This is a collection of machine learning topics. We have to learn more in order to let machines learn from us.
In our past machine learning topic, “Ensemble Modeling“, we mentioned how blending helps on improving our prediction. Then in another topic, “Why are people frustrated on Kaggle’s challenge?“, we mentioned how blending ruins a data science competition. Okay, we have a question here, is blending good or bad?
Blending, the Dark Side in Data Science Competition Read More »
Our topic this time is too Russia, so my water just turned into Vodka :]] . Actually, this is all about Kaggle’s competition: Avito Demand Prediction Challenge. Avito is the biggest classified site in the Mother Russia, just likes the Craigslist. Our mission for this time is, predicting the rate that a seller can make a
TFIDF technique and Deal Probability Prediction (in Russian) Read More »
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 »
You have probably read the above comic in 9gag or imgur before. It is a funny joke, but on the other hand, it is also a material for our Machine Learning topic. It sounds weird? Oh yeah, sometimes knowledge comes from strange ideas.
“Do you have a dog?” explained in Machine Learning Read More »
Up to now, we have tried 3 different Kaggle journeys, the Titanic Survivors, the Iowa House Prices and the hand written digits recognition. Those journeys covered popular Machine Learning topics, such as classification, regression, deep learning, and so on. I would suggest fans of Machine Learning to start with those journeys. So we can learn
Click Fraud Detection with Machine Learning Read More »
On last post, we tried our image recognition project with handwritten digits. We used a Convolutional Neural Network (CNN) to train our machine and it did pretty well with 99.47% accuracy. We learnt how a CNN works by actually implementing a model. Today, we move one step further to learn more about the CNN, let’s visualize
Visualize a Convolutional Neural Network Read More »
On our data science journey, we have solved classification and regression problems. What’s next? There is one popular machine learning territory we have not set feet on yet — the image recognition. But now the wait is over, in this post we are going to teach our machine to recognize images by using Convolutional Neural
Python Image Recognizer with Convolutional Neural Network Read More »
When I have a data project in mind and have no idea on where to start modeling, I will always use the Random Forest model. It is not because of its catchy name and the fact that I always misspell it as Rain Forest, it is quick, convenient, easy to understand and, it provides decent
A Beginner Random Random Forest Tutorial Read More »
Previously on CodeAStar: The data alchemist wannabe opened the first door to “the room for improvement”, where he made better prediction potions. His hunger for the ultimate potions became more and more. He then discovered another door inside the room. The label on the door said, “Ensemble Modeling”. This is our last chapter on the
To win big in real estate market using data science – Part 3: Ensemble Modeling Read More »
