A Machine Learning Web App using Streamlit and Python
In this project we attempt to create an interactive web application using Streamlit and Python. Here we train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn on the mushrooms.csv data set from UCI Machine Learning repository and plot evaluation metrics for binary classification algorithms.
If the data set is downloaded from Kaggle (as in the README), open the CSV file and rename the first column from 'class' to 'type' before you begin with the code. The uploaded file in the repository has been updated already. This has been done to avoid any errors since class is a reserved keyword in python.
Install Streamlit using pip as:
pip install streamlit
Run the file app.py using streamlit:
streamlit run app.py
You can now view your Streamlit app in your browser.
Streamlit changes the web application dynamically as we modify and save the app.py file. Thus, we need not run the file each time we modify.