Machine-Learning-Advanced-Techniques

A comprehensive machine learning project demonstrating advanced Python techniques for data science applications. This project covers regularization, hyperparameter optimization, evaluation metrics, ensemble methods, and production-ready code organization using object-oriented programming principles.

What This Project Does

This project implements advanced machine learning workflows to predict weekday patterns from commit data using user ID, lab name, trial count, and commit hour features.

The implementation progresses from basic regularization techniques to production-ready pipeline architecture.

Files in src Directory

• regularization.ipynb
      Implements L1 and L2 regularization for logistic regression and tree-based models, demonstrating overfitting prevention techniques

• gridsearch.ipynb
      Automates hyperparameter optimization using GridSearchCV with cross-validation

• metrics.ipynb
      Comprehensive evaluation using precision, recall, F1-score, ROC curves, and AUC metrics beyond basic accuracy

• ensembles.ipynb
      Combines multiple models using voting classifiers, bagging, and stacking techniques

• pipelines.ipynb
      Production-ready implementation using scikit-learn pipelines and object-oriented design patterns

Key Techniques

• Regularization
      Using sklearn.linear_model with L1/L2 penalty parameters for feature selection and overfitting prevention

• Cross-validation
      With GridSearchCV for systematic hyperparameter optimization

• ROC curve analysis
      Using sklearn.metrics.roc_curve for threshold optimization

• Ensemble methods
      Including VotingClassifier and BaggingClassifier

• Pipeline architecture
      Using sklearn.pipeline.Pipeline for reproducible workflows

Technologies and Libraries

• scikit-learn 0.23.1
      Core machine learning algorithms and utilities

• tqdm 4.46.1
      Progress tracking for long-running operations

• Jupyter Notebook
      Interactive development environment

• pandas
      Data manipulation and analysis

• numpy
      Numerical computing foundation

Project Structure

├── src/
│   ├── ex00/
│   ├── ex01/
│   ├── ex02/
│   ├── ex03/
│   └── ex04/
├── data/
└── README.md

src/ - Contains all exercise implementations organized by topic

data/ - Stores datasets and model outputs for reproducible results

ex00/ through ex04/ - Individual exercise directories containing focused implementations of specific machine learning concepts

The src/ directory organizes each advanced technique into separate modules, allowing for focused study and implementation of specific machine learning concepts.

The data/ directory maintains all datasets and intermediate results for consistent experimentation across different approaches.