Automation is essential for all industries, and machine learning opens new perspectives in this area. Companies have to keep up-to-date, and their dependence on data science is becoming greater than ever.
Machine Learning techniques give computers the ability to learn from past experience and recognize patterns. Sophisticated training algorithms are used to allow predictive models to predict outcomes. Thus allowing companies to better take action (preemptively before a failure take place for example).
Scorelab is a company of this digital era, combining knowledge from the latest academic research in machine learning, industry expertise and entrepeneurship experience to innovate.
Our team, composed of mathematicians, computer scientists and statisticians, develops custom algorithms to meet the needs of each project. Machine learning is at the heart of our development, for in-house projects or those developed with our partners and clients.
The Global Wine Score has been the first project developed by our team. It is a worldwide rating which assesses wines with a single score, providing comprehensive and comparable information for all industry players.
It is an average adjusted score aggregating the major wine critics. It takes into account their ways of rating and their respective scales to provide an indicator minimizing the experts subjectivity.
A website has been developed globalwinescore.com and it is already in production used daily by users worldwide.
We develop predictive algorithms and innovative investment strategies for our clients.
We find small signals and patterns using cutting edge techniques in machine learning and robust statistical analysis in order to predict the commodities financial markets.
Our rigorous methodology and innovative approach help our clients in taking better decisions on the market.
Scorelab develops for Ashler & Manson the solution preacor.fr.
Based on the experience of the ASHLER & MANSON brokers, associated with the machine learning algorithm developed by our team, PREACOR is enriched with new applications added daily.
Thousands of anonymized loan applications are used in the training and development of PREACOR model. Dozens of criteria have been added to the score calculation to make the PREACOR model robust and innovative.
Scorelab collaborates with IECB (Institut Européen de Chimie et Biologie) on a genomic study. It is a large meta-analysis of multiple genomics experiments on the nematod Caenorhabditis elegans. This analysis aggregates 1600 runs of experiments using RNA sequencing technologies. The data used comes from published studies sharing their datasets on the NCBI (National Center for Biotechnology).
The goal of this work is to understand the functions of genes and the cells’ types in which genes are active. Our statistical tools allow the comparison of experiments with different degrees of sensibility. This work aims to reveal unexpected correlations between genes or experiments. This is still under development.
Scorelab develop algorithms for personalized recommandation of wines. We aggregate our experiences in wine data collection and analysis as well as our skills in machine learning and recommander systems. It allows us to develop and train machine learning models specifically for wine tastes. We learn individuals taste and wines characteristics to generate a personalized wine score.
This score reflects the wines you will probably love and the ones you will have in disgust. It is then applied to your user profile to recommend you wines. A prototype has been developed to test its relevance. Further development are in progress for commercial use.
We work on the personalized recommender engine of this application. The model in use is an extension of Collaborative Topic Regression.
It is based on both topic modeling of the papers textual content and the users 'likes' on the papers. The application is an open source project and is expected for August 2017.
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