Every business owner wants to make revenue prediction, so he or she can have better marketing decisions. On Kaggle, the data science community site, there is a challenge on making a store’s revenue prediction. And that is the topic we are looking for. The store in this challenge is none other than the Google Merchandise Store. …
So far, we have entered several Kaggle’s machine learning competitions. Most of the time, we use Kaggle’s free kernel to solve the puzzles. From the Deal Probability Prediction challenge, we reached the limit of Kaggle’s kernel — the 17 GB RAM limit, while we were training our model. When we added more training data or …
AWS Tutorial: Deep Learning on Amazon Web Services Read More »
Other than our last hand writing challenge, there is another Kaggle challenge featuring image recognition — TGS Salt Identification Challenge. But this time, we are going for an “upgrade”. As we are dealing with object detection. It is all about salt. In this challenge, our mission is finding geophysical images that contain salt. Oh wait, does it sound weird? Actually …
U-Net and IoU for Object Detection in Image Processing Read More »
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?
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 »
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 …
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 »
Okay, yes, once again, it is a catchy topic. BUT, this post is indeed trying to help people (including me) to gain an upper hand in real estate market, using data science. From our last post (2 months ago, I will try to update this blog more frequently :]] ), we learn that we can …
To win big in real estate market using data science – Part 1 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 …
