Heart Disease Prediction Project

This project utilizes machine learning techniques to predict the presence of heart disease based on clinical and demographic data.

Key Features

Data Cleaning and Preprocessing: The dataset (processed_cleveland.csv) is cleaned by handling missing values and outliers.
Outlier Detection: Outliers in the target variable (num) are identified and removed using the Interquartile Range (IQR) method.
Feature Selection: Harris Hawk Optimization (HHO) algorithm is employed to select the most relevant features for predicting heart disease.
Model Training: A Random Forest Classifier is trained on the selected features to build the predictive model.
Model Evaluation: Accuracy metrics are calculated to evaluate the performance of the trained model.
README: A README file (README.md) is included to provide detailed instructions on installation, usage, and credits for the project.

Ensure you have Python 3.x installed. Install necessary dependencies using pip:

pip install numpy matplotlib pandas scikit-learn zoofs

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
README.md		README.md
completed_code1 (1).ipynb		completed_code1 (1).ipynb
processed_cleveland (1).csv		processed_cleveland (1).csv