Data Science
Predict the fare amount of future rides using regression analysis
Ever wonder how your Uber ride prices keep changing so often? Why not learn how by creating the prediction model yourself?
Certified by
Role
Data Scientist
Industry
Transportation
No. of Subscribers
94
Level
Intermediate
Time Commitment
60 Hours
Duration
60 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 a data scientist, your task is to predict the fare amount of future rides using regression analysis. By accurately estimating the fare amount, ride-sharing companies can optimize pricing strategies, provide transparency to riders, and ensure fair compensation for drivers.
Explore
the following work techniques
Python programming
Regression analysis
Bridging the gap
The fare amount of a ride is influenced by various factors such as distance, duration, traffic conditions, time of day, and demand. Your goal is to develop a regression model that can predict the fare amount based on these factors. You will work with a dataset that includes historical ride data, including the fare amount and relevant features.
Apply
the following skills
Analytical Skills
Data Analysis
Expected output
In this project, you will build a regression model to predict the fare amount of future rides. Your model should be trained on historical ride data and evaluated using appropriate metrics. Additionally, you will provide insights and recommendations based on the model's predictions.
Create
the following deliverables
Regression model to predict fare amounts
Evaluation metrics for model performance
What you’ll need before starting
Basic knowledge of Python programming
Familiarity with regression analysis concepts
