This module equips students with the skills to analyse large datasets and develop machine learning (ML) models for predictive analytics.

Students are able to use both classical ML and deep learning models, and explore a variety of data-related challenges you may be facing.

Master's level students in groups of 2-3 will analyse your company data set(s), perform exploratory data analysis (EDA), feature engineering and other techniques to build a model, evaluate its performance, make refinements and communicate the business-related findings to you in a short presentation.

While we make every effort to align student interests with your project needs, we cannot guarantee project selection. Final confirmation of participating students will be provided by the last week of October or first week of November.

Deliverables are negotiable and will seek to align the needs of the learners and the organization. 

Some final project deliverables might include: 

• Comprehensive analysis report of the dataset with key insights
• Developed and deployed machine learning model with documented code (Python / Jupyter notebook)
• Evaluation metrics and model performance
• Refinement plan for model improvement based on evaluation results
• Business-stakeholder presentation summarising the findings and capabilities