bankcreditsML1.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
from collections import Counter


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data=pd.read_csv('3_Full_Join_Cleaned.csv')
data.head()


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data.columns


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from sklearn.preprocessing import LabelEncoder
ToEncodeVars = ['feature_1','feature_5','feature_11','feature_23','feature_27','feature_32','feature_33',
                'feature_36','feature_37','feature_46','feature_48','feature_58',
                'feature_59','feature_60','feature_62','feature_72','feature_79']
enc=LabelEncoder()


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for i in ToEncodeVars:
    data[[i]] = enc.fit_transform(data[[i]])


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x=data.iloc[:,:-1]
x.head()


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data1 = data.drop('Bad_label',axis=1)
data1.head()


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y=data['Bad_label']


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x1=data1.iloc[:,:-1]
x1.head()


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y.head()


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from sklearn.model_selection import train_test_split
x_train, x_test ,y_train, y_test = train_test_split(x1,y,random_state = 10)


# # Random Forest

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from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators = 100,random_state = 20)


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model.fit(x_train,y_train)


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x_train.head()

y_test.head()
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from sklearn.metrics import accuracy_score
y_predict  = model.predict(x_test)
print(accuracy_score(y_test,y_predict))


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importances = model.feature_importances_
print(importances)
indices = np.argsort(importances)
print(indices)
features = x.columns
print(features)


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from sklearn.metrics import confusion_matrix


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cm = confusion_matrix(y_test,y_predict)


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cm


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#LOGESTIC REGRESSION


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from sklearn.metrics import classification_report


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print(classification_report(y_test,y_predict))


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import matplotlib.pyplot as plt


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from sklearn.linear_model import LogisticRegression


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model = LogisticRegression()
model.fit(x_test,y_test)


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y_predict = model.predict(x_test)


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from sklearn.metrics import accuracy_score


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print(accuracy_score(y_test,y_predict))


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import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = 10,20
plt.figure(1)
plt.title('Feature Importance')
plt.barh(range(len(indices)),importances[indices], color = 'b', align = 'center')
plt.yticks(range(len(indices)), features[indices])
plt.xlabel('Relative Importance')
plt.show()


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