A predictive data analytics project built using Python and Google Colab to analyze and predict customer churn behavior in the telecommunication industry. The project utilizes an Ensemble Tree Method to achieve a prediction accuracy of 80%.
Customer churn is a critical issue in the telecommunication sector. This project focuses on:
- Analyzing customer data to identify patterns and factors contributing to churn.
- Building a predictive model using Ensemble Tree Methods to forecast customer churn with high accuracy.
- Data Analysis: Provides insights into customer behavior and churn trends.
- Predictive Modeling: Uses advanced machine learning techniques to predict churn.
- High Accuracy: Achieves an 80% prediction accuracy.
- Python
- Google Colab
- Machine Learning (Ensemble Tree Method)
The project uses a Telecommunication dataset containing customer information such as demographics, service usage, and subscription details.
- Clone the repository.
git clone https://github.com/Stellar-Cretaceus/Telco-Churn-Predictor cd telco-data-churn-predictor - Open the project in Google Colab.
- Follow the steps in the Jupyter Notebook to:
- Import and preprocess the dataset.
- Train the model using the Ensemble Tree Method.
- Evaluate the model's performance.
The model achieves:
- 80% accuracy in predicting customer churn behavior.
- Key factors influencing churn can be identified.
- Helps telecommunication companies proactively retain customers and reduce churn rates.
- Google Colab for providing an excellent platform for data analysis and model development.
- Inspiration from industry churn prediction use cases.