Restaurants

Predicting Order Wait Time using Machine Learning

Predicting Order Wait Time using Machine Learning

In order to provide a better customer experience, one of the world’s largest full-service dining companies required a solution that leverages machine learning to predict how long customers will have to wait for their order to be served.

Client Challenges and Requirements

  • Limited factors available in real-time
  • Online and table-top orders data segregation

Tools & Technologies We Used

Python
scikit-learn
AWS Sagemaker
AWS Lambda
AWS API Gateway
AWS DynamoDB

Key Results

Predicted order wait time with 87% accuracy

Predicted order wait time with 87% accuracy

Improved customer experience

Improved customer experience

Increased customer base

Increased customer base

Client Challenges and Requirements

  • Limited factors available in real-time
  • Online and table-top orders data segregation

Bitwise Solution

Bitwise created a prediction model to help the client give accurate wait times to customers who have ordered food.

test (4)

Strategy to distinguish real-time (high frequency) and static (low frequency) factors​

feature (1)

Implementation of caching technique for static factors in DynamoDB​

support (7)

Development of statistical standardization methods and advanced feature engineering for online and table-top order segregation​

synchronize (1)

Real-time predictions using end-to-end pipelines on AWS Lambda and API gateway

Tools & Technologies We Used

Python

scikit-learn

AWS Sagemaker

AWS Lambda

AWS API Gateway

AWS DynamoDB

Key Results

Group 3

Predicted order wait time with 87% accuracy

relationshipCreated with Sketch.

Improved customer experience


target (8)Created with Sketch.

Increased customer base

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