Utah Money Watch’s Post

Fancy taking a look at Maryland's Data Center world with me? Fine, buckle up. From Baltimore to Frederick, Maryland's buzzing with Data Center activity. Here's what I know: ✅ Maryland's right in the middle of all the action on the East Coast, plus, it's super close to "Data Center Alley" in NoVA, where all the big dogs hang out. ✅ It's close to D.C. and has super fast internet connections. ✅ Being close to D.C. means Maryland's Data Centers can help the government with top-secret stuff. ✅ Why Baltimore? Compared to other big cities, they don't cost as much. Also, Maryland gives special treats like tax incentives to businesses, making it even cheaper. ✅ Plus, Baltimore has a focus on Healthcare and AI - it's a perfect playground for innovation and networking. It sounds like my kind of place. What are your thoughts? #Datacentre #ai #artificialintelligence #maryland

🎉 Excited to Announce: Successful Completion of My Latest Machine Learning Project on USA House Price Prediction! 🏡💡 👨💻 I've leveraged advanced algorithms and extensive data analysis to develop a model that accurately predicts house prices across various regions in the United States. This project has been an incredible journey into the world of predictive analytics and real estate market trends. Key Highlights: 📊 Data Collection: Compiled a diverse dataset covering features such as location, square footage, amenities, and more to train the predictive model. 🔍 Exploratory Data Analysis (EDA): Conducted in-depth EDA to understand correlations, outliers, and patterns in the data, uncovering valuable insights for model development. ⚙️ Feature Engineering: Engineered new features and transformed existing ones to enhance model performance and capture nuanced relationships within the dataset. 🤖 Model Selection: Explored and experimented with various machine learning algorithms including Linear Regression, Decision Trees, and Random Forest to identify the most suitable model for accurate predictions. 📈 Evaluation Metrics: Assessed model performance using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to ensure accuracy and reliability. 📊 Insights & Visualization: Visualized predictions and key findings through interactive charts and graphs for intuitive interpretation of house prices across different regions. 🔗 [Detailed Report Attached] This report summarizes the project, methodologies, findings, and implications. Your thoughts and feedback are welcome as I continue to refine and extend the capabilities of this predictive model. 🌟 Special Thanks to #Learnbay for the support and resources throughout this project. Let's connect and discuss more about predictive analytics and real estate trends! 🚀 #MachineLearning #DataScience #HousePricePrediction #PredictiveAnalytics #RealEstate #EDA #FeatureEngineering #ModelSelection #DataVisualization #Learnbay

If you're anything like us, you have a mountain of #research papers and #data analyses to read. Unless your superpower is disrupting the space-time continuum, you're likely looking for trusted sources to help cut through the noise. That's where we come in -- a social science research lab building, researching, and scaling fair technology where we live, learn, and work. Our approach is academic-quality research using real-time data in partnership with tech platforms in key impact areas: 🏘 #Housing 📖 #Education ⚙ #Workforce We're cutting through the noise by highlighting important research findings from our lab and our affiliates. Because what's the value of a research finding when it's left unfound? ➕ Follow for findings straight in your feed 💬 Comment with questions or suggestions 🔁 Share your favorites #researchmatters #evidencematters #evidencebased #datadriven

📺 EVENT! Join a special Data for Policy conversation on 3rd April! Algorithmic Rents: How Big Tech Platforms Control Attention and Shape Markets: A Conversation  📅 Date: 3rd April 1700 GMT ℹ Free to attend on Zoom, approx. 60 min with Q&A 👉 Register here: https://lnkd.in/e-cGxEjP We invite you to join an online event marking the publication of a landmark report from the UCL Institute for Innovation and Public Purpose about how today’s big tech platforms use the power they hold over the attention of billions of users to shape the markets.   Join authors Tim O'Reilly (Founder and CEO of O'Reilly Media), Mariana Mazzucato and Ilan Strauss (UCL Institute for Innovation and Public Purpose) in a discussion about their findings and how the work was produced. This new report outlines a new theory of algorithmic attention rents: as platforms grow, they become increasingly capable of extracting rents from users and suppliers through algorithmic control over attention. Their work focuses on advertising models that harvest, monetize and resell attention, but it has relevance to other online business models, and calls for regulation that mandates the disclosure of metrics that platforms use to measure attention.   👉 Read the report #OpenAccess: https://lnkd.in/eRBzStFt 👉 More info on the event: https://lnkd.in/e9xT2FjF Zeynep Engin, PhD (Data for Policy CIC) and Jon Crowcroft (The Alan Turing Institute & University of Cambridge Department of Computer Science and Technology) – two of the Founding Editors-in-Chief of Data & Policy (an #OpenAccess journal at Cambridge University Press ) - host the conversation. #platforms #algorithms #businessmodels #attention #regulation

I’m so proud of this partnership between Resultant and the Indiana Department of Workforce Development! What began as a “what if” dream project ten years ago is now delivering astonishing results. A decade back I was talking with Joshua Richardson about the potential of state longitudinal data systems (SLDS) to unlock solutions that are both targeted to the individual and incredibly effective across the population. Being the brilliant guy he is, Josh immediately applied this concept to one of the biggest problems our society faces today: unemployment. In collaboration with CIO Chris Henderson and a team of partners from multiple agencies across the State of Indiana, we designed, tested and launched a first-of-its-kind Workforce Recommendation Engine in October of last year. This system uses machine learning and AI to leverage a universe of longitudinal data that exists across state government. These insights are then mobilized to help job seekers within the unemployment system find the quickest and most effective opportunities to increase their income and jumpstart their careers. It’s been gratifying to watch thousands of Hoosiers discover a targeted training, certification, or industry swap that helped click their career into place. For me personally, it’s a deeply fulfilling win, to see government data mobilized to improve individual lives in such a tangible way. And this is just the start. We're now gearing up to start incorporating additional data sets from other domains such as health, criminal justice, and social services. Check out this whitepaper for more details, and comment or message me if you want to know more! I could talk about this all day. 😎 Whitepaper here: https://lnkd.in/gqs7aUvJ

Initial Data Offering (IDO): Brain Wikipedia Page Views The Brain Wikipedia Page Views dataset tracks the number of views on Wikipedia pages for the top 1000 U.S. companies, roughly aligning with the Russell 1000 index, as an indicator of interest in these companies. It uses a "buzz" metric to determine whether a company is experiencing an unusual increase in page visits over different time frames, such as the previous day, week, or month. The aim is to offer an alternative method for gauging investor interest in a specific company, serving as a complement to attention metrics derived from news or other sources. See more details, request an introduction, and join the community in the link below

Want to learn about data centers in your community? I wrote an article about their financial impacts at the local level. There are plenty of unknowns about what new AI data centers mean for employment, but the message is clear that data centers are big fiscal winners. Hyperscale data centers could be big tax generators for communities looking to fund schools, fire departments, and other essential local services. https://lnkd.in/eVqdxaHZ

Entropy and Information Gain are fundamental concepts in decision tree algorithms, helping to determine which features best split the data to improve classification performance. By calculating entropy, we measure the impurity of groups, and by computing information gain, we assess how much a feature reduces that impurity. Features with higher information gain provide more valuable insights, helping the model make more accurate predictions. Ultimately, understanding and applying these concepts allows data scientists to build more efficient and interpretable models, ensuring that decisions are based on meaningful patterns in the data. #DataScience #MachineLearning #DecisionTrees #Entropy #InformationGain #Classification #FeatureSelection #DataAnalysis

Join us into learn more about Machine Learning with Shani Vanlerberghe!! 

I'm excited to announce the launch of my latest project on Hugging Face Spaces: the Ultimate Asset Analyzer! The Ultimate Asset Analyzer is designed for anyone interested in financial markets, from seasoned investors to beginners. Here’s what it offers: Advanced Predictive Analytics: Utilizing deep learning models, this tool analyzes historical financial data to predict future asset prices, helping you make informed investment decisions. User-Friendly Interface: Built with Gradio, the interface is intuitive and easy to use. Simply input the asset ticker, and the tool will provide detailed predictions and insights. Comprehensive Data Analysis: Integrates data from Yahoo Finance, normalizes it, and applies sophisticated machine learning algorithms for accurate predictions. Real-Time Updates: Stay ahead of the market with real-time data analysis and predictions. Visualizations and Insights: Provides detailed visualizations and insights, making it easier to understand market trends and asset performance. Technical Indicators: Includes popular technical analysis indicators like RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence) to help you make better trading decisions. Customizable Analysis: Allows users to input specific parameters and customize the analysis according to their needs. Data Export: Enables users to export analysis results for further use and reporting. Whether you're looking to enhance your investment strategy or explore the power of machine learning in finance, the Ultimate Asset Analyzer is a valuable resource. Check it out here: https://lnkd.in/eXa-6qJs and share your feedback! Disclosures Purpose: This code is designed to estimate the price of a user-inputted asset for a user-inputted number of days. It is publicly available and intended for informational and educational purposes only. Data Sources: The code utilizes the yfinance library to access data from Yahoo Finance. The user acknowledges that the use of Yahoo Finance data is subject to Yahoo's terms of service, and the author of this code does not have any specific agreement with Yahoo Finance. User Input: Users are responsible for providing valid input in the form of a ticker symbol. The code does not validate the input, and incorrect or invalid input may lead to errors or inaccurate predictions. Predictive Model Disclaimer: The predictions generated by this code are based on limited historical data and a mathematical model. They are provided for informational purposes only and should not be construed as financial or investment advice. #MachineLearning #Finance #DeepLearning #DataScience #Investing #HuggingFace #OpenSource #FinancialAnalysis