In Part 1 of this article we successfully scraped the data we needed and in this Part 2 we are going to move on to the next steps in the Machine Learning Cycle, which is Exploratory Data Analysis. The full code on Github and the scraped dataset for this Part 2 can be found here. Let's dive straight with the exploratory data analysis. What to Expect from Exploratory Data Analysis There is some confusion out there whether to split the test and training dataset before doing EDA or not.I would say the problem comes when you go into too much detail during your EDA.
Currently, I am preparing to sit in for the AWS Machine Learning Speciality Exam, and I know for such exams, the best way to prepare is to go quickly through the concepts (inhaling) and start building projects (exhaling). So I have decided to build a machine learning app using Python and AWS Sagemaker , which will predict land prices based on the specified neighborhood and the size of land required ,as inputs to the model. The goal is to use this project so together we walk through the machine learning lifecycle and also introduce you to the powerhouse of machine l
Traditional Business Intelligence systems have reached their limit Gone are those days when telecom companies used to have an easy ride and make money with little effort. Most markets are now becoming highly saturated and telecom companies today, are currently suffering from huge competition .A Telecom Company spends averagely above $100 to acquire a new subscriber. It is a huge loss when those subscribers leave the franchise to other competitors within a couple of months of their acquisition. The customer lifetime value is bound to drop significantly, as competitors en
90% of the data in the world today has been created in the last two years only. And the truth is, the amount of data generated by people and companies keep increasing exponentially every single day. We are now in the age of Big data where the speed, variety and complexity of data is only ever going to increase. A company can either chose to stay as an emotion driven culture (using gut feelings from a few leaders) or move towards a data driven culture. Emotion driven culture is ideal when the leaders are experts, company data is not available and the company’s data scien
Data is the new oil… For those business owners and Top Management who still have not realized it, they need to be aware that not only have the Steam and Computer Industrial revolutions passed a long time ago, even the Digital Revolution is gone already.We are now in the Fourth Industrial revolution of Cloud computing, Artificial Intelligence and Data. You might not be aware but each day we consume products from data science and machine learning. After watching a video on you tube, you receive a recommendation for a similar video based on the previous video you watche