We start with a presentation of 1Tap then we do a gentle introduction to Machine Learning.

1. 1
Roman Orac, 1Tap Machine Learning & Data Analysis
A Gentle introduction to Machine Learning

2. 1Tap is a Automated Accounting Platform
For the Self Employed*
* Sole Trader, Sole Proprietor, Freelancer, Contractor, Independent, Non Incorporated
Businesses
Fully

3. The Self Employed can’t buy the stuff they want
Profit…Welfare…
Taxes…
No idea
That is a problem for
the new year...
Denied...
Hopefully I get better
real soon...
Credit…
6

4. Making Self Employment
> Employment
Our Mission

5. 1Tap Receipts
Take a photo Data Extracted Tax Return updated Customers Love it
1 2 3 4

6. The foundation of our apps
Ruby on Rails
Restful JSON API
4.0 Code Climate GPA

7. Enough about us …
What is Machine Learning Anyway?

8. What is Machine Learning?
Training data
Machine Learning
algorithm
ClassifierNew samples Prediction
Pre-processing
● Machine Learning is the science of getting computers to act without
being explicitly programmed

9. Predict survival on the Titanic
In 1912 the Titanic sank, killing 1,502 out
of 2,224 passengers and crew.
Some groups of people were more
likely to survive than others.

10. Let’s look at the data
Abbreviations
● Embarked: Port of embarkation
○ C = Cherbourg
○ Q = Queenstown
○ S = Southampton
● Parch: Number of parents/children
aboard
● Pclass: Passenger's class
● SibSp: Number of siblings/spouses
aboard
● Survived: Survived (1) or died (0)
● Ticket: Ticket number

11. Understanding the data
● Distributions of the fare of passengers who survived or did
not survive
● Many passengers with cheaper fares died
● Is fare a good predictive variable?

12. Most Important Step: Data preprocessing
Original data Preprocessed data
preprocessing
● Clean the data
● Encode attributes
● Fill in missing values
● Add new attributes

13. Decision Tree
● Use training set and build a decision tree model
● Use the model to predict new samples

14. What types of problems do we solve with
ML at 1Tap?

15. Receipt categorization
Initial receipt categorization
based on company’s industry
deterministic categorization
many mis-categorization
The Numbers
600K categorized receipts
40K users
80K new receipts every month

16. Receipt categorization with ML
Categorizing receipts in a smarter and more contextual way

17. ● Features:
○ user’s profession
○ vendor name, date, expense total and text
● Preprocessing:
○ Filter receipts
○ Recategorize most obvious receipts
● Train a classifier that categorizes receipts
● This approach improves categorization as receipt text adds more context
Receipt categorization with ML

18. Questions?

21. Come talk to
us over pizza!
Nejc, Human
Resources
Roman, Machine
Learning
Vesna, Head of
Product