Heart Disease Prediction

This repository contains a project that predicts the presence of heart disease using various machine learning algorithms. The project uses a dataset that contains several health metrics to train models and evaluate their performance.

Project Overview

This project aims to predict whether a person has heart disease based on features such as age, sex, cholesterol level, resting blood pressure, and more. We use a combination of exploratory data analysis (EDA) and machine learning algorithms to evaluate the prediction accuracy.

The models implemented include:

• Random Forest Classifier
• Gradient Boosting Classifier
• Decision Tree Classifier

Technologies Used

• Python
• Pandas: for data manipulation
• NumPy: for numerical computations
• Matplotlib: for data visualization
• Seaborn: for plotting heatmaps and data correlations
• Scikit-learn: for machine learning model implementation

Dataset

The dataset used in this project is the heart disease dataset. It contains features such as:

• Age
• Sex
• Chest pain type (cp)
• Resting blood pressure (trestbps)
• Cholesterol (chol)
• Maximum heart rate achieved (thalach)
• and others...

The target variable (target) is binary:

• 0: No Heart Disease
• 1: Heart Disease

Exploratory Data Analysis

We performed EDA to understand the distribution of the data and visualize the correlations between various features. Key graphs and plots include:

• Bar plot showing distribution of patients with and without heart disease.
• Histograms of categorical and continuous variables based on the target label.
• Correlation heatmap of features.

Machine Learning Models

We implemented three machine learning models to predict heart disease:

1. Random Forest Classifier: A robust ensemble method using decision trees.
2. Gradient Boosting Classifier: An advanced boosting algorithm for better accuracy.
3. Decision Tree Classifier: A simple tree-based model.

We applied normalization and one-hot encoding to preprocess the data and used a train_test_split to create training and testing sets.

Model Evaluation

For each model, we evaluated performance using the following metrics:

• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix

The model scores were visualized in a bar plot, comparing the training and testing accuracy for all models.

User Data Simulation

The project also generates user-specific data for predictions, simulating health metrics for new patients. The Random Forest classifier was trained on this simulated data with a test accuracy of around 50%.

How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/heart-disease-prediction.git

2. Install required packages:

   pip install -r requirements.txt

3. Run the notebook or script to train the models and view results.

Results

Accuracy Comparison (Iteration 1)

Model	Training Accuracy %	Testing Accuracy %
Random Forest Classifier	85.25%	81.82%
Gradient Boosting Classifier	86.84%	84.09%
Decision Tree Classifier	78.69%	75.00%

Accuracy Comparison (Iteration 2)

Model	Training Accuracy %	Testing Accuracy %
Random Forest Classifier	79.31%	75.00%
Gradient Boosting Classifier	82.47%	81.82%
Decision Tree Classifier	74.07%	70.45%

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