Summary of What is Predictive Modeling and How Does it Work?
Summary of Main Ideas and Concepts
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Definition of Predictive Modeling:
Predictive Modeling is a mathematical process used in Predictive Analytics to forecast future events based on historical data and patterns.
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Process of Predictive Modeling:
- Data Collection: The process begins with gathering current or historical data.
- Algorithm Creation: Data scientists develop algorithms and statistical models.
- Training Models: These models are trained using subsets of data.
- Running Models: The trained models are then applied to the full dataset to generate predictions.
- Multiple Models: Often, several models are used together to enhance prediction accuracy.
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Differences Between Predictive Modeling and Predictive Analytics:
While often used interchangeably, Predictive Modeling is specifically the hands-on aspect of the broader Predictive Analytics applications.
- Common Modeling Methods:
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Applications of Predictive Modeling:
Widely used in various fields including:
- Meteorology (weather forecasting)
- Online advertising and marketing
- Spam filters
- Fraud detection
- Customer Relationship Management (CRM)
- Capacity planning
- Change management
- Disaster recovery
- Engineering
- Medical diagnosis
- Security management
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Considerations for Effective Predictive Modeling:
- Data Preparation: Acquiring, sorting, cleansing, and preparing data is crucial and often takes about 80% of the total process.
- Avoid Overfitting: Care must be taken to prevent models from memorizing data points instead of generalizing outcomes.
- Addressing Barriers: Planning for technical and organizational challenges is essential, especially when dealing with decentralized data systems.
- Business Insight vs. Statistical Significance: Statistical significance does not always equate to actionable business insights.
Methodology/Instructions for Predictive Modeling
- Step 1: Collect current or historical data.
- Step 2: Create algorithms and statistical models.
- Step 3: Train the models using subsets of the data.
- Step 4: Run the trained models against the full dataset.
- Step 5: Use multiple models to improve prediction accuracy.
- Step 6: Prepare data thoroughly (80% of the effort).
- Step 7: Avoid overfitting and ensure models generalize well.
- Step 8: Plan for technical and organizational barriers.
- Step 9: Ensure Predictive Modeling projects align with real business challenges.
Speakers/Sources Featured
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Category
Educational