stages/model_training.py

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()