The Customer Churn Prediction project is a data-driven approach aimed at identifying customers likely to discontinue services with a telecom company. By utilizing machine learning with a TensorFlow model, this project analyzes historical customer data to uncover churn patterns. The process covers everything from data preprocessing to model evaluation, providing an essential tool for proactive measures in customer retention. With churn prediction, telecom companies can enhance customer retention strategies, ultimately boosting profitability in this competitive sector.
We’re predicting telecom customer churn to help companies retain customers and reduce turnover.
Customer behavior and service usage patterns are directly linked to their likelihood of churn. Understanding these patterns allows us to predict who might leave.
The Telco Customer Churn dataset was used, containing customer demographics, account information, and usage patterns.
- Cleaned and preprocessed the data, handled missing values, and converted categorical variables into numerical formats.
- Balanced the dataset using SMOTE to deal with imbalances in the target variable (Churn).
Built a neural network using TensorFlow, trained it on the preprocessed data, and evaluated its performance.
Analyzed the results and identified key predictors of churn, such as tenure and service usage.
Presented the findings and insights, offering recommendations on improving retention strategies based on model predictions.
To run this project, install the necessary dependencies:
pip install tensorflow