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yuwa4103 / repository
In our daily life, dietary habits are becoming an important element that impacts a lot of aspects. As college students, our group interested in the food choices and preferences of college students. Examining the eating practices of college students is always relevant to improving health education. Today, a lot of college students have trouble keeping good dietary habits. Some of them stress eat, some eat food with little nutrition, some don’t have regular meals. This research project focuses on the food choices and preferences of college students, using the dataset to analyze the current dietary habits of students and nutrition information of college students. We also want to use ‘business logic’ to discover the internal relations for each attribute from the dataset. Our dataset is a summary of 126 student‘s responses. The dataset includes information on food choices, GPA, nutrition, preferences on food type, parents' influence, the relativity of health and other data collected from the survey. This dataset contains 61 columns and various types including nutrition information, students’ taste preference of different cuisines, their parents cook, sports, weight, grade. Because our original dataset contains very extensive information,(like demographics, sports, parents, etc) not only for food, So we consider a comprehensive database to build upon because this dataset includes several different fields with different data. Building a database makes data management more easily. We can discover more relationships between fields once the data is structured and organized, collecting and storing more new data into the same database is also considerable and efficient. More specific, our group selected columns that we are interested in from 61 columns as a new dataset, classified by characteristics to eight fields, which are demographics, habits, parents, food-type, sports, calories, healthy and comfort_food. They used a new dataset to analyze the relations which are inside each field or relations between each field. Then we listed the questions which we are interested in, like What’s the favorite_type for college students? What’s the relation between GPA and veggies_day? What the difference between male and female thinking about calories? How important is nutrition information for today's college students? Are these kids likely to have a different taste compared to others? etc.
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In our daily life, dietary habits are becoming an important element that impacts a lot of aspects. As college students, our group interested in the food choices and preferences of college students. Examining the eating practices of college students is always relevant to improving health education. Today, a lot of college students have trouble keeping good dietary habits. Some of them stress eat, some eat food with little nutrition, some don’t have regular meals. This research project focuses on the food choices and preferences of college students, using the dataset to analyze the current dietary habits of students and nutrition information of college students. We also want to use ‘business logic’ to discover the internal relations for each attribute from the dataset. Our dataset is a summary of 126 student‘s responses. The dataset includes information on food choices, GPA, nutrition, preferences on food type, parents' influence, the relativity of health and other data collected from the survey. This dataset contains 61 columns and various types including nutrition information, students’ taste preference of different cuisines, their parents cook, sports, weight, grade. Because our original dataset contains very extensive information,(like demographics, sports, parents, etc) not only for food, So we consider a comprehensive database to build upon because this dataset includes several different fields with different data. Building a database makes data management more easily. We can discover more relationships between fields once the data is structured and organized, collecting and storing more new data into the same database is also considerable and efficient. More specific, our group selected columns that we are interested in from 61 columns as a new dataset, classified by characteristics to eight fields, which are demographics, habits, parents, food-type, sports, calories, healthy and comfort_food. They used a new dataset to analyze the relations which are inside each field or relations between each field. Then we listed the questions which we are interested in, like What’s the favorite_type for college students? What’s the relation between GPA and veggies_day? What the difference between male and female thinking about calories? How important is nutrition information for today's college students? Are these kids likely to have a different taste compared to others? etc.