The access to Big Data processing engines, infrastructure and Cloud computing allows organisations and companies to implement traditional and more modern, AI-based machine learning and data mining algorithms that can be used on massive datasets. Whether your organisation is interested in clustering your customers, classification of products or predicting the value of sales, we can support you in:
– implementing specific statistical and machine learning approaches suitable for your business cases and/or research questions (Machine Learning and Artificial Intelligence Methods),
– preparing, processing and managing data for appropriate analytical, predictive or prescriptive methods (Data Engineering),
– scaling up your existing data processing and analytics workflows to accommodate for huge amounts of data and more computationally demanding machine learning techniques (Data Mining Infrastructure).


Diversity of projects, bespoke solutions

Predictive analytics projects and their requirements vary based on numerous essential factors. Some may utilise unstructured or textual data e.g. product reviews or social media statuses, other may contain high-frequency transactional information such as payment records or website behaviours, and still some others may include large amounts of multivariate numeric data gathered over longer period of time. In most cases, derived models should achieve very high and stable accuracy metrics with potentially fast learning and testing times. Depending on your research area and business questions, we can offer you bespoke Big Data, data integration, blending and predictive analytics solutions to suit your specific fields of interest, data-related problems and data processing infrastructure in-situ.

Data Integration


Many organisations struggle to optimise the machine learning algorithms and their efficiency due to the amount and type of data they collect, quality of databases or processing engines and infrastructure they use, or simply because of the lacking computational efficiency of algorithms they apply.


Decision making process

At Mind Project, we consider all possible factors that can affect your analytical capabilities. We design data processing workflows and predictive algorithms to suit your existing or planned architecture keeping in mind that your main goal is to develop a robust, powerful model which will enhance your business operations and give you a competitive edge on the market. Our goal is to provide you with an algorithmic solution which will:

  • Be easily communicated to stakeholders and integrated into your current processes to enhance data transparency and quality control,
  • Translate to actionable results for your organisation, both short-, and long-term,
  • Improve your data-driven decision making process,
  • Grow your operational and organisational capabilities in efficient, controlled and evidence-based manner.


A variety of approaches to suit your needs

Our statistical and machine learning approaches utilise both traditional methods such as linear/polynomial or logistic regressions, decision and regression trees, Bayesian algorithms, additive models etc. but also their ensembles e.g. random forests, adaptive boosting, Bayesian networks and advanced methods currently associated with artificial intelligence and cognitive computing such as different types of multi-layered neural networks (Deep Learning), kernel-based approaches e.g. support vector machines and other. We do all the data engineering work, model optimisation and testing for you, so you and your team can focus on operations and growing the business.

Network Analysis Algorithm


Machine learning and AI solutions with hands-on experience

Data scientist

Most of our experience in applied machine learning and AI comes from our involvement in large scale projects in business, banking, finance, health and public issues/governance. We specialise in topics related to spatio-temporal analytics e.g.:

  • Geo-social models (i.e. of customers, medical patients, behaviours, migration etc.),
  • Time-series forecasting (i.e. sales, volumes, measuring and predicting website engagement, forecasting unusual events),
  • And a combination of both (e.g. estimating and predicting changes in user profiles across time for different geographical areas, measuring social activity e.g. entrepreneurship, local council spending or levels of crime in specific local authority districts).


If you are interested in our predictive analytics, machine learning and AI services, please contact us to discuss further.