Practical ML/AI Guide for Businesses

 AI allows investors to process real-time datasets from various sources such as stock prices, financial reports, social media sentiment, portfolio management resources, etc. Further, AI can make predictions on market movements, optimize investment portfolios, manage trading strategy in terms of ROI-risk balance, and automatically build a risk-aware customized portfolio. AI plays a key role in financial risk analysis - a process of assessing and evaluating potential risks and their impact on an organization, project, or decision-making. AI-Powered Stock Technical Indicators: Thanks to high-level automation and integration of multiple tasks, AI can simultaneously analyze hundreds of technical indicators, run simulations and forecasts, perform real-time technical analysis, and generate trading signals and alerts at a speed and frequency that is impossible for a human trader. AI Breakout Trading Strategies implemented as a stock scanner in Python. AI can download historical data

  (the image was created using Visme [1]). Introduction Stock markets are analyzed either technically or fundamentally [2]. Fundamental analysis studies supply and demand relationships that define the stock price at any given time.  Technical analysis uses specialized methods of predicting prices by analyzing past price patterns and levels.  T here are many techniques used to examine stock price lines and patterns [2]: bar or high/low/close charts moving averages trend lines channels cycles resistance and support planes corrections double tops and bottoms head and shoulders formation trading volume open interest.  Theere are numerous limitations of these techniques: moving averages responds to general trends only is not highly precise short-term moving averages can give false indications, especially in times of volatile prices Trend lines work best with sustained trends positioning of trend lines is subjective and takes practice trends must be established before they become recognizabl

  https://www.canva.com/design/DAE7oU6O6QQ/share/preview?token=xH-OB2oXeQSrennmqMC2hw&role=EDITOR&utm_content=DAE7oU6O6QQ&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton Acknowledgements with the ML/AI contribution https:// hiscidatmlai.blogspot.com/2022/02/digita l-transformation-all-way.html … and @VismeApp #Graphics via ref https:// visme.co/?ref=al24 Thanks to Mugdha Paithankar [1] and https:// kaggle.com/uciml/breast-c ancer-wisconsin-data … [2] for the shared open-source content! Introduction Breast Cancer (BC) continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management [3].  The use of ML/AI models in combination with statistical explarotary data analysis (EDA) has become a predominant area of cancer research as a part of HealthTech data science/analytics [1,2]