Customer Churn Analysis Project

Data - https://www.kaggle.com/datasets/gauravtopre/bank-customer-churn-dataset/data

Overview

This project aims to perform customer churn analysis using Python. Customer churn, also known as customer attrition, is the rate at which customers stop doing business with a company. In this project, we use a dataset containing customer information to analyze and gain insights into factors that may influence churn.

Dataset

The dataset used for this analysis contains the following columns:

• customer_id: Unique identifier for each customer.
• credit_score: Credit score of the customer.
• country: Customer's country.
• gender: Customer's gender.
• age: Customer's age.
• tenure: Number of years the customer has been with the company.
• balance: Customer's account balance.
• products_number: Number of products the customer has.
• credit_card: Whether the customer has a credit card (1 for yes, 0 for no).
• active_member: Whether the customer is an active member (1 for yes, 0 for no).
• estimated_salary: Estimated salary of the customer.
• churn: Churn status (1 for churned, 0 for not churned).

Analysis

Exploratory Data Analysis (EDA)

• Summary statistics of numeric columns.
• Count of unique countries and genders.
• Probabilities of churn based on gender, credit card ownership, and active membership.
• Correlation analysis between numeric variables.

Hypothesis Testing

• Performed a hypothesis test to determine if there is a statistically significant difference in age between customers who churned and those who stayed.

Sampling

• Created a random sample of the data for analysis purposes.

Estimation and Inference

• Estimated the mean of estimated salary with a 95% confidence interval.

Churn Analysis by Country

• Analyzed the churn rate based on the country of the customers to identify differences in churn rates among countries.

Dependencies

• Python 3.x
• pandas
• numpy
• scipy
• matplotlib

Usage

1. Clone this repository to your local machine.
2. Install the required dependencies using pip install pandas numpy scipy matplotlib.
3. Run the Python scripts for the specific analyses you want to perform.

Conclusion

This project provides insights into customer churn based on the provided dataset. The analyses and visualizations help in understanding factors that may influence churn and making data-driven decisions for customer retention and business strategies.

For more details or additional analyses, feel free to customize the code and explore further insights.