Update pages/Life_cycle_of_ML.py

pages/Life_cycle_of_ML.py

@@ -88,30 +88,3 @@ The life cycle of Machine Learning (ML) involves several stages, from defining t
 
 # Render HTML content in Streamlit
 st.markdown(html_content, unsafe_allow_html=True)
-
-# Display the diagram
-st.markdown(html_content, unsafe_allow_html=True)
-
-# Add buttons for interaction
-st.subheader("Interactive 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.")