Tree-based survival analysis from scratch
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Updated
Sep 2, 2024 - Python
Tree-based survival analysis from scratch
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
In this section we will be predicting diabetes using classification machine-learning algorithms
Implementing Tree-based algorithms from scratch (Decision Tree, Random Forest, and Gradient Boosting) from scratch and comparing it to the scikit-learn implementation.
Group academic research project focuses on predicting term deposit subscriptions for bank clients through data science, data analytics, and machine learning.
This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl
Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .
Comparison of multiple machine learning algorithms for leaf classification.
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
Kaggle competition: predicting bikeshare demand with regression techniques. Linear/Lasso/Ridge Regression, KNN, Decision Tree, Random Forest, AdaBoost, XGBoost.
Kaggle competition: predicting forest cover type with multiclass classification algorithms. Logistic Regression, SVC, KNN, Decision Tree, Random Forest, XGBoost, AdaBoost, LightGBM, & Extra Trees.
Data Analysis with R
A machine learning project, predicting hourly bike rentals in Seoul.
Tree-based algorithms for solving a game of Flappy Bird.
Analyzing the binary gender difference in lead roles using statistical machine learning
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.
Linear & logistic regression, model assessment and selection, and gradient boosted trees
All the course work of supervised and unsupervised algorithms and projects.
Random Forests Tree-Based Model in Machine Learning (exercise using Iris data)
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