Project Title: Predicting Customer Churn in a Telecom Company using Machine Learning
The aim of this project is to predict customer churn in a telecom company using machine learning techniques. Customer churn is the rate at which customers stop using a company's services, and predicting it can help the company identify customers who are at risk of leaving, and take proactive measures to retain them.
As a student, you can start by understanding the concept of customer churn and how it affects a telecom company's business. You can then collect and preprocess a dataset of customer information, such as demographic data, call and text usage, billing information, and other customer data.
After preprocessing the data, you can perform exploratory data analysis to identify patterns and trends that may indicate a likelihood of churn. You can then use various machine learning techniques, such as logistic regression, decision trees, random forests, and support vector machines (SVMs) to build predictive models.
You can evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1 score. Once the models have been trained and evaluated, you can deploy them to predict customer churn in real-time.
The final deliverable can be a report detailing the methodology, findings, and recommendations for the telecom company.
- A detailed analysis of the customer data and the factors that contribute to customer churn in the telecom industry.
- A set of machine learning models that can predict customer churn with high accuracy.
- A user-friendly web interface that allows the telecom company to input customer data and get predictions in real-time.
- A comprehensive report that details the methodology, findings, and recommendations for the telecom company.
Tools and Technologies:
As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline: Week 1: Understanding the concept of customer churn and the telecom industry Week 2-3: Data Collection and Preprocessing Week 4-5: Exploratory Data Analysis and Feature Engineering Week 6-7: Model Development and Evaluation Week 8: Report Writing and Presentation.