Welcome back to our exciting quest of making money in football betting using machine learning! In our last post, we have attempted to feed the dataset bluntly into a few machine learning algorithms without much feature engineering and the best accuracy score was a disheartening 53%. While 53% is very shameful, it served as a
One day, when I was looking around for interesting data science toy projects to play with, an article from a machine learning tutorial website caught my attention. It says, “How to predict football results easily using machine learning”. Interesting, I thought, and I clicked into the article. As I was reading the article with a
If you are on this page, chances are you have heard of the incredible capability of XGBoost. Not only it “boasts” higher accuracy compared to similar boasted tree algorithms like GBM (Gradient Descent Machine), thanks to a more regularized model formalization to control over-fitting, it enables many Kaggle Masters to win Kaggle competitions as well.
You stumble upon some intriguing patient cancer dataset that seems to be the last remaining puzzle towards solving the human war against cancer that will make this world a better place for everyone and you excitedly download the dataset. Your data analysis usually go through these standard processes: 1) Load data 2) Do some pre-processing of