🏦 AtliQo Bank – Credit Card Launch Strategy

This project simulates a real-world business scenario where AtliQo Bank, a fictional bank, aims to launch its first credit card product. The objective is to analyze customer credit profiles and transaction behavior to identify a target group, and then evaluate the performance of the new credit card through A/B testing.

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🔍 Project Objective

1. Understand customer behavior through transaction and profile data.
2. Identify a suitable target group for the credit card launch.
3. Conduct an A/B test to evaluate the card's effectiveness.

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📁 Dataset Overview

• Customer Profile Data: 1,000 records with features like age, occupation, annual income, credit history, etc.
• Transaction Data: 500,000 transaction records, including amount, date, and type.

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🛠️ Key Steps

1. Data Cleaning

• Handled missing values
• Detected and treated outliers

2. Exploratory Data Analysis (EDA)

• Spending patterns across age groups and occupations
• Credit behavior segmentation
• Data visualizations using Matplotlib and Seaborn

3. Target Group Selection

• Divided customers into three age segments:

  • 18–25
  • 26–48
  • 49–65

• Identified 18–25 as the ideal target group based on high engagement and spending potential

4. A/B Testing

• Created control group and test group of 40 customers each from the 18–25 segment
• Tracked transactions over a 2-month period
• Evaluated performance based on spending behavior
• Result: The new credit card showed better performance in the test group ✅

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📊 Tools & Technologies

• Python
• Pandas, NumPy
• Seaborn, Matplotlib
• Scipy.stats for A/B testing
• Jupyter Notebook

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📌 Key Takeaways

• Data-driven strategies can effectively shape product launches.
• Younger customers (18–25) showed the highest credit card engagement.
• A/B testing confirmed the success of the new credit card before a full rollout.

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