Update stages/model_training.py

stages/model_training.py

@@ -1,5 +1,29 @@
-import streamlit as st
-
-def main():
-    st.title("Model Training")
-main()
+import streamlit as st
+
+def main():
+    st.title("Step 7: Model Selection and Training:robot_face:")
+
+    st.markdown("""
+
+        After preparing your data, it's time to **choose the right model** and **train** it. Here's the essential process:
+
+        :nerd_face:**Model Selection**:  
+        - Pick a model that suits your data (classification, regression, etc.).  
+        - Start simple (e.g., Logistic Regression) and explore complex ones (e.g., Random Forest, XGBoost).
+
+        :nut_and_bolt:**Training the Model**:  
+        - Train the model on your training data.  
+        - Use **cross-validation** to ensure it generalizes well.  
+        - Fine-tune hyperparameters for better accuracy.
+
+        **Why it matters?**  
+        The right model and proper training are crucial for accurate predictions. The goal is to make sure the model captures patterns from your data.
+
+        **Common Models**:
+        - **Linear Models**: Logistic Regression, Linear Regression
+        - **Tree-based Models**: Decision Trees, Random Forest, XGBoost
+        - **Deep Learning**: Neural Networks (for complex problems)
+    """)
+
+    st.divider()
+main()