In this final assignment, you will write a data analysis report that demonstrate concepts covered in the course using Python. Your report should between four (4) and seven (7) single-spaced pages with the following sections: introduction, data, methods, results, discussion, conclusion. Explanations of what is required within each section are below. You are going to find a dataset and mine and analysis.
CIS3090 Business Intelligence Fall 2019 Due on December 14th 11:59PM Instruction In this final assignment, you will write a data analysis report that demonstrate concepts covered in the course using Python. Your report should between four (4) and seven (7) single-spaced pages with the following sections: introduction, data, methods, results, discussion, conclusion. Explanations of what is required within each section are below. You are going to find a dataset and mine and analysis. Dataset Your dataset can consist of any data you feel is appropriate for descriptive or predictive analytics, (regression or classification). Some examples include data related to sports information, census or government statistics, scientific observations, social-network information, social media data (movies, music, etc.), consumer behaviors, socioeconomic data, and security information (e.g., intrusion detection logs). The following links are to sites that contain (or link to) public datasets. Use these to get started or if you are having trouble finding data. · www.data.gov · www.kaggle.com/datasets · www.archive.ics.uci.edu/ml/datasets.html · www.aws.amazon.com/datasets · www.data.worldbank.org · www.pewinternet.org/datasets · www.labrosa.ee.columbia.edu/millionsong · www.sports-reference.com · www.wunderground.com/history · www.yelp.com/academic_dataset · www.developer.bestbuy.com/apis · www.ll.mit.edu/mission/communications/cyber/CSTcorpora/ideval/data Mine the Dataset You should perform data preprocessing (as needed), exploratory analysis of the dataset (including visualizations), and testing and evaluation of the model. Project Document Outline The introduction is where you provide a brief description of your personal motivation for the project and frame your report. Tell the reader why they care about the results you are about to present and what is the question you will be answering is important. A description of your dataset including what type of data it contains, how many attributes, how many instances, what the class labels are if its classification. Any additional challenges such as messy or missing values. The data analysis section is where you describe your data showing exploratory analysis through charts and tables showing basic data distributions (e.g., frequency diagrams, scatter plot diagrams), and “interesting” relationships between attributes. Your job for this part is to give more insight into your dataset that is relevant for the regression of the classification task. Explain what the data is and anything about that data that is especially interesting. In the methods section, explain the method(s) you use to analyze the data. Discuss how the method works, why it was well suited for your data, and how you applied it. The results section is where you describe what the analysis resulted in. Again, it is generally appropriate to use charts or tables here to illustrate important findings. The discussion section is the place to explain your findings. Why do you think you found the results you did and what do you think they mean? This is generally the only section where it is appropriate to make educated guesses about the phenomena you observed. A brief description of your new data mining topic you learned. Finally, the conclusion should provide the analysis' answer to the question posed in the introduction along with a brief description of why the analysis answered the way it did, which should be consistent with your discussion section. Additionally, you may wish to posit questions raised by your analysis or areas for future analysis. In data analysis reports, it is also common to have a recommendation. Describe any key components of the code. Project Submission Submit your project to blackboard by the due date, no late submissions will be accepted. You should submit: 1. Your source code in a well-documented Jupyter Notebook and dataset files. Example runs of your program. 2. Your project report First_Lastname_FinalProject.docx. Grading This project is worth 100 points + 10 points bonus. The 10 points bonuses if you demonstrate that you have implemented new data mining topic or method that was not covered in class. · 50 Points for implementation. Your code must be your own code and well documented and clear. · 50 points for technical reporting final project document and format.