amazon machine learning im einsatz: smartes marketing - aws machine learning web day
TRANSCRIPT
Targeted Marketing with Amazon Machine Learning
Barbara Pogorzelska,
Technical Program Manager
Agenda
• Problem description
• Downloading, editing and uploading the data
• Datasource creation
• ML model creation
• Model evaluation
• Batch prediction
• Clean up
Problem Description
Machine learning & the use case
Machine learning is the technology that automatically finds
patterns in your data and uses them to make predictions for
new data points as they become available
Problem
How to identify potential customers for targeted marketing
campaigns?
Data available
Publicly available banking and marketing dataset from the
University of California at Irvine (UCI) repository
Amazon Machine Learning tutorial
Amazon Machine Learning tutorial
Data (see http://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
Training data
41188 data points
20 attributes
binary output
Batch predictions
4119 data points
Bank client data
1 - age 2 - job (admin., blue-collar, entrepreneur, …)3 - marital (single, divorces, married, …) 4 - education (basic.4y, basic.6y, university.degree, …)5 - default: has credit in default? 6 - housing: has housing loan?
7 - loan: has personal loan?
Related with the last contact of the current campaign
8 - contact: communication type: (cellular, telephone) 9 - month: last contact month of year10 - day_of_week: last contact day of the week 11 - duration: last contact duration, in seconds
Other attributes
12 - campaign: number of contacts performed during this
campaign and for this client
13 - pdays: number of days that passed by after the client
was last contacted from a previous campaign
14 - previous: number of contacts performed before this
campaign and for this client
15 - poutcome: outcome of the previous marketing
campaign
Social and economic context attributes
16 - emp.var.rate: employment variation rate
17 - cons.price.idx: consumer price index
18 - cons.conf.idx: consumer confidence index
19 - euribor3m: euribor 3 month rate - daily indicator
20 - nr.employed: number of employees
Output variable (desired target)
21 - y - has the client subscribed a term deposit?
Three steps to create a prediction
Downloading, editing and uploading the data
Storing the data on S3
• Download from https://s3.amazonaws.com/aml-sample-data/banking.csv and
https://s3.amazonaws.com/aml-sample-data/banking-batch.csv
– Replaced yes/no with 1/0
• Store data on S3
Datasource creation
Datasource creation
Datasource creation
Datasource creation
Datasource creation
Datasource creation
Three steps to create a prediction
ML model creation
Model creation – default model
Model creation – default model
Model creation – default model
Model creation – default model
Model evaluation
Model evaluation – default model
Model evaluation – default model
Model evaluation – default model
Data Insights
Data Insights
Data Insights
Data Insights
Data Insights
Data Insights
Model creation – custom model
Model creation – custom model
Model creation – custom model
Model creation – custom model
Three steps to create a prediction
Batch predictions
Batch predictions - default model
Batch predictions - default model
Batch predictions - default model
Batch predictions - default model
Batch predictions - default model
Batch predictions - default model
Batch predictions - default model
Batch predictions - default model
Batch predictions - custom model
Batch predictions - custom model
Batch predictions - custom model
Batch predictions - custom model
Clean up
Clean up your account
To delete the input data used for training, evaluation, and batch prediction steps
1. Open the Amazon S3 console.
2. Navigate to the S3 bucket where you stored the banking.csv and banking-batch.csv.
3. Select the two files and the .writePermissionCheck.tmp file.
4. Choose Actions, Delete.
5. When prompted for confirmation, choose OK.
To delete the predictions generated from the batch prediction step
1. Open the Amazon S3 console.
2. Navigate to the bucket where you stored the output of the batch predictions.
3. Select the batch-prediction folder.
4. Choose Actions, Delete.
5. When prompted for confirmation, click OK.
Try out machine-learning-samples from github
https://aws.amazon.com/de/machine-learning/
Get Started on AWS with
Amazon Machine Learning