catalaosoares's Stars
apache/superset
Apache Superset is a Data Visualization and Data Exploration Platform
Datalux/Osintgram
Osintgram is a OSINT tool on Instagram. It offers an interactive shell to perform analysis on Instagram account of any users by its nickname
tpn/pdfs
Technically-oriented PDF Collection (Papers, Specs, Decks, Manuals, etc)
Ignitetechnologies/Mindmap
This repository will contain many mindmaps for cyber security technologies, methodologies, courses, and certifications in a tree structure to give brief details about them
daffainfo/AllAboutBugBounty
All about bug bounty (bypasses, payloads, and etc)
jakejarvis/awesome-shodan-queries
🔍 A collection of interesting, funny, and depressing search queries to plug into shodan.io 👩💻
seleniumbase/SeleniumBase
📊 Python's all-in-one framework for web crawling, scraping, testing, and reporting. Supports pytest. UC Mode provides stealth. Includes many tools.
fbdesignpro/sweetviz
Visualize and compare datasets, target values and associations, with one line of code.
PacktPublishing/The-Kaggle-Book
Code Repository for The Kaggle Book, Published by Packt Publishing
anthwlock/untrunc
Restore a truncated mp4/mov. Improved version of ponchio/untrunc
microsoft/powerbi-desktop-samples
Power BI Desktop sample files for the monthly release. Here you can find the PBIX files used in the monthly release videos.
ErikEJ/SqlCeToolbox
SQLite & SQL Server Compact Toolbox extension for Visual Studio, SSMS (and stand alone)
pberkes/big_O
Python module to estimate big-O time complexity from execution time
varunkashyapks/Books
Books related to AI/ML/DL/GENAI
hhsm95/FacebookPostsScraper
Scraper for posts in Facebook user profiles, pages and groups
jhnwr/rotatingproxies
kdoren/jambox-pi-gen
Start jamming online easily with a Raspberry Pi, an audio interface, and this free Jambox image file. Just download/burn/boot/jam. Choose from multiple jamming apps: Jamulus, SonoBus, JackTrip, JamTaba, JammerNetz or HpsJam. User interface is any web browser on same local network. Pre-built image file is available under "Releases".
uezo/TinySeleniumVBA
A tiny Selenium wrapper written in pure VBA
perchrh/sanction_list_search
name search for people and entities on the EU, OFAC and UN sanction lists
scriptzteam/Awesome-Shodan-Queries
🔍 A collection of interesting, funny, and depressing search queries to plug into shodan.io 👩💻
assoft-portugal/CryptoSAF-T-SAF-T-Utils
Ferramentas para descaracterização (cifra) e reversão (decifra) do ficheiro SAF-T.
robocorp/example-ie-mode-edge
Automating with Selenium and Edge in IE compatibility mode.
pvilas/fin_sanctions
Consult the lists of UN, EU and US persons, groups and entities subject to financial sanctions
keepers305/Song-Sheet-Sharing-Web-Pages
Web application to allow online musicians to see song sheets selected by another Jam Member
SHIVITG/Titanic-ML-Disaster-Prediction
Titanic Data Science Solutions This notebook is the solution to the Titanic: MACHINE LEARNING for Disaster Workflow stages The competition solution workflow goes through seven stages described in the Data Science Solutions book. Question or problem definition. Acquire training and testing data. Wrangle, prepare, cleanse the data. Analyze, identify patterns, and explore the data. Model, predict and solve the problem. Visualize, report, and present the problem solving steps and final solution. Submiting the results. Problem Statement Competition sites like Kaggle define the problem to solve or questions to ask while providing the datasets for training your data science model and testing the model results against a test dataset. The question or problem definition for Titanic Survival competition is described here at Kaggle. Workflow goals The data science solutions workflow solves for seven major goals. Classifying. We may want to classify or categorize our samples. We may also want to understand the implications or correlation of different classes with our solution goal. Correlating. One can approach the problem based on available features within the training dataset. Which features within the dataset contribute significantly to our solution goal? Statistically speaking is there a correlation among a feature and solution goal? As the feature values change does the solution state change as well, and visa-versa? This can be tested both for numerical and categorical features in the given dataset. We may also want to determine correlation among features other than survival for subsequent goals and workflow stages. Correlating certain features may help in creating, completing, or correcting features. Converting. For modeling stage, one needs to prepare the data. Depending on the choice of model algorithm one may require all features to be converted to numerical equivalent values. So for instance converting text categorical values to numeric values. Completing. Data preparation may also require us to estimate any missing values within a feature. Model algorithms may work best when there are no missing values. Correcting. We may also analyze the given training dataset for errors or possibly innacurate values within features and try to corrent these values or exclude the samples containing the errors. One way to do this is to detect any outliers among our samples or features. We may also completely discard a feature if it is not contribting to the analysis or may significantly skew the results. Creating. Can we create new features based on an existing feature or a set of features, such that the new feature follows the correlation, conversion, completeness goals. Charting. How to select the right visualization plots and charts depending on nature of the data and the solution goals.
SantosJGND/SI_NOTEBOOKS
Statistical Inference Class Notebooks