This is a Streamlit web application for analyzing retail prices and training a Decision Tree Regressor model to predict prices based on various features.
- Python 3.6+
- Streamlit: Install it using
pip install streamlit - Pandas: Install it using
pip install pandas - Plotly: Install it using
pip install plotly - Scikit-learn: Install it using
pip install scikit-learn
Install the required dependencies:
pip install -r requirements.txtUsage Place your dataset named retail_price.csv in the root directory of the project.
Run the Streamlit app using the following command:
streamlit run app.pyThe app will open in your default web browser, allowing you to select visualizations and train the Decision Tree Regressor model.
Features Choose from various visualizations such as histograms, box plots, scatter plots, and more. Train and visualize the performance of a Decision Tree Regressor model. Contributing Contributions are welcome! Please feel free to fork this repository and submit pull requests.