Build a predictive model for Otto Group Product Classification

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.


Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

Customer Love

Read All Reviews

Mike Vogt

Information Architect at Bank of America

I have had a very positive experience. The platform is very rich in resources, and the expert was thoroughly knowledgeable on the subject matter - real world hands-on experience. I wish I had this... Read More

Camille St. Omer

Artificial Intelligence Researcher, Quora 'Most Viewed Writer in 'Data Mining'

I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More

What will you learn

Understanding the problem statement and importing the file
Initializing the libraries and understand it's use
Check for null values and necessary imputations
Use and interpret the summary function in R
Use a Box plot to identify the outliers and handle them
Applying ensembling model Decision Tree for a Multi-Class Classification problem
Preparing the dataset and training the model
Parameter tuning for better results
How to use Confusion Matrix for a Multi-Class Classification problem
Making final predictions from the trained model

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.

Similar Projects

Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

In this project, we are going to talk about insurance forecast by using regression techniques.

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

Curriculum For This Mini Project

03h 58m