GAC Revenue Forecasting
Project description
The company we are working with plan their budgets, hours, revenue, etc. Based on that, they try to forecast the revenue they will make in the following months. They rely on experience and simple calculations to do so. Our project makes forecasts using machine learning models to make this process automatic and more precise.
Context
Budget planning and revenue forecasting are critical activities for the company we are collaborating with. Currently, their approach relies on experience and straightforward calculations to estimate revenue, allocate hours, and manage budgets for upcoming months. While this traditional method provides a general direction, it lacks precision and scalability, especially as business dynamics grow more complex.
To address these challenges, our project introduces an automated and data-driven forecasting solution. By leveraging machine learning models, we aim to enhance the accuracy and reliability of revenue projections. This approach not only streamlines the forecasting process but also equips the company with actionable insights, enabling better decision-making and resource management.
Results
The preduct has 3 main parts: a linear regression model that is the main part of the project and the one that does the predictions. To show the results of this model and to display it in a better way, a dashboad has been developed. This is done in PowerBI and has an intuitive interface. The purpose of this is to interprete and play with the results of the model for every human. Finally, an API has been developed to link these two items and make them work in the correct way. It also gives more freedom to the user to play with the model as you are not limited to the options the dashboard provides.
About the project group
Mix of IT students and business students.