This project developed a Random Forest Classifier to recommend Smart or Ultra cell phone plans for fictional telecommunication company Megaline's legacy plan users based on usage patterns, achieving 80% accuracy on test data. The model provides a strong foundation for aligning plan offerings with customer behavior to improve satisfaction. Future refinements could further enhance predictive accuracy and drive plan conversions.
π Supervised Machine Learning π©π½βπ» Classification and Regression Models π§ͺ Scikit Learn π³ Decision Tree and Random Forest Models π€ Logistic Regression Models π― Evaluation Metrics for Model Quality including Accuracy and Mean Square Error βοΈ Tuning Hyperparameters βοΈ Model Comparison and Selection πͺ Jupyter Notebook ππ» Splitting Data
- This project uses pandas, train_test_split, DecisionTreeClassifier, accuracy_score, RandomForestClassifier, LogisticRegression, and DummyClassifier. It requires python 3.9.6.