Heart Disease Prediction Model

Overview This project aims to predict the likelihood of heart disease based on various health parameters using machine learning techniques.

Dataset The dataset used contains information about patients, including their age, sex, cholesterol levels, and other relevant medical indicators.

Model Used Logistic Regression was employed for this prediction task due to its interpretability and effectiveness in binary classification problems.

Accuracy Training Data Accuracy: 85.24% Testing Data Accuracy: 80.49% Usage To use the model for prediction:

Provide input data in the format: (age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal). The model will predict whether the person is likely to have heart disease or not. Dependencies numpy pandas scikit-learn Files heart_disease_prediction.ipynb: Jupyter notebook containing the code. data.csv: Dataset used for training and testing. How to Run Install necessary dependencies (numpy, pandas, scikit-learn). Run heart_disease_prediction.ipynb in a Python environment that supports Jupyter notebooks. Future Improvements Explore other machine learning algorithms. Fine-tune hyperparameters for better accuracy.