This is an individual assignment.
The final deliverables include a 4-page IEEE-format report, code implementation and a detailed GitHub readme file.
Project 2 is due Monday, November 14 @ 11:59 PM. Find the complete rubric in the Canvas assignment.
The dataset you will be working with is available for download here:
- training.ipynb ---- code used for training the datasets.
- test.ipynb ---- code used for testing the datasets.
- Project 2 - Dimentionality Reduction.ipynb ---- description of project 1
- Model ---- contains all models generated from the training.ipynb.
- Dataset ---- contains LDA, t-SNE and PCA components I get from question 3 and original dataset.
- Project_1_report.docx ---- report in docx form
- Project_1_report.pdf ---- report in pdf form
- training.pdf ---- pdf form of training.ipynb
- test.pdf ---- pdf form of test.ipynb
- README.md ---- this file.
The models are in the Model file which is in my Google Drive, you need to down load from here.
This file doesn't include 4 original dataset, so you need to load these data before running my code.
Training: Training code do not need any parameters to input. It generates totally 10 models in file Model and 3 numpy arrays in file Dataset. And these models and data will be use in test code.
Test: Test code has already loaded the datasets and resampled dataset, and all models have been loaded too. Except running all codes, you do not need any operation in Jupyter.