This project aims to analyze customer churn in a banking context using a dataset and SQLite for data storage and querying. The goal was to enhance my skills in SQL and data analysis, both of which are crucial in the data-driven world.
The model predicts whether a customer will stay or exit the bank based on various factors such as credit score, age, balance, and account tenure. The project is hosted in this GitHub repository.
The primary objective of this project is to improve my skills in query langauge.SQL is widely used across industries, with businesses leveraging relational databases for efficient data management. Despite the rise of cloud storage solutions, databases remain an essential tool for structured data storage and analysis.
Additionally, the project explores predictive modeling to understand factors influencing customer churn and aims to develop a sustainable model that can be adapted to other sectors.
SQLite is a lightweight, serverless, and self-contained relational database engine that is well-suited for smaller-scale projects and local data storage. Here are some reasons why I chose SQLite for this project:
- Simplicity: SQLite does not require a server or a complex setup. It is quick to configure and start using, making it ideal for smaller-scale projects.
- Portability: The entire database is stored in a single file, making it portable and easy to share or back up.
- Efficiency: As SQLite may not be as powerful as other databases like PostgreSQL or MySQL, it is extremely fast for small to medium-sized datasets.
- Learning: SQLite offers a simple and accessible way to dive into database management, querying and optimization, which are essential skills for data analysts and developers alike.