Using below datasets:

https://www.kaggle.com/kemical/kickstarter-projects

https://www.kaggle.com/rhuebner/human-resources-data-set

https://www.kaggle.com/kondla/carinsurance

https://www.kaggle.com/lukamauto/insurance-claim-info-for-vehicles-being-serviced

https://www.kaggle.com/iabhishekofficial/mobile-price-classification

https://www.kaggle.com/prakharrathi25/banking-dataset-marketing-targets

You have the choice of performing the four different analyses we learned in this course (association rule analysis, clustering, numeric prediction, and classification) on 1, 2, 3, or 4 of the above datasets. For example, you can perform all four analyses on the same dataset, on four different datasets (one for each), or two or three analyses on the same dataset. The requirement is to perform all of the four analyses and provide appropriate discussions of the results. Please do not stop after getting all the results; you need to discuss insights based on various results you obtained. If you face new data preprocessing challenges for a certain attribute (e.g., a date- or time-related attribute), please do your own research to resolve it or exclude the attribute from the analysis.

Use a different .ipynb file for each of the four analyses (even if they are all on the same dataset). You can put your explanations and discussions in the .ipynb files. If there is additional information (e.g., graphs) that you cannot include in the .ipynb file, you can also submit one additional Word file.