Update pages/Life_cycle_of_ML.py

pages/Life_cycle_of_ML.py

@@ -84,6 +84,8 @@ The life cycle of Machine Learning (ML) involves several stages, from defining t
     </marker>
   </defs>
 </svg>
+st.markdown(html_content, unsafe_allow_html=True)
+
 # Initialize session state for tracking the current lifecycle step
 if "step" not in st.session_state:
     st.session_state.step = 0
@@ -93,19 +95,19 @@ ml_lifecycle = [
     {
         "title": "1️⃣ **Problem Statement**",
         "description": """
-        ****:Understanding the problem and setting objectives for the ML model.
+        **Goal**:
         """
     },
     {
         "title": "2️⃣ **Data Collection**",
         "description": """
-        ****:Gathering relevant data for model training.
+        **Goal**: 
         """
     },
     {
         "title": "3️⃣ **Data Preparation**",
         "description": """
-        ****: Initial analysis to understand the dataset's basic properties.  
+        **Goal**: Clean and preprocess the data to make it usable.  
         - Steps:  
             - Handle missing values and outliers.  
             - Normalize or scale numerical features.  
@@ -117,62 +119,68 @@ ml_lifecycle = [
     {
         "title": "4️⃣ **Feature Engineering**",
         "description": """
-        ****:Select or create the most relevant features for the model.  
+        **Goal**: Select or create the most relevant features for the model.  
         - Techniques:  
             - Feature Selection: Choose the most important columns (e.g., using correlation).  
             - Feature Creation: Combine or transform existing features.  
+        - Example: Extracting 'time spent on website' as a feature from raw session logs.
         """
     },
     {
         "title": "5️⃣ **Model Selection**",
         "description": """
-        ****:Choose the right ML algorithm for your problem.  
+        **Goal**: Choose the right ML algorithm for your problem.  
         - Factors to consider:
             - Problem type: Classification, Regression, Clustering, etc.  
             - Data size and structure.  
+        - Example: Using Logistic Regression for binary classification (e.g., spam detection).
         """
     },
     {
         "title": "6️⃣ **Training**",
         "description": """
-        ****: Train the ML model using training data.  
+        **Goal**: Train the ML model using training data.  
         - Process:  
             - Split data into training and validation sets.  
             - Use the training data to fit the model.  
+        - Example: Training a Random Forest on customer purchase data.
         """
     },
     {
         "title": "7️⃣ **Evaluation**",
         "description": """
-        ****: Assess the model's performance using metrics.  
-        - Metrics:
+        **Goal**: Assess the model's performance using metrics.  
+        - Common Metrics:
             - Classification: Accuracy, Precision, Recall, F1-Score.  
             - Regression: Mean Squared Error (MSE), R² Score.  
+        - Example: Evaluating a churn prediction model using accuracy on the test set.
         """
     },
     {
         "title": "8️⃣ **Deployment**",
         "description": """
-        ****: Integrate the trained model into a production environment.  
+        **Goal**: Integrate the trained model into a production environment.  
         - Steps:
             - Create an API for model predictions.  
             - Monitor performance on real-world data.  
+        - Example: Deploying a sentiment analysis model as a REST API.
         """
     },
     {
         "title": "9️⃣ **Monitoring & Maintenance**",
         "description": """
-        ****:Ensure the model continues to perform well over time.  
-        - Monitor:
+        **Goal**: Ensure the model continues to perform well over time.  
+        - Monitor for:
             - Data drift: Changes in data distribution.  
-            - Model decay: Performance deterioration.
+            - Model decay: Performance deterioration.  
+        - Example: Regularly retraining a sales forecasting model with new data.
         """
     },
 ]
 
 # Display title and description
 st.title("📊 Machine Learning Life Cycle")
-st.write("Explore the detailed view of life cycle of a Machine Learning project by clicking  the steps below:")
+st.write("Explore the detailed life cycle of a Machine Learning project by clicking through the steps below:")
 
 # Buttons for navigation
 col1, col2, col3 = st.columns([1, 1, 1])
@@ -190,8 +198,4 @@ st.markdown(current_step['description'])
 
 # Reset button
 if st.button("🔄 Restart"):
-    st.session_state.step = 0
-"""
-
-# Render HTML content in Streamlit
-st.markdown(html_content, unsafe_allow_html=True)
+    st.session_state.step = 0