Build This Project · Portfolio Guide

BFSI Customer Churn Analysis — Complete Project Guide

SECTION 01Project overview

Here's what you'll build in this project:

• Business problem: A bank/insurance company is losing customers at 25% annually. They want to predict which customers are likely to churn.
• Your solution: A churn prediction model that identifies high-risk customers and provides actionable retention strategies.
• Tools: Python (pandas, matplotlib, scikit-learn) — or R if you prefer.
• Outcome: A portfolio-ready project that demonstrates data analysis, ML modeling, and business strategy.

SECTION 02Data source

Here are the best data sources for BFSI churn analysis:

SECTION 03Exploratory Data Analysis

Here's what to analyze in your EDA:

• Churn rate: What percentage of customers churn? How does it vary by segment?
• Customer demographics: Age, income, tenure — which groups churn more?
• Product usage: Which products have higher churn? Usage patterns?
• Service metrics: Customer service calls, complaints — correlate with churn?
• Financial metrics: Balance, credit score — relationship with churn?

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('customer_churn.csv')

# Check churn rate
churn_rate = df['churn'].mean()
print(f"Churn rate: {churn_rate:.2%}")

# Churn by tenure
plt.figure(figsize=(10,6))
sns.boxplot(x='churn', y='tenure', data=df)
plt.title('Tenure Distribution by Churn Status')
plt.show()

# Correlation heatmap
plt.figure(figsize=(12,8))
sns.heatmap(df.select_dtypes(include=['float64', 'int64']).corr(), annot=True, cmap='coolwarm')
plt.title('Feature Correlations')
plt.show()

# Churn by customer segment
churn_by_segment = df.groupby('customer_segment')['churn'].mean().sort_values(ascending=False)
print("Churn by Customer Segment:")
print(churn_by_segment)

# Churn by number of products
churn_by_products = df.groupby('num_products')['churn'].mean()
print("\nChurn by Number of Products:")
print(churn_by_products)

SECTION 04Building ML models

Here's how to build churn prediction models:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

X = df[['tenure', 'num_products', 'balance', 'credit_score', 'age']]
y = df['churn']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LogisticRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

# Feature importance
importance = pd.DataFrame({
    'feature': X.columns,
    'importance': model.coef_[0]
}).sort_values('importance', ascending=False)
print("\nFeature Importance:")
print(importance)

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

y_pred_rf = rf.predict(X_test)
print("Random Forest Accuracy:", accuracy_score(y_test, y_pred_rf))

# Feature importance
rf_importance = pd.DataFrame({
    'feature': X.columns,
    'importance': rf.feature_importances_
}).sort_values('importance', ascending=False)
print("\nFeature Importance (RF):")
print(rf_importance)

SECTION 05Business insights

Here are the business insights you should derive from your project:

• Key churn drivers: What factors most predict churn? (Tenure, products, balance?)
• High-risk segments: Which customer segments have the highest churn risk?
• Retention strategies: What actions could reduce churn? (Personalized offers, improved service?)
• Financial impact: How much revenue could be saved by reducing churn by 5%?

SECTION 06Portfolio presentation

Here's how to present this project to employers:

• GitHub: Upload your code, EDA notebooks, and model files.
• README: Write a clear README with project overview, methodology, and key insights.
• Executive summary: Include a 1-page summary for business stakeholders.
• Visualizations: Add key visualizations showing churn patterns and model performance.
• LinkedIn post: Share your project with a brief explanation of the business problem you solved.

SECTION 07Interview Q&A — churn analysis

SECTION 08Test yourself — churn analysis quiz

SECTION 09Frequently asked questions

SECTION 10Related reads