Unit 868: Predictive Analytics

1. Introduction to Predictive Analytics

• Definition and scope of predictive analytics
• Importance in business and decision-making processes
• Comparison with other analytics types:
  • Descriptive analytics
  • Prescriptive analytics
• Examples and applications in various industries

2. Data Collection and Preparation for Predictive Analytics

• Sources and types of data
• Data collection methods and best practices
• Data cleaning techniques:
  • Handling missing values
  • Removing duplicates
  • Correcting inconsistencies
• Data transformation:
  • Normalization and scaling
  • Encoding categorical variables
  • Data integration and aggregation

3. Exploratory Data Analysis (EDA) for Predictive Analytics

• Purpose and importance of EDA
• Summary statistics:
  • Measures of central tendency
  • Measures of dispersion
• Data visualization techniques:
  • Histograms, box plots, scatter plots
  • Correlation matrices and heatmaps
• Identifying patterns, trends, and anomalies
• Detecting outliers and data distribution insights

4. Predictive Modeling Techniques

• Overview of predictive modeling
• Regression analysis:
  • Linear regression
  • Logistic regression
• Classification techniques:
  • Decision trees
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)
• Clustering methods:
  • k-Means clustering
  • Hierarchical clustering
• Time series analysis:
  • Components of time series
  • ARIMA models
• Guidelines for selecting appropriate techniques based on data and problem

5. Model Evaluation and Selection

• Importance of model validation
• Performance metrics:
  • Accuracy
  • Precision, Recall, and F1 Score
  • ROC curve and AUC
  • Mean squared error (MSE) and root MSE
• Cross-validation techniques
• Overfitting and underfitting concepts
• Model selection strategies

6. Feature Selection and Engineering

• Role of features in predictive modeling
• Feature selection methods:
  • Filter methods
  • Wrapper methods
  • Embedded methods
• Feature engineering:
  • Creating new features
  • Transforming existing features
• Dimensionality reduction techniques:
  • Principal Component Analysis (PCA)
• Feature importance analysis

7. Model Deployment and Monitoring

• Steps in deploying predictive models
• Integration with production systems
• Monitoring model performance over time
• Handling model drift and data changes
• Updating and retraining models

8. Ethical Considerations in Predictive Analytics

• Understanding bias in data and models
• Privacy and data security concerns
• Fairness and transparency in predictive modeling
• Responsible and ethical use of analytics in decision-making
• Regulatory and legal considerations

9. Case Studies in Predictive Analytics

• Business problem identification and solution design
• Examples from finance, healthcare, marketing, and manufacturing
• Lessons learned and best practices
• Discussion on innovation driven by predictive analytics