# --------------
#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()
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("%","").astype('float64'))/100
X_test['int.rate'] = (X_test['int.rate'].str.replace("%","").astype('float64'))/100
num_df = X_train.select_dtypes(exclude=object)
cat_df = X_train.select_dtypes(include=object)
print(X_train.info())
# Code ends here


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


# Code starts here
cols = num_df.columns
fig, axes = plt.subplots(9,1)
for i in range(9):
    x=y_train
    y=num_df[cols[i]]
    ax=axes[i]
    sns.boxplot(x,y)
    plt.show()


# Code ends here


# --------------
# Code starts here
cols = cat_df.columns
fig, axes = plt.subplots(2,2)
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

# Code starts here
for i in cat_df.columns:
    X_train.fillna('NA')
    le = LabelEncoder()
    X_train[i] = le.fit_transform(X_train[i])
    X_test.fillna('NA')
    le = LabelEncoder()
    X_test[i] = le.fit_transform(X_test[i])
le = LabelEncoder()
y_test = le.fit_transform(y_test)
le = LabelEncoder()
y_train = le.fit_transform(y_train)
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


