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

About Me

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

Automates the creation of a data science tutorial with machine learning using Serper API and OpenAI. Four agents (Researcher, Writer, Developer, Reviewer) collaborate to research, write, code, and review, resulting in a complete tutorial with code examples. Includes setup instructions for using API keys and environment configuration.

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.

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 developed a predictive model to estimate additional profits from two loyalty programs at a major retailer. By analyzing growth rates, revenues, and customer behavior, the model distinguished between organic growth and profits driven by loyalty campaigns.

✨ 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 developed a model to analyze and track the profitability of contracts at a law firm. It integrated data on revenue, attorney costs, contract expenses, billable hours, and indirect costs to evaluate individual contract performance. The model provided valuable insightslater evolved into a customized system still in use today

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

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

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