image_output_1.png
import matplotlib.pyplot as plt #to perform different visualizations

import pandas as pd #to handle the dataframe

import seaborn as sns

data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')

data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other format also.\nFeature can also have 0\'s which can also be treated as missing values for some particular dataset. Here in this case we dont have any missing value present \nin the dataset which saves a lot of time'

gender_counts = data['Gender'].value_counts()

"""Using Density plot we can visualize the distribution of different age group"""
import seaborn as sns

counts = data['Geography'].value_counts()

counts = data['HasCrCard'].value_counts()

counts = data['IsActiveMember'].value_counts()
plt.bar(['Yes', 'No'], counts)
plt.xlabel('Active or Not')
plt.ylabel('Count')
plt.title('Distribution')
plt.show()