Upload 2 files

app.py

@@ -11,8 +11,7 @@ from eda import (average_sales_by_region, average_sales_and_profit_over_time,
                  segment_vs_region_distribution, sales_vs_profit_across_segments,
                  category_composition_for_profit_and_sales)
 
-# Load the model for predictions
-model = joblib.load('best_model.pkl')
+from prediction import make_prediction
 
 # Load the dataset for EDA
 @st.cache_data
@@ -68,8 +67,9 @@ elif selection == "Make a Prediction":
             'Sales', 'Discount', 'Quantity', 'Sub-Category'
         ])
         
-        # Preprocess and predict (You'll need to adjust this part based on how your model expects input)
-        # For example, you might need to transform 'input_features' to match the expected input format of your model
-        predicted_profit = model.predict(input_features)  # Adjust this line as necessary
+
+
+        predicted_profit = make_prediction(input_features)  # Adjust this line as necessary
         
+
         st.write(f'Predicted Profit: {predicted_profit:.2f}')

prediction.py

@@ -52,7 +52,6 @@ model = joblib.load('best_model.pkl')
 preprocessor = joblib.load('preprocessor.pkl')
 
 def make_prediction(input_features):
-    encoded_features = preprocessor.transform(input_features)
-    processed_features = pipeline.transform(encoded_features)
+    processed_features = pipeline.transform(input_features)
     prediction = model.predict(processed_features)
     return prediction[0]