**As usual, please contact me if you want to propose a talk, or sponsor the meetup (food, drinks, location).**



Programme (tentative):



- Mathis



Abstract: Making the results of a machine learning project easily reproducible is often no easy feat. We have to work with (or against) OS and language level package managers as well as constantly evolving library APIs and hardware drivers. With the nix package manager it is possible to unambiguously specify the entire dependency tree of your projects on Linux distributions and on macOS. We'll show in examples how you can use nix to describe your projects.



- Alexandre Gerbeaux



on AutoML



- Kris Kasidit Methajarunon



Kris M will present his Silver Medal Solution for Kaggle’s Home Credit Default Competition.



· About the competition :



o Home Credit Default Risk : https://www.kaggle.com/c/home-credit-default-risk



o The aim of this competition is to predict whether loan applicants will default eventually in the future, given their information such as income, occupation, age, payment history, and many other features.



o To date, this is considered one of the most participated competition on Kaggle with 7,198 teams.



· Presentation Highlights:



o An overall of my solution (silver medal, ranked 50 / 7,198 or top 0.7% ) . If you attended my last talk, I was hoping to win a gold medal, which needs to be within top 0.5% . Unfortunately I didn’t make the cut, needed another 0.2% .



o In particulars, we will be looking at :



§ Feature engineering, arguably the most important part of this competition



§ Handling categorical variables : one hot encoding vs ordinal labelling vs target mean encoding



§ The Three Trees : XGBM vs LGBM vs CatBoost



§ A touch of winner solution, how and what these guys did differently



o About him : https://www.linkedin.com/in/krismetha/