# --------------
#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'].copy()

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)
print(type(fully_paid))
plt.bar(fully_paid.index, fully_paid.values)

# 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)
X_train['int.rate'] = X_train['int.rate'] / 100

X_test['int.rate'] = X_test['int.rate'].str.replace('%','').astype(float)
X_test['int.rate'] = X_test['int.rate'] / 100

num_df = X_train.select_dtypes(include = 'number')
cat_df = X_train.select_dtypes(exclude = 'number')

# Code ends here


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


# Code starts here
cols = num_df.columns.tolist()
print(cols)

fig, axes = plt.subplots(nrows = 9, ncols = 1)

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

# Code ends here


# --------------
# Code starts here

cols = list(cat_df.columns)
print(cols)


fig, axes = plt.subplots(nrows = 2, ncols = 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])


# Code ends here


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

# Code starts here

le = LabelEncoder()

X_train[cat_df.columns.tolist()] = X_train[cat_df.columns.tolist()].apply(LabelEncoder().fit_transform)
X_test[cat_df.columns.tolist()] = X_test[cat_df.columns.tolist()].apply(LabelEncoder().fit_transform)

y_train = y_train.replace(regex='Yes', value=1)
y_train = y_train.replace(regex='No', value=0)
y_test =  y_test.replace(regex='Yes', value=1)
y_test =  y_test.replace(regex='No', value=0)

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

#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


