Machine Learning Portfolio

Personal machine learning portfolio with notebook-based projects focused on regression, classification, and dimensionality reduction, plus a small coding-challenges section.

Repository Overview

MachineLearningPortfolio/
├── classification_projects/
│   └── 1 - Obesity_Risk_with_classifications.ipynb
├── linear_regression_projects/
│   ├── 1 - CLVT_with_linear_regression.ipynb
│   └── 2 - House_Price_with_Linear_Regression.ipynb
├── dimensionality_reduction/
│   └── 1 - High_Dimensional_Datascape.ipynb
├── coding_challenges/
│   ├── codewars/
│   │   └── test.ipynb
│   └── HackerRank/
│       ├── battery.ipynb
│       ├── polynomial_regression_office_prices.py
│       └── tests.ipynb
├── data/
│   ├── CLTV_project/
│   ├── house_price_project/
│   ├── obesity_risk_project/
│   └── high_dimensional_datascape_proyect/
├── requirements.in
├── requirements.txt
└── LICENSE

Projects

Linear Regression

• CLTV with Linear Regression
  Predicts Customer Lifetime Value (CLTV) from historical customer features.
• House Price with Linear Regression
  Builds and evaluates a house-price prediction workflow.

Classification

• Obesity Risk with Classification
  Trains classification models for obesity-risk prediction.

Dimensionality Reduction

• High Dimensional Datascape
  Explores high-dimensional data reduction and visualization techniques.

Coding Challenges

• Codewars practice notebook
  Small notebook-based practice problems and experiments.
• HackerRank battery notebook
  Notebook work for HackerRank exercises.
• HackerRank office prices script
  Script version that reads from standard input for the HackerRank judge.
• HackerRank tests notebook
  Notebook used to validate and iterate on challenge solutions.

Data

Project datasets and generated outputs are grouped by project under data/.
Each subfolder contains the files needed by its corresponding notebook (for example train.csv, test.csv, submissions, and plots).

Getting Started

1. Clone the repository:

   git clone https://github.com/davidsmv/MachineLearningPortfolio.git
   cd MachineLearningPortfolio

2. (Optional) Create and activate a virtual environment.
3. Install dependencies:

   pip install -r requirements.txt

4. Launch Jupyter and open any notebook from a project folder.

Notes

• requirements.in is the source dependency list.
• requirements.txt is the pinned environment file used for reproducible installs.

Author

David Martínez
GitHub: @davidsmv
Email: david-martinezv@outlook.com

License

Licensed under the MIT License. See LICENSE for details.