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.

Testing different supervised machine learning algorithms to predict credit risk

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

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

A full stack classification machine learning project.

Identify credit card risk using Machine Learning algorithms

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

Demonstrating how changes in input image resolution affect the algorithm's output

Machine learning app to identify credit risk

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

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.

Predict credit risk with machine learning techniques.

Review score prediction using text on the Amazon Fine Food dataset

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.

Using six different machine learning algorithms to evaluate credit data and compare each model’s accuracy, precision, and recall scores in relation to the data’s credit risk.