Telecom Churn Prediction is a data science project that aims to predict customer churn in a telecommunications company. Customer churn refers to the phenomenon where customers leave the company's services for various reasons, such as switching to a competitor or discontinuing the service altogether. This project uses machine learning techniques to identify factors that contribute to churn and build predictive models to anticipate customer attrition.
In this project, we analyze historical customer data to:
- Explore and preprocess the dataset.
- Perform exploratory data analysis (EDA) to gain insights into customer behavior.
- Create predictive models using machine learning algorithms.
- Evaluate the performance of the models and choose the best one.
- Deploy the chosen model for real-time predictions or use it to identify customers at risk of churn.
The dataset used for this project can be found in the data directory. It contains the following columns:
customer_id: Unique identifier for each customer.contract_duration: The duration of the customer's contract.monthly_charges: The amount the customer is charged monthly.total_charges: The total amount charged to the customer.churn: The target variable indicating whether the customer has churned (1) or not (0).
To run this project locally, follow these steps:
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Clone the repository:
git clone https://github.com/your-username/telecom-churn-prediction.git