https://github.com/ahmad-ali-rafique/heart-disease-detection-model
A comprehensive project for detecting heart disease using machine learning, including data processing, model training, and evaluation metrics with AUC curve analysis.
https://github.com/ahmad-ali-rafique/heart-disease-detection-model
artificial-intelligence data datascience heart-disease machine-learning modeling prediction-model
Last synced: 2 months ago
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A comprehensive project for detecting heart disease using machine learning, including data processing, model training, and evaluation metrics with AUC curve analysis.
- Host: GitHub
- URL: https://github.com/ahmad-ali-rafique/heart-disease-detection-model
- Owner: Ahmad-Ali-Rafique
- Created: 2024-06-09T11:44:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-12T08:25:01.000Z (over 1 year ago)
- Last Synced: 2025-03-05T16:14:53.489Z (7 months ago)
- Topics: artificial-intelligence, data, datascience, heart-disease, machine-learning, modeling, prediction-model
- Language: Jupyter Notebook
- Homepage:
- Size: 82 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Heart Disease Detection Model
### Introduction
I am a data enthusiast with a passion for leveraging machine learning to solve real-world problems. My focus lies in the healthcare domain, where I aim to utilize data-driven approaches to improve patient outcomes and advance medical research.
### Skills and Expertise
- **Data Processing**: Expertise in cleaning, transforming, and preparing datasets for analysis.
- **Machine Learning**: Proficient in training and tuning various machine learning models.
- **Evaluation Metrics**: Skilled in evaluating model performance using metrics such as accuracy, precision, recall, F1 score, and AUC curves.
- **Programming Languages**: Python, R
- **Tools and Libraries**: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
### Project Highlights
- **Data Processing**: Implemented robust data cleaning and preprocessing steps to handle missing values, outliers, and feature engineering.
- **Model Training**: Trained multiple machine learning models, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.
- **Model Evaluation**: Assessed model performance using a variety of evaluation metrics, with a focus on the Area Under the Curve (AUC) for ROC analysis.
- **Visualization**: Created detailed visualizations to interpret model results and feature importances.
### Contact
Feel free to reach out if you have any questions or suggestions regarding this project.
- **Email**: arsbussiness@gmail.com
- **LinkedIn**: [Ahmad Ali Rafique](https://www.linkedin.com/in/ahmad-ali-rafique/)
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## [Ahmad Ali CV](https://drive.google.com/file/d/1bNLIx1j85e8ax21ZEC0DPt5C1at8vHjv/view?usp=sharing)
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