<filename>Decision-Trees:-Loan-Defaulters/code.py<gh_stars>0
# --------------
#Importing header files

import pandas as pd
from sklearn.model_selection import train_test_split

# Code starts here
data = pd.read_csv(path)
X = data.drop(['customer.id','paid.back.loan'], axis=1)
y = data['paid.back.loan']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)



# Code ends here


# --------------
#Importing header files
import matplotlib.pyplot as plt

# Code starts here
fully_paid = y_train.value_counts()
fully_paid.plot(kind='bar')

# Code ends here


# --------------
#Importing header files
import numpy as np
from sklearn.preprocessing import LabelEncoder


# Code starts here
X_train['int.rate']=(X_train['int.rate'].str.replace('%','').astype('float'))/100
X_test['int.rate']=(X_test['int.rate'].str.replace('%','').astype('float'))/100
num_df = X_train.select_dtypes(include=[np.number])
print(num_df)
cat_df = X_train.select_dtypes(exclude=[np.number])
print(cat_df)


# Code ends here


# --------------
#Importing header files
import seaborn as sns


# Code starts here
cols = num_df.columns
fig ,axes = plt.subplots(nrows = 9 , ncols = 1, figsize=(5,15))

for i in range(0,9):
    sns.boxplot(x=y_train, y=num_df[cols[i]], ax=axes[i])


# Code ends here


# --------------
# Code starts here
cols = cat_df.columns
fig ,axes = plt.subplots(nrows = 2 , ncols = 2, figsize=(5,15))
for i in range(2,2):
    sns.countplot(x=X_train[cols[i*2+j]], hue=y_train, ax=axes[i,j])

# Code ends here


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#Importing header files
from sklearn.tree import DecisionTreeClassifier

# Code starts here

for col in cat_df.columns:
    X_train[col].replace('NaN','NA',inplace=True)

    X_test[col].replace('NaN','NA',inplace=True)

    le=LabelEncoder()

    X_train[col]=le.fit_transform(X_train[col])

    X_test[col]=le.transform(X_test[col])

y_train=y_train.str.replace('No','0').replace('Yes','1').astype('int')
y_test=y_test.str.replace('No','0').replace('Yes','1').astype('int')
model = DecisionTreeClassifier(random_state=0)
model.fit(X_train, y_train)
acc = model.score(X_test, y_test)
print(acc)

# Code ends here


# --------------
#Importing header files
from sklearn.model_selection import GridSearchCV

#Parameter grid
parameter_grid = {'max_depth': np.arange(3,10), 'min_samples_leaf': range(10,50,10)}

# Code starts here
model_2 = DecisionTreeClassifier(random_state=0)
p_tree = GridSearchCV(estimator=model_2, param_grid=parameter_grid, cv=5)
p_tree.fit(X_train, y_train)
acc_2 = p_tree.score(X_test, y_test)
print(acc_2)


# Code ends here


# --------------
#Importing header files

from io import StringIO
from sklearn.tree import export_graphviz
from sklearn import tree
from sklearn import metrics
from IPython.display import Image
import pydotplus

# Code starts here
dot_data = export_graphviz(decision_tree=p_tree.best_estimator_, out_file=None, feature_names=X.columns, filled = True, class_names=['loan_paid_back_yes','loan_paid_back_no'])
graph_big = pydotplus.graph_from_dot_data(dot_data)


# show graph - do not delete/modify the code below this line
img_path = user_data_dir+'/file.png'
graph_big.write_png(img_path)

plt.figure(figsize=(20,15))
plt.imshow(plt.imread(img_path))
plt.axis('off')
plt.show() 

# Code ends here


