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.
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)
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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Cloning the Repository:
- Clone this repository to your local machine using
git clone.
- Clone this repository to your local machine using
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Exploring Projects:
- Navigate to each project folder to access the code files, datasets, and README for detailed information about the classification project.
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Running Projects:
- Feel to run the code included in each project file, analyze the data, and understand the classification process.
This repository is licensed under the MIT License. See the LICENSE file for more details.
For any questions, suggestions, or collaboration opportunities, please feel free to reach out via [mawadamhd12@gmail.com].