With Breast Cancer Awareness Month upon us, what better way to raise awareness than to discuss building a breast cancer screening model in Python? By understanding the patterns and barriers associated with non-compliance, we can formulate meaningful, timely interventions that enable early detection.

This talk bridges the gap between contemporary academic research and practical real-world application with an actual use case that also serves as an approachable introduction to applied machine learning. From problem definition to data exploration, modeling, and experiment design; this talk has you covered! Hopefully the discussion will also help to raise general screening awareness.

• Introductions (2 min)
• Framing the problem (5 min)
• Discuss common barriers (5 min)
• Data curation & exploration (10 min)
• Interesting patterns & insights (5 min)
• Model training & evaluation (5 min)
• Experiment design (5 min)
• Questions (3 min)