🚀 Machine Learning Pipeline Notebooks 📊

Welcome to the Machine Learning Pipeline Notebooks repository! If you're looking to master the art of data-driven decision-making, you're in the right place. This collection of Jupyter notebooks is designed to be your comprehensive guide to understanding and implementing the entire machine learning workflow. From data preprocessing to model evaluation and deployment, we've got you covered every step of the way! 💡

Overview of Notebooks 📝

1. 01-machine-learning-pipeline-data-analysis.ipynb: Dive into data analysis techniques that unveil the hidden stories in your dataset. Learn how to visualize data distributions, identify outliers, and perform exploratory data analysis that lays the foundation for robust modeling.

2. 02-machine-learning-pipeline-feature-engineering.ipynb: Discover the secrets behind crafting powerful features from raw data. Explore techniques such as normalization, one-hot encoding, and text embedding to transform data into insightful representations that drive model performance.

3. 03-machine-learning-pipeline-feature-selection.ipynb: Uncover the art of feature selection, where you'll learn strategies to choose the most informative variables while avoiding overfitting. Dive into techniques like recursive feature elimination and feature importance ranking.

4. 04-machine-learning-pipeline-model-training.ipynb: It's time to build, train, and fine-tune machine learning models! Understand different algorithm families, learn about hyperparameter tuning, and validate your models to ensure they're ready for real-world predictions.

5. 05-machine-learning-pPipeline-scoring-new-data.ipynb: Once your model is trained, discover how to use it to make predictions on new data. Learn about model persistence, loading saved models, and the process of scoring unseen data.

6. 06-feature-engineering-with-open-source.ipynb: Dive deep into open-source libraries that simplify complex feature engineering tasks. Harness the power of libraries like Featuretools and tsfresh to automate advanced feature extraction.

7. 07-feature-engineering-pipeline.ipynb: Elevate your feature engineering skills by creating an end-to-end feature engineering pipeline. Explore techniques for handling missing data, encoding categorical variables, and engineering time-based features.

8. 08-final-machine-learning-pipeline.ipynb: Bring it all together! Construct a final machine learning pipeline that encapsulates all the steps from data analysis to model deployment, ensuring a seamless and repeatable process.

Feel free to explore these notebooks at your own pace, adapt them to your projects, and unleash your data-driven potential. Happy learning and coding! 🎉

Author: Vidhi Waghela LinkedIn: Connect with me