This Streamlit application allows users to build, train, validate, and use a Random Forest model to predict one spectral reflectance band based on others from a given dataset. The interface is user-friendly, providing easy selections for input and output variables, adjusting test data rate, and visualizing model performance and feature importance.

Ensure you have the following libraries installed:

• streamlit
• pandas
• matplotlib
• seaborn
• scikit-learn
• numpy

Install the required libraries using:

pip install streamlit pandas matplotlib seaborn scikit-learn numpy

1. Run the Streamlit application:

   streamlit run streamlit_app.py

• Variable Selection: Select one output variable (target) and at least one input variable (features).
• Test Data Rate: Adjust the rate of test data using a slider.

1. Title and Description: Provides an overview of the application.
2. Dataset Information: Details about the dataset used.
3. Model Information: Displays selected output variable, input variables, training data shape, and test data shape.
4. Scatter Plot: Scatter plot comparing predicted vs. observed values for the test data, showing R-squared and RMSE values.
5. Feature Importance Chart: Bar chart showing the importance of each feature in the model.
6. Model Prediction:
   • User input fields for entering values of input variables.
   • Displays the predicted value of the output variable.
   • Bar chart showing the predicted output value.

This project is licensed under the MIT License. See the LICENSE file for details.