MM - List different phases in data analytics life cycle and explain Model Building phase in detail.

Creating mind maps for the Data Analytics Lifecycle and the detailed Model Building phase can significantly enhance your recall and understanding. Here are keywords and short phrases that you can use to structure your mind maps:

Data Analytics Lifecycle Mind Map

Problem Definition
- Define objective
- Scope identification
- Impact analysis
Data Acquisition and Filtering
- Source identification
- Data collection
- Cleaning and filtering
Data Exploration
- Pattern analysis
- Trend identification
- Visualization
Data Preparation
- Preprocessing
- Feature engineering
- Normalization
Model Building
- Technique selection
- Training/testing split
- Algorithm application
Model Evaluation
- Performance metrics
- Validation
- Test results
Deployment
- Integration
- User interface
- Automation
Model Monitoring and Maintenance
- Continuous monitoring
- Adjustment
- Retraining

Model Building Phase Mind Map

Selection of Techniques
- Algorithm type
- Problem relevance
- Technique comparison
Splitting the Data
- Training set
- Testing set
- Validation set (optional)
Model Training
- Parameter settings
- Learning process
- Hyperparameter optimization
Cross-Validation
- K-fold validation
- Performance consistency
- Error estimation
Feature Importance and Selection
- Importance ranking
- Feature reduction
- Model simplification
Ensemble Techniques
- Bagging
- Boosting
- Stacking
Tuning and Optimization
- Grid search
- Random search
- Best parameters

These keywords and phrases will guide you in building comprehensive mind maps that encapsulate both the overall lifecycle of data analytics and the specific tasks involved in the model building phase. Each term not only serves as a recall point but also as a potential area for deeper exploration.