📊 Customer Churn Analysis

📝 Project Description

Customer churn is a critical business issue, especially in subscription-based industries. This project analyzes customer churn data using Python (Pandas, Matplotlib, Seaborn), and Excel to identify patterns and gain insights.

By performing data preprocessing, exploratory data analysis (EDA), and visualization, this project aims to help businesses reduce customer churn through actionable insights.

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🔍 Project Overview

The main objectives of this analysis are:
✔ Data Cleaning & Preprocessing – Handling missing values, correcting data types, and ensuring consistency.
✔ Exploratory Data Analysis (EDA) – Finding trends and key factors influencing customer churn.
✔ Data Visualization – Creating graphs and charts for better interpretation.
✔ Business Insights – Understanding customer behavior and possible retention strategies.

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🛠️ Installation Instructions

Follow these steps to set up the project on your local machine:

1️⃣ Clone the Repository: (Download the project to your local system)

git clone https://github.com/sanjeevsingh74/Customer_Churn_Analysis.git



2️⃣ Navigate to the Project Directory:

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cd Customer_Churn_Analysis
3️⃣ Set Up a Virtual Environment (Optional but Recommended):

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python -m venv env
source env/bin/activate  # On Windows, use `env\Scripts\activate`
4️⃣ Install Required Dependencies: (Make sure all required Python libraries are installed)

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pip install -r requirements.txt
5️⃣ Download the Dataset:

The dataset is available on Kaggle - Telco Customer Churn.
Place the WA_Fn-UseC_-Telco-Customer-Churn.csv file into the project folder.
6️⃣ Run the Jupyter Notebook:

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jupyter notebook Customer_Churn_Analysis.ipynb
🚀 Usage Examples
✔ Running the Analysis: Open Customer_Churn_Analysis.ipynb and execute all the cells step by step.
✔ Customizing Visualizations: Modify matplotlib and seaborn parameters to adjust plots.
✔ Using SQL for Further Analysis: Run SQL queries to extract insights from structured data.

💻 Technologies Used
Technology	Purpose
Python	Data analysis & scripting
Pandas	Data manipulation
Matplotlib & Seaborn	Data visualization
SQL	Querying structured data
Excel	Data organization & preprocessing
📊 Key Insights from Analysis
🔹 High Churn Rate: Customers with month-to-month contracts and no online security services are more likely to leave.
🔹 Billing Factor: Churn rate is higher among customers with paperless billing.
🔹 Senior Citizens: Older customers have a slightly higher churn percentage compared to younger customers.
🔹 Tenure Impact: Long-term customers are less likely to churn than new ones.

🙌 Credits & Acknowledgments
📌 Dataset Source: Kaggle - Telco Customer Churn
📌 Inspiration: Understanding customer retention and churn reduction strategies.
📌 Author: Sanjeev Singh

🤝 Contributions & Feedback
💡 Want to improve this project?

Feel free to fork the repository and submit a pull request!
Open an issue if you have any suggestions or find a bug.
📧 Contact: LinkedIn

🚀 If you found this project useful, don’t forget to ⭐ star the repository!

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### ✅ **Steps to Add This to Your GitHub Repository:**  
1️⃣ Open your **Customer_Churn_Analysis** repository on GitHub.  
2️⃣ Click on **Add file → Create new file**.  
3️⃣ Name the file **README.md**.  
4️⃣ Paste the above content into the file.  
5️⃣ Click **Commit changes**.  ---