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Build a predictive model for Otto Group Product Classification

Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.
4.64.6

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What will you learn

  • Classification Problem
  • Machine Learning
  • xgboost
  • magrittr
  • BlackBox Model

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Language used: Python

Project Description

The dataset has 93 features for more than 200,000 products with the Test Data containing 144K rows and Training Data containing 61K rows. This data science project is a supervised , multinomial classification problem. The dataset has a total of 9 possible product lines and the objective is accurately make class predictions on 144,000 unlabeled products based on the 93 features for the products provided in the dataset.

Instructors

 
Jeeban

Senior Statistical Analyst

"I enjoy 3 things in Analytics - Machine Learning, Image/Video Processing, Natural Language Processing."