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.

Videos

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.

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

Given a customer's search query and the returned product in text format, your predictive model needs to tell whether it is what the customer was looking for.

In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

Curriculum For This Mini Project

19-Mar-2016
03h 58m