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Predict The Daily Number of Units Sold of Different Products

DESCRIPTION

Dataset Used: PredictionsFor4April2019.csv

Problem Statement: ABC Company has made a model to predict the daily number of units sold of different products


Import Necessary Packages

import pandas as pd

Read Dataset

df = pd.read_csv("PredictionsFor4April2019.csv")
df.head()

Output:








df['error'] = ((df.PredValue - df.ActualValue) ** 2)
df.head()

Output:








RMSE for Country DE

rmse_DE = df[df.Country_code == 'DE']['error'].mean() ** .5
rmse_DE_round = round(rmse_DE,1)
rmse_DE_round

output:

10.9

RMSE for Country AT

rmse_AT = df[df.Country_code == 'AT']['error'].mean() ** .5
rmse_AT_round = round(rmse_AT,1)
rmse_AT_round

Output:

0.6

RMSE for Country PL

rmse_PL = df[df.Country_code == 'PL']['error'].mean() ** .5
rmse_PL_round = round(rmse_PL,1)
rmse_PL_round

Output:

1.3

Writing into the list

list_of_result_values = []
list_of_result_values.append(rmse_DE_round)
list_of_result_values.append(rmse_AT_round)
list_of_result_values.append(rmse_PL_round)
list_of_result_values

Output:

[10.9, 0.6, 1.3]

Save Output Into csv file

file = open('./output/output.csv','a+')
file.write("Question2 Output" +"\n")
file.write(str(list_of_result_values) +"\n")
file.close()



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