From discipline Intellectual systems and technologies

SVM and Feature Selection course.

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

Deployed model to predict total sales for every item and shop for the next month, from a time-series dataset consisting of daily sales data

Prediction of a productional appliance readings based on anonimized data.

The projects which involve the use of ML concepts

Predict the ratings of players based on various attributes, like crossing, free_kick_accuracy, ball_control.

This workshop is part of the "Machine Learning in R" graduate course held at University of Münster, School of Business and Economics (winter term 2020/21). 🎓

An Unsupervised learning based project where objective is Geolocation based Customer Analysis to identify ownership patters of the cars throughout the country and Market Segmentation to predict revenue that can be extracted from each segment over a life of the car.

Feature selection project for a student competition. Analyzing data for chemists at University of Southampton.

Linear & logistic regression, model assessment and selection, and gradient boosted trees

Music genre classification done in two different ways: 1.Traditional ML and 2.Temporal feature integration/Fusion of decisions

Identify best tweeting practices by hospitals in Illinois during COVID-19. Also find factors affecting popularity of a tweet.

Time Series Transformations Feature Selection with Estimation of Distribution Algorithms

Forecasting Oil Prices with Time Series & Generalized Additive Models for Location, Scale and Shape

Building a model that predicts cancer death rates in the US and to predict the risk that a person from a particular county might be suffering from cancer

TCR Innovation Internship Program