data base辅导、讲解program、Java/Python/c++程序语言调试 辅导Python程序|讲解R语言编程
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Firstly, note that this project is optional. For the final exam part (60%), you may choose
between taking the final exam (on May 27th at 18:00) or developing a project based on a real
database (deadline May 27th).
The project should be based on a real data base.
The final objective is to fit adequate models (studied in this course) to make inference
and predictions that can be of interest of potential users, clients or society.
Projects should be done individually; however, you may share the same data set with
some colleagues and compare results.
The length of the project should be smaller than 12 pages; however, you may include
additional plots, the code and any other information of interest in a number of
appendices.
Project should be based whether on a regression problem (when the output is a
continuous variable) or a classification problem (when the output is a categorical
variable).
The structure of the project should be as following:
1. Introduction
Explain briefly the motivation of the project, the data source and main objectives.
2. Description of variables
Describe clearly each one of the observed variables. Indicate which one is the output
variable and which ones are the input variables (predictors). Indicate clearly if your
project will be based on a regression or a classification problem
3. Descriptive analysis
Show and describe some plots (histograms, boxplots, scatterplots) and data
summaries where you explain the main features of the observed variables.
4. Results using models of type I.
Explain clearly which type of models are going to be used in this section (linear,
logistic, knn, ridge, trees, etc), describe clearly how are they applied, show the main
results in tables and plots and finally, extract the main conclusions from the applied
approach. Comment on the benefits and limitations of the considered type of models.
5. Results using models of type II
Explain clearly which type of models are going to be used in this section (linear
models, knn, trees, etc), describe clearly how are they applied, show the main results
in tables and plots and finally, extract the main conclusions from the applied approach.
Comment on the benefits and limitations of the applied type of models.
6. …
7. Conclusions and extensions
Summarize the main conclusions obtained from the different techniques that have
been considered in your project. Comment on some possible extensions.
8. References
9. Appendix I
10. Appendix II
11. …
Firstly, note that this project is optional. For the final exam part (60%), you may choose
between taking the final exam (on May 27th at 18:00) or developing a project based on a real
database (deadline May 27th).
The project should be based on a real data base.
The final objective is to fit adequate models (studied in this course) to make inference
and predictions that can be of interest of potential users, clients or society.
Projects should be done individually; however, you may share the same data set with
some colleagues and compare results.
The length of the project should be smaller than 12 pages; however, you may include
additional plots, the code and any other information of interest in a number of
appendices.
Project should be based whether on a regression problem (when the output is a
continuous variable) or a classification problem (when the output is a categorical
variable).
The structure of the project should be as following:
1. Introduction
Explain briefly the motivation of the project, the data source and main objectives.
2. Description of variables
Describe clearly each one of the observed variables. Indicate which one is the output
variable and which ones are the input variables (predictors). Indicate clearly if your
project will be based on a regression or a classification problem
3. Descriptive analysis
Show and describe some plots (histograms, boxplots, scatterplots) and data
summaries where you explain the main features of the observed variables.
4. Results using models of type I.
Explain clearly which type of models are going to be used in this section (linear,
logistic, knn, ridge, trees, etc), describe clearly how are they applied, show the main
results in tables and plots and finally, extract the main conclusions from the applied
approach. Comment on the benefits and limitations of the considered type of models.
5. Results using models of type II
Explain clearly which type of models are going to be used in this section (linear
models, knn, trees, etc), describe clearly how are they applied, show the main results
in tables and plots and finally, extract the main conclusions from the applied approach.
Comment on the benefits and limitations of the applied type of models.
6. …
7. Conclusions and extensions
Summarize the main conclusions obtained from the different techniques that have
been considered in your project. Comment on some possible extensions.
8. References
9. Appendix I
10. Appendix II
11. …