Demo: Using Azure Machine Learning to Predict Churn

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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.

Technology

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

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

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

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

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

Demo #1
Importing Data and Exploring Fields in Azure ML

Demo #2
Identifying Missing Values and Imputing Results

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

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

Demo #4
Comparing Logistic Regression and Decision Forest Models

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Demo: Using Azure Machine Learning to Predict Churn

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

11.

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

13.

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

15.

16. Demo #5 Notebooks and Web Services

17. What about Bob?

18.

19. Questions?

20. THANK YOU! What questions do you have?