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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Project Experience

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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