Why is it important to incorporate some storytelling principals into effective data communication?
Learning Goal: I’m working on a python multi-part question and need an explanation and answer to help me learn.DSA 610, Homework #9, Spring 2021Part I:Instructions: Answer each discussion question in your own words. You may use posted resources or other online resources to answer the questions (cite your sources). Thoroughly explain your responses with a minimum of one paragraph (3-5 sentences) in length. Be thoughtful. We will discuss the answers in class.1.Why is it important to incorporate some storytelling principals into effective data communication?2.Give me a draft of the abstract for your final project. What are the essential results you want to communicate? Who is your audience?3.Search the web and include at least three screenshots of data dashboards. Describe the effectiveness of each in communicating information. What commonalities do you notice?4.In what ways does making time series forecasts differ from making machine learning predictions? How does the test/train split have to change to validate the models?5.Why is it important that data analysis not stop with communicating the results?Part II:Instructions: Complete the following tasks in Python. Submit your Jupyter Notebook with the solutions. Illustrate your answers with supporting graphs where appropriate. Use the household_income_expenses_nulls.xlsx data to complete this assignment.1.Import the data and replace the null values with 0.2.You will need to make a dummy variable of the Location variable.3.Make a new variable of total household income from the First and Second Income columns.4.Create some plots of the data.5.Split the data into test and train.6.Using a model type of your choice appropriate to the data, produce the model with the training set, and analyze the quality of the predictions with the test set, using appropriate metrics. (I suggest predicting Debt or Homeownership, but you may predict any of the variables you want from the others, just be sure to explain what you are doing.)7.Are any of the variables poor predictors that can be dropped from the model? How did you determine that?8.Write a short summary of your analysis. What did you learn from your analysis and how could this information be acted upon?