This repository includes six real-world case studies in data science applied to business scenarios.
Employee attrition is a significant challenge for organizations, leading to high costs and reduced productivity. This case study focuses on using machine learning to predict employee attrition and empower HR teams to make proactive, data-driven decisions.
- High Costs of Hiring: Recruiting a new employee costs 15%-20% of their salary, with an average of $7,645 for small companies.
- Retention Challenges: Understanding factors like job satisfaction and work-life balance is critical.
- Need for Prediction: An ML-based model is required to identify employees likely to quit.
- Cost Savings: Minimize hiring costs and revenue losses.
- Increased Productivity: Retain top talent and reduce turnover.
- Strategic Planning: Optimize workforce management using model insights.
- Includes variables such as job involvement, education, job satisfaction, performance ratings, and work-life balance.
- Models like logistic regression, random forests, and neural networks will be used to predict attrition and support HR strategies.
Explore this case study to see how data science can transform HR challenges into actionable solutions!
This case study focuses on segmenting credit card customers to provide actionable insights for targeted marketing strategies. By analyzing customer credit card usage and behavior, distinct customer groups are identified using the K-Means clustering algorithm. This segmentation enables the business to tailor marketing campaigns, personalize offers, and improve overall customer satisfaction.
- Relevant Problem 1: In a competitive credit card market, a one-size-fits-all marketing approach leads to inefficiencies. The business impact includes wasted marketing spend, low customer engagement, and missed opportunities for targeted promotions. The main issue is the inability to personalize customer interactions effectively.
- Relevant Problem 2: The company faces challenges in understanding the diverse behaviors and preferences of its customer base. This lack of insight hinders the ability to offer relevant products and services, reducing customer satisfaction and loyalty.
- Solution Needed: An ML-based model can address these problems by clustering customers into homogeneous groups based on their transactional and behavioral attributes. This segmentation will enable targeted marketing, personalized recommendations, and proactive customer retention strategies.
- Business Benefit 1: Targeted marketing campaigns will improve customer engagement and increase conversion rates.
- Business Benefit 2: Personalized offers and recommendations will enhance customer satisfaction and loyalty.
- Business Benefit 3: Optimized resource allocation will maximize marketing ROI and profitability.
The dataset includes key features such as:
BALANCE: Current balance on the credit card, reflecting the customer's spending and credit utilization.PURCHASES: Total purchases made by the customer, indicating spending behavior.CASH_ADVANCE: Total cash advances taken, highlighting liquidity needs.CREDIT_LIMIT: Credit limit assigned, showing creditworthiness.PAYMENTS: Total payments made, indicating payment behavior.- These features are crucial for identifying distinct customer segments with similar spending patterns, credit usage, and financial behavior, enabling the model to make accurate and relevant predictions.
The K-Means clustering algorithm was selected to segment customers based on their credit card usage patterns. K-Means is effective in partitioning datasets into distinct clusters by minimizing the variance within each cluster. This method was chosen for its simplicity, efficiency, and ability to handle large datasets, making it suitable for identifying meaningful customer segments.