jupyter notebooks + a concise 1-page report.

This repository, Composite Visualization of Microsoft Stock Performance (2010-2022) contains visualization of Microsoft Stock performance from 2010 to 2022. Line graph, correlation heatmap, scatter plot, and histogram were produced in this visualization project.