Data Mining and Machine learning project

Produce a portfolio of studies that applies machine learning methods to generate data insights in a specic topic of interest. The portfolio should critically compare the performance of dierent machine learning methods applied 3 large datasets related to the chosen topic of interest. Number of Methods: In total, you should apply and critically evaluate 5 methods of machine learning for this project to facilitate your discussion: -Regression -Classification -Clustering -Dimensionality Reduction -Ensemble Methods -Neural Nets and Deep Learning -Transfer Learning -Reinforcement Learning -Natural Language Processing -Word Embeddings Notions of Performance: The discussion of performance should be orientated around multiple no- tions of performance. It is not sucient to discuss only accuracy or R2 for the methods applied. Other possibilities include, but are not limited to: Cohen's Kappa, RSME, RSS, Sensit- ivity/Specicity, FMeasure, and MAPE. Methodology: The application of each method must follow an appropriate data mining methodology, where CRISP-DM [1] and KDD [2] are foreseen as most likely to be appropriate. The final report must follow the IEEE conference format and should be between 8-10 double column pages in length (this includes all _gures and references). For this report IEEE referencing style should be used. Microsoft Word and LATEX templates are available at [login to view URL]