An List of my own Powershell scripts, commands and Blogs for windows Red Teaming.

🗡️ Discover our curated list of creative tools to supercharge your next project.

A collection of awesome software, libraries, Learning Tutorials, documents, books, resources and interesting stuff about APIs

Tool for searching information via Telegram, Number Phone and Username.

Securing LLM's Against Top 10 OWASP Large Language Model Vulnerabilities 2024

containing everything about python development and implementations

This project predicts customer churn for a telecom company by analyzing user contracts, personal data, and service usage. It uses pandas for data manipulation and scikit-learn for model building, applying Logistic Regression, Decision Trees, and Gradient Boosting. The aim is to enable proactive customer retention supporting business decisions

Just a bunch of simple tools/scripts that can help you to have fun while using containers for development on either your local machine or on your own container server

This project analyzes a dataset on video game sales to uncover patterns that determine a game's success. The analysis covers user reviews, sales by platform and genre, and regional preferences. Python (pandas, matplotlib) is used for data manipulation and visualization, while various statistical methods explore correlations and trends.

PUBLICATION: High-throughput analysis of adaptation using barcoded strains of Saccharomyces cerevisiae

Impacket is a collection of Python classes for working with network protocols.

This project builds a classification model for Megaline's telecom clients to recommend updated plans based on their usage behavior. It utilizes machine learning algorithms like Decision Trees, Random Forests, and Logistic Regression to maximize accuracy. The goal is to enable plan recommendations, improving customer satisfaction and revenue

✨ Basic template for run iOS & Android devices simultaneously

This project aims to predict customer insurance claims by analyzing personal data and claim history. Using models like Decision Trees, Random Forests, and Logistic Regression, it evaluates customer risk factors and insurance claim frequency. Data preprocessing and feature engineering are employed, while accuracy and F1-score measure effectiveness

This project aims to detect negative movie reviews for the Film Junky Union community by analyzing IMDB data. It uses pandas for data manipulation and scikit-learn for building models, including Logistic Regression and Gradient Boosting. Applies tokenization and TF-IDF are applied to classify reviews as positive or negative

This project focuses on analyzing customer purchasing patterns on Instacart to understand product affinities and shopping behaviors. Data exploration, feature engineering, and collaborative filtering using Python libraries such as pandas and scikit-learn. Helps Instacart optimize product recommendations and improve inventory management

This project forecasts hourly taxi demand for peak times using historical data from airports. It uses pandas for data preparation and scikit-learn for building and evaluating predictive models like Random Forest and Gradient Boosting. The project aims to enhance driver availability during rush hours by predicting the number of future taxi orders

This project identifies optimal locations for oil well drilling using machine learning. It analyses geological data from three regions, the goal is to maximize profit while minimizing risk. Linear regression predicts reserves, and techniques like Bootstrapping assess profitability and risk for each region to guide decision-making on where to drill

This project analyzes taxi trip data in Chicago to identify patterns in passenger preferences and the impact of external factors like weather on ride frequency. SQL is used for data extraction, and pandas/scikit-learn are utilized for exploratory data analysis and hypothesis testing. The outcomes improve marketing strategies and user experience

This project predicts churn for Beta Bank by analyzing client demographics, account details, and behavior using models like Decision Trees, Random Forest, and Logistic Regression. Aims to achieve a high F1 score for precise churn prediction. Class balancing, hyperparameter tuning, and model evaluation are employed to improve performance