Update pages/Life_cycle_of_ML.py

pages/Life_cycle_of_ML.py

@@ -7,63 +7,63 @@ if "step" not in st.session_state:
 # Define ML lifecycle steps with detailed descriptions
 ml_lifecycle = [
     {
-        "title": "1️⃣ **Problem Statement**",
+        "title": "📋 **Problem Statement**",
         "description": """
         **Info**:
         Understand the challenge at hand, establish clear objectives, and set criteria for success.
         """
     },
     {
-        "title": "2️⃣ **Data Collection**",
+        "title": "🧑‍💻 **Data Collection**",
         "description": """
         **Info**: Gather relevant data to train the model, utilizing sources such as surveys, web scraping, and APIs.
         
         """
     },
      {
-        "title": "3️⃣**Simple EDA**",
+        "title": "🔍**Simple EDA**",
         "description": """
         **Info**:  Perform a preliminary analysis to examine the dataset’s key features.
         """
     },
     {
-        "title": "4️⃣ **Data Preprocessing**",
+        "title": "🧹 **Data Preprocessing**",
         "description": """
         **Info**: Clean the data to make sure it is in an appropriate format for further analysis.
         """
     },
     {
-        "title": "5️⃣ **EDA**",
+        "title": "🔎 **EDA**",
         "description": """
         **Info**:Conduct deeper analysis to extract valuable insights and uncover patterns within the data. 
         """
     },
     {
-        "title": "6️⃣ **Feature Engineering**",
+        "title": "🔧 **Feature Engineering**",
         "description": """
         **Info**: Develop new features or refine existing ones to enhance the model’s performance.
         """
     },
     {
-        "title": "7️⃣ **Training**",
+        "title": "🛠️ **Training**",
         "description": """
         **Info**:Train machine learning models using the preprocessed data.
         """
     },
     {
-        "title": "8️⃣ **Testing**",
+        "title": "🧪 **Testing**",
         "description": """
         **Info**:Assess the model’s performance using a separate test dataset to determine its effectiveness.
         """
     },
     {
-        "title": "9️⃣ **Deploying**",
+        "title": "🚀 **Deploying**",
         "description": """
         **Info**:Deploy the trained model into a production environment for real-world use.
         """
     },
     {
-        "title": "🔟 **Monitoring**",
+        "title": "📊 **Monitoring**",
         "description": """
         **Info**:Continuously track the model’s performance in production to ensure it remains effective over time
         """