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https://github.com/mawadamhd/classification
This repository serves as a storage space for classification projects. Organized by projects, each directory houses code files, documentation, and dataset necessary for running and understanding the project. Feel free to explore the projects. Reach out for inquiries, feedback, or collaboration opportiunities.
https://github.com/mawadamhd/classification
classification-algorithms
Last synced: 20 days ago
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This repository serves as a storage space for classification projects. Organized by projects, each directory houses code files, documentation, and dataset necessary for running and understanding the project. Feel free to explore the projects. Reach out for inquiries, feedback, or collaboration opportiunities.
- Host: GitHub
- URL: https://github.com/mawadamhd/classification
- Owner: MawadaMhd
- License: mit
- Created: 2024-03-21T04:30:09.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-30T10:11:02.000Z (9 months ago)
- Last Synced: 2024-03-30T11:23:33.341Z (9 months ago)
- Topics: classification-algorithms
- Language: Jupyter Notebook
- Homepage:
- Size: 16.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Classification
## Overview
This GitHub repository is dedicated to showcasing a collection of classification projects that leverage various machine learning algorithms to categorize and analyze datasets. Each project focuses on different classification tasks and provides insights into the implementation, methodology, and results achieved through the classification process.
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## Projects
### **[Cardiovascular Disease Classification](https://github.com/MawadaMhd/Classification/tree/main/Cardiovascular%20Disease%20Predictive%20Modelling)**
**Description:** This project aimed to develop a machine learning model that accurately predicts the likelihood of cardiovascular disease in individuals based on various risk factors and medical history.
**Algorithms Used:** Logistic Regression, Decision Tree Classifier, KNeighbors Classifier, Random Forest Classifier, SVC.
**Results:** Random Forest Classifier (Accuracy: 98%, F1 Score (0.98, 0.99)
### **[Tumor Benign Malignent Classification](https://github.com/MawadaMhd/Classification/tree/main/Tumors%20BenignMalignant%20Classification%2096)**
**Description:** This project aimed to create a machine learning algorithm that can differentiate between benign and malignant tumors for early detection and treatment planning.
**Algorithms Used:** Logistic Regression, Decision Tree Classifier, KNeighbors Classifier, Random Forest Classifier, SVC.
**Results:** KNN Classifier (Accuracy: 99%, F1 Score (0.98, 0.99)
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## Usage
1. **Cloning the Repository:**
- Clone this repository to your local machine using `git clone`.
2. **Exploring Projects:**
- Navigate to each project folder to access the code files, datasets, and README for detailed information about the classification project.
3. **Running Projects:**
- Feel to run the code included in each project file, analyze the data, and understand the classification process.
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## License
This repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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## Contact
For any questions, suggestions, or collaboration opportunities, please feel free to reach out via [[email protected]].