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

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.

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

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

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

Bài tập lớn môn học máy - TLU

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

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

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