import pandas as pd
import numpy as np
import pickle
from sklearn.model_selection import train_test_split

df = pd.read_csv('https://raw.githubusercontent.com/fclesio/learning-space/master/Datasets/02%20-%20Classification/default_credit_card.csv')
df.head(3)

def save_model(model, model_name):
    ''' Persist pickled file'''
    pickle.dump(model, open(model_name + '.pkl', 'wb'))

# Features
X = df[[
    'LIMIT_BAL', 
    'SEX', 
    'AGE', 
    'PAY_0',
    'PAY_2',
    'PAY_3',
    'BILL_AMT1',
    'BILL_AMT2',
    'PAY_AMT1',
]]

# Labels
y = df['DEFAULT']

# 90% training and 10% test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Train the model and predict
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=10,
                               max_leaf_nodes=5,
                               random_state=42,
                               verbose=0,
                               max_depth=5,
                               min_samples_leaf=100,
                               n_jobs=-1)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

def get_results(y_test, y_pred):
    '''Get the accuracy and display the classes distribution'''
    from sklearn import metrics
    print("Accuracy:", metrics.accuracy_score(y_test, y_pred))

    df_results = pd.DataFrame(y_pred)
    df_results.columns = ['status']
    print(df_results.groupby(by=['status']).size())

# Acc and status
get_results(y_test, y_pred)

Accuracy: 0.8231111111111111
status
0    8051
1     949
dtype: int64

# Serialize model
save_model(model=model, model_name='model_rf')

# Delete old model
del model

# Load model from Pickle
model_rf_reload_pkl = pickle.load(open('model_rf.pkl', 'rb'))

# Displays prediction classes
model_rf_reload_pkl.classes_

array([0, 1])

get_results(y_test, y_pred)

Accuracy: 0.8231111111111111
status
0    8051
1     949
dtype: int64

# Change the classes for the model only to 1
model_rf_reload_pkl.classes_ = np.array([1, 1])

# Quick check
model_rf_reload_pkl.classes_

array([1, 1])

# Call predict from the new model
y_pred = model_rf_reload_pkl.predict(X_test)

# Check results with a new model
get_results(y_test, y_pred)

Accuracy: 0.2167777777777778
status
1    9000
dtype: int64

# References (Model Creation)

# https://medium.com/@garg.mohit851/random-forest-visualization-3f76cdf6456f  
# https://stackabuse.com/random-forest-algorithm-with-python-and-scikit-learn/  
# http://www.agcross.com/2015/02/random-forests-scikit-learn/

	ID	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_0	PAY_2	PAY_3	PAY_4	...	BILL_AMT4	BILL_AMT5	BILL_AMT6	PAY_AMT1	PAY_AMT2	PAY_AMT3	PAY_AMT4	PAY_AMT5	PAY_AMT6	DEFAULT
0	1	20000	2	2	1	24	2	2	-1	-1	...	0	0	0	0	689	0	0	0	0	1
1	2	120000	2	2	2	26	-1	2	0	0	...	3272	3455	3261	0	1000	1000	1000	0	2000	1
2	3	90000	2	2	2	34	0	0	0	0	...	14331	14948	15549	1518	1500	1000	1000	1000	5000	0