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Intro_to_Machine_Learning_Project

This was an Intro to Machine Learning project for TripleTen. πŸ‘©πŸ½β€πŸ’»

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.

Skills Highlighted

πŸ‘€ Supervised Machine Learning πŸ‘©πŸ½β€πŸ’» Classification and Regression Models πŸ§ͺ Scikit Learn 🌳 Decision Tree and Random Forest Models πŸ€” Logistic Regression Models πŸ’― Evaluation Metrics for Model Quality including Accuracy and Mean Square Error βš™οΈ Tuning Hyperparameters βœ”οΈ Model Comparison and Selection πŸͺ Jupyter Notebook πŸ––πŸ» Splitting Data

Installation & Usage

  • This project uses pandas, train_test_split, DecisionTreeClassifier, accuracy_score, RandomForestClassifier, LogisticRegression, and DummyClassifier. It requires python 3.9.6.