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Course Outline
Introduction
- Overview of RapidMiner Studio
- Orientation to the RapidMiner UI and key features
CRISP-DM Methodology in RapidMiner
- Understanding the CRISP-DM framework
- Application in estimating and forecasting values
Data Understanding and Preparation
- Data import and exploration
- Preprocessing and cleaning techniques
- Advanced data transformation methods
Data Modelling with RapidMiner
- Introduction to data modelling
- Selection and application of machine learning algorithms
- Supervised learning algorithms
- Unsupervised learning algorithms
Model Evaluation and Deployment
- Techniques for model evaluation
- Strategies for model deployment
- Model realignment and optimisation
Time Series Analysis and Forecasting
- Fundamentals of time series analysis
- Application of moving average models
- Time series preprocessing and data aggregation
Advanced Time Series Techniques
- Decomposition analysis
- Forecasting with time windows
- Forecasting with feature generation
ARIMA Modelling
- Understanding ARIMA models
- Practical application in RapidMiner
Summary and Next Steps
Requirements
- Basic understanding of data analysis and machine learning concepts
Audience
- Data Analysts
- Business Analysts
- Data Scientists
14 Hours
