Name: Soufiane Fartit
Type: User
Bio: Interests : Data Science, Machine Learning, Python, Computer Vision, AI, Anomaly Detection, Image Processing, Predictive Analytics
Location: Strasbourg
Soufiane Fartit's Projects
API to deploy image->image ML models.
Scrap Linkedin Jobs, and create resumes with custom skills for each job description
Given a Bank customer, can we build a classifier which can determine whether they will leave or not?
Code examples for my Write Better Python Code series on YouTube.
predict price of a car
ML web application for exploring data, features engineering, and model training (classification/regression).
If you are looking to become a Google Cloud Engineer , then you are at the right place. GCPSketchnote is series where I share Google Cloud concepts in quick and easy to learn format.
This is a repo for building out Github Actions and Tricks
Installscript
A large collection of system log datasets for AI-powered log analytics
A toolkit for automated log parsing [ICSE'19, TDSC'18, DSN'16]
MinIO : an s3 compatible storage, as a docker image to be hosted (here on Heroku), and be used like AWS S3
MLflow web application dockerised and deployed on heroku. using AWS RDS and AWS S3 as backend store and Nginx for authentication.
Mini Social Network
A collection of design patterns/idioms in Python
Code snippet to generate a docx resume
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we try to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.