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The Heart Failure Prediction Model utilizes the Random Forest Classifier algorithm, a powerful ensemble learning method, to predict the probability of heart failure in patients.

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DDILLOUD/Heart-Failure-Prediction-Model

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Heart Failure Prediction Model

This project implements a machine learning model using the RandomForestClassifier algorithm to predict heart failure based on various features.

Overview

  • Description: This project aims to predict the likelihood of heart failure in patients based on clinical features.
  • Installation: Clone the repository and install dependencies using pip install -r requirements.txt.
  • Usage: Use predict.py to make predictions with the trained model. See src/README.md for more details.
  • Dataset: The project uses the Heart.csv dataset, which contains information about patients' clinical features.
  • Results: The trained model achieved [accuracy/recall/precision] of [result].
  • Acknowledgments: This project is based on the dataset from Kaggle.

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The Heart Failure Prediction Model utilizes the Random Forest Classifier algorithm, a powerful ensemble learning method, to predict the probability of heart failure in patients.

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