Predicting Customer Churn: A Comprehensive Data Science Approach

Introduction

Customer churn is a critical issue for businesses, as retaining customers is often more cost-effective than acquiring new ones. In this analysis, we leverage data science techniques to identify key factors driving customer churn and build a predictive model.

Complete Code

You can find the complete code and dataset on GitHub

Dataset Overview

The dataset consists of customer demographics, contract details, and service usage information. The target variable is churn, which indicates whether a customer left the company.

Data Preprocessing

• Handled missing values using imputation techniques.

• Encoded categorical variables using one-hot encoding and label encoding where necessary.

• Removed unique identifiers (e.g., customer ID) to avoid bias in the model.

Exploratory Data Analysis (EDA)

• Correlation Heatmap: Explored relationships between numerical features and churn.

• Churn Distribution by Category: Used bar plots to analyze how different groups contribute to churn.

• Key Insights from EDA:

  • Customers with no partners or dependents churned more.

  • Month-to-month contracts had the highest churn rate.

  • Electronic check payments were more common among churned customers.

  • Senior citizens had a higher churn rate.

Feature Importance

To determine the most influential factors in churn prediction, we used:

• Logistic Regression for coefficient-based analysis.

• Random Forest Classifier for feature importance visualization.

Model Training & Evaluation

Models Used:

1. Logistic Regression

2. Random Forest Classifier

Performance Metrics:

Key Takeaways

• Contract Type was the most significant predictor of churn.

• Monthly Charges had a weak correlation with churn.

• Senior citizens and paperless billing customers were more likely to leave.

• Improving customer retention strategies for these groups could reduce churn.

Next Steps

• Hyperparameter tuning to improve model accuracy.

• Feature engineering for better representation of customer behavior.

• Deployment of the model as an API or web dashboard.

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