Telecom Churn Prediction System

This project focuses on predicting customer churn in the telecommunications industry using machine learning techniques. Customer churn refers to the situation when customers stop using a company's services.

The main goal of the system is to analyze customer data and identify patterns that indicate whether a customer is likely to leave the company. This helps telecom companies take proactive actions to retain customers and improve customer satisfaction.

Key Features:

Data preprocessing and cleaning

Exploratory Data Analysis (EDA) to understand customer behavior

Feature selection to identify the most important factors affecting churn

Building and training machine learning models for prediction

Evaluating model performance using accuracy and other metrics

Technologies Used:

Python

Pandas and NumPy for data processing

Matplotlib / Seaborn for data visualization

Scikit-learn for machine learning models

Outcome:

The system successfully predicts customers who are likely to churn, allowing businesses to take preventive actions such as targeted offers or improved customer support to reduce customer loss.