ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

[ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

[NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

Bank customers churn dashboard with predictions from several machine learning models.

Experimental implementations of several (over/under)-sampling techniques not yet available in the imbalanced-learn library.

An Intrusion Detection System (IDS) using ensemble machine learning models and LIME for explainable AI, leveraging the CICIDS-2017 dataset for network intrusion detection with transparent predictions.

Testing different supervised machine learning algorithms to predict credit risk

Conception and deployment of a credit-scoring model, API and interactive dashboard

Fairness-aware ICU mortality prediction using MIMIC-III data and Group-Aware SMOTE

No description provided.

Credit risk analysis using scikit-learn and imbalanced-learn.

Loan Default Prediction: Predict loan default risk using classification models with feature engineering, model training, and evaluation metrics.

Identify credit card risk using Machine Learning algorithms

A full stack classification machine learning project.

Onco-Logic is a comprehensive, multi-modal decision support ecosystem designed to transform cancer care by unifying fragmented patient data. The suite leverages advanced AI and machine learning to provide clinicians and researchers with a holistic understanding of each patient's disease, enabling a new frontier in precision oncology.

End-to-end customer churn prediction project using real Olist Brazilian e-commerce data — EDA, feature engineering, Logistic Regression and Random Forest models, with business recommendations.

Credit Card Fraud Detection using Machine Learning – A classification project that detects fraudulent credit card transactions using supervised learning, with data preprocessing, handling class imbalance, and model evaluation (ROC-AUC, Precision, Recall, F1-score).

AI Based Anomaly Detection System for System Logs

Final project for the end of the course in collaboration with Alessandro Zanzi.

Predict credit risk using a variety of Resampling Models and algorithms.

A predictive ML pipeline to classify online shoppers’ purchase intent and segment customer types – leveraging SMOTE to address class imbalance, applying mRMR for feature selection, and training multiple scikit-learn classifiers and K-Means clustering to drive revenue-boosting insights.

Machine-learning pipeline for predicting stroke risk from patient health and lifestyle data.

What causes a shopper to hit "purchase"?

Built a machine learning pipeline to classify obesity levels and predict BMI from behavioral and demographic data – employing SMOTE for class balancing, training Decision Tree, Random Forest, and Gradient Boosting models, and using feature importance analysis to highlight key lifestyle factors.

Предсказание оттока клиентов из банка

A data science project exploring Portuguese "Vinho Verde" wine quality prediction. Features EDA, feature engineering, ML models, and evaluation using Python, pandas, scikit-learn, and visualization tools.

Use Python and Scikit-learn and Imbalanced-learn to predict credit risk. Compare the strengths and weaknesses of machine learning models. Assess how well a model works.

Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.

Predict credit risk with machine learning models by using different techniques to train and evaluate models with unbalanced classes.

Credit Risk Analysis utilizing imbalanced classification machine learning models