Learn how you can leverage the elastic, on-demand processing power of Microsoft Azure to create faster, more applicable analytics. Data Scientist and Author, Ahmed Sherif, will demonstrate key analytic use cases that can be spun up quickly with minimal effort and maximum return on investment.

1. Azure ML Churn Demo From Scratch
November 2nd, 2017
Twitter: @theAhmedSherif

2. Review our Customer Case
Demo #1: Importing Data
Demo #2: Identifying Missing
Values
Demo #3: Building Logistic
Regression Model
Demo #4: Comparing 2
Scored Models side by side
Demo #5: Notebooks and
Web Services

3. Review Our Customer Case

4. Keep Calm and Minimize Churn
A large multimedia conglomerate is concerned customers are leaving for more online media content
Cord Cutting!!!!
They need to understand who is leaving and what factors are indicators for churn
You work for this Company!
You need to help them figure out what kind of customers are leaving and for what reason

5. Dataset
7,000 records
The following columns
customerID Online Backup
gender Device Protection
SeniorCitizen Tech Support
Partner Streaming TV
Dependents Streaming Movies
tenure Contract
PhoneService Paperless Billing
MultipleLines Payment Method
Internet Service Monthly Charges
Online Security Total Charges

6. Modeling Strategy
The Target Column is Churn
All other variables are Predictors
The output is Binary: Yes or No for Churn
Ideal Candidate for binary classification model
Logistic Regression
Decision Forest

7. Demo #1
Importing Data and Exploring Fields in Azure ML

9. Demo #2
Identifying Missing Values and Imputing Results

10. Data Cleansing
80% of the time a Data Scientist is spent cleaning dirty data
20% of the time a Data Scientist is complaining about cleaning dirty data

12. Demo #3
Building Logistic Regression Model, Scoring, and Evaluating

14. Demo #4
Comparing Logistic Regression and Decision Forest Models

16. Demo #5
Notebooks and Web Services

17. What about Bob?

19. Questions?

20. THANK YOU!
What questions do you have?