Machine Learning for Churn Prediction

Project Overview

Customer churn is a critical metric for businesses, representing the percentage of customers who stop using a company's products or services over a specified period. Accurately predicting churn allows businesses to proactively engage with at-risk customers, potentially reducing churn rates and improving customer retention. In this project, I aimed to develop a machine learning model to predict customer churn using a dataset from a phone/internet service provider.

Project Steps

1. Exploratory Data Analysis (EDA)

Began by loading and exploring the dataset, identifying key characteristics and understanding the distribution of features:

• The dataset contains 7,043 rows and 21 columns.
• There are no missing values, but the TotalCharges column needed to be converted from a string to a numeric type after handling whitespace values.

2. Data Cleaning

To prepare the data:

• Replaced whitespace in the TotalCharges column with NaN values and filled these using the column's median.
• The customerID column, which was not useful for modeling, was dropped.
• The PaymentMethod column was cleaned to simplify the categories.

3. Data Analysis

Conducted a thorough analysis of categorical and numeric features:

• Bar charts and scatter plots were used to identify correlations between features and churn.
• Key insights include the higher likelihood of churn among customers with month-to-month contracts, electronic checks as the payment method, and those lacking additional services like online security and tech support.

4. Data Preparation

To prepare the data for modeling:

• Categorical variables were encoded using LabelEncoder for binary variables and get_dummies for others.
• The dataset was split into training and testing sets, and used RandomUnderSampler to balance the training data.

5. Model Training and Evaluation

Trained and evaluated multiple machine learning models, including:

• Logistic Regression
• Decision Trees
• Support Vector Machines (SVM)

Evaluation Metrics:

• Recall was prioritized as it reflects the proportion of correctly identified churns.
• Precision was also considered to measure the accuracy of the positive predictions.

The models trained on balanced data yielded better recall results, with SVM showing the best performance.

6. Hyperparameter Tuning

Used GridSearchCV to tune the hyperparameters for the SVM and Logistic Regression models:

• The best recall score for SVM was 0.9319 with a poly kernel and C=0.01.
• The Logistic Regression model had a best recall score of 0.832 with liblinear solver and C=0.001.

7. Final Model Evaluation

The tuned SVM model was evaluated on the test set:

• The model achieved a recall of 0.94 for the churn class, indicating strong performance in identifying churning customers.
• The overall accuracy was 55%, which highlights the model's conservative approach to predicting churn, favoring recall over precision.

Conclusion

This project successfully built a churn prediction model using machine learning, with a particular focus on maximizing recall to identify potential churners. The SVM model, after hyperparameter tuning, provided the best performance, making it a valuable tool for businesses aiming to reduce churn and improve customer retention.