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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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
01m
Import Data Sets
02m
Import Libraries
00m
Exploratory Data Analysis
04m
What is the Variation in Prices?
08m
How good is the condition of products?
10m
How good shipping condition is?
08m
What are the most expensive brands?
07m
Do Expensive brands have high prices?
00m
How many categories are there?
02m
Do prices vary by category?
12m
How do you predict the price?
06m
Does Item description impact price?
15m
Document Feature Matrix
07m
N-gram approach to extract features
19m
Creating new features
09m
Predict Prices
02m
Additional features
06m
Implementing in Python
07m
Q n A session
20m