🩺 As part of my CodeAlpha Machine Learning Internship, I developed a model to predict the likelihood of diseases based on patient data using classification algorithms.
- Predicts heart disease risk using patient health metrics.
- Applied Random Forest Classifier for high accuracy.
- Evaluated model with Confusion Matrix, Classification Report, ROC-AUC Score.
- Visualized feature importance to interpret predictions disease risk from medical data
- Used models: Logistic Regression, Random Forest, SVM
- Evaluated with accuracy, precision, recall, and F1-score
- File:
heart.csv(included in repository) - Description: Contains patient health records with attributes such as age, sex, blood pressure, cholesterol levels, and more.
- Target Variable:
HeartDisease(1 = Disease Present, 0 = No Disease)
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Data Exploration
- Checked for missing values and data types.
- Visualized class distribution of target variable.
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Data Preprocessing
- One-hot encoding for categorical variables.
- Train-test split (80% training, 20% testing).
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Model Training
- Algorithm: Random Forest Classifier
- Evaluated with Accuracy, Precision, Recall, F1-score, and ROC-AUC.
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Feature Importance
- Identified top predictors for heart disease risk.
- Python
- Scikit-learn
- Pandas, NumPy
- Matplotlib, Seaborn
- ROC-AUC Score: 0.94
- Top Features: ST_Slope_Up, MaxHR, Oldpeak, Cholesterol, Age, RestingBP, etc.
Confusion Matrix & Classification Report
- Accuracy: 88%
- Precision (Class 1): 0.90
- Recall (Class 1): 0.89
Project completed for the CodeAlpha Machine Learning Internship