Posted: December 10th, 2022
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I have already finished the most important part of this project, which is the creating prediction model part. In total, i write 4 models, and the fourth model will be the model has the lowest RMSE, also the best mode. For each model, please run them to get the result.
This project to be completed individually involves performing steps of predictive analysis including exploring and tidying data, fitting competing models, selecting features, tuning model hyperparameters, interpreting results and presenting findings. You will be given a prediction problem to work on. The lower the RMSE, better the model.
For this project, you are given a set of about 20,000 popular songs and their auditory features. The goal of PAC is to predict the popularity rating using features of the song.
Arriving at good predictions begins with gaining a thorough understanding of the data. This could be gleaned from examining the description of predictors, learning of the types of variables, and inspecting summary characteristics of the variables. Visual exploration may yield insights missed from merely examining descriptive characteristics. Often the edge in predictive modeling comes from variable transformations such as mean centering or imputing missing values. Review the predictors to look for candidates for transformation.
Not all variables are predictive, and models with too many predictors often overfit the data they are estimated on. With the large number of predictors available for this project, it is critically important to judiciously select features for inclusion in the model.
There are a number of predictive techniques discussed in this course, some strong in one area while others strong in another. Furthermore, default model parameters seldom yield the best fit. Each problem is different, therefore deserves a model that is tuned for it.
Finally, predictive modeling is an iterative exercise. It is more than likely that after estimating the model, you will want to go back to the data preparation stage to try a different variable transformation.
This is a short report summarizing the data analysis process and what you learnt from the experience. Your report should include insights from exploring the data, efforts to prepare the data, and analysis techniques explored. The report should cover not only the ingredients of the final analysis but also the failed steps or missteps along the way. The length of the report should be 2-4 pages and must be supplemented by neatly commented R code for the best submission. You can submit the written report in a text editor and R code as R syntax files (i.e., .R files) or you can combine the report and code in a Knit RMarkDown file (i.e., html file). In addition, you are encouraged to submit separate R code files for your other unsuccessful submissions.
The ability to explain and communicate your analytical findings to a general audience is critical to your success in using data to influence decisions at your organization. Equally important is to Keep it Simple and Short. Accordingly, you will construct deliver a succinct presentation supported by just one presentation slide. Specific time allowed for your presentation will depend on class size and will be determined by your instructor, but you should expect it will be 1-3 minutes. Please also write the presentation speech note for me. Your brief presentation should focus on just two issues:
What you did right with the analysis and where you went wrong.
If you had to do it over, what you would do different.
attached are two datasets that need in this project, and the document which includes codes of 4 models.
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