Table of Contents

1. Business Task
2. Use of Machine Learning/Deep Learning in this task
3. Evaluation Metric
4. Exploratory Data Analysis
5. Feature Engineering
6. Existing Solutions
7. My Experimentations
8. Final Model
9. Summary, results, and conclusions
10. Future Work
11. References

The objective of this case study is to suggest an appropriate selling price to a seller who wishes to sell his/her product (usually pre-owned) on the online platform, Mercari, which connects the sellers to the buyers.

This case study is based on the famous Kaggle Competition held in 2018: Mercari Price Suggestion Challenge

Mercari is an online selling/buying platform (similar to OLX in India) where the users can sell/buy used products, so that, the seller gets an appropriate amount for the product which is now not much use to him/her and the buyer gets the product at a lower cost as compared to the market.

As part of this competition, we have to suggest an appropriate price to the seller for the product he/she wishes to sell on the Mercari platform. Here, by “appropriate”, we mean that the price should not be so high that no one buys that product and it should not be so low that the seller is not able to earn a significant profit.

The seller enters the details of the product he/she wishes to sell, like the product’s name, a short description, category, brand, shipping status, and the condition of the product.

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