data analysis
It has been a while since I made the last git commit in the project repository. Although I am a bit busy with my daytime job recently, I will keep the side project rolling. This blog post is about my recent reflection on the project direction. There are two goals
data analysis
in #4, I talked about how to find the hidden APIs of Centaline Android App. In this article, I am going to show you how to write a scraper to scrape the data. You can find the Scrapy Crawler in this Git directory. 3 Levels of Data The scraping was
data analysis
Date: 26 July 2020 In #3, I faced difficulty in having a lot of missing values in the property transaction data. After searching the web, I found that Centaline claimed they have spent 10 million HKD to fill the missing values. Maybe, I should try scraping data from Centaline. Luckily,
machine learning
Date: 13 July 2020 In part 2, we have talked about splitting data into a training set and testing set. In part 3, I would like to share some findings on the first data exploration. Big Problem: Missing Data I did not expect there are so many missing data in
machine learning
In part 1, we have talked about scraping transactional data of apartments. In part 2, let’s talk about splitting data into a training set and testing set. Why we need to split? The short answer is we want to prevent overfitting/memorizing and hope that the model trained can
machine learning
Date: 7 July 2020 Housing is still one of the greatest concerns of Hong Kong people. I think it will be fun to build a model to predict the house price in Hong Kong. Unlike, Kaggle competition, data is not readily available. So, the first step, which I am going