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

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

[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

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

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

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.

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

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

Machine learning app to identify credit risk

Testing different supervised machine learning algorithms to predict 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

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.

A full stack classification machine learning project.

Credit risk is an unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. Use imbalanced-learn and scikit-learn libraries to build and evaluate machine learning models using resampling.

Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.