saba-gul / telecom-churn-prediction-and-analysis

To explore and analyze the Telecom Churn dataset to understand factors contributing to customer churn and to develop a predictive model that can forecast customer churn with high accuracy

Customer Churn Prediction in Telecom Industry

Overview

This project aims to predict customer churn in the telecom industry using machine learning techniques. Customer churn, or the rate at which customers stop doing business with a company, is a critical metric for telecom companies as it directly impacts revenue and profitability. By accurately predicting churn, companies can take proactive measures to retain customers and improve overall business performance.

Table of Contents

• Introduction
• Methodology
• Key Findings
• Recommendations

Introduction

Customer churn, also known as customer attrition, is a critical challenge for telecom companies. It refers to the phenomenon where customers discontinue using the services provided by a company. High churn rates can negatively impact revenue and profitability. Therefore, predicting and reducing churn is essential for maintaining a sustainable business.

Methodology

Data Collection:

• Description of the dataset used.
• Features included and their significance.

Exploratory Data Analysis (EDA):

• Distribution of key variables.
• Relationships between features and churn.

Feature Engineering:

• Deriving new features.
• Selecting relevant features for modeling.

Modeling Approach:

• Utilized logistic regression and random forest classifier.
• Split data into training and testing sets.
• Evaluated model performance using accuracy, precision, recall, and F1-score.

Key Findings

Interpretation of Model Results:

• Coefficients for logistic regression and feature importances for random forest.
• Identification of key factors predicting churn.

Model Performance:

• Performance metrics before and after tuning.
• Improvement in model performance post-tuning.

Actionable Insights:

• Recommendations based on key findings.
• Strategies to reduce churn and improve customer retention.

Recommendations

• Targeted marketing campaigns focusing on month-to-month contracts.
• Improving customer experience for fiber optic users.
• Enhancing security and support features.
• Addressing billing and payment issues.
• Implementing onboarding programs for new customers.