Learn how the exercise files are organized and how you should use them.
- [Instructor] In this lecture, we are going to talk about your exercise files that comes with this course. So what you can do is you can download the exercise files, and unzip them and place them in a folder that is comfortable with you. In this case I have downloaded and put them into a folder called exercise files in my desktop. So you go in there, what you see is there are five folders in there. There is one folder for every lecture. So for each lecture that has an exercise file, the chapter name and the lecture number are there.
You open them up what you would see is you would see a data file like browsing.csv, which you can open in Excel. And also you would see an IPython notebook, and that is something you can open in IPython to read that notebook. So this is the set of code files available to you. So in order for you to open the browing.csv just click on it, it should open in Excel. In order to run IPython notebook, there are multiple ways in which you can run it.
And I believe you should already be familiar with it. The way I do it is I typically go to the exercise files folder, and then in there I'm going to just write the command Jupyter notebook. And once I execute that one it is going to open the notebook for me in the browser. And from here I can navigate to the specific folder, and then click on the notebook to open the notebook. Once you open the notebook it is recommended that you execute the cells, all the cells one by going to cell and then run all.
This notebook is fully executed so you don't have to make any changes for it. All you have to do is go open the cells, and run all and that should give you all the results that you need.
Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review three use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up.
- Understanding the customer life cycle
- Acquiring customer data
- Applying big data concepts to your customer relationships
- Finding high propensity prospects
- Upselling by identifying related products and interests
- Generating customer loyalty by discovering response patterns
- Predicting customer lifetime value (CLV)
- Identifying dissatisfied customers
- Uncovering attrition patterns
- Applying predictive analytics in multiple use cases
- Designing data processing pipelines
- Implementing continuous improvement