Review the dataset size and aim to replace sample datasets with smaller datasets.

CreditRiskPrediction/Data/README.md

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+# Introduction
+
+  The datasets provided here are randomly generated to mimic the type
+  of data that a bank might actually use. These random datasets allow
+  the scripts to be run. A data scientist can replace these random
+  datasets with their own data of the same form to initiate their
+  machine learning.
+
+  We have had two versions here. The full datasets (listed below) used
+  for the blog post were actually quite large and so we have replaced
+  them with smaller datasets. Processing time for the originals was
+  considerable and not practical for quick interactive replication.
+	
+# List of data sets
+
+|  Data Set Name | Link to the Full Data Set   | Full Data Set Size (KB)  | Link to Report |
+| ---:| ---: | ---: | ---: |
+| transactionSimu.csv | [link](https://github.com/Microsoft/acceleratoRs/tree/master/CreditRiskPrediction/Data/transactionSimu_v3.csv) | 22,565 | N/A|
+| demographicSimu.csv | [link](https://github.com/Microsoft/acceleratoRs/tree/master/CreditRiskPrediction/Data/demographicSimu_v3.csv) | 17,445 | N/A|
+| processedSimu.csv | [link](https://github.com/Microsoft/acceleratoRs/tree/master/CreditRiskPrediction/Data/processedSimu.csv) | 27,883 | N/A|
+
+# Description of data sets
+
+* The simulated transaction dataset contains 198,576 records and 11
+  variables. The columns of “transactionID” and
+  “accountID” are identifiers at transaction level and account
+  level, respectively. Each account may have more than one transaction
+  occuring at different times. These transactions record information
+  about the transaction amount, transaction type, location, merchant
+  and so on.
+
+* The simulated demographic dataset contains 184,254 records with 9
+  variables. It has a common key, “accountID”, with the
+  transaction data. The column of “badFlag” is the label of
+  customers or accounts (assuming one customer one account),
+  representing their default status. The other variables show
+  information about the characteristic of customers (e.g., age,
+  education, income) and bank account information (e.g., credit
+  limit).
+
+* The simulated processed dataset contains...