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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:




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:




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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