justatirkey / heart_attack_risk_prediction

This is my Second task of AI\ML internship for Nexus Sofware winter internship.

Heart Attack Risk Prediction using Machine Learning

Project Overview

The "Heart Attack Risk Prediction using Machine Learning" is an intermediate-level project that focuses on developing an intelligent system to predict the likelihood of a person having a heart attack based on various health-related features. The system utilizes machine learning algorithms, specifically logistic regression or support vector machines (SVM), to analyze data from the Kaggle dataset on heart attack risk and make predictions. The project aims to contribute to early heart attack detection and proactive healthcare management.

Project Objectives

1. Data Collection:

   • Utilize the Kaggle dataset on heart attack risk, containing relevant health features such as age, gender, BMI, blood pressure, cholesterol levels, and other key indicators.

2. Data Preprocessing:

   • Perform thorough data cleaning and preprocessing to handle missing values, outliers, and ensure data quality.
   • Normalize or standardize features to bring them to a consistent scale.

3. Feature Selection:

   • Employ feature selection techniques to identify the most influential variables for heart attack risk prediction.
   • Ensure that selected features contribute significantly to the accuracy of the logistic regression or SVM models.

4. Model Development:

   • Implement logistic regression or SVM for heart attack risk prediction.
   • Evaluate and compare the performance of the chosen model using metrics like accuracy, precision, recall, and F1-score.

5. Cross-Validation:

   • Implement cross-validation techniques to assess the generalization performance of the models and mitigate overfitting.

6. Hyperparameter Tuning:

   • Fine-tune the hyperparameters of the logistic regression or SVM model to optimize its performance.

7. Validation and Testing:

   • Conduct extensive testing and validation to ensure the accuracy, reliability, and robustness of the heart attack risk prediction system.

Implementation Details

• Machine Learning Model: Logistic Regression or Support Vector Machines (SVM)
• Programming Language: Python
• Libraries: Scikit-learn, Pandas, NumPy
• Development Environment: Jupyter Notebook or any preferred IDE

How to Run the Project

1. Clone the repository to your local machine.
2. Execute the Jupyter Notebook or run the Python script to perform data preprocessing, model development, and evaluation.
3. Interpret the model results and make predictions based on the provided health-related features.

Results and Evaluation

The final model, whether logistic regression or SVM, will be evaluated based on standard machine learning metrics such as accuracy, precision, recall, and F1-score. The results is presented in the project report or notebook.

Acknowledgments

• This project was developed using the Kaggle dataset on heart attack risk.
• The dataset source: Kaggle - Heart Attack Analysis & Prediction.