Machine-learning-algorims

This project aims to develop a machine learning model that can predict whether or not a song will be liked by a user based on its features such as energy and danceability. We used logistic regression with linear discriminant analysis, quadratic discriminant analysis, naive Bayes, K nearest neighbor, classification tree, bagging, and random forest algorithms to train and test our model. We compared the performance of these algorithms and identified the best model for predicting song likability. Our analysis revealed that the random forest algorithm outperformed the other algorithms with an accuracy of 86%. The results showed that the energy and danceability features were the most important predictors of song likability. This project highlights the power of machine learning in solving real-world problems such as improving music listening experiences. The model developed in this project can be used to recommend songs to users based on their preferences and improve music discovery on streaming platforms. Overall, this project provides insights into the strengths and weaknesses of various machine learning algorithms for classification problems and highlights the potential for further research in this area.