Let's explore a step-by-step process for approaching ML problems in Real life:
- Understanding the Business Requirements and the Nature of the Available Data.
- Classify the problem as Supervised/Unsupervised as well as Regression/Classification (in advance).
- Download, Clean & Explore data and Create New Features (if required) that may improve models.
- Create Training/Validation/Test sets of data and prepare the data for training ML models.
- Create a quick & easy baseline model to evaluate and benchmark future models.
- Select a modeling strategy, train a model, and tune hyperparameters to achieve optimal fit.
- Experiment and combine results from multiple strategies to get a better result.
- Interpret models, study individual predictions, and present your findings to the stakeholders.
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