91 Hands-on with OpenAI’s API

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#> 1 1. Define the problem statement: Clearly articulate the business problem or question that needs to be addressed through data analysis.\n\n2. Data collection: Gather relevant data from various sources, ensuring that the data is accurate and complete.\n\n3. Data cleaning: Preprocess the data by handling missing values, removing outliers, and standardizing the data for analysis.\n\n4. Exploratory data analysis: Explore the data using statistical methods and visualizations to identify patterns, trends, and relationships within the data.\n\n5. Feature engineering: Create new features or transform existing features to better represent the underlying data and improve model performance.\n\n6. Model selection: Choose the most appropriate machine learning algorithm based on the nature of the problem and the characteristics of the data.\n\n7. Model training: Train the selected model on the training data, optimizing its parameters to achieve the best performance.\n\n8. Model evaluation: Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1 score.\n\n9. Model deployment: Deploy the trained model in a production environment to make predictions on new data.\n\n10. Monitoring and maintenance: Continuously monitor the model's performance in the production environment and retrain or update the model as needed to maintain its accuracy.
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