This project focuses on analyzing clinical datasets using Python to derive meaningful insights from patient health records. The analysis includes data cleaning, visualization, and basic predictive modeling to support healthcare decision-making.
- Data Preprocessing – Handle missing values, outliers, and data normalization.
- Exploratory Analysis – Visualize trends using Matplotlib and Seaborn.
- Predictive Modeling – Apply logistic regression and decision trees to identify health patterns.
Built using Pandas, NumPy, Scikit-learn, and Jupyter Notebook, this project highlights how Python can assist in evidence-based healthcare analytics.