₿ Bitcoin 🚀 predictions 🪙

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.

Deciphering how customer's purchasing habits are influenced by wholesale pricing and examining its impact on final retail cost.

M. Anisetti, C. A. Ardagna, A. Balestrucci, N. Bena, E. Damiani, C. Y. Yeun. "On the Robustness of Random Forest Against Data Poisoning: An Ensemble-Based Approach". In IEEE TSUSC, vol. 8 no. 4

This project aims to develop an Intrusion Detection System (IDS) 🕵️‍♂️🕵️‍♂️ using deep learning techniques. The IDS model is designed to detect malicious activities and potential threats within a network by analyzing traffic data.

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

This repository contains a project for predicting heart attacks using a dataset of patient information. The project includes data preprocessing, feature engineering, model training using Random Forest Classifier.

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.

Example machine learning applications for the determination of the residual yield force of corroded steel bars tested under monotonic tensile loading. Data is collected from 26 experimental programs avaialbe in the literature.

🍊 📊 💡 Orange: Interactive data analysis

Machine Learning para analisis de Encuestas de Hogares. Modelos de Random Forest para predecir caracteristicas de hogares argentinos usando EPH y datos del Censo.

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.

Educational notebooks reviewing machine learning models and concepts.

Simple and flexible classical ML module that can be used for recording baseline ML performance.

Predicting bank churn rates with machine learning models (decision trees, random forest, & xgboost)

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

Batch Name: MIP-ML-11 (Machine Learning Intern)

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

This repository includes various trainings and projects about AI and Data Science