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Used machine learning models to predict spam mails, loan, diabetes etc... also done web scraping, sentiment analysis, EDA. Have fun.
Updated
Jun 17, 2024
Jupyter Notebook
End-to-end diabetes risk predictor: XGBoost (0.824 AUC, 73% recall) + SHAP explainability + FastAPI + Streamlit. Handles class imbalance with tuned threshold for clinical screening. 253K CDC BRFSS samples.
Updated
Mar 29, 2026
Python
Identified the optimal order of preprocessing for Pima Diabetes Dataset, how feature selection and upsampling affects the evaluation metrics
used diabetes dataset from kaggle and performed prediction using Logistic Regression, Random Forest, XGBoost
Updated
Jan 2, 2026
Jupyter Notebook
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