Machine Learning applied to business context
How ML can bring value
Data Science
Machine Learning
Innovation
Deep Learning
Thesis project description
The idea is to identify Machine Learning techniques that are appropriate to the existing framework of the company and industry. The main steps of Data Science Methodology will be applied:
- Data Preprocessing with a focus on handling missing values, outliers, and noise.
- Exploratory Data Analysis in order to uncover patterns, correlations, and potential anomalies within the data.
- Model Development: unsupervised/supervised learning, neural networks, basic linear models
- The final step includes translating the results to comprehensive reports and visualizations to communicate them and provide recommendations.
Additional topics can be included.
Managerial value of the research
Collaborating with a student on a machine learning-focused master's thesis can bring new opportunities for innovation, research-driven insights, and access to emerging talent in the field. This partnership can result in novel solutions, a better understanding of challenges, and skill transfer. Machine Learning techniques may lead to cost savings, prototyping benefits. Depening on industry and chosen subject, it can reduce manual tasks, improve the time management and increase revenue.
Research topic motivation
I have a big interest in the Data Science field as it is in constant development. Besides that, I see a huge potential of Machine Learning being applied in different sectors.Particular interest for industries like:- Engineering - Energy sector- Sustainability- Biology/ Chemistry- Medical industry
Why collaborate with us
My thesis partner and I bring to the table a diverse and capable team, unwavering determination, and strong problem-solving and communication skills; expertise Data science, Visualization, Predictive Analytics, Machine Learning, NLP, Big Data Management. Thus, we would like to have more practical experience in solving real-world problems, task in which you can help us succeed.Our partnership promises valuable, actionable insights and solutions that can drive innovation within your organization.

Project information
Project type
Thesis project
Collaboration semester
Spring 2024
Collaboration duration
14 to 19 weeks.
Collaboration start date
2024-01-01
Collaboration end date
2024-02-01
Number of student
1
Collaboration language
English
Interested in a collaboration?
Project Researcher #1
Researcher background
Academic Degree
Master of Science
Faculty
Business
Study programme
MSc in Business Administration and Data Science
Research interest
Graduation
Spring 2024
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