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.


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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.

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Curriculum For This Mini Project

03h 58m