This document discusses Mercari's challenge of maintaining a high number of active users by reducing the average time it takes to sell products. It proposes using natural language processing to build an algorithm that suggests product prices based on user-inputted descriptions. The algorithm would convert text data like product names and brands into word vectors that could then be used in regression models to predict the best selling price.

1. Mercari Price Suggestion
Challenge

2. What is Mercari ?

3. Mobile - First - peer-to-peer shopping
app
which is used globally by more than
55 million active users !

4. No Control over price !

5. What is the challenge ?

6. Maintaining such a high number of
Active Users in the marketplace !

7. One way of achieving this is By
Reducing the average amount of time
it takes to sell a product on the
platform !

8. How quickly a product gets sold
depends on multiple things.
Out of these we are focusing on
"Right Price"

9. Which one is more expensive ?

10. Both of these items appear similar but their prices
vary drastically.
Mercari wants to suggest prices to the user based
on inputted data like Description, Brand Name ?

11. This is what We are solving !

12. We built a Natural Language Processing based
algorithm that suggests product prices
based on
user inputted text descriptions like
Product name , Brand name , item condition.

13. Before we dive into deep analysis
Let's take a quick look at Mercari's
official website

14. These are 10 categories on main home page.

19. Attributes

20. 1 2 3
4 5
6 7

21. Target Variable : Price

22. Now we have all the ingredients ,
let's start cooooking !

24. How to suggest prices ?
Regression ?

25. How to do regression with
Text Data ?

26. Converting Text data
to Word Vectors

28. Once we have all the numerical vectors in place ,
we can run multiple regression models to predict
the price !

29. Thank You