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Data Science

PITS data science team specializes in data processing, cleaning, and knowledge graph creation. Our skills in natural language processing, image recognition, social network analysis, and predictive maintenance can help companies optimize their supply chain, tailor their marketing campaigns, and minimize downtime. We also have experience in building Ontologies using Protégé which is an open-source ontology editor for building intelligent systems. With a passion for leveraging data to solve complex problems, our team is dedicated in delivering insightful and actionable insights to our clients.

Fraud detection: We can help companies identify fraudulent transactions by processing and cleaning large amounts of transactional data and building Machine learning models to uncover patterns of suspicious behavior.

Customer segmentation: We use data processing and cleaning techniques to group customers based on their demographic and behavioral characteristics, creating knowledge graphs to understand the relationships between different segments and tailor marketing campaigns accordingly.

Supply chain optimization: We have the skills in data processing and cleaning techniques to analyze supply chain data and create knowledge graphs that identify bottlenecks, inefficiencies, and opportunities for optimization.

Sentiment analysis: PITS team use natural language processing techniques to clean and analyze large volumes of customer feedback data, creating ML models that identify the sentiment of customers towards different products, services, or brands.

Image recognition: Our team use machine learning algorithms to process and clean image data and build Deep Learning models using tensorflow or pytorch, that help identify objects, people, and patterns within images.

Social network analysis: We have experience in processing and cleaning social media data, creating ML models that show the connections between different users, their interests, and the topics they discuss, helping companies understand their target audience better.

Predictive maintenance: We use data processing and cleaning techniques to analyze sensor data from equipment and build ML models that identify patterns of failure, allowing companies to predict maintenance needs and minimize downtime.

Data Structuring: By collecting Unstructured data from websites and cleaning by implementing various NLP methods, the data could be transformed into structured form. These structured data could be stored and interlinked to each other using knowledge graphs which helps the business to gain insights.

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