[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.

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

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

Identify credit card risk using Machine Learning algorithms

A full stack classification machine learning project.

Data visualization of the NYC restaurant data, and data analysis to gauge if a restaurant located in a high-income area receives a higher health inspection grade. Uses Python (Pandas, Scikit-learn, Imbalanced-learn), PostgreSQL, SQLAlchemy, Tableau, JavaScript (Plotly.js library), HTML, CSS, and Bootstrap.

Engineered 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.

Предсказание оттока клиентов из банка

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.

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

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

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

Machine learning models for predicting credit risk in LendingClub dataset.

Review score prediction using text on the Amazon Fine Food dataset

Predict credit risk with machine learning techniques.

Built and evaluated several machine-learning models to predict credit risk using free data from LendingClub.