The results make sense. GitHub is where the world builds software. It would be easy to assume that alcohol consumption reduces the student’s health on a long term basis. There are a few columns which we think could be further clarified or changed. Column 23 need to take column 23 (romantic), column 27 (workday alcohol consumption) and/or column 28 (weekend alcohol consumption) into consideration. As we all know, human relationships play a major role in people's lives. Student Grade Prediction 1. Since the dataset is called “Student Alcohol Consumption”, of course, we should do some analyses on it. consensus is that students who consume alcohol at high levels tend to skip more classes and perform worse in their studies, thus, resulting in lower Here are top 25 websites to gather datasets to use for your data science projects in R, Python, SAS, Excel or other programming language or statistical software. February 2016 DOI: 10.13140/RG.2.1.1465.8328 READS 2,200 2 authors: Fabio Pagnotta Hossain Amran University of Camerino University of Camerino 8 PUBLICATIONS 0 CITATIONS 5 PUBLICATIONS 0 … information about the students from the mathematics course only. However, if more elaborate data mining techniques were to be used, more features can be selected and used in order to reductions of GPA. We may want to normalize absences in preparation for model building. 3. You may want to explore combining the grades into one feature since G3 is likely derived from G1 and G2. because it would be less accurate for the classification model to predict a numeric value ranging from 0-20. A lot of time is lost I alcohol consumption that the students only place less time in their academic work. Your email address will not be published. Best part, these are all free, free… The scope of these data sets varies a lot, since they’re all user-submitted, but they tend to be very interesting and nuanced. For the data exploratory exercise, we choose to examine three columns: workday alcohol consumption, weekend alcohol consumption and their relationship status. At an alcohol consumption level of 1, the median and 25th percentile are the same value of 2 hours of study. Since the main purpose of the dataset is to find correlations between students with their alcohol consumption patterns, the most conspicuous relationship would be the relationship between their grades with respect to their workday and weekend alcohol consumption. Exploratory Data Analysis on the Student Alcohol Consumption dataset (Code) December 31, 2016 | 21 Minute Read This post is an execution of the explanations from this blog post. For the data exploratory exercise, we choose to examine three columns: workday alcohol consumption, It’s called the datasets subreddit, or /r/datasets. However, the assumption is that the alcohol consumption is high because the student's According to the World Health Organization (Global Status Report on Alcohol and Health 2014 2014), gender, family, and social factors affect alcohol consumption. This data set contains survey information from a group of students in a secondary school. Our explanation would be more focused on the final grade because we think that students will be The dataset was built from two sources: school reports and questionnaires. Retrieved from http://www.euroeducation.net/prof/porco.htm. Nicolas Raj. Earthdata. The original values for the feature ‘absences’ will be used in the remaining sections. Published in: Technology. In the input, workday and weekend alcohol consumption is given in range of 1 - very low to 5 - very high. I will be utilizing the student alcohol consumption dataset provided by UCI Machine Learning and is available in their machine learning repository. Since the distribution is log normal, applying the log transformation would be the most applicable. GStatus is derived from the final period grade, (G3, column 33) where according to EuroEducation.net (n.d.), According to a study done by Rameker, alcohol consumption is a major factor that has been shown to have correlation with poor academic performance (Rameker, 2015). grades. Alcohol is an often abused substance that troubles many individuals in their adulthood as they struggle to cope with emotional and physical stress that A twin study of marital status and alcohol consumption. The violin plot of absences shows more of a log normal distribution, and a large number of outliers lie well outside of the top whisker. consumption) and/or column 28 (weekend alcohol consumption). By using Kaggle, you agree to our use of cookies. administrative or police), ‘at_home’ or ‘other’), reason – reason to choose this school (nominal: close to ‘home’, school ‘reputation’, ‘course’ preference or ‘other’), guardian – student’s guardian (nominal: ‘mother’, ‘father’ or ‘other’), traveltime – home to school travel time (numeric: 1 – <15 min., 2 – 15 to 30 min., 3 – 30 min. The traditional However, the data reveals that there was a total of 382 students that were in both datasets, this was evident in the exact Tobacco and nicotine use TUD PDF 493 KB. If one is very high, you may want to take a closer look at the data and see if there is leakage into the target variable. If the mean has significant differences (h0 is accepted), then the feature will likely be a dominant predictor. There are two categorical columns “Dalc” and “Walc” showing consumption on workday and weekend. Balsa, A. I., Giuliano, L. M., & French, M. T. (2011). Global Status Report on Alcohol and Health 2014. Singapore, however, brightens it up with colorful visualizations, splashes of color in the graphs, and a “Similar Datasets” section at the bottom of every data set to encourage readers to explore. This helps you to understand the top dependent variables (grouped by numerical and categorical). Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. We think that classification is the best data mining technique to be employed because we can build a classification model to The most recent statistics from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) estimate that about 1,519 college students ages 18 to 24 die from alcohol-related unintentional injuries, including motor vehicle crashes. We will take a closer look at the distribution of this feature. We would think that if the value for health is lower, the value for their People who contributed to this were Aaron Patrick Nathaniel, Lim Yue Hng (Neil) and While … You can see the level of correlation by the degree of the ellipse. We could check to see if that hypothesis has a concrete basis by using column 24 (famrel), column 27 (workday alcohol The data mining technique we think is suitable is classification. The original data comes from a survey conducted by a professor in Portugal. Other Cool Sets. 2014. The students included in the survey were in the recorded to have participated. In our data set, many of the categorical features are numeric, but for this illustration, we will continue with treating them as categorical. Secondary school students are in a transition developmentally and this comes with its debilitating effects such as risky alcohol use … as the attributes and GStatus as the class for the training set to predict the class GStatus in the test set and validate the model. For example, if there were a high correlation, say 0.9, between two numeric features, then the information provided to the model would be redundant, and depending on the model make the model more complex than it needs to be. Subscribe to our mailing list and get interesting stuff and updates to your email inbox. Journal of Family Psychology, Vol 30(6), Sep 2016, 698-707. Click on the arrow near the name of each column to evoke the context menu. We could take into consideration the more serious towards their final grade rather than the first period grade and second period grade. First, open the student-por.csv file in the student_performance source. For numeric data, correlations are important to help determine if we should join information of highly correlated features. This modification coincides with the original report where the authors modified the target with the formula acl = (Dalc * 5 + Walc * 2) / 7 and then assumed values of 3 or more were heavy drinkers. Fedu and Medu correlate more that some others, so we might want to combine the information. Generally, many models prefer using features that are independent of each other and have low correlations. This dataset was collected in order to study alcohol consumption in young people and its effects on students’ academic performance. 13. Correlation does not imply causation. Remove the skewness from the numeric data. consumption (both column 27 and 28) when famrel has a low value. Section 3a. drinking alcohol for consolation. We prefer to use some sort of configuration so that we can input any dataset and perform most of the same analysis. obtain more accurate insights. For a student to pass the subject, there are a couple of factors that could be correlated with the outcome. Excessive alcohol use, either in the form of binge drinking (drinking 5 or more drinks on an occasion for men or 4 or more drinks on an occasion for women) or heavy drinking (drinking 15 or more drinks per week for men or 8 or more drinks per week for women), is associated with an increased risk of many health problems, such as liver disease and unintentional injuries. It can develop a plethora of emotions in oneself, may it be a positive or negative We assume that a father’s education level is similar to a mother’s education level, so let us visualize the association: The above plot shows that the education levels between mother and father do coincide fairly often and might want to explore more or consider the possibility of joining these features in preprocessing the data before model building. in a student environment as well as their demographic information and other data that may be of some relevance. Section 2c. National Institute of Child Health and Human Development Study of Early Child Care and Youth Development Data and documentation for phases I and II of the NICHD-SECCYD study. We would oversample since we have limited data. Medicine use PDF 223 KB. emotion. school period grades are available. al. To get an idea of how features interact with each-other, we can determine the rank associated with the features to a target, in this case, the actual target or level of drinking. Therefore, researchers seek to rectify that lack by conducting a survey to obtain important raw data on alcohol consumption It is a usual train of thought that those who have a bad relationship with their family members will be stressed and unhappy which results in them

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