📊 Bravais-Pearson Interactive

An interactive web application to visualize the Pearson correlation coefficient (r) using Streamlit. The app generates linear, nonlinear, and random datasets, calculates correlation values, and provides graphical representations with regression lines.

🔗 Live Demo: Bravais-Pearson Interactive
🔗 GitHub Repository: noluyorAbi/Bravais-Pearson-Interactive

📌 Features

• 📈 Visualize Linear, Quadratic, and Random Relationships
• 🔢 Interactive Parameter Controls for correlation strength, noise, and sample size
• 🧮 Manually Implemented Pearson Correlation Calculation
• 🏹 Real-time Scatter Plots with Regression Lines
• 📊 Statistical Metrics: Pearson’s r, $r^2$ (coefficient of determination), and p-value
• 🎨 Seaborn & Matplotlib Styling for Clean Data Visualization

🚀 Installation

1️⃣ Clone the repository:

git clone https://github.com/noluyorAbi/Bravais-Pearson-Interactive.git
cd Bravais-Pearson-Interactive

2️⃣ Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate     # Windows

3️⃣ Install dependencies:

pip install -r requirements.txt

🎯 Usage

Run the Streamlit app:

streamlit run interactive.py

🖥️ Project Structure

.
├── examples.py          # Example scripts for correlation calculations
├── interactive.py       # Main Streamlit application
└── requirements.txt     # Dependencies for the project

📚 Pearson Correlation Formula

The Pearson correlation coefficient (r) is a measure of the linear relationship between two variables:

$$r = \frac{n \sum xy - (\sum x)(\sum y)}{\sqrt{(n \sum x^2 - (\sum x)^2),(n \sum y^2 - (\sum y)^2)}}$$

Interpretation of r:

• $r = 1$: Perfect positive correlation
• $r = -1$: Perfect negative correlation
• $r = 0$: No correlation

🛠 Dependencies

• numpy
• matplotlib
• scipy
• seaborn
• streamlit

Install all dependencies via:

pip install -r requirements.txt

🤝 Contributing

If you’d like to contribute, feel free to fork the repository and submit a pull request! 🚀