This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl

Tree-based survival analysis from scratch

Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023

Implementation of Decision Tree and Ensemble Learning algorithms in Python with numpy

All the course work of supervised and unsupervised algorithms and projects.

This is a customer loyalty analysis based on historical purchase behavior in R language.

A collection of various applied Machine Learning and Artificial Intelligence projects I have done.

Codes for the paper On marginal feature attributions of tree-based models

Analyzing the binary gender difference in lead roles using statistical machine learning

Kaggle competition: predicting bikeshare demand with regression techniques. Linear/Lasso/Ridge Regression, KNN, Decision Tree, Random Forest, AdaBoost, XGBoost.

Tree-based algorithms for solving a game of Flappy Bird.

Kaggle competition: predicting forest cover type with multiclass classification algorithms. Logistic Regression, SVC, KNN, Decision Tree, Random Forest, XGBoost, AdaBoost, LightGBM, & Extra Trees.

Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .

A machine learning project, predicting hourly bike rentals in Seoul.

Supervised learning and unsupervised in R, with a focus on regression and classification methods.

Random Forests Tree-Based Model in Machine Learning (exercise using Iris data)

Linear & logistic regression, model assessment and selection, and gradient boosted trees

Telecom Churn analysis using various tree based classification models

Homeworks for Statistical Learning course (Prof. Vinciotti) @ University of Trento

Implementing Tree-based algorithms from scratch (Decision Tree, Random Forest, and Gradient Boosting) from scratch and comparing it to the scikit-learn implementation.