🪙 Gold Pathfinder ML Project

Data-Driven Gold Exploration Using Geochemical Assay Analysis

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🎯 Objective

Apply Python-based data science and visualization techniques to analyze geochemical assay data from a gold exploration program.
The project aims to identify pathfinder elements — geochemical indicators that can signal potential gold mineralization zones — and demonstrate how open-source tools can support data-driven mineral exploration.

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🌍 Program Context

This project fulfills the requirements of the
MIT Emerging Talent – Experiential Learning Opportunity (ELO2),
integrating domain expertise (Geoscience) and computational methods (Data Science) to solve real-world challenges.

The project follows the Collaborative Data Science Project (CDSP) milestone framework, emphasizing structured process, documentation, and reflection.

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🧠 Motivation

Traditional gold exploration relies heavily on proprietary mining software and costly fieldwork.
By leveraging Python and open-source data analytics, this project explores how machine learning and geochemical visualization can:

• Identify multi-element geochemical associations.
• Detect anomalies and possible gold pathfinders.
• Reduce exploration cost and time through reproducible analysis.

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🧩 Milestone 0 – Cross-Cultural Collaboration

This milestone establishes the foundation of the project:

• GitHub repository setup and documentation structure.
• Communication and learning frameworks.
• Project constraints and planning for future collaboration.

Although the project is currently conducted individually, it is designed to be collaboration-ready, allowing new team members to join at any stage.

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👤 Current Team

Role	Name	Background
Team Lead	Obay Salih	Geoscientist & Data Science Trainee (MIT Emerging Talent, 2025)
Team Member	Salih Adam	Chemical Engineer & Data Science Trainee (MIT Emerging Talent, 2025)

Potential collaborators welcome for:

• Data visualization and automation.
• Machine learning feature analysis.
• Geospatial data integration.

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⚙️ Tools & Technologies

Category	Tools / Libraries
Programming	Python 3.x
Data Handling	pandas, numpy
Visualization	matplotlib, seaborn, plotly
Geospatial	geopandas, folium
Machine Learning	scikit-learn
Documentation	Markdown, Jupyter Notebooks
Version Control	GitHub

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🧭 Repository Structure

ELO2_Gold_Pathfinder_Project/
│
├── data/
│ ├── raw/ # Original ALS assay data
│ └── processed/ # Cleaned and structured CSV files
│
├── notebooks/
│ ├── 01_data_cleaning.ipynb
│ ├── 02_exploration.ipynb
│ ├── 03_visualization.ipynb
│
├── src/
│ ├── data_preparation.py
│ ├── visualization.py
│
├── docs/
│ ├── group_norms.md
│ ├── communication_plan.md
│ ├── constraints.md
│ ├── learning_goals.md
│ └── meetings/
│ └── meeting_01.md
│
├── reports/
│ ├── milestone_0_reflection.md
│ ├── milestone_1_problem_identification.md
│ ├── milestone_2_data_collection.md │ ├── milestone_3_analysis.md
│ ├── milestone_4_communication.md
│ └── milestone_5_final_presentation.md
│
├── CONTRIBUTING.md
├── .gitignore
└── README.md

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⏱️ Project Timeline (ELO2 Schedule)

Milestone	Period (2025)	Focus
0️⃣ Cross-Cultural Collaboration	Sept 22 – Sept 30	Setup, documentation, collaboration framework
1️⃣ Problem Identification	Oct 1 – Oct 12	Define research question & stakeholders
2️⃣ Data Collection	Oct 13 – Oct 24	Clean, structure, and document ALS data
3️⃣ Data Analysis	Oct 26 – Nov 7	Visualize and analyze geochemical trends
4️⃣ Communicating Results	Nov 9 – Nov 22	Create visual story & interpretation summary
5️⃣ Final Presentation	Nov 23 – Dec 4	Present findings and reflections

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📚 Learning Outcomes

• Integrate geoscience knowledge with data-driven modeling.
• Build a reproducible workflow for mining data analysis.
• Practice open-source collaboration and technical documentation.
• Develop visualization and storytelling skills for scientific communication.

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⚡ Quick Start Guide

1. Clone the Repository

git clone https://github.com/<your-username>/ELO2_Gold_Pathfinder_Project.git
cd ELO2_Gold_Pathfinder_Project

2. Install Dependencies

Create a virtual environment and install required libraries:

python -m venv venv
source venv/bin/activate   # For Linux/Mac
venv\Scripts\activate      # For Windows
pip install -r requirements.txt

3. Launch Jupyter Notebook

jupyter notebook

Open the notebook:

notebook/01_data_cleaning.ipynb

4. Explore the Data

• Load the ALS assay CSV files from the /data/raw/ directory.
• Run the data cleaning and visualization notebooks.
• Save outputs to /data/processed/ and /reports/.

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🧩 Future Work

• Integrate geospatial mapping using GeoPandas and Folium.
• Train a machine learning model to predict elemental correlations.
• Develop a dashboard-style visualization for non-technical stakeholders.

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💬 Acknowledgements

Thanks to the MIT Emerging Talent Program (ELO2, 2025) for providing mentorship, structure, and support in applying data science to real-world domains.

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📫 Contact

Obay Salih

• 🌍 Sudan, China
• Geoscientist | MIT Emerging Talent (Data Science, 2025)

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Salih Adam

• 🌍 Sudan, Egypt
• Chemical Engineer | MIT Emerging Talent (Data Science, 2025)

Small update