[USER (data scientist)]:Great, now that we have the filtered dataset, let's create a scoring system that takes into account the nutritional factors (calories, total fat, saturated fat, trans fat, cholesterol, sodium, sugar, and protein), with more weight given to calories, total fat, saturated fat, and sodium. Please provide a scoring system formula.

[YOU (AI assistant)]: One of the widely used nutrient profiling systems in the US is the Nutrient Rich Foods (NRF) Index. The NRF Index is based on the concept of nutrient density, which is the ratio of beneficial nutrients to the energy content of a food item. The NRF Index takes into account both beneficial nutrients (protein, fiber, vitamins, and minerals) and nutrients to limit (saturated fat, added sugar, and sodium). 
Here's the formula for the NRF Index: NRF Index = (Beneficial Nutrients Score - Nutrients to Limit Score) 
 
Our dataset does not include all the beneficial nutrients considered in the NRF Index, but we can create a modified version using the available data:Comprehensive Modified NRF Index = (Protein + Fiber + Vit_A + Vit_C + Calcium) - (Sat_Fat + Trans_Fat + Sugar + Sodium + Total_Carb) 
To calculate the Comprehensive Modified NRF Index using the column names, you can follow these steps: 
Normalize the nutrient values by dividing them by the recommended daily intake (RDI) for each nutrient. 
Multiply the normalized values by 100 to get the percentage of RDI. 
Sum the percentage of RDI for beneficial nutrients and nutrients to limit separately. 
Subtract the sum of nutrients to limit from the sum of beneficial nutrients.

Here's the code to calculate the Comprehensive Modified NRF Index using the column names: 

'''
 
import pandas as pd   
import pickle
   
# Assuming the dataset is loaded as a DataFrame named 'fastfood'   
   
# Recommended Daily Intake (RDI) values   
rdi_protein = 50   
rdi_fiber = 25   
rdi_vit_a = 5000   
rdi_vit_c = 60   
rdi_calcium = 1000   
rdi_sat_fat = 20   
rdi_trans_fat = 2   
rdi_sugar = 50   
rdi_sodium = 2400   
rdi_total_carb = 300   
   
# Calculate the percentage of RDI for each nutrient   
fastfood['protein_score'] = (fastfood['protein'] / rdi_protein) * 100   
fastfood['fiber_score'] = (fastfood['fiber'] / rdi_fiber) * 100   
fastfood['vit_a_score'] = (fastfood['vit_a'] / rdi_vit_a) * 100   
fastfood['vit_c_score'] = (fastfood['vit_c'] / rdi_vit_c) * 100   
fastfood['calcium_score'] = (fastfood['calcium'] / rdi_calcium) * 100   
fastfood['sat_fat_score'] = (fastfood['sat_fat'] / rdi_sat_fat) * 100   
fastfood['trans_fat_score'] = (fastfood['trans_fat'] / rdi_trans_fat) * 100   
fastfood['sugar_score'] = (fastfood['sugar'] / rdi_sugar) * 100   
fastfood['sodium_score'] = (fastfood['sodium'] / rdi_sodium) * 100   
fastfood['total_carb_score'] = (fastfood['total_carb'] / rdi_total_carb) * 100   
   
# Calculate the Comprehensive Modified NRF Index   
fastfood['comprehensive_modified_nrf_index'] = (fastfood['protein_score'] + fastfood['fiber_score'] + fastfood['vit_a_score'] + fastfood['vit_c_score'] + fastfood['calcium_score']) - (fastfood['sat_fat_score'] + fastfood['trans_fat_score'] + fastfood['sugar_score'] + fastfood['sodium_score'] + fastfood['total_carb_score']) 

print(fastfood)
pickle.dump(fastfood,open("./ref_result/fastfood.pkl","wb"))

'''

[USER (data scientist)]: shouldn't you consider normalizing data in each column?


[YOU (AI assistant)]: Yes, you are right, you can use min-max to normalize your data at first.
''' 
import pandas as pd   
import pickle
   
# Assuming the dataset is loaded as a DataFrame named 'fastfood'   
   
# YOUR SOLUTION:
${pred_code}  

print(fastfood)
pickle.dump(fastfood,open("./pred_result/fastfood.pkl","wb"))

'''