Part I Problem Framing
A common problem across businesses in many industries is that of customer churn. Businesses often have to invest substantial amounts attracting new clients, so every time a client leaves it represents a significant investment lost. Both time and effort then need to be channelled into replacing them. Being able to predict when a client is likely to leave and offer them incentives to stay can offer huge savings to a business. This is the essence of customer churn prediction.
- Tech Stack (a) Windows 10 (b) Python 3.6 (c) Jupyter Notebook (d) Python Modules and Libraries: scikit-learn, matplot
Part II Problem Solution
- Building Process
(a) Define / frame the question of interest (what is the likelihood of churn for each client?)
(b) Extract data from our source system
(c) Explore and clean the data (a simple .csv file in this case) (d) Select relevant features (e) Fit statistical models (f) Make predictions / evaluate models