In this academic project, we implemented and compared betwen regression methods for linear, multi-linear and exponential problems. For the linear case, the implemented algorithms are : Newton method, ...

We will now implement Simple Linear Regression using PyTorch. Let us consider one of the simplest examples of linear regression, Experience vs Salary. We will train a regression model with a given set ...

This repository includes the C language implementation of some fundamental operations such as FFT, matrix inverse, matrix-vector multiplication, linear regression, 1D convolution and determinant of a ...

This paper focuses on predicting wind power using ten distinct models, including Gaussian Process Regression (GPR) with proper tunning of kernel function. Thereafter, the same wind power prediction is ...

Multiple Linear Regression¶ 9.1. Preliminaries¶ As before, we need to start by: Loading the Pandas and Statsmodels libraries. Reading the data from a CSV file. Fixing the column names using Panda’s ...

The second model we employ is Exponential Smoothing, a powerful tool for forecasting time series data. Both models are trained and tested using actual data obtained from the Jordanian Department of ...

8.3. Regression diagnostics¶. Like R, Statsmodels exposes the residuals. That is, keeps an array containing the difference between the observed values Y and the values predicted by the linear model. A ...

Learn how to choose between linear and nonlinear regression models for your data analysis, based on fit, inference, and simplicity criteria. Agree & Join LinkedIn ...

The diagram in Figure 2 gives you a rough idea of support vector regression for a scenario where there is just one predictor variable x. Each dot is a training data item. The red line is the linear ...