- Data preparation: collection and preprocessing of relevant data (cleaning, formatting, and transforming)
- Feature selection: most relevant features (variables) that are likely to have an impact on the outcome
- Model selection: different algorithms can be used for predictive modeling, depending on the nature of the problem and the characteristics of the data
- Training the model: model is trained using historical data, where the relationship between the features and the target variable is known
- Validation and evaluation: common evaluation metrics include accuracy, precision, recall, F1 score, and mean squared error
- Hyperparameter tuning: parameter that control the learning process
- Deployment: make predictiions on new, incoming data
- Continuous monitoring and maintenance: predictive models require ongoing monitoring to ensure that they remain accurate over time
Teknikal
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