This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl
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Updated
Apr 19, 2024 - C
This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl
Implementation of Decision Tree and Ensemble Learning algorithms in Python with numpy
All the course work of supervised and unsupervised algorithms and projects.
Open-source Survival Analysis library
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
A collection of various applied Machine Learning and Artificial Intelligence projects I have done.
This is a customer loyalty analysis based on historical purchase behavior in R language.
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.
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 methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .
This is a repository with exercises extracted from the book "Introduction to machine learning with R" from Scott V. Burger. It will help you gain a solid foundation in machine learning principles. Using the R programming and then move into more advanced topics such as neural networks and tree-based methods.
Codes for the paper On marginal feature attributions of tree-based models
A machine learning project, predicting hourly bike rentals in Seoul.
Group academic research project focuses on predicting term deposit subscriptions for bank clients through data science, data analytics, and machine learning.
Linear & logistic regression, model assessment and selection, and gradient boosted trees
Telecom Churn analysis using various tree based classification models
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)
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