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Use the Mercari Dataset with dynamic pricing to build a price recommendation algorithm using machine learning in R to automatically suggest the right product prices.
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Business Objective
Clothing has strong seasonal pricing trends and is heavily influenced by brand names, while electronics have fluctuating prices based on product specs.
Mercari, Japan’s biggest community-powered shopping app, knows this problem deeply. They’d like to offer pricing suggestions to sellers, but this is tough because their sellers are enabled to put just about anything, or any bundle of things, on Mercari's marketplace.
In this project, Mercari’s challenging us to build an algorithm that automatically suggests the right product prices.
Data Overview
There are two files available. They are train.tsv and test.tsv
Both are tab separated files
The following are the data fields
Source https://www.kaggle.com/c/mercari-price-suggestion-challenge/overview/description
Aim
To predict the price of the product using the given description and other information
Tech Stack
Approach
Exploratory data analysis is the process of analysing the dataset to understand its characteristics. In this step, we perform the following.
Machine learning algorithms for regression can understand the input only in the form of numbers and hence it is highly essential to convert the non - numeric data that we have to numeric data by providing them labels.
This step involves the process of filling the missing values in appropriate ways so that the data is not lost.
Various regression algorithms are applied on the dataset and the model that suits best for the dataset is selected. The models that we apply for this dataset are
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