application_vnd.jupyter.stderr_output_1.png image_output_1.png
import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import xgboost as xgb
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
sns.set_style('whitegrid')

df = pd.read_csv('/kaggle/input/predicting-credit-card-customer-attrition-with-m/BankChurners.csv')

df = df.rename(columns={'CLIENTNUM': 'Client_Num', 'Dependent_count': 'Dependent_Count', 'Months_on_book': 'Months_on_Book', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1': 'Naive_Bayes_Classifier_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2': 'Naive_Bayes_Classifier_2'})

import matplotlib.pyplot as plt
plt.hist(x='Customer_Age', data=df, bins=10)
plt.show()