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Curve Fitter Tool 🧮

License: MIT

A simple and interactive Python application for curve fitting, built using Streamlit for the GUI and a CLI tool for terminal-based usage.
It supports fitting Straight Line, Parabolic, and Geometric curves to given (x, y) data points using least squares approximation.


🧠 Features

✅ Streamlit-based interactive GUI
✅ Command-line (CLI) mode for terminal users
✅ Fits multiple curve types:

  • Straight Line: y = a + bx
  • Parabola: y = a + bx + cx²
  • Geometric Curve: y = axᵇ

✅ Displays calculated constants and regression equations
✅ Visualizes the curve fitting results
✅ Easy-to-use modular code structure


⚙️ Installation

1. Create a virtual environment (recommended)

python3 -m venv myenv

source myenv/bin/activate # On Linux/macOS
myenv\Scripts\activate # On Windows

2. Clone the repository

git clone https://github.com/nezchan0/Curve-Fitter.git
cd curve-fitter/Curve-Fitting-Tool

3. Install dependencies

pip install -r requirements.txt

🖥️ GUI Usage (Streamlit App)

1. Run the Streamlit App

streamlit run streamlit_app.py

2. Features in GUI

  • Upload or input x and y data
  • Choose curve type to fit
  • View calculated coefficients
  • Plot curve and data points

🧾 CLI Usage (Terminal)

1. Run the CLI tool

python cli_main.py

2. Example Session

Enter the Values of the X and Y coordinates
X values: 1 2 3 4 5
Y values: 2.2 4.5 6.1 8.2 10.1

Enter the type of curve you want to fit for the given data:
Press 1: Straight Line
Press 2: Parabola
Press 3: Geometric Curve
Press 123: To Exit

🌐 Live Link & Preview

Check out the live Streamlit Curve Fitter Tool here:
🔗 Live Demo -> https://nezchan0-curve-fitter-curve-fitting-toolstreamlit-app-c7r9ti.streamlit.app/

Preview

Curve Fitter Tool Preview


📜 License

This project is licensed under the MIT License — feel free to use, modify, and distribute it freely, with attribution.


🧑‍💻 Author

Alok Kumar Maurya – Developer | Email: alok05.maurya@gmail.com


About

An interactive Streamlit app for curve fitting — fit straight lines, parabolas, and geometric curves to your data using least squares. Built with NumPy, Pandas, and Matplotlib.

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