Need guidance to do this.

Final Project Requirement: Please see attached for a word template for the final report. The final report should include the following items: 1. Find a business area and define a data scinece quesiton that can be tested and analyzed. Background information and the motivation of study should be included. 2. Find a dataset, which can be used to answer your question. 3. Determine the type of model based on the question (descriptive, predictive), and pick the best set of variables for your analysis. 4. Describe the research question and your dataset. 5. Do the data description analysis using statistical numbers and graphs (using table and graphs) 6. Do the data analysis to answer the research question (describe the model, run the model, and show the results). 1. You need to use at least one model learned from the class (including: K-means clustering, hierarchical clustering, linear regression, logistic regression, decision tree, SVM) 7. Do the model evaluation (evaluate the model to see whether it is good) 8. Discuss potential problems and improvements Final Project Requirement: Please see attached for a word template for the final report. The final report should include the following items: 1. Find a business area and define a data scinece quesiton that can be tested and analyzed. Background information and the motivation of study should be included. 2. Find a dataset, which can be used to answer your question. 3. Determine the type of model based on the question (descriptive, predictive), and pick the best set of variables for your analysis. 4. Describe the research question and your dataset. 5. Do the data description analysis using statistical numbers and graphs (using table and graphs) 6. Do the data analysis to answer the research question (describe the model, run the model, and show the results). 1. You need to use at least one model learned from the class (including: K-means clustering, hierarchical clustering, linear regression, logistic regression, decision tree, SVM) 7. Do the model evaluation (evaluate the model to see whether it is good) 8. Discuss potential problems and improvements