Reference no: EM132396754

Assignment

Making Sense of Data-Learning and Comparing Classification Models for a Dataset

This assignment is an opportunity for you to investigate different classification approaches; the idea is to apply different classification techniques to a challenging dataset, to compare the results, to potentially enhance the accuracy of the learnt models via selecting better parameters/preprocessing/using kernels/incorporating background knowledge and to summarize your findings in a report. You will also learn how to work in a team and will get some practical experience in comparing and evaluating different classification methods.
Datasets to be used in the project include: Use R software.

Dataset:

Activity recognition with healthy older people using a batteryless wearable sensor Data Set.

Use the following approaches:

It is mandatory to use these 2 approaches to obtain classification models for the dataset you chose above:

1. Neural Networks

Next select any 1 from the following approaches to obtain classification models for the dataset you chose above:

2. KNN

3. Random Forest

4. Decision Trees

5. Naïve Bayes

Other requirements for Assignment:

1. Comparing the experimental results, write a paragraph or two trying to explain/speculate why, in your opinion one classification algorithm outperformed the others.

2. Include a brief discussion in your report, how you have selected the parameters of particular data mining algorithms.

3. Finally, at the end of your report provide a 1-2 paragraphs summary that summarizes your most important findings of Assignment

4. Your report must contain all the results you obtained for the classification models.