Credit risk analysis for credit card applicants

Binary and Categorical Focal loss implementation in Keras.

Fast and customizable framework for automatic ML model creation (AutoML)

A multi-platform GUI for bit-based analysis, processing, and visualization

Code for Java Deep Learning Cookbook

autosklearn-zeroconf is a fully automated binary classifier. It is based on the AutoML challenge winner auto-sklearn. Give it a dataset with known outcomes (labels) and it returns a list of predicted outcomes for your new data. It even estimates the precision for you! The engine is tuning massively parallel ensemble of machine learning pipelines…

Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0

Detecting Autism Spectrum Disorder in Children With Computer Vision - Adapting facial recognition models to detect Autism Spectrum Disorder

A set of deep learning models for FRB/RFI binary classification.

2017-CCF-BDCI-企业经营退出风险预测：9th/569 (Top 1.58%)

1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

Malicious URL detector using keras recurrent networks and scikit-learn classifiers

2018 - Kaggle - TalkingData AdTracking Fraud Detection Challenge: Silver medal (银牌)

2018-腾讯广告算法大赛-相似人群拓展(初赛)：10th/1563 (Top 0.64%)

Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.

Perform analytics on a large airline dataset with Spark and build an XGBoost model to predict flight cancellations.

Glaucoma detection automation project. Trained a binary image classifier using CNNs and deployed as a streamlit web app. It takes eye (retinal scan) image as input and outputs whether the person is affected by glaucoma or not.

2018-JData-联通-基于移动网络通讯行为的风险用户识别：Baseline 0.77