Need Part-2 & Part-3 items:
Based on USA elections 2020 data, Reference: https://www.kaggle.com/unanimad/us-election-2020 File Names: governors_county.csv governors_county_candidate.csv governors_state.csv house_county.csv house_county_candidate.csv house_state.csv president_county.csv president_county_candidate.csv president_state.csv senate_county.csv senate_county_candidate.csv senate_state.csv
Planning to identify the following questions to help investigate my problem statement. How many presidential votes exist at count level? How many candidate’s context in elections for presidential position from each party and number of votes individual got to be elected as presidential? How many votes cumulatively used by citizen to elect presidential and any elector votes if exist any? Are there any states / county level numbers so close by to determine the close proximity votes the losing party missed, which might help in future to recover from such counties. Along with governor elections data analysis, How does data analysis look like for congress house representative elections? How does data analysis look like for governor representative elections? How does data analysis look like for senate representative elections?
Need answers to below questions: Data importing and cleaning steps are explained in the text and in the Github exercises. (Tell me why you are doing the data cleaning activities that you perform). Follow a logical process. With a clean dataset, show what the final data set looks like. However, do not print off a data frame with 200+ rows; show me the data in the most condensed form possible. What do you not know how to do right now that you need to learn to import and cleanup your dataset? Discuss how you plan to uncover new information in the data that is not self-evident. What are different ways you could look at this data to answer the questions you want to answer? Do you plan to slice and dice the data in different ways, create new variables, or join separate data frames to create new summary information? Explain. How could you summarize your data to answer key questions? What types of plots and tables will help you to illustrate the findings to your questions? Ensure that all graph plots have axis titles, legend if necessary, scales are appropriate, appropriate geoms used, etc.). What do you not know how to do right now that you need to learn to answer your questions? Do you plan on incorporating any machine learning techniques to answer your research questions? Explain. Overall, write a coherent narrative that tells a story with the data as you complete this section. Summarize the problem statement you addressed. Summarize how you addressed this problem statement (the data used and the methodology employed). Summarize the interesting insights that your analysis provided. Summarize the implications to the consumer (target audience) of your analysis. Discuss the limitations of your analysis and how you, or someone else, could improve or build on it. In addition, submit your completed Project using R Markdown or provide a link to where it can also be downloaded from and/or viewed.