In our internal project, we had a special challenge: to network an old, ur-analogue product, a pinball machine from 1987, with modern technology and thus make the concept of the Internet of Things (IoT) tangible. The goal was to collect data and develop predictive models for the game.

To overcome the challenge, we opted for a solution based on the use of Raspberry Pis, camera and machine learning algorithms. By using two Raspberry Pis, the pinball machine could be networked with various sensors and actuators to collect data in real time. A camera was added to capture unstructured image data showing the current game state. Machine learning was then used to develop predictive models and create customised D3 visualisations to analyse and display game play.

The result was impressive: the game on the pinball machine was visualised in real time on two monitors and a pattern recognition system identified the current score based on the recorded image data. This gave the team a new opportunity to explore the Internet of Things with modern technology and to apply data analysis methods. The project illustrates how artificial intelligence and machine learning can be used to develop innovative applications even on supposedly old-fashioned devices.

To implement the project, we used Python as the programming language and TensorFlow as the machine learning framework. The combination of these technologies allowed us to effectively process the pinball machine data and develop predictive models.

Overall, the project shows how innovative technologies such as IoT and Artificial Intelligence can be used to open up new possibilities for data analysis and support companies in their growth.

This example shows how the concept of the Internet of Things can be made tangible through collaboration with our company. The combination of Raspberry Pis, Machine Learning algorithms and D3 visualisations allowed us to develop predictive models and collect data in real time. The result is an innovative application that shows how Artificial Intelligence and Machine Learning can be used on old devices like a pinball machine.

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Our telecommunications client had difficulties in identifying user journeys and sources of error in the registration process. It was also important for him to calculate and visualise key figures from the registration process. The client needed a comprehensive data analysis tool that could help them optimise the registration process and carry out future projects independently.

We provided the client with a comprehensive solution by developing a data model for extracting the log data from the Hadoop infrastructure. We used Hive to implement the ETL process. We also mapped out the metrics and user journeys, and conducted pair programming as a team with a member of the client's staff. Furthermore, we coached one of the client's employees during the data science project so that he can carry out similar projects independently in the future.

The result was a Tableau dashboard that visualises the key figures and user journeys and is available for detailed analysis. The client was able to optimise their customer journey analysis and better plan future projects. By training the employee, the client was also able to independently carry out similar projects and gain valuable insights from their data. The project was a complete success and helped our client to take their business to the next level.

As data analytics experts, we are proud to have helped our client successfully meet their data analytics requirements. We specialise in the implementation of data analysis projects and rely on state-of-the-art technologies such as Hadoop, Hive and Tableau. If you too would like to implement a successful customer journey analysis, don't hesitate to choose us as a partner for your next project. We are the right partner for you and offer comprehensive solutions tailored to your individual requirements. Contact us today to find out more!

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