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

pages/3Life Cycle of Machine Learning Project.py

@@ -55,12 +55,12 @@ st.markdown("""
     }
     .step {
         position: absolute;
-        width: 180px; /* Increased size from 150px */
-        height: 70px; /* Increased size from 60px */
-        font-size: 15px; /* Slightly larger font size */
+        width: 150px; /* Increased size from 150px */
+        height: 60px; /* Increased size from 60px */
+        font-size: 13px; /* Slightly larger font size */
         color: black;
         font-weight: bold; /* Bold text */
-        border-radius: 35px;
+        border-radius: 30px;
         display: flex;
         justify-content: center;
         align-items: center;
@@ -118,3 +118,28 @@ st.markdown(
     </div>
     """,
     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.
+""")