Diabetes Data Cleaning, Normalization, and Visualization with Python

This project demonstrates a complete workflow for preparing and exploring diabetes datasets using Python. The key steps include:

• Data cleaning and handling missing values
• Removing duplicates and outliers
• Feature scaling and normalization
• Exploratory data analysis with correlation heatmaps, pairplots, and distribution plots
• Preparing data for machine learning models

Tools used: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn

The workflow helps uncover patterns and relationships among diabetes-related features, making the dataset ML-ready.