Use of AI Modeling in Determination of Premium Prices for the Insurance Industry

See attachment. Let me know if you need to add extra pages.
Assignment Summary: This assignment is to select a Dataset and analyze it (through the lens of answering a business question) with the appropriate AI techniques in Python you learned during the semester in Chapters 12, 13, 15 or 16. You may NOT use any of the datasets used as examples in the book for those chapters. You may NOT use any of the datasets used in homework exercises assigned this semester. If you use one of these datasets, your grade on the paper will be zero. This semester we learned several AI techniques in Python to analyze data. You should think about data that interests you and how it could be analyzed with AI to inform a business problems/issues/qustions. You should identify a dataset that exists that you can use (there is no requirement to collect your own data). There are many web sites that provide datasets – you’ll need to spend some time looking for a dataset that you are interested in. The dataset must have at least 10,000 cases/items and at least 5 features per item. The database must be publicly available and in English. You might try (but there are many other sites available): https://aihub.cloud.google.com( https://www.tensorflow.org/datasets/catalog/overview Assignment Details: This assignment is submitted in 2 parts both due on the same day/time as noted in CourseSite. Part 1: Word document submitted via TurnItIn.com link. Part 2: Jupyter notebook with code containing all revelant analyses that you used to write the paper. The notebook should include Markdown cells or comments in the code codes giving details on what each code block is doing. These can be short but must clearly explain in English what the code is doing (example: The following code implements the KneighborsClassifier and trains the model). Word Document Deliverable: The focus of this paper is to describe the data you will use for the final project, what analyses you choose to do on the data, and what you concluded from the analyses. The paper must include the following sections, numbered with these exact headings: 1.     Introduction: What is the dataset you are using and where did you find it? Give the exact URL in the reference section at the end of the paper. Describe the data in full sentences including how the data was collected, when it was collected, how many data points are in the dataset and how many variables (columns) are in the dataset. What does each data point (meaning each row) represent? For example, in the California Housing dataset, each row is a census block. In the movie data each row is a movie. Include why you think this data is interesting to study in the context of AI. What possible problems could exist with the data (such as issues with the data collection that would make the data false or biased)? If you find more information about the data online (such as what others have analyzed), give all relevant URLs in the reference section. 2.     Detailed Description of Data: For each variable in the dataset, give the descriptors from the describe() command including count of examples, mean, standard deviation, max, min, and various quantiles. Note which columns are numerical and which are categorical. If there are many columns, you only need to include the columns you think you would use for your specific business questions. Detail any interesting observations or discrepancies you see for any columns, such as the data being skewed in a certain direction, having a low or high standard deviation, or a substantial amount of missing data. You must write about any possible bias you see in the data and how to correct the possible bias.You should use tables or other figures here to help understand the descriptive data. Do not provide the Python code, but include graphs and tables from the Python output here. 3.     Three AI Business Questions: What business questions might be posed for this data that you could consider exploring using an AI model in the final project? You must pose at least three questions in three separate paragraphs. Examples from the tutorials would be “Can we predict median housing price in a city block given the total rooms in properties in that city block?” or “Given census data about a person such as age, gender, education and occupation, can we predict whether or not the person earns more than $50,000/year?” For EACH AI business question, give details about which variables (columns) you would use – include which variables are the independent variables and which are the dependent variables. Include any transformations you may need to make to any variables (such as transforming a numerical variable into a binary variable). The business questions must be substantially different from each other. For example, you can’t take the census data income question above and change $50,000 to $100,000 and call that another question. You don’t have to use these questions for the final project, but they are a good starting point. You must include for question why it is interesting – would it affect public policy or business decisions of a particular company, or something else? This must be fairly detailed and could include references to articles about current business issues. 4.     AI Model Analysis: This section has the following subsections: a.     Businss Question and AI Technique: Select ONE of the business questions and ONE AI technique learned in class. You must use a technique from the textbook chapters we covered. For example, classification, sentiment analysis, multiple linear regression, unsupervised machine learning, convolutional neural networks (this is not a complete list). Write a short paragraph on which business problem you selected, and why you selected a particular AI technique to explore the business question. b.     Data Visualization: For the question you are analyzing, provide appropriate visualization. This could be word clouds, scatterplots, bar graphs. The code to produce these should be in the Jupyter Notebook, but you must copy and paste the visualization into your paper. You must number and give each visualization a Title (example could be Figure 1: Scatter Plot of Median House Price by House Age). Write about each visualization you chose in your paper detailing what it means. c.     AI Model Results: Using the relevant output from the Jupyter Notebook, write about the results of your model and any tuning you did (for example, trying different hyperparameters). You should intrepret the results, not just report them. What do they mean in the context of your business problem? 5.     Conclusion: Write a short concluding paragraph about your chosen dataset and business question you analyzed, summing up what you learned by exploring the data and running the AI model.
!!! NOTE: ignore the coding part of the instructions !!!