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This repository provides Python code for analyzing and predicting heart diseases using the UCI Heart Disease dataset. It includes data exploration, preprocessing, and evaluation of machine learning models such as Random Forest, Gradient Boosting, SVM, and Logistic Regression.

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Heart Disease Prediction Challenge

This repository contains a Python script for the Heart Disease Prediction Challenge from Kaggle.

Dataset

The dataset used in this challenge is sourced from Kaggle. You can find the dataset here.

Requirements

To run the Python script, you'll need the following dependencies installed:

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • plotly
  • scikit-learn
  • xgboost

You can install them using pip!

Usage

  1. Clone the repository.
  2. Navigate to the directory containing the Python script.
  3. Ensure the dataset file 'heart_disease_uci.csv' is in the same directory.
  4. Run the Python script.

Figure1

About

This challenge was completed using Python, leveraging libraries such as pandas, numpy, and scikit-learn for data manipulation, visualization, and machine learning modeling.

About

This repository provides Python code for analyzing and predicting heart diseases using the UCI Heart Disease dataset. It includes data exploration, preprocessing, and evaluation of machine learning models such as Random Forest, Gradient Boosting, SVM, and Logistic Regression.

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