Python End-to-end Multiclass Classification Project

3 tutorials

This is a multiclass classification project to classify the severity of road accidents into three categories. This project is based on real-world data, and the dataset is also highly imbalanced. There are three types of injuries: minor, severe, and fatal.

Road accidents are the major cause of unnatural deaths around the world. To reduce accidents and fatalities, all governments work hard to raise awareness about the rules and regulations that must be followed when driving a vehicle on the road.

In this module, we will look at the end-to-end project with source code to develop a machine-learning solution to predict the severity of road accidents to take necessary precautions by the investigation agency.

Partnership: This module was created in collaboration with Avikumar Talaviya - a Mumbai chapter lead at OmdenaAI. Refer here if you would like to find out more about the author.

Helpful prior knowledge


Learning Outcomes

By the end of this module, you will be able to:

  • Apply EDA techniques to get insights of a dataset
  • Perform feature engineering and feature selection techniques
  • Treat an imbalanced dataset using the SMOTENC technique
  • Build, evaluate and export a random forest classification model
  • Build and deploy a Streamlit web application
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