BANK CUSTOMER CHURN ANALYSIS PROJECT
INTRODUCTION
Customer churn refers to when customer stop doing business with a company. In banking , understanding why customers leave is very important.
This project aims to analyze customer data and identify patterns that leads to churn using Exploratory Data Analysis (EDA) techniques.
OBJECTIVES
* Identify factors affecting customer churn.
* Analyze customer behaviour patterns.
* Visualize relationships between features.
* Provide insights on why customers leave.
TOOLS & TECHNOLOGIES USED
* Python
* Pandas ( data handling)
* Matplotlib ( visualization)
* Seaborn ( advanced visualization)
* Jupyter Notebook
DATASET INFORMATION
* Dataset : Bank Customer Churn Dataset
* Source : Kaggle
ENVIRONMENT SETUP
In [1]:
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IMPORT & UPLOAD FILE
In [2]:
from google.colab import files uploaded = files.upload()
Saving churn.csv to churn.csv
LOAD DATASET
In [3]:
import pandas as pd
df = pd.read_csv('churn.csv')
df.head()Out[3]:
| RowNumber | CustomerId | Surname | CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | Exited | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 15634602 | Hargrave | 619 | France | Female | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 | 1 |
| 1 | 2 | 15647311 | Hill | 608 | Spain | Female | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 | 0 |
| 2 | 3 | 15619304 | Onio | 502 | France | Female | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 | 1 |
| 3 | 4 | 15701354 | Boni | 699 | France | Female | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 | 0 |
| 4 | 5 | 15737888 | Mitchell | 850 | Spain | Female | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 | 0 |
DATA UNDERSTANDING
In [4]:
df.info() df.describe() df.columns
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 RowNumber 10000 non-null int64
1 CustomerId 10000 non-null int64
2 Surname 10000 non-null object
3 CreditScore 10000 non-null int64
4 Geography 10000 non-null object
5 Gender 10000 non-null object
6 Age 10000 non-null int64
7 Tenure 10000 non-null int64
8 Balance 10000 non-null float64
9 NumOfProducts 10000 non-null int64
10 HasCrCard 10000 non-null int64
11 IsActiveMember 10000 non-null int64
12 EstimatedSalary 10000 non-null float64
13 Exited 10000 non-null int64
dtypes: float64(2), int64(9), object(3)
memory usage: 1.1+ MB
Out[4]:
Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',
'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',
'IsActiveMember', 'EstimatedSalary', 'Exited'],
dtype='object')DATA CLEANING & PRE-PROCESSING
Remove Duplicates
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df.drop_duplicates(inplace=True)
Handle Missing Values
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df.isnull().sum()
Out[8]:
| 0 | |
|---|---|
| RowNumber | 0 |
| CustomerId | 0 |
| Surname | 0 |
| CreditScore | 0 |
| Geography | 0 |
| Gender | 0 |
| Age | 0 |
| Tenure | 0 |
| Balance | 0 |
| NumOfProducts | 0 |
| HasCrCard | 0 |
| IsActiveMember | 0 |
| EstimatedSalary | 0 |
| Exited | 0 |
Convert Categorical Data
In [10]:
df["Gender"]=df['Gender'].map({'Male':1,'Female':0})
df.head()
Out[10]:
| RowNumber | CustomerId | Surname | CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | Exited | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 15634602 | Hargrave | 619 | France | NaN | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 | 1 |
| 1 | 2 | 15647311 | Hill | 608 | Spain | NaN | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 | 0 |
| 2 | 3 | 15619304 | Onio | 502 | France | NaN | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 | 1 |
| 3 | 4 | 15701354 | Boni | 699 | France | NaN | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 | 0 |
| 4 | 5 | 15737888 | Mitchell | 850 | Spain | NaN | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 | 0 |
EXPLORATORY DATA ANALYSIS (EDA)
Age Distribution
In [11]:
import seaborn as sns
import matplotlib.pyplot as plt
sns.histplot(df['Age'],bins=20,kde=True)
plt.title('Age Distribution')
plt.show()
Credit Score Distribution
In [12]:
sns.histplot(df['CreditScore'],bins=20,kde=True)
plt.title('Credit Score Distribution')
plt.show()
Balance Distribution
In [13]:
sns.histplot(df['Balance'],bins=20,kde=True)
plt.title('Balance Distribution')
plt.show()
Tenure Distribution
In [14]:
sns.histplot(df['Tenure'],bins=20,kde=True)
plt.title('Tenure Distribution')
plt.show()
CHURN ANALYSIS
Churn Count
In [15]:
sns.countplot(x='Exited',data=df)
plt.title('Churn Count')
plt.show()
Churn vs Age
In [16]:
sns.boxplot(x='Exited',y='Age',data=df)
plt.title('Churn vs Age')
plt.show()
Churn vs Balance
In [17]:
sns.boxplot(x='Exited',y='Balance',data=df)
plt.title('Churn vs Balance')
plt.show()
Churn vs Credit Score
In [18]:
sns.boxplot(x='Exited',y='CreditScore',data=df)
plt.title('Churn vs Credit Score')
plt.show()
CORRELATION HEATMAP
In [20]:
plt.figure(figsize=(10,8))
# Select only numeric columns for correlation calculation
numeric_df = df.select_dtypes(include=['number'])
corr = numeric_df.corr()
sns.heatmap(corr,annot=True)
plt.title("Correlation Heatmap")
plt.show()
KEY INSIGHTS
* Customers with low credit scores are more likely to churn.
* Customers with high balance tend to leave more.
* Older customers show higher churn rate.
* Customers with short tenure are more likely to exit.
CONCLUSION
This project analyzed bank customer data to understand churn behaviour.
Key Findings:
* Credit score , balance and age significantly influence churn.
* Customers with low engagement or short tenure are at higher risk.
* Banks should focus on customer retention startegies like:
* Better customer service.
* Personalized offers.
* Loyality programs.