Machine Learning Pipeline (using scikit-learn)
Pipeline in Machine Learning
A pipeline in machine learning is a sequence of data processing steps that includes data cleaning, feature extraction, model selection and training, and model evaluation.
1) Importing the required libraries and loading the dataset.
2) Creating a Custom preprocessing function that basically removes missing data, and outliers. Other required preprocessing steps can also be included in this function, depending on the specifics of your dataset.
3) Split the data and create the pipeline.
4) Apply cross-validation and hyperparameter tuning.
5) Fit the pipeline to the data and Model Evaluation.
6) Advantages of using a pipeline
By automating multiple workflows and linking them, a machine-learning pipeline helps streamline and speed up the process. 1. Consistency 2. Automation 3. Improved accuracy 4. Reproducibility 5. Better code organization 6. Model Deployment.
