This project predicts the likelihood of cardiovascular disease based on patient health data. The goal is to identify key risk factors and develop a predictive model for early diagnosis.
- Data preprocessing and exploratory data analysis (EDA)
- Feature engineering and selection
- Machine learning model development and evaluation
- Model interpretability and visualization
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
The dataset includes medical parameters such as:
- Age
- Blood Pressure
- Cholesterol Levels
- Heart Rate
- Lifestyle Factors
- Logistic Regression
- Random Forest Classifier
- Support Vector Machine (SVM)
- XGBoost
The models are evaluated based on accuracy, precision, recall, and AUC-ROC score. The best model provides reliable predictions for cardiovascular disease risk.
📂 Cardiovascular-Disease-Prediction
│-- 📁 data (Dataset & processed data)
│-- 📁 notebooks (Jupyter Notebooks)
│-- 📁 models (Trained models)
│-- 📁 images (Code and Results Screenshots)
│-- 📄 README.md (Project documentation)
Include images of code and results in the images folder. Example:
- Clone the repository:
git clone https://github.com/rohitinu6/Cardiovascular-Disease-Prediction.git
- Navigate to the project folder:
cd Cardiovascular-Disease-Prediction - Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook or Python scripts to train and test models.
- GitHub Repository: Cardiovascular Disease Prediction
- Portfolio: Rohit Dubey
- GitHub Profile: rohitinu6
- LinkedIn: Rohit Dubey
- Twitter/X: @rohitdubey003
Machine Learning Cardiovascular Disease Health Prediction Data Science Python EDA
This project is licensed under the MIT License.
💡 For any queries or collaboration opportunities, feel free to connect! 🚀