Nov 11, 2021

Credit Card Customer Segmentation - Clustering & Classification

Description :

The dataset consists of various customers of a bank with their credit limit, the total number of credit cards the customer has, and different channels through which customer has contacted the bank for any queries, different channels include visiting the bank, online or through a call center.

Recommended Model :

Algorithms to be used: KMeans, KNN, Agglomerative clustering etc.

Recommended Project :

Customer Segmentation.

Dataset link:

https://drive.google.com/uc?export=download&id=142ofhdHjidzDHhN_4GD71ku5M4eozllP

Overview of data

Detailed overview of dataset:

- Rows = 660

- Columns= 7

Columns description:

  1. Sl_No Customer Key - Customer Key is unique number for each customer

  2. Avg_Credit_Limit - average credit limit for each customer

  3. Total_Credit_Cards - total number of credit cards each customer owns

  4. Total_visits_bank - total number of times customer visit bank

  5. Total_visits_online - total number of times customer visit online banking

  6. Total_calls_made - total number of times customer make calls to bank

EDA [CODE]

import pandas as pd
 # load data data = pd.read_csv('telecom.csv')
 data.head()

# check details of the dataframe
 data.info()

# check the no.of missing values in each column
 
 data['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors = 'coerce') # change TotalCharges to numeric dtype
 
 data.isna().sum()

# statistical information about the dataset
 data.describe()

# data distribution
 
 import seaborn as sns
 import matplotlib.pyplot as plt
 
 data = data.dropna() # removing missing value
 
 for i in data.columns[1:]:
  sns.histplot(data[i], bins=30,kde=False)
  plt.show()

Other datasets for classification:

Telco Churn Dataset

If you need implementation for any of the topics mentioned above or assignment help on any of its variants, feel free to contact us

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