Customer Churn Prediction for Telco Company¶
Problem Description ¶
In this project, we aim to predict customer churn for a telecommunications company. Customer churn refers to the phenomenon where customers stop using a company's services or products. Predicting churn is crucial for businesses as it allows them to take proactive measures to retain customers and reduce revenue loss.
We are hired as data scientists to develop a machine learning model that can accurately predict which customers are likely to churn. The company has provided us with a dataset containing various features related to customer demographics, account information, and service usage. Our task is to analyze the data, identify key factors contributing to churn, and build a predictive model that can help the company target at-risk customers with retention strategies.
The project will involve several steps, including data preprocessing, exploratory data analysis, feature engineering, model development, and evaluation. We will also explore the possibility of deploying the model using Streamlit to create an interactive web application for the company's use.
Project Structure ¶
We will structure our project as follows:
- Problem Description: This section will provide an overview of the problem we are trying to solve, the importance of churn prediction, and the objectives of our project.
- Exploratory Data Analysis: In this section, we will analyze the dataset to understand the distribution of features, identify patterns, and uncover insights that may help in predicting churn.
- Model Development and Evaluation: Here, we will develop various machine learning models to predict customer churn, evaluate their performance using appropriate metrics, and select the best-performing model.
- Model Deployment with Streamlit: Finally, we will create an interactive web application using Streamlit to allow the company to input customer data and receive churn predictions in real-time.
By following this structured approach, we aim to provide a comprehensive solution to the problem of customer churn prediction for the telecommunications company.