Practical ML/AI Guide for Businesses

  The following is a set of steps intended for ML/AI regression to predict stock prices. The objective is to simulate available historical stock prices of $AAPL using the SciKit Learn library. 1. Install Yahoo finance library !pip install yfinance 2. Let's call  all dependencies that we will use for this exercise  import pandas as pd import numpy as np import math  import seaborn as sns  import matplotlib.pyplot as plt from sklearn import metrics from sklearn.model_selection import train_test_split import yfinance as yf  # We will use this library to upload latest data from Yahoo API %matplotlib inline plt.style.use('fivethirtyeight') 3. Define the ticker you will use nio = yf.Ticker('AAPL') #Display stock information, it will give you a summary description of the ticker nio.info {'zip': '95014', 'sector': 'Technology', 'fullTimeEmployees': 100000, 'longBusinessSummary': 'Apple Inc. designs, manufactures, and

  (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]