Logistic-Regression

After completing this lab you will be able to: * Use Logistic Regression for classification * Preprocess data for modeling * Implement Logistic regression on real world data

Logistic Regression for Telco Customer Churn Prediction

This Jupyter notebook implements a complete Logistic Regression classification model to predict customer churn in the telecommunications industry using Python and scikit-learn. The project demonstrates end-to-end machine learning workflow including data preprocessing, model training, evaluation, and feature importance analysis.[1]

Project Overview

This implementation focuses on predicting which customers are likely to leave a telecommunications company's land-line business for cable competitors. The model achieves approximately 66% accuracy through logistic regression classification and provides insights into the key factors influencing customer churn.[1]

Dataset

The project uses the Telco Customer Churn dataset, a hypothetical dataset representing telecommunications customer data. The dataset contains 200 customer records with 28 features including:[1]

Demographic Features:

• tenure: Length of customer relationship
• age: Customer age
• address: Years at current address
• income: Annual income
• ed: Education level (1-5 scale)
• employ: Years with current employer

Service Features:

• equip: Equipment rental (binary)
• callcard: Calling card service (binary)
• wireless: Wireless service (binary)

Target Variable:

• churn: Customer churn indicator (0=retained, 1=churned)[1]

The dataset exhibits a 29% churn rate (58 out of 200 customers), providing a realistic business scenario for classification modeling.[1]

Key Features

Data Preprocessing:

• Feature selection and subset extraction
• Data type conversion for target variable
• StandardScaler normalization for feature scaling
• Train-test split (80-20 ratio) with random state for reproducibility[1]

Model Implementation:

• Logistic Regression classifier from scikit-learn
• Model training on standardized features
• Prediction and probability estimation
• Log-loss evaluation metric for model performance assessment[1]

Analysis & Visualization:

• Feature coefficient visualization using horizontal bar plots
• Interpretation of coefficient magnitudes and directions
• Probability-based prediction analysis
• Feature importance ranking for business insights[1]

Technical Stack

• Python 3.12.4
• pandas 2.2.3: Data manipulation and analysis
• numpy 2.2.0: Numerical computing
• scikit-learn 1.6.0: Machine learning algorithms and preprocessing
• matplotlib 3.9.3: Data visualization[1]

Model Performance

The trained model achieves a log-loss of approximately 0.626 on the test set, indicating reasonable predictive performance. Log-loss (logarithmic loss or binary cross-entropy) measures the performance of a classification model where predictions are probability values between 0 and 1 - lower values indicate better model performance.[1]

Feature Importance Insights

The model provides interpretable coefficients for each feature, where:

• Large positive coefficients indicate that increases in the feature value lead to higher churn probability
• Large negative coefficients indicate that increases in the feature value lead to lower churn probability
• Small absolute values suggest weaker influence on churn prediction[1]

The visualization clearly shows which customer attributes have the strongest impact on churn decisions, enabling data-driven retention strategies.[1]

Usage

The notebook provides a complete, executable workflow that can be run sequentially from top to bottom. Key sections include:[1]

1. Library installation and imports
2. Data loading and exploration
3. Feature engineering and preprocessing
4. Data standardization
5. Train-test splitting
6. Model training
7. Prediction generation
8. Performance evaluation
9. Feature coefficient analysis and visualization

Practice Exercises

The notebook includes hands-on exercises to explore model behavior with different feature combinations:

• Adding individual features (callcard, wireless)
• Removing features (equip, income, employ)
• Analyzing the impact on log-loss performance[1]

Learning Objectives

After completing this implementation, users will be able to:

• Use Logistic Regression for binary classification tasks
• Preprocess data for machine learning modeling
• Implement end-to-end ML pipelines
• Evaluate model performance using appropriate metrics
• Interpret model coefficients for business insights[1]

Estimated Completion Time

60 minutes[1]

Applications

This implementation is ideal for:

• Customer churn prediction in telecommunications and subscription-based businesses
• Educational purposes for learning classification techniques
• Foundation for more advanced customer retention models
• Business intelligence and customer analytics projects[1]

Dataset link: https://www.kaggle.com/datasets/ezzaldeenesmail/telco-churn-dataset/data