How sifat192/diabetes-prediction-ml works
This is a small notebook-based diabetes classification project. Its documented workflow takes patient data through exploration, preprocessing and feature encoding, a train/test split, logistic-regression training, and evaluation with accuracy and a confusion matrix. The notebook and dataset are present in the tree, but the notebook itself was not sampled; the workflow links below are supported by the README rather than inspected implementation calls.
Main entry points: diabetes_prediction_model.ipynb Explore Data · diabetes_prediction_dataset.csv Patient Dataset
Components 8
Patient Data
- Patient Dataset
diabetes_prediction_dataset.csv
Model Workflow
- Explore Data
diabetes_prediction_model.ipynb - Preprocess Features
diabetes_prediction_model.ipynb - Train/Test Split
diabetes_prediction_model.ipynb - Logistic Regression
diabetes_prediction_model.ipynb
Evaluation
- Evaluate Model
diabetes_prediction_model.ipynb - Performance Metrics
diabetes_prediction_model.ipynb
Other components
- Analyst
Connections 7
- Analyst → Explore Data: runs notebook
README.md - Patient Dataset → Explore Data: loads and explores
README.md - Explore Data → Preprocess Features: precedes
README.md - Preprocess Features → Train/Test Split: precedes
README.md - Train/Test Split → Logistic Regression: trains
README.md - Logistic Regression → Evaluate Model: evaluates
README.md - Evaluate Model → Performance Metrics: reports
README.md