Update pages/3Life Cycle of Machine Learning Project.py

pages/3Life Cycle of Machine Learning Project.py

@@ -120,26 +120,45 @@ st.markdown(
     unsafe_allow_html=True)
 
 
-st.markdown("##Descriptions")
+
 st.markdown("""
-1. **Problem Statement**:
-   - Define the business problem and objectives to guide the ML project.
-2. **Data Collection**:
-   - Gather and integrate relevant data from multiple sources.
-3. **Simple EDA (Exploratory Data Analysis)**:
-   - Perform quick data exploration to understand key patterns and issues.
-4. **Data Preprocessing**:
-   - Clean and prepare the data for modeling (e.g., handle missing values, normalize).
-5. **EDA**:
-   - Conduct detailed analysis to extract insights and visualize trends.
-6. **Feature Engineering**:
-   - Create, transform, or select relevant features to improve model performance.
-7. **Model Training**:
-   - Train machine learning models using suitable algorithms and techniques.
-8. **Model Testing**:
-   - Evaluate the model's performance using metrics such as accuracy or F1 score.
-9. **Model Deployment**:
-   - Integrate the trained model into a production environment for real-world use.
-10. **Monitoring**:
-    - Continuously monitor the model's performance and retrain if necessary.
-""")
+**Problem Statement**:
+   - Clearly define the business or research problem.
+   - Identify the objectives and success criteria.
+
+**Data Collection**:
+   - Collect data from reliable sources.
+   - Ensure the data is relevant and sufficient for the problem.
+
+**Simple EDA (Exploratory Data Analysis)**:
+   - Perform initial data exploration to understand basic patterns.
+   - Quickly check for missing or inconsistent data.
+
+**Data Preprocessing**:
+   - Clean the data by handling missing values and correcting errors.
+   - Transform data types and normalize or scale features as needed.
+
+**EDA**:
+   - Perform detailed analysis to uncover insights.
+   - Visualize data relationships using charts and graphs.
+
+**Feature Engineering**:
+   - Create new features that enhance model performance.
+   - Select or transform existing features to improve relevance.
+
+**Model Training**:
+   - Choose appropriate machine learning algorithms.
+   - Train models and optimize hyperparameters for best performance.
+
+**Model Testing**:
+   - Evaluate the model's performance with test data.
+   - Perform cross-validation and analyze performance metrics.
+
+**Model Deployment**:
+   - Deploy the model into a production environment.
+   - Ensure the model can handle real-time data and user requests.
+
+**Monitoring**:
+    - Continuously monitor the model's performance over time.
+    - Update and retrain the model to maintain accuracy and relevance.
+""")