Blog Category: Use cases


Employee Analytics


Published at: May 19, 2017, 1:33 p.m. | Author: Piotr Płoński


The analytic methods can improve Human Resources (HR) management for companies with large number of employees. It is very easy to give example, how can companies benefit from machine learning methods applied to HR. Let’s assume that training of new employee costs 1000$ and if we can predict which employee is going to leave next month, and propose him/her a bonus program worth 500$ to keep him for next 6 months, we are 500$ on plus and keep experienced, well-trained employee under the hood, with higher morale.




Machine Learning Wars


Published at: Dec. 12, 2016, 1:33 p.m. | Author: Piotr Płoński


Herein the performance of MLJAR on Kaggle dataset from “Give me some credit” challenge is reported. The obtained results are compared with other predictive APIs from Amazon, Google, PredicSis and BigML. This post was inspired with Louis Dorard's [article][1]. [1]: http://www.kdnuggets.com/2015/05/machine-learning-wars-amazon-google-bigml-predicsis.html




Searching for brain regions responsible for kids dyslexia


Published at: Nov. 9, 2016, 4:40 p.m. | Author: Piotr Płoński


Dyslexia is reading disorder - characterized by trouble with reading despite normal intelligence. We used ML methods to find brain regions responsible for this! Check out the paper: "Multi-Parameter Machine Learning Approach to the Neuroanatomical Basis of Developmental Dyslexia" in Human Brain Mapping journal.




Building Binary Classifier on Numer.ai data


Published at: Nov. 9, 2016, 4:20 p.m. | Author: Piotr Płoński


We made a youtube video with instruction how to build a binary classifier on Numer.ai dataset. We use raw data - but you should make some magic, create new features and use MLJAR to find the best models!




Predict stock market on AI tournament


Published at: Sept. 28, 2016, 1:30 p.m. | Author: Piotr Płoński


Machine learning models used by hedge funds for predicting stock market are of course super top secret as well as data used for their creation. However, there is one which makes its data public - Numer.ai. Dataset is encrypted and prediction of stock market is transformed into binary classification problem. Every 7 days (one round time) a new dataset is released and anyone can download it, train model and upload predictions. At the end of round, the best predictions are rewarded - there is no need to model upload. As you can see from leaderboard mljar.com is going really well in this competition.