Update pages/Life_cycle_of_ML.py

pages/Life_cycle_of_ML.py

@@ -93,19 +93,19 @@ ml_lifecycle = [
     {
         "title": "1️⃣ **Problem Statement**",
         "description": """
-        **Understanding the problem and setting objectives for the ML model.
+        ****:Understanding the problem and setting objectives for the ML model.
         """
     },
     {
         "title": "2️⃣ **Data Collection**",
         "description": """
-        **Gathering relevant data for model training.
+        ****:Gathering relevant data for model training.
         """
     },
     {
         "title": "3️⃣ **Data Preparation**",
         "description": """
-        ** Initial analysis to understand the dataset's basic properties.  
+        ****: Initial analysis to understand the dataset's basic properties.  
         - Steps:  
             - Handle missing values and outliers.  
             - Normalize or scale numerical features.  
@@ -117,7 +117,7 @@ ml_lifecycle = [
     {
         "title": "4️⃣ **Feature Engineering**",
         "description": """
-        **Select or create the most relevant features for the model.  
+        ****: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.  
@@ -126,7 +126,7 @@ ml_lifecycle = [
     {
         "title": "5️⃣ **Model Selection**",
         "description": """
-        ** Choose the right ML algorithm for your problem.  
+        ****:Choose the right ML algorithm for your problem.  
         - Factors to consider:
             - Problem type: Classification, Regression, Clustering, etc.  
             - Data size and structure.  
@@ -135,7 +135,7 @@ ml_lifecycle = [
     {
         "title": "6️⃣ **Training**",
         "description": """
-        ** Train the ML model using training data.  
+        ****: Train the ML model using training data.  
         - Process:  
             - Split data into training and validation sets.  
             - Use the training data to fit the model.  
@@ -144,7 +144,7 @@ ml_lifecycle = [
     {
         "title": "7️⃣ **Evaluation**",
         "description": """
-        ** Assess the model's performance using metrics.  
+        ****: Assess the model's performance using metrics.  
         - Metrics:
             - Classification: Accuracy, Precision, Recall, F1-Score.  
             - Regression: Mean Squared Error (MSE), R² Score.  
@@ -153,7 +153,7 @@ ml_lifecycle = [
     {
         "title": "8️⃣ **Deployment**",
         "description": """
-        ** Integrate the trained model into a production environment.  
+        ****: Integrate the trained model into a production environment.  
         - Steps:
             - Create an API for model predictions.  
             - Monitor performance on real-world data.  
@@ -162,7 +162,7 @@ ml_lifecycle = [
     {
         "title": "9️⃣ **Monitoring & Maintenance**",
         "description": """
-        **Ensure the model continues to perform well over time.  
+        ****:Ensure the model continues to perform well over time.  
         - Monitor:
             - Data drift: Changes in data distribution.  
             - Model decay: Performance deterioration.