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.
👀 Supervised Machine Learning 👩🏽💻 Classification and Regression Models 🧪 Scikit Learn 🌳 Decision Tree and Random Forest Models 🤔 Logistic Regression Models 💯 Evaluation Metrics for Model Quality including Accuracy and Mean Square Error ⚙️ Tuning Hyperparameters ✔️ Model Comparison and Selection 🪐 Jupyter Notebook 🖖🏻 Splitting Data
- This project uses pandas, train_test_split, DecisionTreeClassifier, accuracy_score, RandomForestClassifier, LogisticRegression, and DummyClassifier. It requires python 3.9.6.