How to figure out if you are paying the right price for the property you are about to purchase? Welcome to a new data science case study example on YOU CANalytics to identify the right housing price. Pricing is a highly important and specialized function for any business. A right price can make the difference between
In the last post we had started a case study example for regression analysis to help an investment firm make money through property price arbitrage (read part 1 : regression case study example). This is an interactive case study example and required your help to move forward. These are some of your observations from exploratory analysis that you shared
Welcome back to the case study example for regression analysis where you are helping an investment firm make money through property price arbitrage. In the last two parts (Part 1 & Part 2) you started with the univariate analysis to identify patterns in the data including missing data and outliers. In the discussion section of the
Principal component analysis is a wonderful technique for data reduction without losing critical information. Yes, you could reduce the size of 2GB data to a few MBs without losing a lot of information. This is like a mp3 version of music. Many, including some experienced data scientists, find principal component analysis (PCA) difficult to understand.
This is a continuation of our case study example to estimate property pricing. In this part, you will learn nuances of regression modeling by building three different regression models and compare their results. We will also use results of the principal component analysis, discussed in the last part, to develop a regression model. You can find
“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.” – Sherlock Holmes This is a continuation of our regression case study example. In the previous parts, we have learned, as Sherlock Holmes says, to make bricks i.e. develop regression models. In this part, we will learn how to make clay from scratch