Driven by all the hoopla about ChatGPT and the generative AI, I have been also playing with it, please see my previous posts on this. In this post, I want to show you my interaction with ChatGPT to create a simple app for stock price prediction. My initial prompt, shown below, is to ask for a prediction model that will take the closing price of any given stock and predict its next day’s closing price.
Any machine learning model building task begins with a collection of data vectors wherein each vector consists of a fixed number of components. These components represent the measurements, known as attributes or features, deemed useful for the given machine learning task at hand. The n
In my previous post, I had written about principal component analysis (PCA) for dimensionality reduction. In PCA, the class label of each and every example, even if available, is ignored and only the feature values from each example are considered. Thus, PCA is considered an unsupervised approach. The emphasis in PCA is to preserve data variability as much as possible while reducing the dimensionality. Linear discriminant analysis (LDA) on the other hand makes use of class labels as well and its focus is on fi
A common scenario in multiple linear regression is to have a large set of observations/examples wherein each example consists of a set of measurements made on a few independent variables, known as predictors, and the corresponding numeric value of the dependent variable, known as the response. These examples are then used to build a regression model of the following form: