MACHINE LEARNING · CLASSIFICATION
Megaline Customer
Plan Classification
Building a machine learning model to recommend the right mobile plan based on customer behavior.
01 · OBJECTIVE
Can customer behavior predict the right mobile plan?
Megaline wants to migrate customers away from legacy plans. The goal of this project was to build a classification model capable of recommending either Smart or Ultra based on monthly customer behavior.
02 · DATASET
Understanding the customer base
Each observation represents one customer's monthly behavior, including calls, minutes, messages and mobile data usage. The target variable identifies whether the customer uses the Smart or Ultra plan.
03 · METHODOLOGY
Train, validate, test
The dataset was split into three subsets to train the models, compare their performance, and evaluate the final model on unseen data.
04 · MODEL COMPETITION
Which model performs best?
Three classification algorithms were trained under the same train-validation split. Each model was first evaluated with its default configuration and then tuned using hyperparameters.
05 · FINAL RESULT
Does the model actually work?
After selecting Random Forest using the validation set, the final model was evaluated on the untouched test set and compared against a dummy baseline that always predicts the majority class.
06 · TRY THE MODEL
Try a prediction
Adjust monthly customer behavior and test the trained Random Forest model directly in the browser.