Predicting Traffic, Driving Style, and Road Surface Conditions

This project provides an overview on advanced supervised ML techniques and hyperparameter tuning along with data mining and data visualization to predict traffic, driving style and road surface.

  • 3 bar graph
    Difficulty: Advanced

    Prior knowledge or professional experience is strongly recommended.

  • Asset 1
    Duration: Approximately 5 hours

Case Overview

  • Classification algorithms are utilized to identify the category or group to which a data point belongs.
  • With the increase in the number of vehicles on road, there are various consequences on the road traffic and condition. Often, people get stuck in traffic for a long duration, thereby losing both resources and time which also affects a vehicle’s condition.
  • Telematics data allows real-time prediction based on sensors data which helps the organization to amplify the customer experience. Regular monitoring of vehicles can help in early failure detection and can save further damages.
  • Learn to build a robust classification model which can be used by practitioners to predict the condition of Traffic, Driving Style and Road Surface.

What’s included


Lifetime Access

Access this project for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Classification Model Building

Model Evaluation

Hyperparameter Tuning

Data Management

Data Visualization

Hyperparameter Tuning

Linear Regression

Feature Engineering

Data Aggregation


Associated Learning Tracks

Case Context

  • NFBI is a financial institution that lacks a proper financial banking license. NFBI is currently observing increased cases of faulty vehicle loans and mismanagement in the same category.
  • The companies aim to reduce their losses by analyzing their clients loan repayment abilities.
  • The sector also aims in gathering descriptive analysis of various parameters and how they affect borrowers ability to repay loans.

  • Firstly, the data is treated and preprocessed to remove outliers by establishing a floor and cap for the same.
  • Two-Sample t-Tests were applied which revealed that there is a huge disparity in the credit amount of people with or without cars.
  • Random Forest Classification was performed to analyze the creditworthiness of the borrower.

  • The NFBI industries can use such analysis to detect default vehicle loan possibilities and prevent them in time.
  • The analysis can also help in determining degree of credit risks associated with extending credit to a loan applicant.
  • Fraud Detection Model can also analyze trade data and predict borrowers debt obligations

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Head Data and Analytics at CitiBank India

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