Manufacturing Defects Synthetic DataΒΆ

In this notebook we generate some data that will represent measurements of defects in a manufacturing setting.

import numpy as np
import pandas as pd
#generate synthetic data
Factors = []
Outcome = []
numpoints = 2000
for workday, time_per_task  in zip(np.random.normal(loc=.3, scale=.05, size=numpoints), np.random.normal(loc=.05, scale=.01, size=numpoints)):
    Factors.append([workday, time_per_task])
    Outcome.append( 0*workday**2/(time_per_task**2) + 1/time_per_task**1.5 + 1000*workday**1.5)
data = pd.DataFrame(Factors, columns=['Workday', 'Time per Task'])
data['Defect Rate'] = Outcome
data['Defect Rate']/= data['Defect Rate'].max()*10
data['Defect Rate'] += np.random.normal(scale=.003, size=len(data['Defect Rate']))
data.head()
Workday Time per Task Defect Rate
0 0.303114 0.060810 0.023022
1 0.263133 0.052325 0.023017
2 0.230397 0.065387 0.015868
3 0.265632 0.044866 0.032806
4 0.298651 0.038648 0.035234
data.to_csv('Manufacturing_Defects_Synthetic_Data.csv')