269 skills found · Page 6 of 9
mohsinchd / Learnica BackendLearnica's backend repository serves as the robust foundation of our online learning platform, powered by Node.js and Express.js, forming part of the MERN stack. Designed with scalability and security in mind, the backend efficiently handles data processing, user authentication, and course management.
valentineashio / Online Payments Fraud Detection Dataset Case StudyA Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.
DivyaKarade / Deep Learning Classification Based Model For Screening Compounds With HERG Inhibitory ActivityDeveloping a Deep learning classification-based model for screening pharmaceutical compounds with hERG inhibitory activity (cardiotoxicity) and using the model to screen CAS antiviral database to identify compounds with cardiotoxicity potential. The data is derived from "Drug Discovery Hackathon 2020: PS ID: DDT2-13" (https://innovateindia.mygov.in/ddh2020/problem-statements/) Details related to the project can also be derived from: (https://youtu.be/7tqaPmYQmCM) Note: The solution for the above problem statement is solved with Deep learning classification based model instead of linear discriminant analysis model as written in the problem statement. Details of the project: In silico prediction of cardiotoxicity with high sensitivity and specificity for potential drug molecules would be of immense value. Hence, building a classification-based machine learning models, capable of efficiently predicting cardiotoxicity will be critical. A data set of diverse pharmaceutical compounds with hERG channel inhibitory activity (blocker/non-blocker) is provided. The SMILES notations of all compounds are given. The set of compounds divided into a training set and a test set using 70:30 ratios. Simple, reproducible and easily transferable classification models developed from the training set compounds using 2D descriptors. The models were validated based on the test set compounds. The models is having the following quality: Training Set: ROC AUC for training set: 0.977280 Classification accuracy for training set: 0.986058 Precision for training set: 0.993124 Sensitivity/Recall for training set: 0.990235 F1 score for training set: 0.991677 Confusion matrix: [[ 892 33] [ 47 4766]] Test set: ROC AUC for test set: 0.649767 Classification accuracy for test set: 0.813670 Precision for test set: 0.883061 Sensitivity/Recall for test set: 0.990235 F1 score for test set: 0.889050 Confusion matrix: [[ 165 243] [ 215 1835]] The best model was also used to classify CAS antiviral database compounds for hERG channel inhibitory activity and a list of compounds with cardiotoxicity potential was being generated in the form of .csv file.
resibots / Kaushik 2018 Multi DexSource code for "Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards" (CoRL 2018)
generalroboticslab / Sym2realOfficial repo for Sym2Real: Symbolic Dynamics with Residual Learning for Data-Efficient Adaptive Control
microsoft / Imitation Learning In Modern Video GamesAccompanying code for "Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games" publication
ekoahamdutivnasti / VRINDAV.R.I.N.D.A. (Virtual Responsive Intelligent Neural Data-driven Assistant) is a Python-based AI designed for efficient task automation and intelligent interactions. Leveraging NLP and machine learning, VRINDA excels in understanding and responding to user inputs with precision and adaptability.
visresearch / DemsThis repository includes official implementation and model weights of Data-Efficient Multi-Scale Fusion Vision Transformer.
Cyizere-Happy / C WorkHere’s a glimpse into my journey learning the C programming language 💻, including implementing linked lists 🔗, mastering sorting algorithms 📊, and building CRUD operations ✍️ to efficiently create, read, update, and delete data structures.
harshika0926 / Battery Management System Using Machine LearningMachine learning can enhance a BMS by improving SOC and SOH estimation, detecting faults, and optimizing control policies. By leveraging data from the battery and other sensors, ML can lead to more accurate and efficient battery management, enhancing safety, performance, and sustainability.
arvindselvamoorthy15 / Optimizing Electric Vehicle Charging Using Reinforcement LearningThis project leverages reinforcement learning to optimize electric vehicle (EV) charging schedules. By analyzing historical charging data, weather, and traffic patterns, the model minimizes energy costs and balances grid loads. It efficiently determines the best times to charge EVs, ensuring grid stability and reducing energy expenses.
DaviRP2018 / Courses Rest Apis With Flask And PythonAre you tired of boring, outdated, incomplete, or incorrect tutorials? I say no more to copy-pasting code that you don’t understand. Welcome to the bestselling REST API course on Udemy! I'm Jose. I'm a software engineer, here to help you truly understand and develop your skills in web and REST API development with Python and Flask. Production-ready REST APIs with Flask This course will guide you in creating simple, intermediate, and advanced REST APIs including authentication, deployments, databases, and much more. We'll start with a Python refresher that will take you from the very basics to some of the most advanced features of Python—that's all the Python you need to complete the course. Using Flask and popular extensions Flask-RESTful, Flask-JWT, and Flask-SQLAlchemy we will dive right into developing complete, solid, production-ready REST APIs. We will also look into essential technologies Git, Heroku, and nginx. You'll be able to... Create resource-based, production-ready REST APIs using Python, Flask, and popular Flask extensions; Handle secure user registration and authentication with Flask. Using SQLAlchemy and Flask-SQLAlchemy to easily and efficiently store resources to a database; and Understand the complex intricacies of deployments and the performance of Flask REST APIs. But what is a REST API anyway? A REST API is an application that accepts data from clients and returns data back. For example, a REST API could accept text data from the client, such as a username and password, and return whether that is a valid user in the database. When developing REST APIs, our clients are usually web apps or mobile apps. That's in contrast to when we make websites, where the clients are usually the users themselves. Together we'll develop a REST API that not only allows clients to authenticate but also to store and retrieve any data you want from a database. Learning this will help you develop any REST API that you need for your own projects! I pride myself on providing excellent support and feedback to every single student. I am always available to guide you and answer your questions. I'll see you on the inside. Take your first step towards REST API mastery! O que você aprenderá Connect web or mobile applications to databases and servers via REST APIs Create secure and reliable REST APIs which include authentication, logging, caching, and more Understand the different layers of a web server and how web applications interact with each other Handle seamless user authentication with advanced features like token refresh Handle log-outs and prevent abuse in your REST APIs with JWT blacklisting Develop professional-grade REST APIs with expert instruction Há algum requisito ou pré-requisito para o curso? Some prior programming experience in any programming language will help. The course includes a full Python refresher course. All software used in the course is provided, and completely free Complete beginners may wish to take a beginner Python course first, and then transition to this course afterwards Para quem é este curso: Students wanting to extend the capabilities of mobile and web applications by using server-side technologies Software developers looking to expand their skill-set by learning to develop professional grade REST APIs Those looking to learn Python while specifically catering to web services
Kiven-ykw / DMAB For Dynamic Beamforming With Local ObservationMain data and model for submitted paper: Energy-Efficient Multi-Cell Beamforming via Multi-Agent Reinforcement Learning
MetAILab / FengYuan DAFengYuan Data Assimilation Inference providing efficient inference using ONNX models. This module applies deep learning models to data assimilation tasks, combining background fields and observation data to generate more accurate analysis fields.
unum-cloud / ExamplesLearning Unum's efficient data-processing tools one cool project at a time
mrinal1704 / Credit Card Transaction Fraud Detection Using Supervised Machine Learning With An Imbalanced DatasetCredit card fraud is a burden for organizations across the globe. Specifically, $24.26 billion were lost due to credit card fraud worldwide in 2018, according to shiftprocessing.com. In this project, our goal was to build an effective and efficient model to predict fraud. We analyzed a real-world dataset that contained a list of government related credit card transactions over the 2010 calendar year. The data presented a supervised problem as it included a column showing the transaction’s fraud label (whether a transaction was fraudulent or not). It also contained identifying information about each transaction such as the credit card number, merchant, merchant state, etc. The dataset had 96,753 records and 10 data fields. We first described and visualized each of the 10 data fields, cleaned the dataset, and filled in missing values. Then we created many variables and performed feature selection. Finally, we created a variety of machine learning models (both linear and nonlinear) and highlighted our results.
marcgarnica13 / Ml Interpretability European FootballUnderstanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
sowndkrish / ML Keystroke Dynamics Using Behavioural BiometricsThis thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision shape function to classify different users. To reduce the classification error, it is essential to identify the important features from the raw data. In an effort to confront the generation of features from attributes an efficient feature extraction algorithm has been developed, obtaining high classification performance are now being sought. To handle the multi-class problem, the random forest classifier is used to identify the users effectively.
Daniblit / Ensemble Predictive Model Forecasting AMGEN Stock Price At Year End 31sThe basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.
luigibonati / DEALData efficient active learning for machine learning potentials
