Your task for this assignment is to perform the data preparation necessary to train the above model; however, you will not actually train any models for this assignment. You must construct a Jupyter notebook that reads in data from the supplied Excel file, then performs the necessary data manipulations to obtain two Pandas DataFrames. The first DataFrame must be called features, which is your feature matrix. The features DataFrame must contain the following feature columns in this exact order: days_cube, days_sq, days, temp, pop_dense_sq, pop_dense, pop. These features correspond to the features present in the equation above. Note that days_cube and days_sq are the cubed and squared values of days, respectively. days is the number of days since the outbreak began, which begins with zero. pop_dense_sq is the squared value of pop_dense, and pop is the city population. The second DataFrame contains target values and must be called response, which are the accumulated case counts for all capital cities over time. Your task is to construct these two Pandas DataFrames using the data provided. Some things to note: There are 27 capital cities in Brazil. Each capital city has 151 days of recorded data starting from day zero, which is the day of the first outbreak. One of the Excel sheets spells a capital city Aracaju with an accent, whereas the other sheets spell the same capital city as Aracaju - this may or may not affect you, but beware. Population density is defined as the population of a city divided by the area in kilometers squared of the city. You should begin by watching the video called Pandas DataFrame and completing the PandasDataFrame_template.ipynb. This video and notebook provide you with a review and some new materials about DataFrames - they will prepare you for completing Assignment 2