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