Fabien Mangeant et Vincent Feuillard (Airbus): “scikit-learn for predictive maintenance at Airbus” Julien Sananikone (PriceMinister), Benjamin Guinebertière (Microsoft), Samuel Charron (Data Publica), Thomas Cabrol (Dataiku): “Industrial uses of scikit-learn” (business roundtable) Bio: Julien Sananikone is Project manager at PriceMinister. Benjamin Guinebertière is technical evangelist at Microsoft. Benjamin works with startups and companies of different sizes to help them technically adopt Microsoft Azure cloud, should they use Big Data, Machine Learning or other technologies. He also speaks at conferences, writes (blogs, …) and takes feedback. Combining a strong background in webmining and big data technologies with a deep interest in machine learning, Samuel Charron has earned the title of Big Data Sergeant at Data Publica, where he alternates between contributions to the development of the infrastructure of the C-Radar product and stints of rapid prototyping with Python data science libs (such as pandas, sklearn). In particular, he works on NLP related problems using unstructured web-scraped data to enrich corporate information. Thomas is Dataiku’s Chief Data Scientist. Thomas started his career in data mining for large retail and telecommunications companies. Then he lead the data mining team and geo-marketing initiatives at Apple Europe. He was lead data scientist at qunb, a data visualization startup. He spent most of his career deriving value from large datasets using cutting-edge innovations. He is fluent in all the data scientists linguas: R, Python, SAS…

1. PriceMinister
Product catalog management
with
Scikit-Learn
PriceMinister
Product catalog management
with
Scikit-Learn
April 3rd, 2015

2. PriceMinister Catalog ManagementPriceMinister Catalog Management
●
25+ Millions products, 150+ product types, 250+
categories
●
Professionnal and individual sellers, many listing
channels
●
Machine Learning : a much discussed topic within
PriceMinister / Rakuten, still discussing ...
●
And then came « Scikit-Learn »
– No data scientist background, little knowledge
of python (django)
– I Followed the very clear tutorial : « Working
with text data »

3. UGC – User generated ClassificationUGC – User generated Classification
Machine Learning => Multi-class classification
●
TF-IDF: big USER dictionary (most discriminative
words)
●
Classifier: linear model (SGD)
●
Labelling: products title already labelled on site by
products type (good and bad classification)
●
Training: 1000 samples per class (103 categories, no
cultural products)
●
Implementation : scikit-learn, scipy, numpy, pandas,
Falcon, uwsgi (less than 30 lines of code)

4. UGC – User generated ClassificationUGC – User generated Classification

5. First ResultsFirst Results
mean accuracy of test : 0.67 => not that badmean accuracy of test : 0.67 => not that bad

6. Something is coming …Something is coming …
Stationery: Green FolderStationery: Green Folder
Clothes: Green ShirtClothes: Green Shirt
Green PlantGreen Plant