Machine Learning-based Predictive Analytics for Industry Workflow

Machine Learning Model Training

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Business Workflow Step: Machine Learning Model Training This workflow step involves training machine learning models using historical data to enable predictive analytics. It begins with data preparation, where relevant data is collected, cleaned, and transformed into a format suitable for model training. Next, feature engineering is performed to select the most relevant variables that will contribute to the model's accuracy. The data is then split into training and testing sets to evaluate the model's performance using metrics such as precision, recall, and F1 score. Model selection involves choosing the most suitable algorithm based on the problem type and dataset characteristics. Hyperparameter tuning is also performed to optimize the model's performance. Once the model is trained, it is evaluated for accuracy and then deployed into production, where it can be used to make predictions on new data.