Heart Disease Prediction Project

This Python-based data science project analyses a heart disease dataset and trains a Logistic Regression model to predict the presence of heart disease, using data preprocessing, Exploratory Data Analysis (EDA), and machine learning. The code is structured for use in vscode and consists of modular .py script files.

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Objective

Explore and model the "Heart Disease UCI" dataset to identify key features affecting patients' health conditions and build a machine learning model for prediction.

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Dataset

The dataset used is the "Heart Disease UCI" dataset, which can be found on Kaggle (you will need to download heart.csv and place it in the same directory as the Python script). It contains various features related to patients' health conditions and a binary target variable indicating the presence or absence of heart disease. This dataset has been used to explore data cleaning, visualization, and application of machine learning for survival prediction.

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Project Structure

heart_disease_project/
├── src
    └── data/     : Stores heart disease dataset in CSV file format
        └── heart.cvs : Heart disease dataset
    └── figures/  : Stores images     
    └── models/   : Strores models   
    └── data_loader.py: For data loading and preprocessing.
    └── eda.py: For Exploratory Data Analysis
    └── main.py: The main script to orchestrate the execution.
    └── model_trainer.py: For model training
├── .gitignore    : Project documentation
├── Heart_Disease_Prediction_Presentation.pptx
├── LICENCE
├── README.md
└── requirements.txt : List of Python dependencies  

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Technologies Used:

• Python 3.8+
• pandas, numpy
• seaborn, matplotlib
• scikit-learn, joblib
• powerpoint
• csv file
• vscode

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Setup Instructions

1. Clone the repository

https://github.com/AAdewunmi/Heart-Disease-Prediction-Analysis-Project.git

2. Create a virtual environment (macOS/Linux)

# Set up virtual environment
python3 -m venv venv
source venv/bin/activate    # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run scripts
python data_loader.py
python eda.py
python model_trainer.py
python main.py

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Workflow

This project follows a structured machine learning workflow to ensure reproducibility, clarity, and modular development. Below are the key steps in the workflow:

1. Data Loading & Preprocessing

• Load the dataset from data/heart.csv.
• Display basic information, summary statistics, and check for missing values.
• Handle missing values using SimpleImputer for numerical features.
• Prepare the dataset for analysis.

Script: data_loader.py Function: load_and_preprocess_data()

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2. Exploratory Data Analysis (EDA)

• Visualize the distribution of the target variable (AHD).
• Generate histograms for key numerical features such as Age, Chol, MaxHR, etc.
• Analyze relationships using scatter plots (e.g., Chol vs RestBP).
• Generate a correlation heatmap to identify feature relationships.

Script: eda.py Function: perform_eda(df) Outputs: Saved to the figures/ folder as PNG files

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3. Model Training & Evaluation

• Encode categorical features using one-hot encoding.
• Split the dataset into training and testing subsets.
• Train a Logistic Regression model using scikit-learn.
• Evaluate model performance using metrics: accuracy, precision, recall, F1-score, and confusion matrix.

Script: model_trainer.py Function: train_and_evaluate_model(df)

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4. Model Saving

• Trained model and feature columns are saved using joblib.
• Artifacts stored in the src/models/ directory for future reuse or deployment.

Output Paths:

• src/models/logistic_regression_model.pkl
• src/models/model_columns.pkl

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5. Project Orchestration

• The entire workflow (data loading, EDA, model training) is executed via the main driver script.

Script: main.py Entry Point: main()

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Output

The scripts will:

• Print basic information about the dataset.
• Generate and save several Exploratory Data Analysis (EDA) visualizations as .png files (e.g., distribution of the target variable, histograms of numerical features, a scatter plot, and a correlation matrix).
• Train a Logistic Regression model on the data.
• Print the accuracy, classification report, and confusion matrix of the model on the test set.

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Next Steps (Potential Improvements)

• Explore other classification models (e.g., Random Forest, Support Vector Machines).
• Perform feature scaling and selection.
• Conduct more in-depth EDA.
• Fine-tune the model hyperparameters.

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Power Point Presentation Outline

Here is an outline of a power point presentation for this project.

Heart Disease Prediction Project

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Contact

If you have questions or suggestions, feel free to reach out or open an issue.

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Author

Adrian Adewunmi – GitHub