DevelopersHub-AI-ML-Internship-Task2

Task 2: Heart Disease Prediction

Objective

Build a model to predict whether a person is at risk of heart disease based on their health data.

Dataset

• Heart Disease UCI Dataset (Kaggle)
• 303 rows, 14 columns

Libraries Used

• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

Model Applied

• Logistic Regression

Results

• Training Accuracy: 85.12%
• Testing Accuracy: 85.25%
• ROC-AUC Score: 0.903

Key Findings

• Chest Pain Type is the strongest predictor
• Higher number of major vessels = lower heart disease risk
• Model detects 91% of actual heart disease cases

Internship

DevelopersHub Corporation - AI/ML Engineering Internship