🧠 Real-World Data Science Projects

Welcome!
This repository brings together case studies and practical solutions developed to solve real-world problems using Data Science, Machine Learning, and Artificial Intelligence.
Each project is based on a real scenario, focusing on delivering actionable insights and measurable results.

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🗂 Structure

Each project folder contains:

• README.md → Problem description, context, objectives, and methodology.

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. 
|   ├── 01_data_understanding.ipynb
│   ├── 02_data_preparation.ipynb
│   ├── 03_modeling.ipynb
│   └── 04_evaluation.ipynb
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         cookie_cutter_framework and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── src   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes cookie_cutter_framework a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │
    └── plots.py                <- Code to create visualizations

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📌 Features

• Projects based on real business and industry challenges.
• Use of structured methodologies such as CRISP-DM and DataOps.
• Focus on best practices: version control, reproducibility, and clear documentation.
• Diverse applications: forecasting, classification, anomaly detection, optimization, and exploratory analysis.

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🔍 Navigation

• For shorter and experimental projects, check the cases_study folder.

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📢 Note

This is a living repository and will be continuously updated with new projects covering various sectors:

• 🏭 Industry & Manufacturing
• 🛢️ Oil & Gas
• 🏦 Finance
• 🛒 Retail & E-commerce
• 🏥 Healthcare

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💡 Tip: Read each project's README.md to understand the problem, tested approaches, and obtained results.