"oxayavongsa/projects" is a public GitHub repository serving as a diverse AI/ML Project Portfolio. Using Python coding and Juptyer notebook for multiple methodologies to model statistical algorithms.

Scikit-learn compatible decision trees beyond those offered in scikit-learn

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

Explore my Codsoft ML Internship tasks

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

Estimación de turbidez en el agua a la entrada de la planta de tratamiento de SAMEEP, utilizando los productos Sentinel-2 MSI L2A y aprendizaje automático.

ABC random forests for model choice and parameter estimation, pure C++ implementation

Here we have fully implemented a number of algorithms related to machine learning

Generalized Random Forests

Some of the topics, algorithms and projects in Machine Learning & Deep Learning that I have worked on and become familiar with.

Diabetes is a medical disorder that affects how the body uses food for energy. When blood sugar levels rise, the pancreas releases insulin. If diabetes is not managed, blood sugar levels can rise, increasing the risk of heart attack and stroke. We used Python machine learning to forecast diabetes.

Explore network traffic analysis with Machine Learning! This project utilizes Decision Trees, Random Forests, and K-Nearest Neighbors (K-NN) to predict optimal actions for network sessions. We evaluate classifier performance in accuracy, precision, recall, and F1-score.

This project focuses on analysing the performance of various Machine Learning models available in python's scikit-learn package when trying to predict wine classification

A crop recommendation API using a machine learning with an accuracy of 99.18%.

This project focuses on predicting the income of individuals based on a diverse set of demographic and socio-economic features. Using the Adult Income dataset, I used a Random Forest model to address this classification task.

Credit Score Classification in R using various algorithms

P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.

A short data science project about analyzing datas from a fictional travel blog.

Predicting Customer Churn using Data Mining and Machine Learning techniques - Logistic Regression, Decision Trees and Random Forests