This project implements a heart disease predictor using a Random Forest Classifier to predict the likelihood of heart disease based on various health and lifestyle parameters.
The dataset used (heart_disease.csv) contains the following columns:
- Age: Age of the patient
- Sex: Gender of the patient (1 = male, 0 = female)
- CP: Chest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic)
- Trestbps: Resting blood pressure (in mm Hg)
- Chol: Serum cholesterol (in mg/dl)
- Fbs: Fasting blood sugar > 120 mg/dl (1 = true, 0 = false)
- Restecg: Resting electrocardiographic results (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy)
- Thalach: Maximum heart rate achieved
- Exang: Exercise induced angina (1 = yes, 0 = no)
- Oldpeak: ST depression induced by exercise relative to rest
- Slope: The slope of the peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping)
- Ca: Number of major vessels (0-3) colored by fluoroscopy
- Thal: Thalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect)
- Target: Presence of heart disease (1 = presence, 0 = absence)
- numpy (version 1.26.4): For numerical computations and array operations.
- pandas (version 2.1.4): For data manipulation and analysis.
- matplotlib (version 3.4.2): For creating visualizations of the data.
- seaborn (version 0.12.2): For statistical data visualization.
- sklearn(version 1.2.2): For loading and processing data
1.Clone the Repository:
git clone https://github.com/Niharika-Varshney/Heart_Disease_Predictor.git
cd Heart_Disease_Predictor
2.Open and Run the Jupyter Notebook:
- Navigate to the repository directory and open
Heart_Disease_Predictor.ipynbusing Jupyter Notebook or Jupyter Lab. - Execute the cells to run and explore the heart disease predictor.
heart_disease.csv: Dataset containing heart disease data.Heart_Disease_Predictor.ipynb: Jupyter Notebook containing the code for data analysis, model training, and evaluation.