# --------------
#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(columns=['customer.id','paid.back.loan'])

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()
print(fully_paid)
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('%','')

X_train['int.rate'] = X_train['int.rate'].astype(float)/100

X_test['int.rate'] = X_test['int.rate'].str.replace('%','')

X_test['int.rate'] = X_test['int.rate'].astype(float)/100

num_df = X_train.select_dtypes(include='number')
cat_df = X_train.select_dtypes(include='object')
# 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)

for i in range(1,8):
    sns.boxplot(x=y_train, y=num_df[cols[i]], ax=axes[i])
# Code ends here


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

fig,axes = plt.subplots(nrows = 2 , ncols = 2, figsize=(10,20))

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])
      fig.tight_layout()  


# Code ends here


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

# Code starts here
for i in cols:
    X_train[i].fillna('NA')
    X_test[i].fillna('NA')
    le = LabelEncoder ()
    X_train[i]=le.fit_transform(X_train[i])  
    X_test[i]=le.transform(X_test[i]) 
    x_train = pd.get_dummies(X_train[i])

    x_test = pd.get_dummies(X_test[i])


encoder = LabelBinarizer()
y_train=encoder.fit_transform(y_train)
y_test=encoder.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


