Alejandro de la Fuente

Alejandro de la Fuente

Data Scientist / Postdoctoral fellow

Machine Learning Projects in Python

Published:

Problem

Mastering machine learning requires understanding both theory and practice — how algorithms behave with real data, how to prepare features, and how to evaluate model performance. Here, I showcase a collection of hands-on machine learning projects. Each project demonstrates end-to-end implementation of key algorithms, emphasising data preparation, model training, evaluation, and interpretation.

Approach

  • Created a structured repository with subprojects covering:
    • Regression: Linear and Polynomial Regression
    • Classification: Logistic Regression, K-Nearest Neighbours (KNN), Decision Trees, Random Forests, Support Vector Machines (SVM)
    • Ensemble Methods: Gradient Boosting, XGBoost
    • Clustering: K-Means, Hierarchical Clustering
    • Dimensionality Reduction: Principal Component Analysis (PCA)
    • Natural Language Processing (NLP): Naive Bayes text classification and TF-IDF feature extraction
    • Deep Learning: Neural Networks using TensorFlow and Keras
  • Implemented complete data preprocessing → model training → evaluation → visualization workflows using Scikit-learn and supporting libraries.
  • Emphasised algorithmic intuition through visual diagnostics (e.g., decision boundaries, feature importance, ROC curves).

Stack

  • Language: Python 3
  • Libraries: scikit-learn, pandas, numpy, matplotlib, seaborn, xgboost, tensorflow, keras
  • Environment: Jupyter Notebook, Git/GitHub
  • Concepts: EDA, data manipulation, supervised & unsupervised learning, model validation, scaling, feature engineering, interpretability, neural networks

Structure

Each section of the repository represents a standalone ML project:

  1. Linear Regression
  2. Logistic Regression
  3. K-Nearest Neighbors (KNN)
  4. Decision Trees and Random Forests
  5. Support Vector Machines (SVM)
  6. K-Means Clustering
  7. Principal Component Analysis (PCA)
  8. Recommender Systems
  9. Natural Language Processing (NLP)
  10. Neural Nets and Deep Learning with TensorFlow and Keras
  11. Cross-validation
  12. Introduction to Big Data and PySpark workflows

Results and Impact

  • Developed a complete, modular portfolio of ML workflows covering predictive and unsupervised methods.
  • Strengthened understanding of data preparation, evaluation metrics, and model trade-offs.
  • Strengthened proficiency in data storytelling and visualisation using modern Python tools.
  • This repository establishes a practical foundation for model interpretability and applied machine learning, bridging exploratory data analysis and advanced AI workflows. It complements the Python OOP Mini-Systems, EDA Projects, and Coding Challenges repositories as part of a coherent learning progression.

Links & Resources