BitPredictor - A cutting-edge machine learning-based solution for predicting cryptocurrency prices. Harnessing the power of advanced algorithms and data analysis techniques, this system aims to provide accurate and timely forecasts for Bitcoin and other cryptocurrencies.

A machine learning method for the discovery of the minimum marker gene combinations for cell type identification from single-cell RNA sequencing

Implementations of main Machine Learning Agorithms from scratch: Gaussian Mixture Model, Gradient Boosting, Adam, RMSProp, PCA, QR, Eigendecomposition, Decision Trees etc.

The objective of this project is to showcase the use of Machine Learning models to answer the question of loan default prediction based on certain parameters from the German bank dataset.

Common machine learning algorithm implementations

Welcome to the Lotto 6 aus 49 Prediction Project! This repository contains predictive models and analyses for Lotto 6 aus 49, a popular lottery game in Germany. Through statistical analysis, machine learning models, and simulations, we aim to provide insights and predictions to help lottery enthusiasts make informed decisions. BE AWARE! FUN PROJECT

Empower your real estate decisions with our data-driven model, delivering precise rental predictions for landlords and comprehensive insights for tenants in a dynamic market landscape.

This project leverages cutting-edge technologies like blockchain and machine learning to build trust and combat corruption in government systems. Secure Land Registry: Eliminates fraud with tamper-proof land ownership records. Automated Traffic & Challan System (using edge computing): Ensures transparency in traffic enforcement and reduces bribery

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

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.

Analyzed employee attrition using Python and data science libraries. Explored factors such as job role, department, and demographics to understand patterns influencing attrition. Random Forest demonstrated superior performance with an accuracy rate of 94%.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Two algorithms based on linear programming to discover classification rules for interpretable learning.

This repository focuses on building both a random forest classifier and regressor, building them from scratch using only NumPy for faster array processing.

Proyecto de Titulación: Cálculo de Pérdidas Esperadas basado en 3 modelos de Credit Scoring para una institución financiera del Ecuador

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.

This project employs multiple regression analysis to identify key determinants of employee salaries, such as experience and education, using R. Through extensive data analysis and model comparisons between Linear Regression and Random Forest, the study offers insights for effective salary structuring and employee retention strategies.

cyber attack detection

Machine Learning inference engine for Microcontrollers and Embedded devices

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