Data Science
Develop a predictive model to accurately forecast hourly traffic volumes at different road junctions based on historical traffic data
Explore the factors influencing traffic congestion at different junctions, and build a model that will help solve the widespread problem of congestion through targeted interventions
Certified by
Role
Data Scientist
Industry
Transportation
No. of Subscribers
73
Level
Intermediate
Time Commitment
60 Hours
Duration
45 days
Tools you’ll learn
Here’s What You Work On
About the Company
Ride-sharing services like Uber have revolutionized transportation by providing convenient and affordable rides.
As their core business of transportation services solidifies, ride-sharing companies are now solving other relevant problems that optimise their core business offering.
This Menternship is an opportunity to solve one such problem of traffic congestion, a growing problem across metropolitan cities around the world.
As their core business of transportation services solidifies, ride-sharing companies are now solving other relevant problems that optimise their core business offering.
This Menternship is an opportunity to solve one such problem of traffic congestion, a growing problem across metropolitan cities around the world.
Explore
the following work techniques
Time Series Analysis
Hyperparameter Tuning
Model Development, Evaluation & Refinement
Bridging the gap
Commonly seen in urban areas during peak hours, traffic congestion can result from various factors such as road capacity limitations, traffic incidents, road work, and high vehicle density, the last one becoming a more serious problem with the growth of vehicle ownership in urban households.
In this Menternship, you will work on traffic congestion prediction, identifying peak hours, Junction Comparison & Analysis and evaluate the performance of predictive models to forecast hourly traffic volumes at different road junctions.
Apply
the following skills
Algorithm Development & Implementation
Data Analysis & Modelling
Data Visualization
Exploratory Data Analysis (EDA)
Expected output
As part of this Menternship, you will be required to submit Trained Models for Traffic Congestion Prediction, Reports on Peak Hour Traffic Analysis and Comparative Analysis of Junctions and finally, a Report on the Model Evaluation and Refinement.
Create
the following deliverables
Trained Models (Jupyter Notebook)
Report on Peak Hour Traffic Analysis
Report on Comparative Analysis of Junctions
Report on Model Evaluation & Refinement
What you’ll need before starting
Python
Statistical Analysis
Time Series Analysis
Hyperparameter Tuning
Statistical Analysis
Time Series Analysis
Hyperparameter Tuning
