Pinned Repositories
android-swf-player
Automatically exported from code.google.com/p/android-swf-player
ARIMA-Price-Prediction
This repository implements an ARIMA model for predicting financial prices such as stocks, currencies, and cryptocurrencies. It focuses on time series forecasting to capture temporal dependencies and improve prediction accuracy across different financial datasets.
bho
Automatically exported from code.google.com/p/bho
Bidirectional_LSTM-Price-Prediction
This repository implements a Bidirectional LSTM model for predicting financial prices like stocks, currencies, and cryptocurrencies. It leverages sequential data in both forward and backward directions, enhancing the accuracy and reliability of price predictions.
BrokenLinkChecker_Python
This Python web crawler traverses a website, verifies resource links (CSS, JS, images, videos, iframes), and identifies broken links with HTTP errors (400-599)
BrokenLinkChecker_WinApp-
A Windows Forms web crawler that extracts and checks resources (CSS, JavaScript, images, etc.) on web pages for availability. It logs errors (e.g., 404, 500) and displays them in a data grid. The crawler supports concurrent requests, URL queuing, and saving error details to CSV.
COVID-19-detection
COVID-19 detection by using VGG network
GRU-Price-Prediction
Time Series Price Prediction using Gated Recurrent Units (GRU) for financial assets. This project predicts open, high, low, and close prices of assets like cryptocurrencies, forex, and commodities using machine learning. Includes data pre-processing, GRU model construction, and performance evaluation with metrics and visualizations.
LSTM-Price-Prediction
This repository contains an implementation of the LSTM (Long Short-Term Memory) model for predicting financial asset prices, including Open, High, Low, and Close prices. It includes code for price prediction of assets like Bitcoin, Gold, EUR/USD, and the S&P 500 index.
Price-Prediction
A repository analyzing AI models for predicting Bitcoin, EUR/USD, Gold, and S&P 500 prices. Includes statistical, ML, and neural network methods evaluated using R-squared, MSE, and MAE. Provides insights into model performance, highlighting SVR, ARIMA, and ensemble methods as top performers for financial forecasting.
taleblou's Repositories
taleblou/Price-Prediction
A repository analyzing AI models for predicting Bitcoin, EUR/USD, Gold, and S&P 500 prices. Includes statistical, ML, and neural network methods evaluated using R-squared, MSE, and MAE. Provides insights into model performance, highlighting SVR, ARIMA, and ensemble methods as top performers for financial forecasting.
taleblou/COVID-19-detection
COVID-19 detection by using VGG network
taleblou/GRU-Price-Prediction
Time Series Price Prediction using Gated Recurrent Units (GRU) for financial assets. This project predicts open, high, low, and close prices of assets like cryptocurrencies, forex, and commodities using machine learning. Includes data pre-processing, GRU model construction, and performance evaluation with metrics and visualizations.
taleblou/LSTM-Price-Prediction
This repository contains an implementation of the LSTM (Long Short-Term Memory) model for predicting financial asset prices, including Open, High, Low, and Close prices. It includes code for price prediction of assets like Bitcoin, Gold, EUR/USD, and the S&P 500 index.
taleblou/ARIMA-Price-Prediction
This repository implements an ARIMA model for predicting financial prices such as stocks, currencies, and cryptocurrencies. It focuses on time series forecasting to capture temporal dependencies and improve prediction accuracy across different financial datasets.
taleblou/LinearRegression-Price-Prediction
This repository implements a Linear Regression model for predicting prices of financial instruments like stocks, currencies, and cryptocurrencies. It includes data preprocessing, model training, and evaluation using performance metrics like Mean Squared Error and R-squared.
taleblou/TemporalConvolutionalNetworks-Price-Prediction
This repository implements a Temporal Convolutional Network (TCN) model for predicting financial instrument prices, including currencies, stocks, and cryptocurrencies. It uses advanced techniques like gradient boosting to improve prediction accuracy and handle diverse datasets effectively.
taleblou/Bidirectional_LSTM-Price-Prediction
This repository implements a Bidirectional LSTM model for predicting financial prices like stocks, currencies, and cryptocurrencies. It leverages sequential data in both forward and backward directions, enhancing the accuracy and reliability of price predictions.
taleblou/BrokenLinkChecker_Python
This Python web crawler traverses a website, verifies resource links (CSS, JS, images, videos, iframes), and identifies broken links with HTTP errors (400-599)
taleblou/BrokenLinkChecker_WinApp-
A Windows Forms web crawler that extracts and checks resources (CSS, JavaScript, images, etc.) on web pages for availability. It logs errors (e.g., 404, 500) and displays them in a data grid. The crawler supports concurrent requests, URL queuing, and saving error details to CSV.
taleblou/CatBoostRegressor-Price-Prediction
This repository implements the CatBoostRegressor model for predicting prices of financial instruments like stocks, currencies, and cryptocurrencies. It uses gradient boosting to capture patterns in price movements, improving the accuracy and robustness of price forecasts.
taleblou/GradientBoostingRegressor-Price-Prediction
This repository contains an implementation of a Gradient Boosting Regressor model for predicting prices of financial instruments, such as currencies, stocks, and cryptocurrencies. The model uses gradient boosting techniques to capture patterns in price movements and improve prediction accuracy.
taleblou/KNeighborsRegressor-Price-Prediction
This repository implements the KNeighbors Regressor (KNN) model for predicting financial instrument prices such as stocks, currencies, and cryptocurrencies. It leverages gradient boosting techniques to improve accuracy by capturing complex patterns in price movements.
taleblou/MultiProxyURLLoader-Python
Load URLs through multiple proxy servers seamlessly using Python
taleblou/MultiProxyURLLoader-WinApp
A Windows app for simultaneously loading multiple URLs via various proxy types, including HTTP, HTTPS, SOCKS4, and SOCKS5, ideal for web testing and bypassing geographic restrictions.
taleblou/RandomForestRegressor-Price-Prediction
This repository implements a Random Forest Regressor for price prediction in financial markets, including stocks, currencies, and cryptocurrencies. It uses gradient boosting techniques to improve the model's accuracy and robustness for forecasting financial data across different datasets.
taleblou/SearchTextInImages_Python
This script extracts text from images using EasyOCR, searches for specific predefined strings, and saves the results in a CSV file. It processes images in bulk from a specified directory, providing a streamlined way to analyze and search image text efficiently.
taleblou/SearchTextInImages_WinApp
taleblou/SEOPageChecker_Python
This Python script analyzes SEO performance by inspecting HTML content, titles, meta descriptions, headings, links, images, and structured data. It checks HTTPS, mobile-friendliness, and broken links to deliver insights for optimization.
taleblou/SEOPageChecker_WinApp
SEO Page Analyzer is a C# Windows Forms app that analyzes SEO metrics for any URL. It evaluates titles, meta descriptions, headings, images, links, word count, and page load time. Built with HttpClient and HtmlAgilityPack, it provides actionable insights for improving web page SEO
taleblou/ServerMonitoring_Python
This Python script monitors a URL's performance, logging metrics like ping time, loading time, response size, status code, and errors into a CSV file every 30 seconds. Ideal for tracking website health and performance over time. Customizable and easy to use.
taleblou/ServerMonitoring_WinApp
ServerMonitor is a Windows Forms application that monitors up to 6 URLs, tracking ping time, loading time, response size, status code, and error message. It logs data to a CSV file, visualizes status with progress bars, and plays an alert sound for errors, updating every set interval.
taleblou/SimpleBlockchain
SimpleBlockchainCPP is a basic blockchain implementation written in C++. It includes block mining, transaction management, and chain validation, using SHA-256 hashing from OpenSSL. This project serves as an educational resource for understanding blockchain fundamentals in a simple, lightweight manner.
taleblou/Support-and-Resistance
taleblou/SVR-Price-Prediction
This repository implements an SVR model for predicting prices of financial assets like stocks, currencies, and cryptocurrencies. It uses gradient boosting to capture complex patterns in price data, improving the accuracy of predictions across various datasets.
taleblou/URLVulnerabilityScanner_Python
This Python script scans URLs for vulnerabilities like SQL injection, XSS, open ports, weak session management, and more. It generates a CSV report with detailed findings. Use it for authorized security testing to identify risks and improve website defenses.
taleblou/URLVulnerabilityScanner_WinApp
Penetration and Vulnerability Scanner is a Windows Forms app for security analysis. It detects vulnerabilities like SQL Injection, XSS, open directories, CSRF, and more. Designed for developers and security pros to identify and mitigate risks in web applications.
taleblou/WaveNet-Price-Prediction
This repository implements a WaveNet model for predicting financial instrument prices, such as currencies, stocks, and cryptocurrencies, using advanced AI techniques like gradient boosting to capture intricate patterns in price movements.
taleblou/WordPressVulnerabilityScanner_Python
A Python script to scan WordPress sites for vulnerabilities like version disclosure, exposed wp-config.php, XML-RPC issues, REST API exposure, user enumeration, and outdated plugins/themes. Logs results to a CSV file for easy analysis. For educational use only.
taleblou/XGBoost-Price-Prediction
This repository implements an XGBoost model for predicting the prices of financial instruments, such as stocks and cryptocurrencies. Using gradient boosting techniques, it aims to capture patterns in price movements, enhancing prediction accuracy across various datasets.