Challenge submitted on HackerRank and Kaggle.
Algorithm challenges are made on HackerRank using Python.
Data Science and Machine Learning challenges are made on Kaggle using Python too.
The goal of this challenge is to build a model that predicts the count of bike shared, exclusively based on contextual features. The first part of this challenge was aimed to understand, to analyse and to process those dataset. I wanted to produce meaningful information with plots. The second part was to build a model and use a Machine Learning library in order to predict the count.
I've recently discovered the Chris Albon Machine Learning flash cards and I want to download those flash cards but the official Twitter API has a limit rate of 2 weeks old tweets so I had to find a way to bypass this limitation : use Selenium and PhantomJS.
Purpose of this project : Check every 2 hours, if he posted new flash cards. In this case, download them and send me a summary email.
Modern face recognition with deep learning and HOG algorithm. Using dlib C++ library, I have a quick face recognition tool using few pictures (20 per person).
As a soccer fan and a data passionate, I wanted to play and analyze with soccer data.
I don't know currently what's the aim of this project but I will parse data from diverse websites, for differents teams and differents players.
Kaggle playground to predict the total ride duration of taxi trips in New York City.
Use satellite data to track the human footprint in the Amazon rainforest.
Deep Learning model (using Keras) to label satellite images.
Project inspired by Chuan Sun work
How can we tell the greatness of a movie ?
Scrapping and Machine Learning