Customer Churn Prediction In Telecommunication Industry Using Statistical Machine Learning Algorithms

This project aims to predict customer churn in a telecommunication dataset. The analysis includes data preprocessing, exploratory data analysis, feature engineering, model building, and evaluation. The goal is to identify the best predictive model for customer churn and provide actionable insights to reduce churn rates.

Table of Contents

• Dataset
• Project Overview
• Installation
• Usage
• Modeling Techniques
• Results
• Conclusion
• Contributing
• License

Dataset

The dataset used in this project contains customer information from a telecommunications company, including features such as:

• CustomerID
• Gender
• SeniorCitizen
• Partner
• Dependents
• Tenure
• PhoneService
• MultipleLines
• InternetService
• OnlineSecurity
• OnlineBackup
• DeviceProtection
• TechSupport
• StreamingTV
• StreamingMovies
• Contract
• PaperlessBilling
• PaymentMethod
• MonthlyCharges
• TotalCharges
• Churn

Project Overview

The project follows these main steps:

1. Data Loading and Preprocessing
2. Exploratory Data Analysis (EDA)
3. Feature Engineering
4. Model Building and Evaluation
5. Model Comparison and Selection
6. Conclusion and Insights

Installation

To run this project, you need to have the following packages installed:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• xgboost
• imbalanced-learn

You can install the required packages using the following command:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost imbalanced-learn