Update pages/3_Life Cycle Of ML Project.py

pages/3_Life Cycle Of ML Project.py

@@ -33,15 +33,24 @@ st.markdown("Click on a stage to learn more about it.")
 if st.button("Overview"):
     display_content("Overview")
 
-# Buttons for each stage with colors and emojis
-st.button("🌟 Problem Definition", on_click=display_content, args=("Problem Definition",), key="problem_definition", help="Identify the problem you want to solve and set clear objectives and success criteria.", style="background-color: #FF6347; color: white;")
-st.button("📊 Data Collection", on_click=display_content, args=("Data Collection",), key="data_collection", help="Gather relevant data from various sources and store it in a structured format.", style="background-color: #4682B4; color: white;")
-st.button("🛠️ Data Preparation", on_click=display_content, args=("Data Preparation",), key="data_preparation", help="Clean, transform, and engineer features from the data to prepare it for modeling.", style="background-color: #32CD32; color: white;")
-st.button("🔍 Exploratory Data Analysis (EDA)", on_click=display_content, args=("Exploratory Data Analysis (EDA)",), key="eda", help="Visualize and analyze the data to understand its distributions and relationships.", style="background-color: #FFD700; color: black;")
-st.button("🤖 Model Selection", on_click=display_content, args=("Model Selection",), key="model_selection", help="Choose appropriate machine learning algorithms and develop a baseline model.", style="background-color: #FF4500; color: white;")
-st.button("🏋️ Model Training", on_click=display_content, args=("Model Training",), key="model_training", help="Train the model using the training data and optimize its parameters.", style="background-color: #1E90FF; color: white;")
-st.button("📈 Model Evaluation", on_click=display_content, args=("Model Evaluation",), key="model_evaluation", help="Assess the model's performance using various metrics and cross-validation techniques.", style="background-color: #8A2BE2; color: white;")
-st.button("🚀 Model Deployment", on_click=display_content, args=("Model Deployment",), key="model_deployment", help="Integrate the trained model into a production environment and monitor its performance.", style="background-color: #FF1493; color: white;")
-st.button("🔧 Model Maintenance", on_click=display_content, args=("Model Maintenance",), key="model_maintenance", help="Periodically retrain the model with new data and update features as needed.", style="background-color: #00CED1; color: white;")
-st.button("📝 Documentation and Reporting", on_click=display_content, args=("Documentation and Reporting",), key="documentation_reporting", help="Document the entire project and share the results and insights with stakeholders.", style="background-color: #FF69B4; color: white;")
-
+# Buttons for each stage with emojis
+if st.button("🌟 Problem Definition"):
+    display_content("Problem Definition")
+if st.button("📊 Data Collection"):
+    display_content("Data Collection")
+if st.button("🛠️ Data Preparation"):
+    display_content("Data Preparation")
+if st.button("🔍 Exploratory Data Analysis (EDA)"):
+    display_content("Exploratory Data Analysis (EDA)")
+if st.button("🤖 Model Selection"):
+    display_content("Model Selection")
+if st.button("🏋️ Model Training"):
+    display_content("Model Training")
+if st.button("📈 Model Evaluation"):
+    display_content("Model Evaluation")
+if st.button("🚀 Model Deployment"):
+    display_content("Model Deployment")
+if st.button("🔧 Model Maintenance"):
+    display_content("Model Maintenance")
+if st.button("📝 Documentation and Reporting"):
+    display_content("Documentation and Reporting")