This repository hosts a Jupyter notebook detailing a machine learning project focused on predicting heart disease. The project utilizes various data analysis and machine learning techniques to identify patterns and make predictions regarding the presence of heart disease in patients.
The Jupyter notebook contains a step-by-step analysis, starting from data preprocessing to model selection and evaluation. The dataset used in this project includes a range of variables such as age, sex, cholesterol levels, and other vital signs that are considered important indicators of heart health.
- Data Cleaning and Preprocessing: The notebook begins with cleaning the dataset to handle missing values and normalize the data.
- Exploratory Data Analysis (EDA): Before diving into predictive modeling, the notebook explores the data through various visualizations and statistics to uncover insights and trends.
- Feature Selection: Techniques are applied to select the most significant features that contribute to heart disease.
- Model Building: Several machine learning models are trained and tested, including logistic regression, decision trees, and random forests, among others.
- Model Evaluation: The models are evaluated based on their accuracy, precision, recall, and F1 score to determine the most effective approach for prediction.
- Hyperparameter Tuning: The notebook includes hyperparameter tuning to enhance the performance of the chosen model.
- Python: The primary programming language used for analysis.
- Pandas and NumPy: For data manipulation and numerical computations.
- Matplotlib and Seaborn: For data visualization.
- Scikit-learn: For implementing machine learning algorithms and model evaluation.
To explore the heart disease prediction analysis, clone the repository, and ensure you have the necessary Python packages installed. You can run the Jupyter notebook to see the analysis process and try out the models with the dataset provided.
Contributions to this project are welcome. Whether it's improving the models, offering new insights into the data, or enhancing the data visualization, your input can help make this project even more robust.
This project is an excellent resource for anyone interested in the application of machine learning in healthcare or for those looking to understand how to implement and evaluate predictive models with real-world data.