Ensembles of Oblique Decision Trees

Developed a ML assisted stock trading strategy to long or short a stock by training a random forest learner (random tree with bagging), details see the Final-Project-Report.

Machine Learning algorithms from-scratch implementation. It covers most Supervised and Unsupervised algorithms. Homework assignments and Projects for graduate level Machine Learning Course taught by Dr Manfred Huber at UTA during Spring 21

This repository consists of folders which include some of the courseworks I have completed in my Data Science MSc at KCL.

Bài tập lớn môn học máy - TLU

Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.

In this project, I trained a decision tree model, as well as three variants of a decision tree (random forest, gradient boosting, and bagging) to predict the class of data/images from two datasets

Machine Learning prototype that assess the performance of 1 Decision Tree (no bagging) vs multiple Decision Trees (with bagging)

Code of the Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN on ICEEM 2023

This analytical journey encompasses the following methodologies and techniques: 🔍 Exploratory Data Analysis (EDA): Comprehensive exploration to identify patterns, correlations. 🛠 Feature Engineering: Innovating from the existing dataset to enhance model classification. 📐 XGboost, GBDT, RF : Constructing bagging and boosting models using sklearn

Data visualization provides a good, organized pictorial representation of the data which makes it easier to understand, observe, analyze.In this Pproject, we will provide codes for visualizing data using Python.

e2e machine learning pipeline using a config based approach for classification problems. Supports grouping and grading classifiers in addition to online learning algorithms