Objective: The goal of this assignment is to implement the K$-$Nearest Neighbors $($KNN$)$ algorithm for image classification using the CIFAR$-10$ dataset. You are required to approach this task using two methods:

Utilize a built$-$in function from an existing library, such as scikit$-$learn.

Implement the algorithm independently.

Tasks and Requirements:

Algorithm Implementation:

Employ the KNN algorithm for image classification on the CIFAR$-10$ dataset using both a pre$-$existing library function and a self$-$coded version.

Analyze and compare the performance of both implementations.

Performance Improvement Strategies:

Develop and apply strategies to enhance the performance of your self$-$implemented KNN algorithm. Focus on aspects like accuracy or computational efficiency.

Comprehensive Report:

Prepare a detailed report encompassing the following sections:

Background and Method Introduction: Provide an overview of the KNN algorithm and its application in image classification.

Dataset and Tasks Description: Describe the CIFAR$-10$ dataset and outline the specific classification tasks undertaken.

Algorithms Used: Elaborate on the implementation details of both the library$-$based and self$-$implemented algorithms. Attach screenshot of the codes whenever necessary.

Results: Present and discuss the classification results obtained from both approaches.

Methods of Improvements: Discuss the strategies employed to enhance the performance of your algorithm, focusing on accuracy and speed.

Submission Format:

Submit your work in the form of Jupyter Notebook $(.$ipynb$)$ and HTML files, along with the final report.

Grading Criteria:

Implementation of the Algorithm $(40\%)$: Demonstrated ability to effectively implement the KNN algorithm using both the library function and self$-$coded method $(20\%$ for each$).$

Preliminary Results $(20\%)$: Ability to achieve reasonable initial results from the implemented algorithms.

Algorithm Improvement and Validation $(20\%)$: Thoughtful considerations and implementations for validating and improving your algorithm, including techniques like cross$-$validation, hyper$-$parameter tuning, and efficient coding practices.

Report Quality $(20\%)$: Overall quality, clarity, organization, and thoroughness of the submitted report.