Update app.py

app.py

@@ -118,19 +118,30 @@ if st.button("Predict"):
 
 
 st.write("""Features Used:
+
 The following are the input Varibles from the End user which needs to be enter, and then the application will predict whether 
 the particular Product has the chances of having Backorder or not.
+
 1: Product: Name of the product.
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 2: Lead_time: The average number of days taken to deliver the product after placing the order.
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 3: Demand: The number of units of the product demanded during a specific time period.
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 4: In_stock: The number of units of the product currently available in the inventory.
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 5: Price: The selling price of the product.
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 6: Advertising: The amount spent on advertising the product during a specific time period.
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 7: Weather: Weather condition during a specific time period that could affect the demand for the product.
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 In a retail scenario, weather could be measured in terms of temperature in Fahrenheit or Celsius,
 and since temperature affects the demand for products such as clothing, food, and beverages. It is also one of the important factor
 to be considered for causal analysis of Supply chain management.
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 Target Column/Prediction: 
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 Backordered: A binary variable indicating whether the product was backordered (1) or not (0) during a specific 
              time period. This is the target variable that we want to predict""")