Final Data and Scenario Project

Final Project Requirement

Students will be provided with a data set that contains business records from a company’s past transactions. They will also be provided with a scenario that describes a set of problems related to the business practices that generated this data. Students are also to evaluate the data file and decide how best to analyze the data based on the scenario that they have been provided. They will then conduct their analysis by running three data mining strategies that produce defendable and significant results. Using the results of each of the three analyses, students will also select and produce three MS Excel visualizations that show the significant of the results. Lastly, the analyses, their results, and the related visualizations are to be used to produce a persuasive argument in the form of an 6-8 page report that will describe any recommended actions to fully address the problems described in the initial scenario. Components % of Grade Data Analysis and Analytics Tools 40% Interpretation 30% Style Including Structure, Flow, Grammar, and Spelling 10% Representation 10% Application 10% TOTAL 100%
Final Project Requirement

Students will be provided with a data set that contains business records from a company’s past transactions. They will also be provided with a scenario that describes a set of problems related to the business practices that generated this data. Students are also to evaluate the data file and decide how best to analyze the data based on the scenario that they have been provided. They will then conduct their analysis by running three data mining strategies that produce defendable and significant results. Using the results of each of the three analyses, students will also select and produce three MS Excel visualizations that show the significant of the results. Lastly, the analyses, their results, and the related visualizations are to be used to produce a persuasive argument in the form of an 6-8 page report that will describe any recommended actions to address the problems described in the initial scenario fully.
Components % of Grade
Data Analysis and Analytics Tools 40%
Interpretation 30%
Style Including Structure, Flow, Grammar, and Spelling 10%
Representation 10%
Application 10%
TOTAL 100%

Here is the process that I learn from the class : ( Maybe it is not the correct order, but that’s what I hear from the teacher )

1. Pick your data set ( Regression )
2. Introduction : literature review ( references )
3. Method
– Descriptive Statistics : 3 Numerical and 3 Cat ( Example about table from excel )
– Inferential Statistics: confidential ( summarize ) If we have skills data do not use Mean, std, and range 1. Pick your data set ( Regression )
4. Result
5. Conclusion
6. Recommendation
7. Abstract

Here is extra information about the case that might help you easier to find :

Case Study 2: Student Alcohol Consumption
Context:
The data were obtained in a survey of students math and portuguese language courses in secondary school. It contains a lot of interesting social, gender and study information about students. You can use it for some EDA or try to predict students final grade.

Content:
Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:

school – student’s school (binary: ‘GP’ – Gabriel Pereira or ‘MS’ – Mousinho da Silveira)
sex – student’s sex (binary: ‘F’ – female or ‘M’ – male)
age – student’s age (numeric: from 15 to 22)
address – student’s home address type (binary: ‘U’ – urban or ‘R’ – rural)
famsize – family size (binary: ‘LE3’ – less or equal to 3 or ‘GT3’ – greater than 3)
Pstatus – parent’s cohabitation status (binary: ‘T’ – living together or ‘A’ – apart)
Medu – mother’s education (numeric: 0 – none, 1 – primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
Fedu – father’s education (numeric: 0 – none, 1 – primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
Mjob – mother’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
Fjob – father’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. 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 – 1 hour)
studytime – weekly study time (numeric: 1 – 10 hours)
failures – number of past class failures (numeric: n if 1<=n<3, else 4) schoolsup - extra educational support (binary: yes or no) famsup - family educational support (binary: yes or no) paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) activities - extra-curricular activities (binary: yes or no) nursery - attended nursery school (binary: yes or no) higher - wants to take higher education (binary: yes or no) internet - Internet access at home (binary: yes or no) romantic - with a romantic relationship (binary: yes or no) famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent) freetime - free time after school (numeric: from 1 - very low to 5 - very high) goout - going out with friends (numeric: from 1 - very low to 5 - very high) Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high) Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) health - current health status (numeric: from 1 - very bad to 5 - very good) absences - number of school absences (numeric: from 0 to 93) These grades are related with the course subject, Math or Portuguese: G1 - first period grade (numeric: from 0 to 20) G2 - second period grade (numeric: from 0 to 20) G3 - final grade (numeric: from 0 to 20, output target) Additional note: there are several (382) students that belong to both datasets. These students can be identified by searching for identical attributes that characterize each student, as shown in the annexed R file. Source Information P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. Fabio Pagnotta, Hossain Mohammad Amran. Email:fabio.pagnotta@studenti.unicam.it, mohammadamra.hossain '@' studenti.unicam.it University Of CamerinoCase Study 2: Student Alcohol Consumption Context: The data were obtained in a survey of students math and portuguese language courses in secondary school. It contains a lot of interesting social, gender and study information about students. You can use it for some EDA or try to predict students final grade. Content: Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets: school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira) sex - student's sex (binary: 'F' - female or 'M' - male) age - student's age (numeric: from 15 to 22) address - student's home address type (binary: 'U' - urban or 'R' - rural) famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3) Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart) Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education) Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education) Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other') Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. 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 - 1 hour) studytime - weekly study time (numeric: 1 - 10 hours) failures - number of past class failures (numeric: n if 1<=n<3, else 4) schoolsup - extra educational support (binary: yes or no) famsup - family educational support (binary: yes or no) paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) activities - extra-curricular activities (binary: yes or no) nursery - attended nursery school (binary: yes or no) higher - wants to take higher education (binary: yes or no) internet - Internet access at home (binary: yes or no) romantic - with a romantic relationship (binary: yes or no) famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent) freetime - free time after school (numeric: from 1 - very low to 5 - very high) goout - going out with friends (numeric: from 1 - very low to 5 - very high) Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high) Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) health - current health status (numeric: from 1 - very bad to 5 - very good) absences - number of school absences (numeric: from 0 to 93) These grades are related with the course subject, Math or Portuguese: G1 - first period grade (numeric: from 0 to 20) G2 - second period grade (numeric: from 0 to 20) G3 - final grade (numeric: from 0 to 20, output target) Additional note: there are several (382) students that belong to both datasets. These students can be identified by searching for identical attributes that characterize each student, as shown in the annexed R file. Source Information P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. Fabio Pagnotta, Hossain Mohammad Amran. Email:fabio.pagnotta@studenti.unicam.it, mohammadamra.hossain '@' studenti.unicam.it University Of Camerino

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