Part 1 Data Preprocessing & Cleaning Write a written report. Clean and preprocess the data for the project These should be included in the report

1) Loading the Data into R 2) Data Visualization & Summarization 3) Data Cleaning 4) Data Preprocessing – to prepare the data for modeling

Part 2 On a separate doc Write all the methods and results in a final report. The final report should have the following sections.

1) Abstract 2) Introduction 3) Data Description (brief) 4) Data Visualization (brief) 5) Data Cleaning and Preprocessing (brief) 6) Data Modeling

a) Which algorithms are finally used, a brief introduction to them 7) Evaluation

a) Performance comparison of the different models. b) Which model you would prefer to use based on your results? Why?

8) Discussion a) Discuss the results b) What have you learned from this project?

9) Conclusion a) Summary and future direction

10) References

At end- give all of these. Separate Doc- Written report Separate Doc- Final Report Presentation Slides Code

Data set : https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records#

Previous assignment. DON’T DO THIS…

https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records#
https://www.homeworkmarket.com/questions/project-20115465

1) Introduce the reader to the project that you have planned to perform and the objective of the project. Briefly explain your plan to carry out the project. Also, answer the following questions in the introduction. ? What is your motivation for performing this data mining task that you have selected for this project? ? What is the predicted outcome/goal of your project? ? How do you plan to reach your goal? 2) Data description – Provide a description of the data. If data needs cleaning or noise removal or dimensionality reduction mention those here. Answer the following questions. ? What data will you be using? How is formatted? What is the size of the data? How will you clean the data? How will you process the data? 3) Proposed Data mining tasks – For example, if you are going to perform a classification task which classification algorithms you are going to use and why. ? What hypothesis/hope do you have? How would you prove or disprove your hypothesis? What methods will you use? What software tools will you use? How much programming does it need? How do you validate that your method is working? How does that relate to proving your hypothesis? 4) Conclusion