Diabetes Risk Classification

Coursework comparison between XGBoost and logistic regression on imbalanced healthcare data.

Trained and benchmarked XGBoost and Logistic Regression on an imbalanced diabetes dataset. Achieved 75.3% with XGBoost and 81.2% with Logistic Regression, recommending the simpler model.

Key Outcomes

Classifier comparison on imbalanced data

Simpler model selected after evaluation

Healthcare-focused classification case study

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Context

Problem and Context

Trained and benchmarked XGBoost and Logistic Regression on an imbalanced diabetes dataset. Achieved 75.3% with XGBoost and 81.2% with Logistic Regression, recommending the simpler model.

Approach

Approach and Architecture

Model comparison on imbalanced data to select a simpler, robust classifier.

Implementation

Implementation Details

XGBoost

Logistic Regression

Model Comparison

GridSearchCV

Results

Results and Tradeoffs

This project is presented as a concise technical overview rather than a full-length narrative case study.

Classifier comparison on imbalanced data

Simpler model selected after evaluation

Healthcare-focused classification case study

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