All the course work of supervised and unsupervised algorithms and projects.
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Updated
Jun 8, 2021 - Jupyter Notebook
All the course work of supervised and unsupervised algorithms and projects.
This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl
Analyzing the binary gender difference in lead roles using statistical machine learning
Telecom Churn analysis using various tree based classification models
Kaggle competition: predicting bikeshare demand with regression techniques. Linear/Lasso/Ridge Regression, KNN, Decision Tree, Random Forest, AdaBoost, XGBoost.
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
Implementing Tree-based algorithms from scratch (Decision Tree, Random Forest, and Gradient Boosting) from scratch and comparing it to the scikit-learn implementation.
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.
This is a customer loyalty analysis based on historical purchase behavior in R language.
Linear & logistic regression, model assessment and selection, and gradient boosted trees
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.
A collection of various applied Machine Learning and Artificial Intelligence projects I have done.
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)
Homeworks for Statistical Learning course (Prof. Vinciotti) @ University of Trento
Tree-based algorithms for solving a game of Flappy Bird.
Implementation of Decision Tree and Ensemble Learning algorithms in Python with numpy
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 .
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