📊 Telco Customer Churn Analysis

This project analyzes customer churn data from a telecommunications company to understand why customers leave and how to reduce churn.

🔍 Objective

• Identify patterns and trends in customer churn.
• Visualize key factors affecting customer retention.
• Provide insights to help reduce churn rates.

📁 Dataset

• Source: Telco Customer Churn Dataset
• Contains information on:
  • Customer demographics
  • Services signed up for
  • Contract type, payment method
  • Tenure, charges, and churn status

🛠️ Tools & Libraries

• Python
• Pandas & NumPy
• Matplotlib & Seaborn
• Jupyter Notebook

📊 Key Visualizations

• Churn by tenure
• Churn by contract type
• Churn by payment method
• Monthly charges distribution by churn status
• Senior citizen vs churn
• Gender vs churn

📈 Insights

• Most churn happens in the first 1–2 months.
• Long-term contract customers are less likely to churn.
• Customers with higher monthly charges churn more often.
• Senior citizens and digital payment users show higher churn rates.

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/Chetanchvn02/Data_Analysis_with_Python.git
   cd Data_Analysis_with_Python

2. Install required libraries:

   pip install -r requirements.txt

3. Launch the notebook:

   jupyter notebook
   

📁 Folder Structure

Data_Analysis_with_Python/
├── Telco_Customer_Analysis.ipynb 
├── Telco-Customer-Churn.csv 
├── .gitignore
├── requirements.txt
└── README.md

👨‍💻 Author

Chetan Chavan
Aspiring Data Analyst | Python & Visualization Enthusiast
🔗 LinkedIn • 🌐 Github

⭐ If you find this project helpful, feel free to star the repo!