https://github.com/melvinmo/itf_course_archive

This repository contains my solutions to the assignments from the Information Technology Fundamentals undergraduate course.
https://github.com/melvinmo/itf_course_archive

blockchain-technology recommendation-system wireless-network

Last synced: 4 days ago
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This repository contains my solutions to the assignments from the Information Technology Fundamentals undergraduate course.

• Host: GitHub
• URL: https://github.com/melvinmo/itf_course_archive
• Owner: MelvinMo
• Created: 2023-09-02T09:09:17.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-02T09:19:06.000Z (about 1 year ago)
• Last Synced: 2023-09-03T11:20:51.303Z (about 1 year ago)
• Topics: blockchain-technology, recommendation-system, wireless-network
• Language: Python
• Homepage:
• Size: 3.14 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ITF_Archive

This repository contains my solutions to the assignments from the Information Technology Fundamentals undergraduate course.

## Assignemnt 1

In Assignment 1, I answered a set of questions on networking topics like 802.11, Bluetooth, SNMP, and mobile IP. I then described the role of beacon frames and probe frames in 802.11, explained the relationship between BER and SNR, and discussed RTS/CTS and packet flows in various wireless network scenarios.

## Assignment 2

For Assignment 2, I implemented SVD and KNN, two collaborative filtering recommendation algorithms, using the Surprise library in Python. Then I evaluated their performance using cross-validation, RMSE, and train/test splits with an analysis of the prediction accuracy.

More technically speaking, I used the Surprise library to implement and evaluate SVD and KNN recommender systems on the MovieLens dataset. Then I performed k-fold cross-validation to compare RMSE and MAE. After that, I split the data into train and test sets so I could fit models on the train set and make predictions on the test set to analyze accuracy. Followed by calling the predict() function directly to make rating predictions for given user-item pairs.

## Assignment 3

In this assignment, I programmed and implemented a simple blockchain by solving proof-of-work puzzles. In that code, I defined a block class to represent blocks in the chain with properties like block number, data, and hash value. The Blockchain class managed the chain, including block appending and mining. The code mined and appended blocks to the blockchain using ledger data and math problems from JSON files. Finally, it printed the information for each block in the chain.

I also discussed the blockchain technologies Quorum and Stellar and compared their features like consensus protocols, ledger types, cryptocurrencies, and smart contract support.