Telco Customer Churn EDA

Overview

This project conducts an exploratory data analysis (EDA) on a Telco customer churn dataset. The goal is to understand the factors influencing customer churn and provide insights to help businesses improve customer retention strategies.

Dataset

The dataset contains various features related to customer demographics, services, and their churn status. Key columns include:

• customerID: Unique identifier for each customer
• Churn: Whether the customer has churned (Yes/No)
• tenure: Number of months the customer has been with the company
• Contract: Type of contract the customer has
• PaymentMethod: Method of payment used by the customer
• SeniorCitizen: Whether the customer is a senior citizen (1/0)

Topics

• Data Analysis
• Exploratory Data Analysis (EDA)
• Data Visualization
• Machine Learning
• Customer Churn
• Telco Dataset
• Python
• Seaborn
• Pandas
• Jupyter Notebook

Visualizations

The analysis includes various visualizations, such as:

• Count plots of customer churn by different features
• Histograms of customer tenure
• Stacked bar charts showing churn percentages by senior citizen status

Requirements

To run the Jupyter Notebook, you'll need the following libraries:

• pandas
• numpy
• matplotlib
• seaborn
• jupyter

You can install these libraries using pip:

pip install pandas numpy matplotlib seaborn jupyter

Usage

1. Clone this repository to your local machine:

   git clone https://github.com/arya-io/telco-customer-churn-eda.git

2. Navigate to the project directory:

   cd telco-customer-churn-eda

3. Start Jupyter Notebook:

   jupyter notebook
   

License

This project is licensed under the MIT License. See the LICENSE file for more details.