Mercari Price Suggestion Challenge Data Science Project

Mercari Price Suggestion Challenge Data Science Project

Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.
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Data Analytics Leader, IQVIA

This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More

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What will you learn

Understanding the problem statement
Importing the dataset from AWS
Importing important libraries and understanding its significance
Understanding CSR Matrix and hstack
Performing basic EDA and checking for null values
Creating function for handling null values
Performing slicing and making function for converting variables into categorical types
Merging two or more Dataset
Using TFIDF and Count Vectorizer for analyzing textual data
Applying LabelBinarizer for textual data
Sparse matrix its use and implementation
Selecting models Light GBM and Ridge as model
Defining parameters for the models
Training the model and using the model for making predictions
Saving the final predictions in CSV format

Project Description

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 machine learning project, we will build an algorithm that automatically suggests the right product prices. You’ll be provided user-inputted text descriptions of their products, including details like product category name, brand name, and item condition.

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Curriculum For This Mini Project

Problem Statement
Import Data Sets
Import Libraries
Exploratory Data Analysis
What is the Variation in Prices?
How good is the condition of products?
How good shipping condition is?
What are the most expensive brands?
Do Expensive brands have high prices?
How many categories are there?
Do prices vary by category?
How do you predict the price?
Does Item description impact price?
Document Feature Matrix
N-gram approach to extract features
Creating new features
Predict Prices
Additional features
Implementing in Python
Q n A session