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Install Python and essential libraries like FastAPI, Scikit-learn, and Pandas. Ensure you're using a virtual environment to manage dependencies effectively.
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Use Scikit-learn's wine dataset and convert it to a Pandas DataFrame for easier manipulation. This dataset includes features and target wine classes.
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Analyze the dataset by viewing its structure, summary statistics, and class distributions. This helps understand the data before preprocessing.
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Split the dataset into training and test sets, then scale the features. This ensures the model performs well with normalized inputs.
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Train a Logistic Regression model using the preprocessed data. Save the trained model to a file for future use.
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Evaluate the model's performance on test data by calculating accuracy. This determines how well the model generalizes to unseen data.
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Create a basic FastAPI app with a root endpoint. This acts as the foundation for serving predictions via HTTP requests.
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Create a basic FastAPI app with a root endpoint. This acts as the foundation for serving predictions via HTTP requests.
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Define an endpoint to accept wine features in JSON format and return the predicted wine class. Use Pydantic to validate input data.
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Run the FastAPI server and test the prediction endpoint with sample inputs using tools like cURL or Postman to verify functionality.