Telco Churn Classification Project

This content originally appeared on DEV Community and was authored by Ndumbe

Telco Churn Classification Project

Introduction

This Telco Churn Classification Project was designed to address the critical challenge of customer retention in the telecommunications industry. By leveraging data analytics and machine learning, the project aims to identify patterns and factors contributing to customer churn, enabling proactive strategies to retain customers. The primary objective is to transform raw customer data into actionable insights, guiding business decisions to enhance customer satisfaction and reduce churn rates.

Project Structure

The project follows a systematic approach divided into several key stages:

• Data Loading and Connection: A secure connection to the database was established using the pyodbc library. Environment variables were employed to ensure security during the extraction of customer data.

• Data Cleaning and Preprocessing: Data inconsistencies, missing values, and outliers were addressed to ensure the integrity of subsequent analyses. This step involved handling null values in columns like TotalCharges and converting data types where necessary.

• Exploratory Data Analysis (EDA): Visualizations and statistical summaries were generated using libraries such as seaborn and matplotlib. This phase uncovered trends, correlations, and key factors influencing customer churn.

• Feature Engineering: New features were derived from existing data to enhance model performance. For instance, tenure was categorized into bins to better understand its relationship with churn.

• Model Development: Machine learning models were built using sklearn. Algorithms such as logistic regression, decision trees, and random forests were employed to predict churn probabilities.

• Visualization and Reporting: Interactive dashboards were created using plotly for dynamic exploration of results. Additionally, Power BI was used to replicate visualizations for stakeholder presentations.

Technical Content

The project incorporated various technical methodologies:

1. Database Connectivity:

   • A connection string was configured using environment variables for secure access.
   • Data was extracted from the SQL Server database using SQL queries within Python.

2. Data Cleaning:

   • Missing values in TotalCharges were imputed with median values or dropped for rows with minimal impact.
   • Categorical variables like InternetService and Contract were encoded for model compatibility.

3. Exploratory Data Analysis (EDA):

   • Heatmaps revealed correlations between features like tenure and churn.
   • Bar charts illustrated churn rates across different contract types.
   • Example code snippet:

    sns.countplot(data=data, x='Churn', hue='Contract')
    plt.title('Churn by Contract Type')
    plt.show()
   

4. Machine Learning Models:

   • Logistic regression was implemented for baseline predictions.
   • Random forest classifiers provided improved accuracy by capturing feature interactions.
   • Example code snippet:

    from sklearn.ensemble import RandomForestClassifier
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
   

5. Visualization Tools:

   • Interactive graphs using Plotly allowed stakeholders to explore trends dynamically (example notebook link).

• Power BI dashboards provided a user-friendly interface for non-technical users (Power BI report link).

Conclusions and Recommendations

The Telco Churn Classification Project yielded significant insights into customer behavior:

• Customers with month-to-month contracts exhibited higher churn rates compared to those on annual or biennial contracts.
• Features like tenure, payment method, and internet service type strongly influenced churn predictions.

Recommendations:

1. Introduce loyalty programs or discounts for customers on month-to-month contracts to encourage long-term commitments.
2. Enhance customer service for high-risk groups identified by the model (e.g., customers with fiber optic internet).
3. Invest in collecting additional data points (e.g., customer satisfaction scores) to improve predictive accuracy.

This project underscores the importance of data-driven strategies in mitigating customer churn. By adopting a structured analytics workflow, businesses can proactively address challenges and foster stronger customer relationships.

This content originally appeared on DEV Community and was authored by Ndumbe

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