This repository contains an implementation of a machine learning pipeline for analyzing and predicting diabetes-related outcomes using patient data.
- Data Preprocessing: Handles missing values and performs data cleaning.
- Exploratory Data Analysis (EDA): Generates insights and visualizations.
- Machine Learning Models: Implements classification models for predicting diabetes outcomes.
- Feature Importance Analysis: Uses permutation importance to analyze key factors.
- Clone the repository:
git clone https://github.com/your-username/your-repo-name.git
- The project uses diabetic_data.csv, which contains medical records related to diabetes.
- Ensure the dataset is placed in the same directory as the notebook.
- Open and run the Jupyter Notebook (final.ipynb).
- The notebook will perform data preprocessing, model training, and evaluation.
- View classification metrics and feature importance.