Update app.py

app.py

@@ -14,12 +14,12 @@ def fetch_stock_data(symbol, start_date, end_date):
 
 # Function to create features for the model
 def create_features(data):
-    data.index = pd.to_datetime(data.index)  # Convert index to datetime
-    data['Year'] = data.index.year
-    data['Month'] = data.index.month
-    data['Day'] = data.index.day
-    data['Hour'] = data.index.hour
-    data['Minute'] = data.index.minute
+    data['Date'] = data.index
+    data['Year'] = data['Date'].dt.year
+    data['Month'] = data['Date'].dt.month
+    data['Day'] = data['Date'].dt.day
+    data['Hour'] = data['Date'].dt.hour
+    data['Minute'] = data['Date'].dt.minute
 
     return data
 
@@ -28,20 +28,37 @@ def train_model(data):
     features = ['Year', 'Month', 'Day', 'Hour', 'Minute']
     target = 'Close'
 
+    if len(data) == 0:
+        st.write("Not enough data for training.")
+        return None
+
     X = data[features]
     y = data[target]
 
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    if len(data) <= 1:
+        st.write("Not enough data for splitting.")
+        return None
+
+    try:
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        if len(X_train) == 0 or len(X_test) == 0:
+            st.write("Not enough data after splitting.")
+            return None
+
+        model = RandomForestRegressor()
+        model.fit(X_train, y_train)
 
-    model = RandomForestRegressor()
-    model.fit(X_train, y_train)
+        # Evaluate the model
+        predictions = model.predict(X_test)
+        mse = mean_squared_error(y_test, predictions)
+        st.write(f"Mean Squared Error: {mse}")
 
-    # Evaluate the model
-    predictions = model.predict(X_test)
-    mse = mean_squared_error(y_test, predictions)
-    st.write(f"Mean Squared Error: {mse}")
+        return model
 
-    return model
+    except ValueError as e:
+        st.write(f"Error during train-test split: {e}")
+        return None
 
 # Streamlit UI
 def main():
@@ -61,19 +78,20 @@ def main():
         # Train the model
         model = train_model(stock_data)
 
-        # Predict the stock price for a specific date (e.g., the last date in the dataset)
-        prediction_date = stock_data.index[-1]
-        prediction_features = [[
-            prediction_date.year,
-            prediction_date.month,
-            prediction_date.day,
-            prediction_date.hour,
-            prediction_date.minute
-        ]]
-        predicted_price = model.predict(prediction_features)[0]
-
-        st.subheader(f"Predicted Stock Price on {prediction_date} (UTC):")
-        st.write(f"${predicted_price:.2f}")
+        if model:
+            # Predict the stock price for a specific date (e.g., the last date in the dataset)
+            prediction_date = stock_data['Date'].iloc[-1]
+            prediction_features = [[
+                prediction_date.year,
+                prediction_date.month,
+                prediction_date.day,
+                prediction_date.hour,
+                prediction_date.minute
+            ]]
+            predicted_price = model.predict(prediction_features)[0]
+
+            st.subheader(f"Predicted Stock Price on {prediction_date} (UTC):")
+            st.write(f"${predicted_price:.2f}")
 
 # Run the Streamlit app
 if __name__ == '__main__':