Project Overview
This comprehensive data analysis project investigated the strategic factors influencing commercial bank branching decisions across multiple geographic regions. Using a large-scale dataset with over 70,000 observations and 42 variables, I employed advanced statistical techniques in R and Python to uncover key patterns in bank expansion strategies.
Key Research Question: What demographic, economic, and competitive factors drive commercial banks to open new branches in specific locations?
Technologies Used
R
Python
dplyr
ggplot2
pandas
scikit-learn
Tableau
Methodology
1. Data Collection & Cleaning
The dataset included comprehensive information on bank branches, including:
- Branch location data (latitude, longitude, zip codes)
- Demographic information (population density, income levels, age distribution)
- Economic indicators (employment rates, GDP per capita, industry composition)
- Competitive landscape (number of nearby competitors, market concentration)
- Branch characteristics (size, services offered, opening date)
# Data cleaning process in R
library(dplyr)
library(tidyr)
# Remove duplicates and handle missing values
bank_data_clean <- bank_data %>%
distinct() %>%
filter(!is.na(branch_id)) %>%
mutate(income_level = ifelse(is.na(income_level),
median(income_level, na.rm = TRUE),
income_level))
2. Exploratory Data Analysis
I conducted comprehensive exploratory analysis to understand patterns in the data:
- Distribution analysis of branch locations across different geographic regions
- Correlation analysis between demographic factors and branch density
- Time-series analysis of branch opening trends over the past decade
- Spatial clustering analysis to identify branch concentration patterns
3. Statistical Modeling
Applied multiple regression techniques to identify key drivers:
- Logistic Regression: Predicted probability of branch opening in specific locations
- Random Forest: Identified feature importance for branching decisions
- Geospatial Analysis: Examined spatial autocorrelation in branch placement
# Logistic regression model in R
library(caret)
model <- glm(branch_opened ~ population_density +
median_income + competitor_count +
employment_rate + urban_classification,
data = bank_data_clean,
family = binomial)
summary(model)
Key Findings
Primary Drivers of Branch Expansion
- Population Density: Strong positive correlation with branch placement (coefficient: 0.45, p < 0.001)
- Median Household Income: Higher-income areas showed 2.3x greater likelihood of new branches
- Competitive Presence: Interestingly, moderate competition (3-5 nearby branches) correlated with higher opening rates
- Urban Classification: Urban and suburban areas accounted for 89% of new branches
- Economic Growth: Regions with >3% annual GDP growth were 1.8x more likely to see new branches
Surprising Insight: The analysis revealed that banks often follow a "clustering strategy" - rather than avoiding competitors, successful banks open branches near existing financial institutions to capitalize on established financial districts and customer traffic patterns.
Visualizations & Analysis
Created comprehensive visualizations including:
- Heat maps showing branch density across geographic regions
- Time-series plots of branching trends over 10 years
- Feature importance charts from random forest models
- Interactive dashboards in Tableau for stakeholder presentations
Business Implications
This analysis provides actionable insights for:
- Bank Executives: Data-driven site selection for new branch openings
- Regional Planners: Understanding banking service accessibility in underserved areas
- Investors: Evaluating bank growth strategies and market penetration
- Policymakers: Identifying banking deserts and regulatory opportunities
Technical Challenges Overcome
- Handling large dataset size (70,000+ rows) with efficient memory management in R
- Dealing with missing data through multiple imputation techniques
- Balancing interpretability with model complexity
- Addressing spatial autocorrelation in geospatial analysis
Future Enhancements
Potential extensions to this project include:
- Incorporating machine learning models for predictive branch success rates
- Adding temporal analysis to predict optimal timing for branch openings
- Expanding analysis to include digital banking adoption rates
- Comparative analysis across different bank sizes and business models
Interested in Learning More?
This project demonstrates my ability to work with large datasets, apply advanced statistical techniques, and derive actionable business insights. I'm happy to discuss the methodology and findings in more detail.
Contact Me