The document discusses preparing data for machine learning models. It describes how real-world data is often messy and unstructured, requiring transformations like cleaning, labeling, aggregation, and structuring to make it suitable for ML tasks. The document provides examples of common data transformations including denormalizing, adding labels, handling missing values, and structuring output in CSV format. It emphasizes that the goal of transformations is to end up with tabular data where each row is an observation and each column is a feature.

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2. BigML, Inc 2
Basic Transformations
Making Data Machine Learning Ready
Poul Petersen
CIO, BigML, Inc

3. BigML, Inc 3Topic Models
In a Perfect World…
Q: How does a physicist milk a cow?
A: Well, first let us consider a spherical cow...
Q: How does a data scientist build a model?
A: Well, first let us consider perfectly formatted data…

4. BigML, Inc 4Topic Models
The Dream
CSV Dataset Model Profit!

5. BigML, Inc 5Topic Models
The Reality
CRM
Web Accounts
Transactions
ML Ready?

6. BigML, Inc 6Topic Models
Obstacles
• Data Structure
• Scattered across systems
• Wrong "shape"
• Unlabelled data
• Data Value
• Format: spelling, units
• Missing values
• Non-optimal correlation
• Non-existant correlation
• Data Significance
• Unwanted: PII, Non-Preferred
• Expensive to collect
• Insidious: Leakage, obviously correlated
Data Transformation
Feature Engineering
Feature Selection

7. BigML, Inc 7Topic Models
The Process
• Define a clear idea of the goal.
• Sometimes this comes later…
• Understand what ML tasks will achieve the goal.
• Transform the data
• where is it, how is it stored?
• what are the features?
• can you access it programmatically?
• Feature Engineering: transform the data you have into
the data you actually need.
• Evaluate: Try it on a small scale
• Accept that you might have to start over….
• But when it works, automate it!!!!

8. BigML, Inc 8
Data Transformations

9. BigML, Inc 9Topic Models
BigML Tasks
• Will this customer default on a loan?
• How many customers will apply for a
loan next month?
• Is the consumption of this product
unusual?
• Is the behavior of these customers
similar?
• Are these products purchased
together?
• What are the thematic contents of
these documents?
• Based on trends, how much of this
product will I sell next month?
Goal ML Task
Models / Ensembles
Deepnets / LR
Models / Ensembles
Deepnets
Anomaly Detection
Cluster
Association Discovery
Topic Models
Time Series

10. BigML, Inc 10Topic Models
Classification
CategoricalTrainingTesting
Predicting

11. BigML, Inc 11Topic Models
Regression
NumericTrainingTesting
Predicting

12. BigML, Inc 12Topic Models
Anomaly Detection

13. BigML, Inc 13Topic Models
Cluster Analysis

14. BigML, Inc 14Topic Models
Association Discovery

15. BigML, Inc 15Topic Models
ML Ready DataInstances
Fields (Features)
Tabular Data (rows and columns):
• Each row
• is one instance.
• contains all the information about that one instance.
• Each column
• is a field that describes a property of the instance.

16. BigML, Inc 16Topic Models
Data Labeling
Unsupervised Learning Supervised Learning
• Anomaly Detection
• Clustering
• Association Discovery
• Classification
• Regression
The only difference, in terms of
ML-Ready structure is the
presence of a "label"

17. BigML, Inc 17Topic Models
Data Labelling
Data is often not labeled
Create labels with a transformation
Name Month - 3 Month - 2 Month - 1
Joe Schmo 123,23 0 0
Jane Plain 0 0 0
Mary Happy 0 55,22 243,33
Tom Thumb 12,34 8,34 14,56
Un-Labelled Data
Labelled data
Name Month - 3 Month - 2 Month - 1 Default
Joe Schmo 123,23 0 0 FALSE
Jane Plain 0 0 0 TRUE
Mary Happy 0 55,22 243,33 FALSE
Tom Thumb 12,34 8,34 14,56 FALSE
Can be done at Feature
Engineering step as well

18. BigML, Inc 18Topic Models
SF Restaurants Example
https://data.sfgov.org/Health-and-Social-Services/Restaurant-Scores/stya-26eb
https://blog.bigml.com/2013/10/30/data-preparation-for-machine-learning-using-mysql/
create database sf_restaurants;
use sf_restaurants;
create table businesses (business_id int, name varchar(1000), address varchar(1000), city varchar(1000), state varchar(100),
postal_code varchar(100), latitude varchar(100), longitude varchar(100), phone_number varchar(100));
load data local infile './businesses.csv' into table businesses fields terminated by ',' enclosed by '"' lines terminated by
'rn' ignore 1 lines;
create table inspections (business_id int, score varchar(10), idate varchar(8), itype varchar(100));
load data local infile './inspections.csv' into table inspections fields terminated by ',' enclosed by '"' lines terminated
by 'rn' ignore 1 lines;
create table violations (business_id int, vdate varchar(8), description varchar(1000));
load data local infile './violations.csv' into table violations fields terminated by ',' enclosed by '"' lines terminated by
'rn' ignore 1 lines;
create table legend (Minimum_Score int, Maximum_Score int, Description varchar(100));
load data local infile './legend.csv' into table legend fields terminated by ',' enclosed by '"' lines terminated by 'rn'
ignore 1 lines;

19. BigML, Inc 19
Transformations Demo #1

20. BigML, Inc 20Topic Models
Data Cleaning
Homogenize missing values and different types in the same
feature, fix input errors, correct semantic issues, types, etc.
Name Date Duration (s) Genre Plays
Highway star 1984-05-24 - Rock 139
Blues alive 1990/03/01 281 Blues 239
Lonely planet 2002-11-19 5:32s Techno 42
Dance, dance 02/23/1983 312 Disco N/A
The wall 1943-01-20 218 Reagge 83
Offside down 1965-02-19 4 minutes Techno 895
The alchemist 2001-11-21 418 Bluesss 178
Bring me down 18-10-98 328 Classic 21
The scarecrow 1994-10-12 269 Rock 734
Original data
Name Date Duration (s) Genre Plays
Highway star 1984-05-24 Rock 139
Blues alive 1990-03-01 281 Blues 239
Lonely planet 2002-11-19 332 Techno 42
Dance, dance 1983-02-23 312 Disco
The wall 1943-01-20 218 Reagge 83
Offside down 1965-02-19 240 Techno 895
The alchemist 2001-11-21 418 Blues 178
Bring me down 1998-10-18 328 Classic 21
The scarecrow 1994-10-12 269 Rock 734
Cleaned data
update violations set description = substr(description,1,instr(description,' [ date violation corrected:')-1) where instr(description,'
[ date violation corrected:') > 0;

21. BigML, Inc 21
Transformations Demo #2

22. BigML, Inc 22Topic Models
Define a Goal
• Predict rating: Poor / Needs Improvement / Adequate /
Good
• This is a classification problem
• Based on business profile:
• Description: kitchen, cafe, etc.
• Location: zip, latitude, longitude

23. BigML, Inc 23Topic Models
Denormalizing
business
inspections
violations
scores
Instances
Features
(millions)
join
Data is usually normalized in relational databases, ML-Ready
datasets need the information de-normalized in a single dataset.
create table scores select * from businesses left join inspections using (business_id);
create table scores_last select a.* from scores as a JOIN (select business_id,max(idate)
as idate from scores group by business_id) as b where a.business_id=b.business_id and
a.idate=b.idate;
Denormalize
ML-Ready: Each row contains all the information about that one instance.
create table scores_last_label select scores_last.*, Description as score_label from
scores_last join legend on score <= Maximum_Score and score >= Minimum_Score;
Add Label

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Transformations Demo #3

25. BigML, Inc 25Topic Models
Structuring Output
• A CSV file uses plain text to store tabular data.
• In a CSV file, each row of the file is an instance.
• Each column in a row is usually separated by a comma (,) but other
"separators" like semi-colon (;), colon (:), pipe (|), can also be used.
Each row must contain the same number of fields
• but they can be null
• Fields can be quoted using double quotes (").
• Fields that contain commas or line separators must be quoted.
• Quotes (") in fields must be doubled ("").
• The character encoding must be UTF-8
• Optionally, a CSV file can use the first line as a header to provide the
names of each field.
After all the data transformations, a CSV (“Comma-Separated
Values) file has to be generated, following the rules below:
select * from scores_last_label into outfile "./scores_last_label.csv";
select 'name', 'address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'score_label' UNION select name, address, city,
state, postal_code, latitude, longitude, score_label from scores_last_label into outfile "./scores_last_label_headers.csv" ;

26. BigML, Inc 26
Transformations Demo #4

27. BigML, Inc 27Topic Models
Define a Goal
• Predict rating: Poor / Needs Improvement / Adequate / Good
• This is a classification problem
• Based on business profile:
• Description: kitchen, restaurant, etc.
• Location: zip code, latitude, longitude
• Number of violations, text of violations

28. BigML, Inc 28Topic Models
Aggregating
User Num.Playbacks Total Time Pref.Device
User001 3 830 Tablet
User002 1 218 Smartphone
User003 3 1019 TV
User005 2 521 Tablet
Aggregated data (list of users)
When the entity to model is different from the provided data, an
aggregation to get the entity might be needed.
Content Genr
e
Duration Play Time User Device
Highway
star
Rock 190 2015-05-12
16:29:33
User001 TV
Blues alive Blues 281 2015-05-13
12:31:21
User005 Tablet
Lonely
planet
Tech
no
332 2015-05-13
14:26:04
User003 TV
Dance,
dance
Disco 312 2015-05-13
18:12:45
User001 Tablet
The wall Reag
ge
218 2015-05-14
09:02:55
User002 Smartphone
Offside
down
Tech
no
240 2015-05-14
11:26:32
User005 Tablet
The
alchemist
Blues 418 2015-05-14
21:44:15
User003 TV
Bring me
down
Class
ic
328 2015-05-15
06:59:56
User001 Tablet
The
scarecrow
Rock 269 2015-05-15
12:37:05
User003 Smartphone
Original data (list of playbacks)
create table violations_aggregated select business_id,count(*) as violation_num,group_concat(description) as violation_txt from
violations group by business_id;
create table scores_last_label_violations select * from scores_last_label left join violations_aggregated USING (business_id);
tail -n+2 playlists.csv | cut -d',' -f5 | sort | uniq -c
tail -n+2 playlist.csv | awk -F',' '{arr[$5]+=$3} END {for (i in arr) {print arr[i],i}}'
SET @@group_concat_max_len = 15000
select 'name', 'address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'violation_num', 'violation_txt', 'score_label'
UNION select name, address, city, state, postal_code, latitude, longitude, violation_num, violation_txt, score_label from
scores_last_label_violations into outfile "./scores_last_label_violations_headers.csv" ;

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Transformations Demo #5

30. BigML, Inc 30Topic Models
Pivoting
Different values of a feature are pivoted to new columns in the
result dataset.
Content Genre Duration Play Time User Device
Highway star Rock 190 2015-05-12 16:29:33 User001 TV
Blues alive Blues 281 2015-05-13 12:31:21 User005 Tablet
Lonely planet Techno 332 2015-05-13 14:26:04 User003 TV
Dance, dance Disco 312 2015-05-13 18:12:45 User001 Tablet
The wall Reagge 218 2015-05-14 09:02:55 User002 Smartphone
Offside down Techno 240 2015-05-14 11:26:32 User005 Tablet
The alchemist Blues 418 2015-05-14 21:44:15 User003 TV
Bring me down Classic 328 2015-05-15 06:59:56 User001 Tablet
The scarecrow Rock 269 2015-05-15 12:37:05 User003 Smartphone
Original data
User Num.Playback
s
Total Time Pref.Device NP_TV NP_Tablet NP_Smartphone TT_TV TT_Tablet TT_Smartphone
User001 3 830 Tablet 1 2 0 190 640 0
User002 1 218 Smartphone 0 0 1 0 0 218
User003 3 1019 TV 2 0 1 750 0 269
User005 2 521 Tablet 0 2 0 0 521 0
Aggregated data with pivoted columns

31. BigML, Inc 31Topic Models
Time Windows
Create new features using values over different periods of time
Instances
Features
Time
Instances
Features
(millions)
(thousands)
t=1 t=2 t=3
create table scores_2013 select a.business_id, a.score as score_2013, a.idate as idate_2013 from inspections as a JOIN ( select
business_id, max(idate) as idate from inspections where substr(idate,1,4) = "2013" group by business_id) as b where a.business_id =
b.business_id and a.idate = b.idate;
create table scores_over_time select * from businesses left join scores_2013 USING (business_id) left join scores_2014 USING (business_id);

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Transformations Demo #6

33. BigML, Inc 33Topic Models
Updates
Need a current view of the data, but new data only comes in
batches of changes
day 1day 2day 3
Instances
Features

34. BigML, Inc 34Topic Models
Streaming
Data only comes in single changes
data stream
Instances
Features
Stream
Batch
(kafka, etc)

35. BigML, Inc 35Topic Models
Prosper Loan Life Cycle
Submit
Cancelled Withdraw Expired
FundedBids Current
Q: Which new listings make it to funded?
Q: Which funded loans make it to paid?
Q: If funded, what will be the rate?
Classification
Regression
Classification
Goal ML Task
Defaulted
Paid
Late
Listings Loans

36. BigML, Inc 36Topic Models
Prosper Example
Data Provided in XML updates!!
export.sh
fetch.sh
“curl”
daily
import.py
XML
bigml.sh
Model
Predict
Share in gallery
Status
LoanStatus
BorrowerRate
Denormalization with join

37. BigML, Inc 37Topic Models
Prosper Example
• XML… yuck!
• MongoDB has CSV export and is record based so it is easy
to handle changing data structure.
• Feature Engineering
• There are 5 different classes of “bad” loans
• Date cleanup
• Type casting: floats and ints
• Would be better to track over time
• number of late payments
• compare predictions and actuals
• XML… yuck!
Tidbits and Lessons Learned….

38. BigML, Inc 38
Tools

39. BigML, Inc 39Topic Models
Tools
• Command Line?
• join, cut, awk, sed, sort, uniq
• Automation
• Shell, Python, crontab, etc
• Talend
• BigML: bindings, bigmler, API, whizzml
• Relational DB
• MySQL
• Non-Relational DB
• MongoDB

40. BigML, Inc 40Topic Models
Talend
https://blog.bigml.com/2013/10/30/data-preparation-for-machine-learning-using-mysql/
Denormalization Example

41. BigML, Inc 41Topic Models
Summary
• Data is usually awful
• Requires clean-up
• Transformations
• Consumes an enormous part of the effort in
applying ML
• Techniques:
• Denormalizing
• Aggregating / Pivoting
• Time windows / Streaming
• What a real Workflow looks like and the tools required