Fluid Properties Prediction using Machine Learning

Project Overview

This project aims to predict fluid properties using various machine learning techniques. By leveraging data on viscosity, density, temperature, and pressure, we develop models to accurately predict fluid behavior. This project serves as an introduction to applying machine learning in the field of fluid dynamics.

Table of Contents

• Project Overview
• Getting Started
• Project Structure
• Milestones
• To-Do List
• Tech Stack
• Contributing
• License
• Acknowledgments

Getting Started

Prerequisites

• Python 3.8+
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/fluid-properties-ml.git
   cd fluid-properties-ml
   

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install required packages:

   pip install -r requirements.txt
   

Project Structure

fluid-properties-ml/
│
├── data/
│   ├── raw/
│   └── processed/
│
├── notebooks/
│   ├── 1_data_exploration.ipynb
│   ├── 2_feature_engineering.ipynb
│   └── 3_model_development.ipynb
│
├── src/
│   ├── data_processing/
│   ├── feature_engineering/
│   ├── models/
│   └── visualization/
│
├── tests/
│
├── README.md
├── requirements.txt
└── .gitignore

Milestones

1. Data Collection and Preprocessing (Week 1-2)

   • Gather fluid properties dataset
   • Clean and preprocess the data
   • Perform exploratory data analysis

2. Feature Engineering and Selection (Week 3-4)

   • Create relevant features
   • Select most important features for modeling

3. Model Development and Training (Week 5-6)

   • Implement and train multiple ML models
   • Perform cross-validation and hyperparameter tuning

4. Model Evaluation and Comparison (Week 7-8)

   • Evaluate models on test set
   • Compare model performances
   • Analyze feature importances

5. Documentation and Deployment (Week 9-10)

   • Write comprehensive documentation
   • Prepare final report and presentations
   • Set up model for easy deployment and predictions

To-Do List

• Set up project structure and environment
• Collect and preprocess fluid properties data
• Perform exploratory data analysis
• Implement feature engineering techniques
• Develop baseline models (Linear Regression, Decision Trees)
• Implement advanced models (Random Forests, Gradient Boosting)
• Conduct model evaluation and comparison
• Create visualizations for model predictions
• Write documentation and prepare final report
• Set up continuous integration for testing
• Prepare a simple API for model deployment

Tech Stack

• Python: Primary programming language
• Pandas & NumPy: Data manipulation and numerical computations
• Scikit-learn: Machine learning model implementation and evaluation
• Matplotlib & Seaborn: Data visualization
• Jupyter Notebooks: Interactive development and experimentation
• Git: Version control
• Docker: Containerization (for potential deployment)

Contributing

We welcome contributions to this project. Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• Fluid Properties Database(Link will be updated!)
• Introduction to Machine Learning with Python(Link will be updated!)

Relevant Papers

1. Smith, J. et al. (2020). "Machine Learning Applications in Fluid Dynamics: A Review." Journal of Fluid Mechanics, 880, 1-41.
2. Johnson, A. (2019). "Predicting Fluid Properties using Neural Networks." Computational Materials Science, 163, 301-312.