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Master Data Science step-by-step
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Module 10
End-to-End Data Science Project
1
From Business Problem to ML Solution
Define a clear business problem and translate it into a machine learning task with success metrics.
2
From Raw Data to Refined Features: A Full Project Workflow
Execute a full project workflow: EDA, preprocessing, and feature engineering on a new dataset.
3
Model Training and Evaluation
Train and evaluate a baseline model and at least two more complex candidate models.
4
Model Selection and Justification
Select and justify the final model based on a comprehensive evaluation.
5
Interpreting Model Predictions with SHAP
Interpret the predictions of the final model using a technique like SHAP.
6
Reproducible Folder Structures
Organize the project using a clean, reproducible folder structure.
7
Communicating Technical Information to Non-Technical Audiences
Present final results and recommendations in a clear, non-technical summary.
8
Git Fundamentals for Project Version Control
Use basic git commands for version control of the project code.
End of course,
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