466 skills found · Page 3 of 16
bioinfoDZ / RISCA R package for integrated scRNA-seq data analysis using the RPCI algorithm
zhangzuomin / AI Trade Intelligence Decision PlatformThe Artificial Intelligence Trade Intelligence Decision Platform is a comprehensive solution that integrates advanced data analysis with intelligent algorithms, featuring excellent maintainability and scalability, serving as an important tool for promoting intelligent trade upgrades and scientific decision-making.
wangys96 / Bayesian Stock Market SentimentA stock market text sentiment analysis website. A股舆情分析, web-crawler, bayesian algorithm, SQL, django, data-visualization.
dMLTquant / DMLTresearchBeginner friendly guide into the world of investing, quant data analysis and algorithmic trading.
loveboyz / ProteoLizard AlgorithmToolkitAdvanced machine learning algorithms for processing ion-mobility mass spectrometry (IMS-MS) raw data to enable high-throughput, efficient analysis leveraging GPUs and modern hardware.
sharmaroshan / Insurance Claim PredictionIn this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.
yuval-khachatryan / Data Structures And Algorithm Analysis SolutionsSolutions for Data Structures and Algorithm Analysis in C++ 4th edition by Mark Allen Weiss.
dblhlx / PyBSASeqA novel algorithm with high detection power for BSA-Seq data analysis - the significant structural variant method
jmscslgroup / StrymA real time CAN vehicle data logging, analysis and visualization tool to work with USB-CAN Interface for developing in-vehicle AI algorithms for self-driving and automation
archienorman11 / Thesis Bitcoin ClusteringThe Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.
rohanmistry231 / Parkinsons Disease ClassificationA Python-based machine learning project for classifying Parkinson's disease using patient data and algorithms like XGBoost and Random Forest. Includes data preprocessing, feature analysis, and model evaluation with Scikit-learn and Pandas for accurate predictions.
sharmaroshan / Wine Quality PredictionsPredicting the Quality of Red Wine using Machine Learning Algorithms for Regression Analysis, Data Visualizations and Data Analysis.
sharmaroshan / Big Mart Sales PredictionUsing Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.
ajayshewale / Sentiment Analysis Of Text Data Tweets This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small (just over 5900 examples) and the data within it was highly skewed, which greatly impacted on the difficulty of building a good classifier. After creating a lot of custom features, utilizing bag-of-words representations and applying the Extreme Gradient Boosting algorithm, the classification accuracy at the level of 58% was achieved. Analysing the public sentiment as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like the stock exchange.
sharmaroshan / Students Performance AnalyticsStudents Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades
TheTavakoli1 / PyAI TutorialsPyAI-Tutorials — A comprehensive repository for mastering algorithms, Python, data analysis, and AI. It offers structured code examples, tutorials, and projects to help learners deepen their understanding. Each lesson is organized into separate branches for better clarity and version control. 🚀
JustinGuese / Python Tradingbot FrameworkPython algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.
serkannpolatt / DATA SCIENCE FOR FINANCEThis repository features data science projects focused on financial data analysis and forecasting. The projects apply machine learning algorithms to analyze stock market data, predict trends, and optimize investment strategies.
zahta / Graph Machine LearningCourse: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation learning and graph neural networks, algorithms for the world wide web, reasoning over knowledge graphs, and social network analysis.
Ilyushin / EconomicIntelligenceThe project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.
