Update pages/Life_cycle_of_ML.py

pages/Life_cycle_of_ML.py

@@ -9,89 +9,63 @@ ml_lifecycle = [
     {
         "title": "1️⃣ **Problem Statement**",
         "description": """
-        **Goal**:
-        Understand the problem you are trying to solve, define the objectives, and determine the success criteria for the project.
+        **Info**:
+        Understand the challenge at hand, establish clear objectives, and set criteria for success.
         """
     },
     {
         "title": "2️⃣ **Data Collection**",
         "description": """
-        **Goal**: 
-        Collect relevant data to train the model.
-        - Sources: Surveys, web scraping, APIs, etc.
-        - Example: Collecting data from customer interactions on a website.
+        **Info**: Gather relevant data to train the model, utilizing sources such as surveys, web scraping, and APIs.
+        
+        """
+    },
+     {
+        "title": "3️⃣**Simple EDA**",
+        "description": """
+        **Info**:  Perform a preliminary analysis to examine the dataset’s key features.
         """
     },
     {
-        "title": "3️⃣ **Data Preparation**",
+        "title": "4️⃣ **Data Preprocessing**",
         "description": """
-        **Goal**: Clean and preprocess the data to make it usable.  
-        - Steps:  
-            - Handle missing values and outliers.  
-            - Normalize or scale numerical features.  
-            - Encode categorical data (e.g., one-hot encoding).  
-        - Tools: Python libraries like Pandas, NumPy, or OpenCV for images.  
-        - Example: Removing null values from a customer dataset.
+        **Info**: Clean the data to make sure it is in an appropriate format for further analysis.
         """
     },
     {
-        "title": "4️⃣ **Feature Engineering**",
+        "title": "5️⃣ **EDA**",
         "description": """
-        **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.
+        **Info**:Conduct deeper analysis to extract valuable insights and uncover patterns within the data. 
         """
     },
     {
-        "title": "5️⃣ **Model Selection**",
+        "title": "6️⃣ **Feature Engineering**",
         "description": """
-        **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).
+        **Info**: Develop new features or refine existing ones to enhance the model’s performance.
         """
     },
     {
-        "title": "6️⃣ **Training**",
+        "title": "7️⃣ **Training**",
         "description": """
-        **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.
+        **Info**:Train machine learning models using the preprocessed data.
         """
     },
     {
-        "title": "7️⃣ **Evaluation**",
+        "title": "8️⃣ **Testing**",
         "description": """
-        **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.
+        **Info**:Assess the model’s performance using a separate test dataset to determine its effectiveness.
         """
     },
     {
-        "title": "8️⃣ **Deployment**",
+        "title": "9️⃣ **Deploying**",
         "description": """
-        **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.
+        **Info**:Deploy the trained model into a production environment for real-world use.
         """
     },
     {
-        "title": "9️⃣ **Monitoring & Maintenance**",
+        "title": "🔟 **Monitoring**",
         "description": """
-        **Goal**: Ensure the model continues to perform well over time.  
-        - Monitor for:
-            - Data drift: Changes in data distribution.  
-            - Model decay: Performance deterioration.  
-        - Example: Regularly retraining a sales forecasting model with new data.
+        **Info**:Continuously track the model’s performance in production to ensure it remains effective over time
         """
     },
 ]