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

Menterns at work

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

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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

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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

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What you’ll need before starting

Basic knowledge of Python programming

Familiarity with regression analysis concepts

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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?

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