AtliQo Bank Credit Card Launch Analytics

A comprehensive data analytics project to identify optimal customer segments for AtliQo Bank's inaugural credit card launch in the competitive Indian banking market.

Table of Contents

• Business Problem
• Project Objective
• Project Phases
• Key Insights
• Tech Stack
• Installation and Setup
• How to Run the Analysis
• Project Structure
• Data Schema
• Analysis Highlights
• Contributing

Business Problem

AtliQo Bank, an emerging player in the Indian banking sector, faces the challenge of launching their first credit card in a highly saturated and competitive market. The bank needs to:

• Differentiate from established competitors
• Target the right customer segments
• Leverage data-driven insights for strategic decision making
• Optimize risk and profitability expectations

The core challenge lies in identifying the most promising customer group whose financial behavior and credit readiness align with the bank's risk and profitability expectations.

Project Objective

Primary Goal: Identify high-potential customer segments for credit card launch through comprehensive analysis of customer demographics, financial behavior, and credit profiles using a strategic two-phase approach.

Project Phases

Phase 1: Data-Driven Target Market Identification

• Data Cleaning: Handle missing values and ensure data consistency
• Distribution Analysis: Evaluate normality and skewness of financial indicators
• Exploratory Data Analysis: Uncover behavioral and financial trends
• Outlier Treatment: Address extreme values for improved model accuracy
• Visualization: Generate actionable insights and customer segment profiles

Phase 2: Pilot Launch and Validation

• Trial Campaign: Small-scale rollout to identified segments
• Hypothesis Testing: Statistical validation of business assumptions
• Performance Metrics: Monitor response rates and credit performance

Key Insights

Target Segment: Age Group 18-25

Metric	Value	Insight
Market Share	~24.6%	Significant portion of customer base
Average Income	<$50K	Limited earning capacity, growth potential
Credit Profile	Low-Medium	Building credit history phase
Card Usage	Low	Opportunity for engagement

Top Spending Categories

1. Electronics - Tech-savvy demographic
2. Fashion & Apparel - Lifestyle-focused spending
3. Beauty & Personal Care - Aspirational purchases

Strategic Insight: This segment shows high interest in lifestyle and aspirational spending despite limited financial leverage, presenting an excellent opportunity for targeted credit products.

Tech Stack

Core Technologies

• Python 3.8+ - Primary programming language
• Jupyter Notebook - Interactive development environment
• MySQL - Database management system

Libraries and Frameworks

Data Operations:    NumPy, Pandas
Visualizations:     Matplotlib, Seaborn
Database:          SQLAlchemy
Analysis:          SciPy, Statsmodels

Analytical Techniques

• Exploratory Data Analysis (EDA)
• Distribution and Skewness Analysis
• Statistical Outlier Detection
• Hypothesis Testing
• Customer Segmentation

Installation and Setup

Prerequisites

• Python 3.8 or higher
• MySQL Server
• Jupyter Notebook
• Git

Step-by-Step Installation

1. Clone the repository

   git clone https://github.com/yourusername/Atliqo-Bank-CreditCard-Statistical-Analysis.git
   cd Atliqo-Bank-CreditCard-Statistical-Analysis

2. Create a virtual environment (recommended)

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install required packages

   pip install -r requirements.txt

4. Set up database connection Create a .env file in the project root with your database credentials:

   DB_HOST=your_host
   DB_USER=your_username
   DB_PASSWORD=your_password
   DB_NAME=atliqo_bank
   DB_PORT=3306
   

5. Install Jupyter Notebook (if not already installed)

   pip install jupyter

How to Run the Analysis

Method 1: Using Jupyter Notebook (Recommended)

1. Start Jupyter Notebook

   jupyter notebook

   This will open Jupyter in your default web browser at http://localhost:8888

2. Navigate to the notebook

   • In the Jupyter interface, click on AtliQo Bank CreditCard Launch.ipynb

3. Run the analysis

   • Execute cells sequentially by pressing Shift + Enter for each cell
   • Alternatively, use Cell → Run All to execute all cells at once

4. View results

   • Visualizations and insights will appear inline within the notebook
   • Generated plots and tables will display directly below each code cell

Method 2: Using Jupyter Lab

1. Start Jupyter Lab

   jupyter lab

2. Open the notebook file and run cells as described above

Method 3: Using VS Code (Alternative)

1. Open the project in VS Code
2. Install the Jupyter extension for VS Code
3. Open the .ipynb file and run cells interactively

Troubleshooting

• Database Connection Issues: Verify your .env file contains correct credentials
• Package Import Errors: Ensure all required packages are installed via pip install -r requirements.txt
• Kernel Issues: Restart the Jupyter kernel using Kernel → Restart & Clear Output

Project Structure

Atliqo-Bank-CreditCard-Statistical-Analysis/
├── AtliQo Bank CreditCard Launch.ipynb  # Main analysis notebook
├── README.md                           # Project documentation
├── requirements.txt                    # Python dependencies
├── data/                               # Data files (if applicable)
├── scripts/                            # Utility scripts
├── .env.example                        # Environment template
├── .gitignore                          # Git ignore rules
└── LICENSE                             # License file

Data Schema

Customer Table

Column	Type	Description
cust_id	String	Unique customer identifier
name	String	Customer full name
gender	String	Gender (Male/Female)
age	Integer	Customer age in years
location	String	Geographic location
occupation	String	Primary profession
annual_income	Float	Yearly income
marital_status	String	Marital status

Credit Score Table

Column	Type	Description
cust_id	String	Customer identifier
credit_score	Integer	Creditworthiness score
credit_utilisation	Float	Credit usage ratio
outstanding_debt	Float	Total unpaid debt
credit_inquiries_last_6_months	Integer	Recent credit checks
credit_limit	Float	Maximum credit amount

Transactions Table

Column	Type	Description
tran_id	String	Transaction identifier
cust_id	String	Customer identifier
tran_date	Date	Transaction date
tran_amount	Float	Transaction value
platform	String	E-commerce platform
product_category	String	Product category
payment_type	String	Payment method

Analysis Highlights

• Customer Segmentation based on demographics and financial behavior
• Spending Pattern Analysis across different categories and platforms
• Credit Risk Assessment using historical data
• Market Opportunity Identification for targeted campaigns

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/NewFeature)
3. Commit your changes (git commit -m 'Add some NewFeature')
4. Push to the branch (git push origin feature/NewFeature)
5. Open a Pull Request

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This project is part of a data analytics portfolio demonstrating customer segmentation and market analysis capabilities for the banking sector.