ramtiin Goto Github PK

anomaly-detection-on-stock-market-using-autoencoders

attention-based-lstm-network-for-predicting-times-series

Forex price movement forecast

automated-design-of-symmetric-autoencoders-using-genetic-algorithms

This project presents a novel approach for optimizing the architecture of symmetric, undercomplete autoencoders for dimensionality reduction using a genetic algorithm.

credit-card-fraud-detection

In this project, I explore different methods for detecting credit card fraud transactions; including using the Catboost algorithm with undersampling & oversampling methods, and using an almost new approach, by using deep learning and autoencoder.

detecting-machine-generated-text

The findings of this research reveal several intriguing disparities between human and AI text generation. I demonstrated that these differences could be successfully utilized by classifiers to distinguish between human and AI-generated text.

distributed-random-forests

multiclass-classification-of-dry-beans-using-machine-learning

name-nationality-classification-with-machine-learning

Predicting nationality only by using people's names with machine learning.

predicting-stock-prices-using-fb-prophet

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. In this notebook I'm going to try forecasting Google stock price using facebook's prophet model.

predicting-youtube-dislikes-using-machine-learning

I used Catboost for training a model on the numerical features of every YouTube video (e.g., the number of views, comments, likes, etc.) along with sentiment analysis of the video descriptions and comments using the VADER sentiment analysis model.

stockgpt

A Python script to analyze financial news and publish them on a Telegram channel.

traffic-forecasting-using-different-methods

Time series prediction using deep learning

twint

An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.

watermarking-algorithm-analysis-on-bart-model-using-cnn-dataset

word-clouds-with-sentiment-analysis-of-the-most-recent-tweets

Downloaded tweets from the most popular news agencies and extract keywords from them. In the next steps, I plotted a word cloud and did a sentiment analysis for tweets that have the keywords.