tools-techniques
There are 34 repositories under tools-techniques topic.
Whitecat18/Powershell-Scripts-for-Hackers-and-Pentesters
An List of my Powershell scripts, commands and Blogs for windows Red Teaming.
xiaoluoboding/techstack.tools
🗡️ Discover our curated list of creative tools to supercharge your next project.
CScorza/CStools
Tool for searching information via Telegram, Number Phone and Username.
exajobs/APIs-tools-collection
A collection of awesome software, libraries, Learning Tutorials, documents, books, resources and interesting stuff about APIs
GURPREETKAURJETHRA/LLM-SECURITY
Securing LLM's Against Top 10 OWASP Large Language Model Vulnerabilities 2024
CScorza/CScorzaAnalysis
Capacità di collegare i dati raccolti da fonti diverse - Ability to link data collected from various sources
op10y/python-project
containing everything about python development and implementations
realdanizilla/CrewAI_Agents
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.
realdanizilla/Instacart
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
dhaanpaa-lab0/container-helper-scripts
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
Kuromaea/TemplateDartFlutter
✨ Basic template for run iOS & Android devices simultaneously
QuantEcon/lecture-tools-techniques
Tools & Techniques for Computational Economics
realdanizilla/Beta-Bank
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
realdanizilla/Crazy-Taxi
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
realdanizilla/Insurance
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
realdanizilla/Interconnect
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
realdanizilla/Junky-Union
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
realdanizilla/Loyalty-Savings
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.
realdanizilla/Megaline-Classification
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
realdanizilla/MNSC
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
realdanizilla/Oily-Giant
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
realdanizilla/Rusty-Bargain
This project develops a machine learning model to estimate used car market values for a pricing app. Using pandas for data manipulation and models like Random Forest, Gradient Boosting, and Linear Regression, it aims to balance prediction quality, speed, and training time. It compares multiple models to find the best fit for predicting car prices
realdanizilla/Videogames
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.
realdanizilla/Zuber
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
VinceFasanello/MM_Code_Supplement
PUBLICATION: High-throughput analysis of adaptation using barcoded strains of Saccharomyces cerevisiae
zebbern/Pentesting-Guide
🧾 | Boost Your Pentesting Knowledge
MirRoR4s/Missing-Semester
Missing Semester 学习总结/核心知识点汇总
srishtydhawan/goatcyberspace
Srishty's Brain Child: Goat Cyber Space Inc.
blackkhawkk/Malware_Analysis
Dynamic and Static
Blacksujit/Cyclops-Quinine-Submission
Cyclops offers a range of tools that make it easier to handle complex ML deployments, monitor applications, and ensure everything runs smoothly.
realdanizilla/COVID19-LATAM
COVID-19 Vaccination Analysis: A Case Study on Latin America. This project examines the impact of COVID-19 vaccinations on case numbers and mortality rates in Latin American countries. Uses SQL for data extraction and Tableau for creating visual insights.
realdanizilla/Drone-Delivery
This project optimizes drone delivery routes by analyzing city coordinates and shipment volumes. It calculates distances between cities and identifies the best location for a warehouse to minimize total travel distance. Using Python and vector-based distance calculations, it ensures efficient drone operations, reducing delivery costs
realdanizilla/Megaline
This project aims to classify telecom customers based on their behavior to recommend optimized service plans. Data preprocessing, feature selection, and machine learning algorithms, including Decision Trees, Random Forest, and Logistic Regression, to maximize accuracy. Enables targeted marketing by predicting the most suitable plan for customers
realdanizilla/vehicles
This project analyzes the market value of used vehicles to identify key factors influencing pricing and provide insights into vehicle valuation. Data cleaning and exploration, feature engineering, visualizations, and the use of tools like pandas, matplotlib, and scikit-learn. Helps users understand how vehicle attributes affect resale prices