Please download and save a local copy of the Restaurant.zip data set. This data set has information about 130 restaurants, their characteristics, cuisines they offer and other descriptive information in Five different files. The data set also has information about 138 customers, their demographics, preferences and descriptive information in three user centered files. The last file lists customer ratings of these restaurants based on food, service and overall quality. Your task is to analyze this data set to create a baseline for comparison with other data mining techniques in the coming weeks. At this time, before we have studied any new data mining approaches, you will analyze the data using your existing data analysis skills (spreadsheets, databases etc.) Some of the tools you might find useful are basic ones like averages, distributions, filtering of the data (possibly by multiple criteria). You could also consider lookup functions and pivot tables. Another solution approach might be to import the files to a relational database. The deliverable for this assignment is a list of five to ten well documented claims that can be supported by the data. These statements should be clearly identified by the underlying data, rather than being broad observations. You could start with your intuitions and test to see if your intuition is actually supported by the data. Some acceptable forms of claims are: "68% of single, male, social drinker customers prefer restaurants with smoking sections while 54% of the customers with kids prefer non-smoking restaurants." "On average, restaurants with valet parking are rated higher (1.76 vs 1.43) by customers with high budgets as compared to the customers with low budgets." You should explain based on what summary statistics you are able to make this claim. Summary file descriptions: (Please refer to the README file for more detailed attribute and data type descriptions.) 1 chefmozaccepts.csv 1314 entries listing payments types accepted by the restaurants. 2 chefmozcuisine.csv 916 entries listing the 59 different cuisines served by the restaurants. 3 chefmozhours4.csv 2339 entries listing the working days/hours of the restaurants. 4 chefmozparking.csv 702 entries listing the type of parking available for the restaurants. 5 geoplaces2.csv 130 entries listing all descriptive attributes of the restaurants across 21 data types. 6 rating_final.csv