Practical ML/AI Guide for Businesses

  ETL Workflow Linear regression is an algorithm of supervised Machine Learning (ML) in which the predicted output is continuous with having a constant slope [1].  Consider a company of real estate with datasets containing the property prices of a specific region. The price of a property is based on essential factors like bedrooms, areas, and parking.  Majorly, a real estate company requires [1]: Define the set of variables  (model features like areas, number of rooms and bathroom, etc.)  that affects the price of a house; Creating a linear model quantitatively related to the house price with model va riables; Examine the accuracy of an output model, i.e. how well the model variables can predict the prices of a house for training, test and validation data. So real estate experts assume that the trained, tested and deployed ML model would be capable of learning and predicting how far people would go in the bidding in order to buy a house, based on selected features. That’s the ML housin

  Problem Breast cancer   (BC) is the uncontrollable growth of malignant cells in the breasts [1]. BC is the most common cancer with the highest mortality rate.  The exact cause of breast cancer is unknown, but some women have a higher risk than others. This includes women with a personal or family history of breast cancer and women with certain gene mutations. Since cancer cells can metastasize, or spread to other parts of the body, it’s important to recognize the symptoms of BC early on. The sooner you receive a BC Diagnosis (BCD) and start treatment, the better your outlook [2].   Conventional BCD involves imaging tests to look for BC spread. Imaging tests use x-rays, magnetic fields, sound waves, or radioactive substances to create pictures of the inside of your body. Imaging tests might be done for a number of reasons including [2]: To look at suspicious areas that might be cancer To learn how far cancer might have spread To help determine if treatment is work