Machine Learning Classification Techniques Project
Google Colab Version in Polish (English version will be added later)
Explore the world of heart disease classification with various machine learning techniques. The project includes an in-depth analysis using 🌳Random Forest, 🪐Support Vector Machine (SVM) with multiple kernels, 👤K-Nearest Neighbors, and 📈Logistic Regression. The analysis is accompanied by a comprehensive report in the repository. available as a PDF in the repository.
Key Project Highlights:
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Methods: Random Forest, SVM, K-Nearest Neighbors, Logistic Regression
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Data Exploration: Detailed statistical analysis and impact assessment of variables on the predicted outcome.
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Data Preparation: Handling missing data, outliers, and variable modifications.
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Model Interpretability: In-depth analysis using ceteris-paribus profiles, partial dependence plots, and SHAP values.
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Visual Presentation: Clear and concise visualizations to enhance understanding.
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Project Report: Focuses on precise data and result analysis.
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For additional details, check the Google Colab Version and the full project report in the repository.