Project 1

Structure of the folders:

├── src/
│   ├── knn_experiment.ipynb
│   ├── decisiontree_experiment.ipynb
|   ├── experiment.ipynb
|   ├── img_preprocessing.ipynb
├── MNIST/
├── Caltech10/

Instructions:

Requirements:

Assuming that your system is in python 2.7 which includes these packages

• opencv-python
• scikit-learn
• matplot-lib
• pillow
• scipy
• numpy
• jupyter

which can be easily installed through pip.

Running Instructions:

Please make sure the folders structure are similar with data included in MNIST and Caltech10 folders for the code to run smoothly.

• Run python jupyter notebook command line in the root folder
• Click on the Kernel button to run all on file img_preprocecssing.ipynb
• Click on the Kernel button to run all on file knn_experiment.ipynb for implementation and experimentation results of KNN algorithm on both datasets
• Click on the Kernel button to run all on file decisiontree_experiment.ipynb for implementation and experimentation results of decision tree algorithm on both datasets
• Feel free to edit the call on the pickle_operating when loading the binarized version of the preprocessing dataset between with and without PCA transformation applied.

Notes: Image preprocessing + Loading the dataset is done in the img_preprocecssing.ipynb file

• MNIST_data_1.pickle and Caltech_data_1.pickle are datasets without PCA preprocessing
• MNIST_data_2.pickle and Caltech_data_2.pickle are datasets with PCA preprocessing