Learn Data Science using Python

Another post starts with you beautiful people! XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. Recently XGBoost is released with it's newer version 1.0.0 which has improvements like performance scaling for multi core CPUs, improved installation experience on Mac OSX, availability of distributed XGBoost on Kubernates etc. In this post we are going to explore it's multi processing capabilities on a real world ml problem  Otto Group Product Classification Challenge . In the end of the post I will share my kaggle kernel link also so that you can explore my complete code. Once you go to the challenge link in Kaggle and start your kernel, first you need to enable the Internet option in the noteb...

Another post starts with you beautiful people! I hope you are enjoying our machine learning journey and now after familiar with many real world problems as we have seen earlier you come to know that with this skill you can make the world a better place to live! To continue our journey today we are going to analyze China's largest Big Data service platform problem and this platform is known as TalkingData About The Problem- TalkingData covers over 70% of active mobile devices nationwide . They handle 3 billion clicks per day, of which 90% are potentially fraudulent . Yes, your read it right! 90% of clicks are fraud and it causes them unnecessary server load. Our Challenge- As a data scientist our task is to  build an algorithm that predicts whether a user will download an app after clicking a mobile app ad. Data- To support our modeling, TalkingData has provided a generous dataset covering approximately 200 million clicks over 4 days which you can download/see from here...