Pressure Drop Prediction

Overview

Accurately predicting pressure drops in fluid systems is critical for designing and optimizing various engineering applications. Traditional methods often face limitations when dealing with complex non-Newtonian fluids. This project explores the potential of machine learning techniques to improve prediction accuracy in such cases.

Features

• Data-Driven Approach: Utilizes experimental data to train machine learning models for pressure drop prediction.
• Algorithm Performance Evaluation: Compares the effectiveness of various machine learning algorithms in this context.
• Non-Newtonian Fluid Focus: Addresses the specific challenges in predicting pressure drops in non-Newtonian fluids.

Repository Structure

• data/: Contains datasets used for training and testing the models.
• models/: Includes trained machine learning models and related scripts.
• main.ipynb: Jupyter Notebook demonstrating data analysis, model training, and evaluation.
• requirements.txt: Lists Python dependencies required for the project.

Getting Started

Prerequisites

• Python 3.7 or higher
• Jupyter Notebook

Installation

1. Clone the repository:

   git clone https://github.com/professionaltarun2004/Pressure-Drop-Prediction.git
   cd Pressure-Drop-Prediction

2. Install dependencies: Create a virtual environment and install the required packages:

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate
   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

   Open main.ipynb to explore the analysis and results.

Usage

1. Data Analysis:

   • Explore the provided datasets to understand the features and target variables.

2. Model Training:

   • Train machine learning models using the provided notebook.

3. Evaluation:

   • Assess the models using relevant metrics to determine prediction accuracy.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Make your changes.
3. Create a pull request with a detailed explanation of the proposed changes.