Customer Behavior Analysis in Megaline Mobile Provider
The project assigned to me during the seventh sprint involves Introduction to Machine Learning.
Throughout this sprint, I dived into the fundamentals of machine learning, with an emphasis on the Scikit Learn library.
As a Data Scientist at Megaline, a prominent mobile operator company, I was entrusted with the responsibility of developing a model to analyze consumer behavior. The goal was to recommend one of Megaline's two newest packages: Smart or Ultra, ensuring a minimum accuracy rate of 0.75.
Upon completion, here's a concise recap of the endeavor: I implemented a machine learning model utilizing the Random Forest algorithm, primarily because it exhibited superior accuracy in comparison to the Decision Tree and Logistic Regression models.
While the initial accuracy of the model was 0.74, it witnessed an improvement post hyperparameter optimization using the RandomForestClassifier. The final accuracy achieved was 0.81, with n_estimators set to 400 and max_depth_best set to 5. The negligible variance between training, validation, and test scores indicates that the model is well-calibrated, exhibiting no signs of overfitting.