[SOLVED] CSC781 Assignment 2-KNN Digits Classifier

Description: 

KNN is a classification algorithm that makes predictions based on the distance between a testing sample and the samples in the training set. Though KNN is a simple algorithm, it may work surprisingly well if the test data and train data are from the same data distribution. For this assignment, you need to build a KNN classifier for digits classification using the scikit-learn digits dataset.

Purpose: 

• Get familiar with Python programming language and the scikit-learn library.
• Develop a KNN algorithm for a given task.

Directions: 

For this assignment, you need to build a KNN classifier from scratch. Below is a detailed instruction of what you may need to do.

• Dataset Preparation
  • You need to load the dataset using datasets.load_digits.
    • More information about the function can be found at: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits
  • After loading the dataset, randomly shuffle the dataset to split the dataset to train/dev/test sets.
    • Use the 70% of data for the train set, 15% for the dev set, and 15% for the test set
    • You need to make sure that the labels and images are still matching after shuffling the data.
    • You may want to use the random shuffle function provided by Numpy.
  • KNN Development
    • You need to write your own distance comparison function
    • Use the train set as the training data, and use the dev set to determine the best K and best distance metric.
      • You may need to test multiple K values and distance metrics to select the optimal ones.
    • Test the Model
      • After the optimal K value and distance metric are selected, test the model using the test set.
    • Submission
      • You need to submit a written report for this assignment.
      • For this report, you need to:
        • Explain what you have done
          • g., what distance metrics you have tested, what K values you have tested, etc.
        • Report the best performance on the test set (in terms of accuracy)
          • You also need to indicate the K value and distance matric for achieving this result
        • Visualize the prediction result
          • Randomly select 10 data samples from the test set and specify the ground truth label and the predicted label for each of the samples.
        • Include your code as an appendix
          • You could save your Colab code as a PDF file and attach it to your report, or you could copy and paste your code into the report.
            • If you want to copy/paste your code, make sure to maintain the appropriate indentation and make the code readable.