The Data Science workflow describes the step-by-step process followed to solve a real-world problem using data.
Identify the business problem. Example: Predict customer churn.
Remove duplicates, handle missing values, fix errors.
Create new useful features from raw data.
Use algorithms like Linear Regression, Decision Trees, KNN, etc.
Deploy using Flask, FastAPI, AWS, Azure, or Docker.
The workflow ensures a structured way to deliver reliable Data Science solutions.
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