17 skills found
JohnStarich / Python Pool PerformanceAnalyzes time and space usage of multiple Python thread and process pools with increasing job pool sizes
ThomasTaus / PoolSeqAnalyze and simulate Pool-Seq time series data
broadinstitute / StarrynightA toolkit for processing, analyzing, and managing optical pooled screening (OPS) image data.
mhorlbeck / GImap ToolsTools for processing and analyzing pooled Genetic Interaction map data
dair-iitd / PoolingAnalysis[EMNLP'20][Findings] Official Repository for the paper "Why and when should you pool? Analyzing Pooling in Recurrent Architectures."
gnosly / TomcatJdbcConnectionTestThe goal of this web app is to show how analyze the tomcat connection pool in case of abandoned connections.
gpp-rnd / PoolaPython package to analyze the results of pooled CRISPR screens
jonmatteochurch / Uintah SvnThe Uintah software suite is a set of libraries and applications for simulating and analyzing complex chemical and physical reactions. These reactions are modeled by solving partial differential equations on structured adaptive grids using hundreds to thousands of processors (though smaller simulations may also be run on a scientist's desktop computer). Key software applications have been developed for exploring the fine details of metal containers (encompassing energetic materials) embedded in large hydrocarbon fires. Uintah's underlying technologies have led to novel techniques for understanding large pool eddy fires as well as new methods for simulating fluid-structure interactions. The software is general purpose in nature and the breadth of simulation domains continues to grow beyond the original focus of the C-SAFE initiative
idrees535 / Uniswap V3 SimulatorA uniswap v3 simulator which can be used to analyze and simulate token's pool launch strategies, liquidity provisioning and management strategies
broadinstitute / Nf Pooled CellpaintingNextflow pipeline for analyzing pooled optical screen (OPS) data.
yonathanpy / Eternalblue ExploitResearch project analyzing EternalBlue exploitation mechanics, SMBv1 behavior, pool grooming, and kernel memory corruption techniques. Strictly for authorized lab use.
Dresel / RocketexplorerRocket Explorer is an open-source community project for analyzing and visualizing the decentralized Rocket Pool network.
256foundation / HashScopeA Bitcoin mining analysis platform with transparent MITM proxy and distributed agent fleet for capturing, analyzing, and testing mining pool behavior.
codeesura / ZkSync Pool Data FetcherzkSync Pool Data Fetcher is a powerful tool for the zkSync blockchain that fetches and analyzes pool data from various Automated Market Makers (AMMs). It leverages Node.js and the ethers.js library to interact with zkSync smart contracts, providing insightful information to DeFi enthusiasts and investors.
chaitanya-327 / AI Goofishai-goofish 🐙 AI-powered IdleFish product monitor that auto-scrapes, analyzes with AI, and sends real-time alerts; a web UI, proxy pool, cookie management, and OpenAI integration.
Tirth8038 / Tweets Sentiment ClassificationThe main aim of the project is to analyze the Twitter data describing the covid situation and to build a text classification model which can distinguish the tweets into 5 categories such as Extremely Negative (0), Negative (1), Neutral (2), Positive (3) and Extremely Positive (4). The provided dataset contains tweets with dimension (37041, 2) and numerical labels with dimension (37041,2) of above categories separately. However, the provided tweets need to be cleaned as it contains irrelevant elements such as mentions (@), HTTP links, HTML tags, punctuation marks and URL. Using the regex function, I removed those elements and Stopwords from tweets. Apart from this, to normalize the terms, I implemented Porter Stemmer and used WordNet Lemmatizer to convert the term to its base form. After this, to convert the words into vectors of equal length, I tokenized the tweets and converted it to sequence and then post padded the sequence with zero and kept the length of largest sequence in tweets as maximum length. After Preprocessing the data, the Tweet dataset has dimension of (37041, 286). For Model Selection, I build 3 different models consisting of one Baseline model such as Multinomial Naive Bayes and 2 advanced Recurrent Neural Network models such as GRU Architecture with a single Embedding layer, 1 Bidirectional layer followed by Global Average Pooling 1D and 2 Dense layers & LSTM Architecture with a single Embedding layer followed by 2 Bidirectional layers and 2 Dense layers. In addition to this, I also tried applying Dropout with a 40% dropout rate during training of RNN models and Early Stopping method for preventing overfitting and evaluated that Early Stopping gave better results than Dropout. For evaluation of models, I splitted the dataset into training,testing and validation split with (80,10,10) ratio and calculated F1 macro, AUC Score on test data and using the Confusion Matrix, I calculated the accuracy by dividing the sum of diagonal elements by the sum of all elements. In addition to this, I plotted training vs. validation loss and accuracy graphs to visualize the performance of models. Interestingly, by not implementing the preprocessing techniques like removing stopwords, Porter Stemmer or WordNetLemmatizer and using just basic text cleaning function in the RNN model with LSTM architecture, the accuracy of the model was increased from 73.87% to 77.1% and had AUC score of 0.95.
Kwamb0 / API HomeworkPart I - WeatherPy In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. Your first objective is to build a series of scatter plots to showcase the following relationships: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude After each plot add a sentence or too explaining what the code is and analyzing. Your next objective is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have. Your final notebook must: Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it’s being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot. Part II - VacationPy Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment. Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry. Create a heat map that displays the humidity for every city from the part I of the homework. heatmap Narrow down the DataFrame to find your ideal weather condition. For example: A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness. Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal. Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number. Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates. Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country. hotel map As final considerations: Create a new GitHub repository for this project called API-Challenge (note the kebab-case). Do not add to an existing repo You must complete your analysis using a Jupyter notebook. You must use the Matplotlib or Pandas plotting libraries. For Part I, you must include a written description of three observable trends based on the data. You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. For max intensity in the heat map, try setting it to the highest humidity found in the data set. Hints and Considerations The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook. You may want to start this assignment by refreshing yourself on the geographic coordinate system. Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors – spending hours investigating their entire code – when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you! Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes). Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters. Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities. Lastly, remember – this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!
