This mini machine learning project aims to predict the likelihood of heart disease using a decision tree algorithm. The project utilizes scikit-learn for creating the decision tree model and making predictions. The dataset is divided into training and testing sets to evaluate the model's performance.
Created under Self Paced Program by Girls Who Code.
- Decision Tree Classifier: Utilizes a decision tree algorithm to predict heart disease.
- Scikit-Learn Integration: Employs the scikit-learn library for model creation, training, and prediction.
- Data Splitting: Divides the dataset into training and testing subsets to evaluate accuracy.
- Python 3.x
- scikit-learn
- pandas
- numpy
- Clone the repository
git clone <repository url> - Navigate to the repository.
cd heart-disease-prediction - Run the script.
python3 main.py
This csv file contains data on factors of heart disease. You can learn more about this dataset in your worksheet in Part 2: https://assets.ctfassets.net/idmfh0ehr6d7/3gBpyycY5ShWzLSG3GuH2K/cb483e835f1d29eeaa74d29d1453a9b8/Intro_to_Machine_Learning_Worksheets.pdf
This file contains helper functions you can call in predict.py.