This one uses the NARX model to predict the forthcoming house price in months of 2017.

[Master] regressr

linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called a simple linear regression. One of the most known and used algorithms that most of tech_specialist use and love is L…

Data Analysis: Merge, Impute, and Interpret

Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable)

Hypothesis-Testing-2-Sample-2-Tail-Test-Drugs-and-Placebos. Note: This python code states both 2-sample 1-tail and 2-sample 2-tail codes. Treatment group mean is Mu1 Contrl group mean is Mu2 2-sample 2-tail ttest Assume Null Hypothesis Ho as Mu1 = Mu2 Thus Alternate Hypothesis Ha as Mu1 ≠ Mu2.

This is a linear regression Machine Learning model which I have created with Coursera' s guided project. This is based on the "chirps dataset" which has 'X' variable as number of chirps by a cricket and 'Y' as the temperature.

Linear Regression in PySpark

Executed Regression modelling, hypothesis testing and statistical analysis to predict factors affecting credit card balances in a firm. Tools & technologies: ANOVA, p-value, R square