SEMMA Methodology of Data Mining Process

Slide Content

The slide presents the SEMMA methodology for data mining, which stands for Sample, Explore, Modify, Model, and Assess. "Sample" involves selecting a representative data subset from available datasets to focus analysis on relevant data. "Explore" is about analyzing data to understand characteristics, identify patterns, and detect anomalies, aiming to gain insight into the underlying structure of the data. "Modify" refers to data transformation, cleaning, handling missing values, outliers, and normalizing data to prepare for modeling. "Model" is the application of machine learning (ML) algorithms to build predictive or descriptive models. Finally, "Assess" involves evaluating the model's performance with the objective of determining model accuracy and effectiveness.

Graphical Look

• The slide has a large header in dark blue with white text showing the title.
• There are five main sections, each represented by a chevron shape in a light teal color.
• Each section has a title in bold text: Sample, Explore, Modify, Model, and Assess.
• Accompanying each section title is a circular icon in a lighter blue shade with a pictograph symbolizing the section's concept:
• A database icon for "Sample"
• A magnifying glass for "Explore"
• A gear for "Modify"
• AI (Artificial Intelligence) letters for "Model"
• A checklist for "Assess"
• To the left of each title is a text box with bullet points explaining the relevant process step.
• The background of the slide is white.

The overall look of the slide is clean and professional, with a color scheme of blues and teals to convey a sense of technology and analysis. The icons are simple and effectively represent each step of the SEMMA methodology.

Use Cases

• To educate an audience about the fundamental steps in data mining process during a training or workshop.
• For presenting the methodology to stakeholders before starting a data mining or analysis project to align expectations and methodologies.
• As a visual aid during project meetings to outline the current phase of a data mining task being undertaken by a team.
• In academic or business conferences to discuss best practices or case studies related to data mining and analytics processes.