Tag: artificialintelligence

  • ML/AI Credit Risk Analytics

    ML/AI Credit Risk Analytics

    This project utilized a variety of machine learning algorithms (RF, KNN, DT, GB, LR, ANN, and SVM) to create credit risk modeling for efficient credit rating. Among these, the Gradient Boosting algorithm showed the highest test accuracy at 81.7%. The project also produced feature importance coefficients that reflect relevant credit ratings or probability of default,…

  • HealthTech ML/AI Q3 ’22 Round-Up

    HealthTech ML/AI Q3 ’22 Round-Up

    Featured Photo by Andy Kelly on Unsplash This blog presents a Q3 ’22 summary of current healthtech ML/AI innovation methods, trends and challenges. Virtual reality, artificial intelligence, augmented reality, and machine learning are all healthcare technology trends that are going to play a vital role across the entire healthcare system. Let’s take a look at…

  • Invest in AI via Macroaxis Sep ’22 Update

    Invest in AI via Macroaxis Sep ’22 Update

    Invest in AI via Macroaxis Sep ’22 Update 4 AI pillars AI thematic idea 20 stocks Asset Allocation Market Capitalization (%) Instrument Composition Market Elasticity Risk/Return Ratio Asse ratings Technical Analysis Correlation Matrix Takeaways Business headlines

  • ML/AI Diamond Price Prediction with R

    ML/AI Diamond Price Prediction with R

    The post discusses an analysis for determining the price of diamonds, aiming to detect possible overpayment. Using R Studio and various machine learning models, including linear regression, XGBoost, SVM, Decision Tree, and Random Forest, the study targets diamonds’ price and related factors. The best performance is provided by the Random Forest model, indicating the primary…

  • All Eyes on ETFs Sep ’22

    All Eyes on ETFs Sep ’22

    ETF Focus September 2022: Is Now A Good Time To Buy ETFs? Cons and Pros of ETFs Sectors On Watch The Pup’s Weekend Dig Zacks.com SeekingAlpha TradingView Barchart AIolux Macroaxis S&P 500 Related Infographic Commodity trends, cryptocurrency, AI optimization, and more.

  • US Real Estate – Harnessing the Power of AI

    US Real Estate – Harnessing the Power of AI

    The content describes a continuation of the use-case series dedicated to real estate (RE) monitoring, trend analysis, and forecast. It focuses on predicting and estimating US house prices using a pre-trained ML model. The content covers various machine learning algorithms, data preprocessing, model training, and evaluation using the Boston housing price dataset. Key takeaways include…

  • Cloud-Native Tech Status Update Q3 2022

    Cloud-Native Tech Status Update Q3 2022

    Cloud Computing Trends Q3 2022, market Share update key services IaaS PaaS FaaS Saas Cloud digital transformation all-the-way DevSecOps CI/Cd GitLab MLOps IoT Tech GCP gateway Big data Deloitte use-cases Stock markets Health Tech Cybersecurity Highlights Events Webinars AWS Storage E-training

  • 10 AI-Powered Websites for Content Writers

    10 AI-Powered Websites for Content Writers

    The content introduces ten innovative AI-powered websites, including Magic Eraser for image editing, Craiyon for AI-generated drawings, Rytr for fast content creation, Thing Translator for language translation via images, AutoDraw for automatic sketching, Fontjoy for AI-based font pairing, Talk to Books for referencing a collection of over 100,000 books, ‘This Person Does Not Exist’ for…

  • ML/AI Wildfire Prediction using Remote Sensing Data

    ML/AI Wildfire Prediction using Remote Sensing Data

    The content is a detailed exploration into the application of machine learning (ML) and artificial intelligence (AI) in predicting wildfires using a dataset acquired in Canada. It covers data preprocessing, ML workflow, exploratory data analysis (EDA), model training, performance evaluation, hyperparameter optimization (HPO), and data resampling techniques like SMOTE and ADASYN. The ML models are…

  • Towards Optimized ML Wildfire Prediction

    Towards Optimized ML Wildfire Prediction: This Python case example stems from the initial research into ML/AI wildfire prediction using the dataset that was downloaded from the UCI Machine Learning Repository.

  • Macroaxis AI Investment Opportunity

    The research presented examines the portfolio optimization potential of Macroaxis’ new artificial intelligence (AI) system. This tool automates processes like asset allocation, portfolio diversification and rebalancing, and equity research, providing a list of suggested investments. The AI makes suggestions based on market conditions and investor risk tolerance, and has demonstrated higher average returns than the…

  • ML/AI Wildfire Prediction

    ML/AI Wildfire Prediction

    A wildfire, forest fire, bushfire, wildland fire or rural fire is an unplanned, uncontrolled and unpredictable fire in an area of combustible vegetation starting in rural and urban areas. Wildland fire is a widespread and critical element of the Earth’s system. Presently, global annual area burned is estimated to be approximately 420 Mha (Giglio et al. 2018), which is greater in area than the country of India. Wildland fires…

  • AI-Guided Drug Recommendation

    AI-Guided Drug Recommendation

    AI-Guided Drug Recommendation in Python using NLP text processing. Key steps: WordCount images, NLP Pre-Processing, NER via spacy, LDA topic modelling, and Word2Vec Vectorization for reviews using pretrained glove model. Input data: the Kaggle UCI ML Drug Review dataset. Applications in the pharmaceutical industry, including drug R&D, drug repurposing, improving pharmaceutical productivity, and clinical trials,…

  • Quant Trading using Monte Carlo Predictions and 62 AI-Assisted Trading Technical Indicators (TTI)

    Algorithmic Trading using Monte Carlo Predictions and 62 AI-Assisted Trading Technical Indicators (TTI): The goal of the here presented pilot study is to develop and test an end-to-end Python-3 script in Jupyter that implements algorithmic trading. Thanks to high-level automation and integration of multiple tasks, the script can simultaneously analyze hundreds of technical indicators, run…

  • The Application of ML/AI in Diabetes

    The study uses machine learning (ML) to predict diabetes in patients. Classifying diabetics is complex, but ML can offer quick and accurate predictions. The study focuses on type 2 diabetes and uses the Pima Indians database for diagnostic measurements. Models were trained with Python and the Anaconda library. Feature engineering and exploratory data analysis revealed…

  • AI-Powered Customer Churn Prediction

    AI-Powered Customer Churn Prediction

    AI-Powered Customer Churn Prediction Churn is a good indicator of growth potential. Churn rates track lost customers, and growth rates track new customers—comparing and analyzing both of these metrics tells you exactly how much your business is growing over time. In this project, we explored the churn rate in-depth and examined an example implementation of…

  • ML/AI Regression for Stock Prediction – AAPL Use Case

    1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise  3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output

  • Macroaxis Wealth Optimization

    Macroaxis is an investment management solution designed to aid small businesses with portfolio creation, asset allocation, and more, using a variety of financial models. It offers a range of features, such as portfolio rebalancing, risk management, and a diverse set of analysis tools, at a starting price of $19.99 per month. The company also provides…

  • Supervised ML/AI Breast Cancer Diagnostics (BCD) – The Power of HealthTech

    Pilots Related to HealthTech Infographic These plots illustrate the most basic application of ML/AI in BCD as the binary classification problem. Classification usually refers to any kind of problem where a specific type of class label is the result to be predicted from the given input field of data. This is a task which assigns a label value…

  • About MLOps

    About MLOps

    Machine Learning (ML), a subset of Artificial Intelligence, enables computers to learn from experience, improving tasks through performance measures. Deployed by businesses across sectors, ML powers various applications such as chatbots, decision support tools, fraud detection, etc. ML uses data analytics concepts like predictive and prescriptive algorithms, and techniques such as supervised, unsupervised, and deep…