Create CleaningUpData.py

maths-prog/MachineLearningDemystified/CleaningUpData.py

@@ -0,0 +1,36 @@
+directory = '/home/nuno/Documents/Jobs/IDInsight'
+
+import pandas as pd
+import numpy as np
+
+## Install the dataframe
+insuranceDataFrame = pd.read_csv(directory + '/insurance.csv')
+
+## Some functions for cleaning up, inspired by R's ifelse function
+
+def ifelse1(x, listOfChecks, yesLabel, noLabel):
+    if x in listOfChecks:
+        return (yesLabel)
+    else:
+        return (noLabel)
+
+def ifelse2(x,listOfChecks, listOfLabels):
+    n = len(listOfChecks)
+    for i in range(n):
+        if x == listOfChecks[i]:
+            return (listOfLabels[i])
+    return None
+
+insuranceDataFrame['sex_numeric'] =insuranceDataFrame['sex'].apply(lambda x: ifelse1(x, np.array(['male']),1,0))
+
+insuranceDataFrame['smoker_numeric'] =insuranceDataFrame['smoker'].apply(lambda x: ifelse1(x, np.array(['yes']),1,0))
+
+insuranceDataFrame['region_numeric'] =insuranceDataFrame['region'].apply(lambda x: ifelse2(x, np.unique(insuranceDataFrame['region']), np.array([0,1,2,3])))
+
+insuranceDataFrame = insuranceDataFrame.drop(["sex", "smoker", "region"],axis=1)
+
+## We save the database
+
+insuranceDataFrame.to_csv(directory +'insurance_clean_continuous.csv', index=False)
+
+