Case Studies

Making a Project Management System more Intelligent


The Client is developing a project management system for enterprises covering various business areas: IT, marketing, education, healthcare, etc. Over 10 years, the product has become overwhelmingly successful and accumulated a substantial amount of data on completed projects. The Client is aiming at the continuous improvement of the product performance to meet user needs. To enhance the quality of the project management system, the Client came up with the idea of leveraging the power of data and applying machine learning.


Eliminate the lack of information managers face estimating the duration of a project People are often wrong in their estimations, but twice as often, employees fail to track the actual status of tasks in the project management system. Therefore, top management faces significant difficulties trying to predict the project end date and whether or not a project will be completed on time. A system capable of providing a more accurate estimation of the actual project end date would come in handy for managers and would increase the overall effectiveness of project management processes within the Client’s system.


A microservice that provides access to the API to evaluate the duration of a project with a higher degree of accuracy The work of the AltaFuturis team was split into the following stages:

  • Conduct the research on a given problem
  • Formulate and test hypotheses
  • Choose a relevant model and validate its quality
  • Prove the predictive potential of the Client’s data
  • Develop a predictive model (based on gradient boosted decision trees)

The AltaFuturis team conducted the research on some popular enhancements in the project management tools. To match user needs and market demands, the list of product features includes the following:

  • Project duration prediction
  • Task duration prediction
  • Efficient allocation of project resources
  • Fixing project scope
  • Prioritizing projects
  • Project budget planning
  • Filling gaps in the employees’ profiles
  • Classification of notifications by importance
  • Project workflow automatization

The Client picked the project duration prediction as the most useful feature to develop.

The overall development process comprises 4 stages.

Stage:Scope of work

  • Data understanding and validation Acquiring, processing, and validating the Client’s data.
  • Feature engineering Converting raw project data into features.
  • Modeling Training the model on the prepared dataset.
  • Deployment Delivery and deployment of the model; providing the Client with a user-friendly interface to access the trained model.

The solution “road map” is in Figure 1.

The choice of the gradient boosting model was due to the volume (enough to abandon linear models but insufficient for neural networks) and the heterogeneous nature of the data. So, the AltaFuturis team has delivered the model capable to efficiently predict the number of calendar days left before the expected project end.


A microservice to be integrated into the Client’s infrastructure and enhance the quality of the project management system The service delivered by AltaFuturis provides access to the API, through which it is possible to effectively evaluate the duration of a project. Predictions can be generated at the planning stage or on any given day during the development process. The model performance is 50-60% higher in terms of accuracy than the estimates that managers explicitly indicated.For projects that didn’t have estimations by managers, the baselines were calculated (a simple method to estimate target variable). The quality of the model exceeds the quality of the baseline models by up to 15% (depending on a segment).

Technologies and Tools

Python, PostgreSQL, LightGBM, Docker