Data Sceintist (Ph.D.) at SoftServe (BS), assoc.prof. at LNU
Data Scientist at SoftServe (Business Systems), Ph.D, associate professor, electronics faculty of Ivan Franko National University of Lviv, Ukraine
My current scientific areas are: Data Mining, Predictive Analytics, Supply Chain analysis, Machine Learning, Information Retrieval, Text Mining, Natural Language Processing, R Analytics, Social Network Analysis, Big Data; semantic field approach in the analysis of semi-structured data.
Тема доповіді: Linear, Machine Learning and Probabilistic Approaches for Predictive Analytics
- We will consider the use of machine learning, linear and Bayesian models in the predictive analytics areas especially for time series modeling.
- For machine learning approach, we will describe XGBoost tree based classifier to obtain high scored classification.
- Using the generalized linear model for logistic regression makes it possible to analyze the influence of the factors under study.
- The Bayesian approach for logistic regression gives the statistical distribution for the parameters of the model. It can be useful in the probabilistic analysis, e.g. risk assessment.
- Results of different model combinations will be shown. We also will describe winner solution of our team on Kaggle competition “Grupo Bimbo Inventory Demand”.