loan.py

import pandas as pd
import numpy as np                     # For mathematical calculations
import seaborn as sns                  # For data visualization
import matplotlib.pyplot as plt        # For plotting graphs
import warnings                        # To ignore any warnings
warnings.filterwarnings("ignore")

train=pd.read_csv("training.csv")
test=pd.read_csv("test.csv")
train_original=pd.read_csv("training.csv")
test_original=pd.read_csv("test.csv")


print(train.columns)
print(train.dtypes)
print(train.shape,test.shape)

# analyse data
print(train['Loan_Status'].value_counts())
print(train['Loan_Status'].value_counts(normalize=True))
train['Loan_Status'].value_counts().plot.bar()
#plt.show()


plt.subplot(221)
train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender')
plt.subplot(222)
train['Married'].value_counts(normalize=True).plot.bar(title= 'Married')
plt.subplot(223)
train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'S-E')
plt.subplot(224)
train['Credit_History'].value_counts(normalize=True).plot.bar(title= 'C-H')
#plt.show()


plt.subplot(131)
train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6), title= 'Dependents')
plt.subplot(132)
train['Education'].value_counts(normalize=True).plot.bar(title= 'Education')
plt.subplot(133)
train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area')
#plt.show()




plt.subplot(121)
sns.distplot(train['ApplicantIncome']);
plt.subplot(122)
train['ApplicantIncome'].plot.box(figsize=(16,5))
#plt.show()

train.boxplot(column='ApplicantIncome', by = 'Education')
plt.suptitle("boxplot")
#plt.show()



plt.subplot(121)
sns.distplot(train['CoapplicantIncome']);
plt.subplot(122)
train['CoapplicantIncome'].plot.box(figsize=(16,5))
#plt.show()


plt.subplot(121)
df=train.dropna()
sns.distplot(df['LoanAmount']);
plt.subplot(122)
train['LoanAmount'].plot.box(figsize=(16,5))
#plt.show()

Gender=pd.crosstab(train['Gender'],train['Loan_Status'])



Gender=pd.crosstab(train['Gender'],train['Loan_Status'])
Married=pd.crosstab(train['Married'],train['Loan_Status'])
Dependents=pd.crosstab(train['Dependents'],train['Loan_Status'])
Education=pd.crosstab(train['Education'],train['Loan_Status'])
Self_Employed=pd.crosstab(train['Self_Employed'],train['Loan_Status'])
Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status'])
Property_Area=pd.crosstab(train['Property_Area'],train['Loan_Status'])

Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(4,4))
#plt.show()

Married.div(Married.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(4,4))
#plt.show()

Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True)
#plt.show()

Education.div(Education.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(4,4))
#plt.show()

Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(4,4))
#plt.show()

Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True, figsize=(4,4))
#plt.show()

Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True)
#plt.show()

train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()
#plt.show()
df=train.dropna()

bins=[0,2500,4000,6000,81000]
group=['Low','Average','High', 'Very high']
train['Income_bin']=pd.cut(df['ApplicantIncome'],bins,labels=group)

Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status'])
Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True,figsize=(10,4))
#plt.show()


bins=[0,1000,3000,42000]
group=['Low','Average','High']
train['Coapplicant_Income_bin']=pd.cut(df['CoapplicantIncome'],bins,labels=group)

Coapplicant_Income_bin=pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])
Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True)
#plt.show()

train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']
bins=[0,2500,4000,6000,81000]
group=['Low','Average','High', 'Very high']
train['Total_Income_bin']=pd.cut(train['Total_Income'],bins,labels=group)

Total_Income_bin=pd.crosstab(train['Total_Income_bin'],train['Loan_Status'])
Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True)
plt.xlabel('Total_Income')
plt.ylabel('Percentage')
#plt.show()


bins=[0,100,200,700]
group=['Low','Average','High']
train['LoanAmount_bin']=pd.cut(df['LoanAmount'],bins,labels=group)
LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])
LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind="bar", stacked=True)
plt.xlabel('LoanAmount')
P = plt.ylabel('Percentage')


train=train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'], axis=1)


train['Dependents'].replace('3+', 3,inplace=True)
test['Dependents'].replace('3+', 3,inplace=True)
train['Loan_Status'].replace('N', 0,inplace=True)
train['Loan_Status'].replace('Y', 1,inplace=True)


matrix = train.corr()
f, ax = plt.subplots(figsize=(9, 6))
sns.heatmap(matrix, vmax=.8, square=True, cmap="BuPu");
#plt.show()

print(train.isnull().sum())

train['Gender'].fillna(train['Gender'].mode()[0], inplace=True)
train['Married'].fillna(train['Married'].mode()[0], inplace=True)
train['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)
train['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)
train['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)

train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)
train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)

print('-------')
print(train.isnull().sum())

test['Gender'].fillna(train['Gender'].mode()[0], inplace=True)
test['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)
test['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)
test['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)
test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)
test['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)



train['LoanAmount_log'] = np.log(train['LoanAmount'])
train['LoanAmount_log'].hist(bins=20)
test['LoanAmount_log'] = np.log(test['LoanAmount'])

#plt.show()

train=train.drop('Loan_ID',axis=1)
test=test.drop('Loan_ID',axis=1)

X = train.drop('Loan_Status',1)
y = train.Loan_Status

#dummies
X=pd.get_dummies(X)
train=pd.get_dummies(train)
test=pd.get_dummies(test)


from sklearn.model_selection import train_test_split
x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size =0.3)

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

model = LogisticRegression()
model.fit(x_train, y_train)




LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=1, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)


pred_cv = model.predict(x_cv)

accuracy_score(y_cv,pred_cv)



pred_test = model.predict(test)
submission=pd.read_csv("Sample_Submission_ZAuTl8O_FK3zQHh.csv")
submission['Loan_Status']=pred_test
submission['Loan_ID']=test_original['Loan_ID']




submission['Loan_Status'].replace(0, 'N',inplace=True)
submission['Loan_Status'].replace(1, 'Y',inplace=True)

pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('logistic.csv')


from sklearn.model_selection import StratifiedKFold

i = 1
kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)
for train_index, test_index in kf.split(X, y):
    print('\n{} of kfold {}'.format(i, kf.n_splits))
    xtr, xvl = X.loc[train_index], X.loc[test_index]
    ytr, yvl = y[train_index], y[test_index]

    model = LogisticRegression(random_state=1)
    model.fit(xtr, ytr)
    pred_test = model.predict(xvl)
    score = accuracy_score(yvl, pred_test)
    print('accuracy_score', score)
    i += 1
pred_test = model.predict(test)
pred = model.predict_proba(xvl)[:, 1]



submission['Loan_Status']=pred_test
submission['Loan_ID']=test_original['Loan_ID']
submission['Loan_Status'].replace(0, 'N',inplace=True)
submission['Loan_Status'].replace(1, 'Y',inplace=True)
pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Logistic2.csv')





train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']
test['Total_Income']=test['ApplicantIncome']+test['CoapplicantIncome']

train['Total_Income_log'] = np.log(train['Total_Income'])
sns.distplot(train['Total_Income_log']);
test['Total_Income_log'] = np.log(test['Total_Income'])

train['EMI']=train['LoanAmount']/train['Loan_Amount_Term']
test['EMI']=test['LoanAmount']/test['Loan_Amount_Term']


train['Balance Income']=train['Total_Income']-(train['EMI']*1000)

test['Balance Income']=test['Total_Income']-(test['EMI']*1000)

train=train.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)
test=test.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)



# optimisation
# logistic regression

X = train.drop('Loan_Status',1)
y = train.Loan_Status




i = 1
kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)
for train_index, test_index in kf.split(X, y):
    print('\n{} of kfold {}'.format(i, kf.n_splits))
    xtr, xvl = X.loc[train_index], X.loc[test_index]
    ytr, yvl = y[train_index], y[test_index]

    model = LogisticRegression(random_state=1)
    model.fit(xtr, ytr)
    pred_test = model.predict(xvl)
    score = accuracy_score(yvl, pred_test)
    #print('accuracy_score', score)
    i += 1
pred_test = model.predict(test)
pred = model.predict_proba(xvl)[:, 1]


submission['Loan_Status']=pred_test            # filling Loan_Status with predictions
submission['Loan_ID']=test_original['Loan_ID'] # filling Loan_ID with test Loan_ID

# replacing 0 and 1 with N and Y
submission['Loan_Status'].replace(0, 'N',inplace=True)
submission['Loan_Status'].replace(1, 'Y',inplace=True)

# Converting submission file to .csv format
pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Log-new1.csv')