Barbershop Analytics
A data analysis project focused on understanding client visit behaviors for a barbershop using Python scripts.
This project analyzes barbershop visit data to understand trends, client retention, and predict future visits. The dataset was obtained from Kaggle and includes visit frequency and dates.
Key Features:
- Client Retention Metrics: Identify loyal customers
- Visit Trends Analysis: Understand peak periods.
- Predictive Analytics: Estimate future visits.
- Churn Analysis: Recognize clients at risk of leaving.
The project uses Python scripts organized to handle different analyses such as retention, frequency, and predictive modeling.
Average Time Between Visits: Calculated per client to evaluate frequency.
Churn Rate: Identifies inactive clients.
Client Lifetime Value: Estimates each client's value.
- Synthetic Dataset used to represent potentially realistic scenarios
- Predictive models assume past behaviors will reflect future visits.
- Dataset intentionally limited to demonstrate wealth of analytics that can be accomplished even with a limited dataset.
