Predictive Analytics with Machine Learning
Overview
Predictive analytics uses historical data to estimate what is likely to happen next, from customer churn to equipment failure and demand levels. This course explains the main machine learning methods in accessible terms and takes participants through a complete predictive project, from framing the question to evaluating and deploying a model.
Who should attend
Data analysts, business intelligence staff, planners, and managers who work with data and want to understand machine learning.
Training methodology
Interactive presentations, live demonstrations of AI tools, hands-on exercises using participants' own work examples, case studies, group discussion, and a personal action plan.
Objectives
By the end of this course, participants will be able to:
- Frame business problems as prediction tasks
- Prepare and explore data for modelling
- Understand regression, classification and forecasting methods
- Evaluate model accuracy and reliability
- Plan deployment and monitoring of predictive models
Course outline
Day 1From Question to Data
- Prediction use cases
- Defining the target and features
- Data sources and quality
- Project framing exercise
- Success measures for a prediction project
- Team roles
Day 2Preparing Data
- Cleaning and transforming
- Exploratory analysis
- Handling missing values and outliers
- Hands-on lab
- Feature engineering basics
- Data privacy in modelling
Day 3Machine Learning Methods
- Regression and classification
- Decision trees and ensembles
- Time-series forecasting
- Choosing the right method
- Clustering and segmentation
- Tools: Excel, Python and AutoML
Day 4Evaluating Models
- Training and test data
- Accuracy measures
- Overfitting and bias
- Explaining predictions
- Cross-validation
- Comparing models exercise
Day 5Deployment and Use
- Putting models into operation
- Monitoring and retraining
- Communicating results to managers
- Project presentation
- Ethics of prediction
- Next steps in learning
