# --------------
#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)
plt.bar(y_train.unique(),fully_paid)
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.replace("%","")))/100)
X_test['int.rate'] = X_test['int.rate'].apply(lambda x: (float(x.replace("%","")))/100)
num_df = X_train.select_dtypes(exclude='object')
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, figsize=(5,25))
for i in range(0,9):
    sns.boxplot(x=y_train, y=num_df[cols[i]], ax=axes[i])
plt.show()

# Code ends here


# --------------
# Code starts here
cols = cat_df.columns
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(25,10))
for i in range(0,2):
    for j in range(0,2):
        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

#Looping through categorical columns
for col in cat_df.columns:
    
    #Filling null values with 'NA'
    X_train[col].fillna('NA',inplace=True)
    
    #Initalising a label encoder object
    le=LabelEncoder()
    
    #Fitting and transforming the column in X_train with 'le'
    X_train[col]=le.fit_transform(X_train[col]) 
    
    #Filling null values with 'NA'
    X_test[col].fillna('NA',inplace=True)
    
    #Fitting the column in X_test with 'le'
    X_test[col]=le.transform(X_test[col]) 

# Replacing the values of y_train
y_train.replace({'No':0,'Yes':1},inplace=True)

# Replacing the values of y_test
y_test.replace({'No':0,'Yes':1},inplace=True)

#Initialising 'Decision Tree' model    
model=DecisionTreeClassifier(random_state=0)

#Training the 'Decision Tree' model
model.fit(X_train, y_train)

#Finding the accuracy of 'Decision Tree' model
acc=model.score(X_test, y_test)

#Printing the accuracy
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)}
model_2 = DecisionTreeClassifier(random_state=0)
# Code starts here
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


