Data Analytics -- 2
Budget: ₹1,200 – ₹1,500 INR
3. Create a line graph. The x-axis is the months in the year. The y-axis is the number of inspections. Have each line represent a different year.
a. Which Month and Year had the highest number of inspections?
b. Examine the behavior of each year. What months tend to have a higher number of inspections compared to other months?
c. What months tend to have a lower number of inspections?
4. Consider the “Results” column.
a. What are the different kinds of results and how many inspections were there for each?
b. Break this information down by Year and show it as a stacked bar chart. Do not include 2022 (because the data is incomplete). Is there anything unusual or any trends you can see in the data?
c. The graph in 4b has total number of records as the y-axis. Change it to be out of 100% so that each category is the percent of the total per year. Choose a column or row depending on where your “Year (Inspection Date)” pill is) Which trends appear stronger with this view?
5. Identify the variables with values that will need to be cleaned up due to spelling mistakes, etc.
6. Consider the Risk and Results categorical variables. Create a graph or chart to help answer the following questions. Remove 2022 from the data.
a. What combinations of values do not exist?
b. How many null values for the Risk variable are there? How many null values of the Results variable are there?
7. Show in a map the total number of inspections per zipcode (Use “Zip” provided by the data). Which zipcodes are the highest? Which are the lowest?
a. If the data collected is supposed to be the Chicago metropolitan area, then are there any zipcodes that might either be a mistake or incorrect value?
a. Which Month and Year had the highest number of inspections?
b. Examine the behavior of each year. What months tend to have a higher number of inspections compared to other months?
c. What months tend to have a lower number of inspections?
4. Consider the “Results” column.
a. What are the different kinds of results and how many inspections were there for each?
b. Break this information down by Year and show it as a stacked bar chart. Do not include 2022 (because the data is incomplete). Is there anything unusual or any trends you can see in the data?
c. The graph in 4b has total number of records as the y-axis. Change it to be out of 100% so that each category is the percent of the total per year. Choose a column or row depending on where your “Year (Inspection Date)” pill is) Which trends appear stronger with this view?
5. Identify the variables with values that will need to be cleaned up due to spelling mistakes, etc.
6. Consider the Risk and Results categorical variables. Create a graph or chart to help answer the following questions. Remove 2022 from the data.
a. What combinations of values do not exist?
b. How many null values for the Risk variable are there? How many null values of the Results variable are there?
7. Show in a map the total number of inspections per zipcode (Use “Zip” provided by the data). Which zipcodes are the highest? Which are the lowest?
a. If the data collected is supposed to be the Chicago metropolitan area, then are there any zipcodes that might either be a mistake or incorrect value?