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23_sensitivity_analysis.py
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23_sensitivity_analysis.py
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import matplotlib.pyplot as plt
import pandas as pd
import os.path
import os.path as osp
import pickle
from sklearn.externals import joblib
import glob
from sklearn.model_selection import train_test_split
def Sensitivity_Analysis(df,txt):
mark_list = ['d']
model = joblib.load("./salepriceprediction.pkl")
d = {}
min_variable = 334
max_variable = 5642
step = 500
for i in range(0,len(df)):
df2=pd.DataFrame(df.iloc[i])
actual=df.iloc[i]['GrLivArea']
df2=df2.T
df3=df2.copy()
var_list=[]
curval = actual.copy()
while(curval <= max_variable):
var_list.append(curval)
curval = curval+step
curval = actual
while(curval >= min_variable):
var_list.append(curval)
curval = curval-step
var_list.sort()
var_list = pd.Series(var_list).drop_duplicates().reset_index(drop=True)
num_point = len(var_list)
for j in range(1,num_point):
df2=df2.append(df3)
df2.reset_index(drop=True,inplace=True)
df2['GrLivArea']=var_list
d[str(i)]=df2
del df2
for key,value in d.items():
df_sense=d[key]
df_sense=pd.DataFrame(df_sense)
predicted_Sense=model.predict(df_sense)
d222={'Input':list(df_sense['GrLivArea']),'Output':predicted_Sense}
df_sensitivity=pd.DataFrame(data=d222)
df_sensitivity.set_index('Input',inplace=True)
df_sensitivity.sort_index(inplace=True)
plt.plot(list(df_sensitivity.index),list(df_sensitivity['Output']))
plt.xlabel('GrLivArea')
plt.ylabel('SalePrice')
del predicted_Sense
del df_sense
plt.grid()
plt.show()
plt.close()
df = pd.read_csv("./23_sens_analysis.csv")
Sensitivity_Analysis(df,'1')