Random Forest Classifier on Heart Disease Dataset

This project involves building a Random Forest Classifier to predict the presence of heart disease based on various medical variables such as age, sex, cholesterol level, and more.

Overview

The main goal of this project is to accurately classify individuals as having or not having heart disease using the Random Forest algorithm. This model is particularly suited for this task due to its robustness and ability to handle complex interactions between features.

Dataset

The dataset includes medical attributes such as age, sex, cholesterol level, and other relevant factors, along with the target variable indicating the presence or absence of heart disease.

Key Features

Data Preprocessing: Cleaning and preparing the data for analysis.

Feature Selection: Identifying the most significant variables for predicting heart disease.

Model Building: Implementing a Random Forest Classifier.

Hyperparameter Tuning: Experimenting with different hyperparameters, such as the number of trees in the forest and the number of variables considered at each split, to optimize the model's performance. Used Grid Search Cross Validation method for hyperparameter tuning.

Model Evaluation: Assessing the model's accuracy, precision, recall, and other metrics to ensure its reliability.