Last time, we tried the Kaggle’s TalkingData Click Fraud Detection challenge. And we used limited resources to handle a 200 million records sized dataset. Although we can make our classification with Random Forest model, we still want a better scoring result. Inside the Click Fraud Detection challenge’s leaderboard, I find that most of the high scoring […]
LGB, the winning Gradient Boosting model Read More »
Previously on CodeAStar: A data alchemist wannabe tried to win big in real estate market, he then used Kaggle’s Housing Regression data set, engineered the features and fit them in a bunch of models. Dang! Nothing fancy happened. But he then discovered “the room”, the room for improvement — model params tuning.
To win big in real estate market using data science – Part 2: Model Params Tuning Read More »
We have learnt how to select a machine learning model, it is time to study another Data Science topic from the Data Science Life Cycle — Data Collection. Yes, we do need to know how to collect data. Unlike our Iris Classification project, which its data set is well prepared. Sometimes, we need to prepare our own
Titanic Survivors Dataset and Data Wrangling Read More »
