jupyter-notebook

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

This repository provides a few demonstration notebooks for the IEOTO (https://ieo.to) service to showcase the functionality that is available via the service API. The API, like the service web UI allows to search simultaneously for remote sensing and in-situ data, download the data. There is also functionality to define data streams (networks) and store own data and perform hosted processing.

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

A very simple set of scripts in order to test multiple LLM models through Jan.ai

Show case repository on how to use Google's OCR library (pytesseract)

Project to evaluate the results and performance of the clustering and rescheduling algorithms for the Social Internet of Energy. Related scientific manuscripts: https://ieeexplore.ieee.org/abstract/document/8784194 https://www.mdpi.com/1424-8220/20/13/3704

Project to evaluate the results and performance of the clustering and rescheduling algorithms for the Social Internet of Energy. Related scientific manuscripts: https://ieeexplore.ieee.org/abstract/document/8784194 https://www.mdpi.com/1424-8220/20/13/3704

Data Visualization of Automatic Weather Station

To build a CNN based model which can accurately detect melanoma. Melanoma is a type of cancer that can be deadly if not detected early. It accounts for 75% of skin cancer deaths. A solution which can evaluate images and alert the dermatologists.

Programming Assignments of Neural Networks and Deep Learning course of deeplearning.ai in coursera

Visualizing algorithms in Jupyter notebook

using tshark to generate csv file - then parsing in jupyter-notebook to find patterns