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Mastering the Machine Learning Workflow - A Practical Guide for Supervised Learning
- The workflow for machine learning involves several steps to ensure the usage of updated data including understanding the need for machine learning and conducting thorough research in the relevant business field.
- Collecting data from various sources and exploring it in tools like Excel or programming languages like Python are crucial initial steps.
- Data preprocessing steps such as handling null values, dealing with imbalanced data, removing duplicate rows, and fixing inconsistencies are necessary for preparing the dataset.
- Exploratory Data Analysis (EDA) helps in identifying patterns, errors, and relationships among variables through visualization techniques.
- Feature engineering involves tasks like train-test splitting, encoding categorical features, and creating new features if required.
- Careful consideration is needed for encoding before train-test splitting to avoid data leakage and ensure model performance on new unseen data.
- Feature selection methods like variance threshold, correlation coefficient, and tree-based feature importance help in selecting relevant features for model training.
- Hyperparameter tuning and evaluating metrics like Regression and Classification are crucial for selecting the best model for the dataset.
- Once the best fitting algorithm is determined, hyperparameters are optimized, and the model trained, it is saved in a serialized object format like .pkl for future use.
- Deploying the saved model for real-time inference can be done by creating an API, commonly using Flask API, and updating the model periodically with changing data is essential for maintaining performance.
- This comprehensive guide provides actionable insights into leveraging machine learning for data-driven decisions, emphasizing the importance of continuous learning and improvement in the field.
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