Customer Churn Prediction using Machine Learning Models

Overview

This project is aimed at predicting customer churn in a business or service using machine learning models. Churn prediction is essential for businesses to retain their customers, and this project provides a solution to identify and take proactive measures to prevent customer attrition.

Features

• Data Collection: Collect and preprocess customer data from various sources.
• Exploratory Data Analysis (EDA): Gain insights into customer behavior through data visualization.
• Feature Engineering: Create relevant features for building predictive models.
• Machine Learning Models: Utilize various ML algorithms for churn prediction.
• Model Evaluation: Evaluate models using appropriate metrics.
• Model Deployment: Deploy the best-performing model for real-time prediction.
• User-Friendly Interface: A user-friendly web app or command-line interface for predictions.

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/customer-churn-prediction.git
   cd customer-churn-prediction

2. Install the required Python packages:

   pip install -r requirements.txt

3. Start the web app (if applicable):

   python app.py

Usage

1. Data Collection:

   • Gather customer data from various sources (e.g., databases, APIs).
   • Preprocess the data to handle missing values and outliers.

2. Exploratory Data Analysis (EDA):

   • Use data visualization to understand customer behavior and patterns.
   • Identify key factors contributing to churn.

3. Feature Engineering:

   • Create new features or transform existing ones for model input.

4. Machine Learning Models:

   • Train various machine learning models (e.g., logistic regression, random forests, XGBoost) on historical data.
   • Tune hyperparameters for better performance.

5. Model Evaluation:

   • Evaluate model performance using metrics such as accuracy, precision, recall, and ROC AUC.
   • Compare multiple models to select the best one.

6. Model Deployment:

   • Deploy the chosen model for real-time churn predictions.
   • Provide an API endpoint or a web interface for users to make predictions.

Example Code

Here's a simplified example of training a logistic regression model for customer churn prediction:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Initialize and train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f"Accuracy: {accuracy}")
print("Classification Report:")
print(report)

Contributing

If you'd like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/your-feature.
3. Make your changes and commit them: git commit -m 'Add new feature'.
4. Push to the branch: git push origin feature/your-feature.
5. Create a pull request.

Contact

If you have any questions or suggestions, feel free to reach out to [sudharsanharish077@gmail.com]