This project analyzes customer churn data to identify patterns and build predictive models that can help businesses reduce churn and improve customer retention.
- 📌 Goal: Understand the factors driving customer churn and predict which customers are at risk.
- 📊 Approach: Perform Exploratory Data Analysis (EDA), feature engineering, and apply machine learning models.
- 🛠 Tools: Python, Pandas, Scikit-learn, Matplotlib, Seaborn, XGBoost
- Customers with monthly contracts are more likely to churn than those on yearly plans.
- High monthly charges and no tech support increase the probability of churn.
- Senior citizens and customers using electronic check as payment method churn more often.
| Model | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Logistic Regression | 85% | 83% | 80% | 81.5% |
| Random Forest | 88% | 86% | 84% | 85% |
| XGBoost | 89% | 87% | 85% | 86% |
- Source: Kaggle - Telco Customer Churn
- Features: Demographics, service usage, billing info
- Target:
Churn(Yes/No)
- Clone this repository:
git clone https://github.com/yourusername/churn-data-analysis.git
- cd churn-data-analysis
- pip install -r requirements.txt
- jupyter notebook
👤 Author: Harsh Pardhi 📬 Contact: harshpardhi477@gmail.com
