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pages/3_Training the Model.py

@@ -171,7 +171,17 @@ def step3_page():
 
     if state == "splitting":
         st.subheader("Step 3: Data Splitting")
-        st.write("Now let's split into training and testing sets. The training set is used to train the machine learning model, and the testing set is used to evaluate its performance.")
+        st.write("Now let's split the dataset into training and testing sets. The training set is used to train the machine learning model, and the testing set is used to evaluate its performance. For that, you have to select the train-test split %.")
+        train_size = st.slider("Select Train Dataset Size (%)", min_value=10, max_value=90, value=70)
+        state = "model selection"
+    else:
+        pass
+
+    if state == "model selection":
+        st.subheader("Step 3: Model Selection")
+        st.write("Different machine learning algorithms can be used for weather forecasting and the choice of model depends on the characteristics of the data and the forecasting task. But for this tutorial, you can select between a 'LightGBM' model and a 'Random Forest' model.")
+        models = ['LightGBM', 'Random Forest']
+        model_choice = st.selectbox('Choose Model', models)
     else:
         pass