Telco Customer Churn Classification

Table of Contents

• Introduction
• Project Overview
• Dataset
• Data Preprocessing
• Feature Scaling
• Model Development
• Results and Evaluation
• Conclusion
• Key Concepts
• Project Structure

Introduction

This project focuses on predicting customer churn using machine learning techniques. A crucial aspect of this project is the data preprocessing stage, with a particular emphasis on feature scaling and its impact on model performance.

Project Overview

The goal is to develop a model that can predict whether a customer is likely to churn based on various attributes. The project demonstrates the importance of proper data preprocessing, including handling missing values, encoding categorical variables, and scaling features.

Project Structure

Data Exploration

• Dataset characteristics
• Visualization of feature distributions

Data Preprocessing

• Handling missing values
• Dealing with categorical variables

Feature Scaling Implementation

• MinMaxScaler
• StandardScaler
• RobustScaler

Visualization of Scaling Effects

Model Training and Evaluation

• Comparison of model performance with different scaling techniques

Analysis of Results

Best Practices and Recommendations

Dataset

The dataset includes information about:

• Customer demographics (gender, age range, partners, dependents)
• Services each customer has signed up for (phone, internet, online security, etc.)
• Customer account information (tenure, contract type, payment method, etc.)
• Billing information (monthly charges, total charges)

Key characteristics:

• No missing values in most columns
• 'TotalCharges' column contains some empty strings
• 'CustomerID' column is not needed for prediction

Data Preprocessing

Handling Missing Values:

• Identified and addressed empty strings in 'TotalCharges'
• Decision made to drop rows with missing values (11 rows) due to small number

Feature Engineering:

• Removed 'CustomerID' column
• Encoded categorical variables

Dealing with Duplicates:

• Checked and removed any duplicate entries

Feature Scaling

This project explores different scaling techniques and their impact on the churn prediction model:

StandardScaler (Standardization):

• Used for features with unknown distribution
• Helps in handling outliers better than normalization

MinMaxScaler (Normalization):

• Applied to bring features to a [0,1] range
• Useful when the boundaries of features are known

RobustScaler:

• Utilized for its robustness to outliers
• Especially useful for 'TotalCharges' which showed high skewness

Key considerations:

• Scaling applied after train-test split to prevent data leakage
• Comparative analysis of model performance with different scalers

Model Development

• Feature selection based on correlation analysis
• Train-test split of the dataset
• Model selection (e.g., Logistic Regression, Random Forest, etc.)
• Model training with different scaled datasets
• Hyperparameter tuning

Results and Evaluation

• Comparison of model performance with different scaling techniques
• Analysis of feature importance
• Evaluation metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC

Conclusion

This project demonstrates the critical role of data preprocessing, particularly feature scaling, in customer churn prediction. Key findings include:

• The impact of different scaling techniques on model performance
• The importance of handling skewed features like 'TotalCharges'
• Best practices for preprocessing in churn prediction tasks

The insights gained from this project can be applied to improve customer retention strategies and optimize business operations.

Key Concepts

Standardization vs. Normalization

• Standardization (Z-score normalization): Rescales features to have a mean of 0 and a standard deviation of 1. It's not bounded by a specific range.
• Normalization (MinMaxScaler): Rescales features to a fixed range, typically [0,1] or [-1,1].

Impact on Machine Learning Algorithms

• Gradient Descent Algorithms: Linear regression, logistic regression, and neural networks benefit from scaled features as it helps in faster convergence.
• Distance-Based Algorithms: K-Nearest Neighbors (KNN), K-means clustering, and Support Vector Machines (SVM) are highly sensitive to feature scales.
• Tree-Based Algorithms: Generally robust to feature scaling, but can still benefit in some cases.

Scaling Techniques

• MinMaxScaler (Normalization):

  X_scaled = (X - X_min) / (X_max - X_min)
  

Use cases:

• When the upper and lower boundaries of features are known
• In image processing, where pixel intensities need to be normalized

StandardScaler (Standardization):

X_scaled = (X - μ) / σ

Where μ is the mean and σ is the standard deviation.

Use cases:

• When the distribution of features is unknown or varies significantly
• For algorithms assuming normally distributed data

RobustScaler:

X_scaled = (X - median) / IQR

Where IQR is the Interquartile Range.

Use cases:

• When dealing with datasets containing significant outliers
• For preserving the relative relationships between outliers and other data points

Best Practices

• Scale after train-test split to prevent data leakage
• Consider scaling the target variable in regression problems
• Handle outliers carefully based on domain knowledge
• Choose the appropriate scaler for your data and algorithm
• Standardize non-normal distributions when appropriate
• Compare model performance with different scaling techniques

Handling Outliers

The project explores the impact of outliers on different scaling techniques:

• MinMaxScaler's high sensitivity to outliers
• StandardScaler's moderate sensitivity
• RobustScaler's effectiveness in handling outliers

Strategies for dealing with outliers are discussed and demonstrated.

Scaling and ML Algorithms

The project analyzes how different ML algorithms are affected by feature scaling:

• Gradient Descent based algorithms
• Distance-based algorithms
• Tree-based algorithms

Practical examples and performance comparisons are provided.