# --------------
#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')
plt.xlabel('paid.back.loan')
plt.ylabel('Count')
plt.show()
# 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'].apply(lambda x: float(x[:-1])/100)

X_test['int.rate'] = X_test['int.rate'].apply(lambda x: float(x[:-1])/100)

num_df = X_train.select_dtypes(include = ['int','float'])

cat_df = X_train.select_dtypes(include = 'object')
# Code ends here


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


# Code starts here
cols = list(num_df.columns)

fig ,axes = plt.subplots(9,1, figsize=(10,90))

for i in range(len(cols)):
    sns.boxplot(x=y_train, y=num_df[cols[i]], ax=axes[i])
plt.show()
# Code ends here


# --------------
# Code starts here
cols = list(cat_df.columns)
print(cols)

fig ,axes = plt.subplots(2,2, figsize=(20,20))

for i in range(2):
    for j in range(2):
        sns.countplot(x=X_train[cols[i*2+j]], hue=y_train, ax=axes[i,j])
plt.show()
# Code ends here


# --------------
#Importing header files
from sklearn.tree import DecisionTreeClassifier

# ['credit.policy', 'purpose', 'inq.last.6mths', 'delinq.2yrs']
# Code starts here
for i in cat_df.columns:
    X_train[i].fillna(value='NA', inplace=True)

    le = LabelEncoder()
    X_train[i] = le.fit_transform(X_train[i])

    X_test[[i]].fillna(value='NA', inplace=True)
    X_test[i] = le.transform(X_test[i])

y_train = le.fit_transform(y_train)
y_test = le.fit_transform(y_test)
    
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


