A machine learning project, predicting hourly bike rentals in Seoul.
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Updated
Jul 29, 2023 - R
A machine learning project, predicting hourly bike rentals in Seoul.
This is a customer loyalty analysis based on historical purchase behavior in R language.
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
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)
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.
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.
Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .
Data Analysis with R
Comparison of multiple machine learning algorithms for leaf classification.
Tree-based survival analysis from scratch
In this section we will be predicting diabetes using classification machine-learning algorithms
Codes for the paper On marginal feature attributions of tree-based models
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
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