Automate Your Data Science Workflow: 9 Automated Machine Learning (AutoML) Frameworks in Python

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Automated Machine Learning (AutoML) is a process that involves automating the end-to-end process of applying machine learning to real-world problems. The goal of AutoML is to make machine learning more accessible to individuals and organizations with limited expertise in the field.

Automated Machine Learning (AutoML) is a valuable tool in various stages of the data science lifecycle. Here are key areas in data science where AutoML can be beneficial:

1. Data Exploration and Understanding:

• AutoML tools can assist in quickly exploring and understanding the structure of the dataset.
• Automated feature engineering helps identify relevant features and relationships.
• Frameworks: Auto-Keras, H2O.ai, DataRobot

2. Feature Engineering:

• AutoML frameworks often automate the process of feature selection and transformation.
• They can handle tasks like one-hot encoding, scaling, and handling missing values.
• Frameworks: Auto-Keras, TPOT, Auto-Sklearn

3. Model Selection:

• AutoML helps choose the most…