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')