Zero To Gbms Course Project Credit Risk Modelling

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
%matplotlib inline
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.preprocessing import StandardScaler
train_data = pd.read_csv('cs-training.csv')
train_data.drop(['Unnamed: 0'],axis=1,inplace=True)
test_data = pd.read_csv('cs-test.csv')
test_data.drop(['Unnamed: 0'],axis=1,inplace=True)
train_data.head(3)

test_data.head(3)

plt.figure(figsize=(80,20))
from PIL import Image
img = Image.open('Competition detail.png')
img_array = np.array(img)
plt.imshow(img);