I do have product id dataframe. I would like to find the best product by scoring each product. For each variable the more the value the better the product score except returns which means more returns less score.Also I need to assign different weight to score for the variables Shipped revenue and returns that maybe increased by 20 percent of their importance.
A scoring function can look like this Score=ShippedUnits+1.2*ShippedRevenue+OrderedUnits-1.2Returns+View+Stock where 0<=Score<=100
Please help. Thank you.
df_product=pd.DataFrame({'ProductId':['1','2','3','4','5','6','7','8','9','10'],'ShippedUnits':
[6,8,0,4,27,3,4,14,158,96],'ShippedRevenue':[268,1705,1300,950,1700,33380,500,2200,21000,24565]
,'OrderedUnits':[23,78,95,52,60,76,68,92,34,76],'Returns':[0,0,6,0,2,5,6,5,2,13],'View':
[0,655,11,378,920,12100,75,1394,12368,14356],'Stock':[24,43,65,27,87,98,798,78,99,231]
})
df_product=pd.DataFrame({'ProductId':['1','2','3','4','5','6','7','8','9','10'],'ShippedUnits':
[6,8,0,4,27,3,4,14,158,96],'ShippedRevenue':[268,1705,1300,950,1700,33380,500,2200,21000,24565]
,'OrderedUnits':[23,78,95,52,60,76,68,92,34,76],'Returns':[0,0,6,0,2,5,6,5,2,13],'View':
[0,655,11,378,920,12100,75,1394,12368,14356],'Stock':[24,43,65,27,87,98,798,78,99,231]
})
df_product['score'] = df_product['ShippedUnits'] +1.2*df_product['ShippedRevenue']+df_product['OrderedUnits']-1.2*df_product['Returns']+df_product['View']+df_product['Stock']
df_product['score']=(df_product['score']-df_product['score'].min())/(df_product['score'].max()-df_product['score'].min())*100
df_product