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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`

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

Output: ```df['error'] = ((df.PredValue - df.ActualValue) ** 2)

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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