Overview
After learning various ML concepts and techniques, it’s time to put everything together by building a complete machine learning pipeline from data loading to model deployment.
Today, you’ll select a dataset of your choice from Kaggle, preprocess the data, engineer features, train and evaluate models, and prepare your pipeline for deployment.
Key Concepts
- Dataset selection and problem definition
- Data preprocessing and cleaning
- Feature engineering and selection
- Model training, tuning, and evaluation
- Pipeline automation with Scikit-learn’s <code>Pipeline</code>
- Saving models with joblib or pickle
- Preparing code for deployment
Practice Exercise
Exercise: Complete ML Pipeline Project
- Choose a dataset from Kaggle Datasets.
- Perform exploratory data analysis and cleaning.
- Engineer meaningful features relevant to the problem.
- Build and tune ML models (try multiple algorithms).
- Use Scikit-learn Pipelines to automate preprocessing and modeling steps.
- Evaluate your model with appropriate metrics.
- Save your final model for deployment.
- Write clear documentation (README) explaining your project.
Resources
Kaggle Datasets
Main resource for today
Scikit-learn Pipelines
How to create and use ML pipelines in Scikit-learn
Building a Machine Learning Pipeline
Comprehensive guide on ML pipelines
Model Persistence with Joblib
Save and load your ML models easily
Complete Today's Task
Mark today's task as complete to track your progress and earn achievements.