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app.py

@@ -1,6 +1,7 @@
 import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
+import numpy as np
 
 # Initialize Flask app with a name
 app = Flask("SuperKart sales prediction app backend")
@@ -15,7 +16,7 @@ def home():
 
 # Define an endpoint to predict sales of the single product in a store
 @app.post('/v1/sales')
-def predict_churn():
+def predict_sales():
     # Get JSON data from the request
     store_data = request.get_json()
 
@@ -50,7 +51,7 @@ def predict_churn():
     );
 
 
-    # Make a churn prediction using the trained model
+    # Make a Sales prediction using the trained model
     prediction = model.predict(encoded_new_data).tolist()[0]
 
     #Calculate the actual price