Work on optimizing the model

WebServer/AIPython/ai-predictor.py

@@ -20,7 +20,7 @@ def Predict():
         df = features.CleanDF(df)
 
     # Drop our predictor
-    df.drop('Target_Close_Tomorrow', axis=1, inplace=True)
+    df.drop('Target_Close', axis=1, inplace=True)
 
     # Lazy load this so it doesnt interfere with yfinance
     from keras.models import load_model
@@ -37,6 +37,6 @@ def Predict():
     # 'predictions' will be a 2D array, flatten it if you want a simple list
     flat_predictions = predictions.flatten().tolist()
 
-    print(f"Predicted Target_Close_Tomorrow: {flat_predictions}")
+    print(f"Predicted Target_Close: {flat_predictions}")
 
     return json.dumps(flat_predictions)