import streamlit as st # Render the life cycle process with interactive buttons st.title("Machine Learning Life Cycle") # HTML content inside a string for correct SVG rendering html_content = """ Problem Statement Data Collection Simple EDA Data Preprocessing EDA Feature Engineering Training Testing Deploying Monitoring """ # Render the SVG st.markdown(html_content, unsafe_allow_html=True) # Interactive buttons for stages st.subheader("Stages in ML Life Cycle") if st.button("Problem Statement"): st.write("Understanding the problem and setting objectives for the ML model.") if st.button("Data Collection"): st.write("Gathering relevant data for model training.") if st.button("Simple EDA"): st.write("Initial analysis to understand the dataset's basic properties.") if st.button("Data Preprocessing"): st.write("Cleaning the data to ensure it's in a usable format.") if st.button("EDA"): st.write("Deeper analysis to gain insights and find patterns in the data.") if st.button("Feature Engineering"): st.write("Creating new features or modifying existing ones to improve model performance.") if st.button("Training"): st.write("Training machine learning models using the processed data.") if st.button("Testing"): st.write("Evaluating the trained model using a test set to assess its performance.") if st.button("Deploying"): st.write("Deploying the model to a production environment.") if st.button("Monitoring"): st.write("Continuously monitoring the model's performance in the production environment.")