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Project Details

Customer Size: Large organization
Country: US
Domain: Event Management
Technology: Python, Flask, Jinja2
Web Service: Service Integration with Web portal and Mobile app
Algorithm: Decision Tree [Modeling of algorithm using Scikit Learn]

Client Profile

A leading analytics company based in the US that sells point-of-sales solutions. They deliver solutions that are easy to install and simple to use, helpful in providing immediate insights for the businesses and generates maximum revenue.

Business Scenario

The client required a point-of-sales solution for catering and event organizing company to predict food consumption at various events and stop inappropriate food wastage. The solution should consider various aspects such as weather and the type of event and help in planning and forecasting. They required a solution that caters to the below features:

  • Real-time food predictions
  • Pre-built Dashboards to provide insights
  • Easy integration of the solution with web portal and mobile apps

Cygnet's Solution

Team Cygnet visited the client to get the deeper insights into the processes by understanding the trends and analytics of the food counters. Cygnet also looked into historical data and business model to create a robust point-of-sales solution. The features taken into consideration during the algorithm formation were number of tickets sold in past, weather, type of event, stadium/ wing capacity and total tickets sold till date.

Linear Regression and Decision Tree Algorithms were created by performing data assessment, data preparation, data modeling, algorithm modeling and integration of score model on the accumulated data. Through these algorithms, a predictive solution was developed that forecasted future orders at the event and generated reports and custom analysis to evaluate business performance.

Our solution supplied insightful data for sharper automated decisions making, and improved the efficiency of logistics and operations. Today, the solution is used at a much wider network of food counters during various events, and its predictions were accurate by 96.45%.

To know more about how we created the point-of-sale predictive solution to stop food wastage and the algorithms, download the PDF version of the case-study.

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