This repository contains mini-projects that apply decision trees and ensemble learning models to various datasets. Each project demonstrates the application of classification techniques to solve real-world problems, showcasing the effectiveness of these models in diverse contexts.
This project implements a decision tree classifier on a synthetic dataset generated using scikit-learn's make_classification
function. The dataset consists of two classes and four features, allowing users to learn the basics of training, optimizing, and visualizing decision tree models.
- Dataset Source: Synthetic dataset generated using scikit-learn.
This project predicts the survival status of cirrhosis patients based on clinical and demographic data. Various machine learning models, including ensemble techniques, are used to classify patient status into categories: death, censored, and censored due to liver transplantation.
- Dataset Source: Cirrhosis Patient Survival Prediction Dataset
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
Feel free to explore each project to understand the methodologies and results in more detail!