Regression determines relationship between dependent and independent variables. Linear regression is most common form, showing straight line of best fit. Simple linear uses one independent variable, multiple uses two or more
Tutorial demonstrates building and training linear regression model using scikit-learn. Data set contains housing prices and features like income and room count. Data is split into training (70%) and test (30%) sets using train_test_split. Model coefficients show impact of variables on predicted values. Performance metrics include mean absolute error, mean squared error, and root mean squared error
AI enables machines to think and make decisions like humans. Machine Learning is a subfield of AI that helps machines learn from data. AI algorithms combine inputs and outputs simultaneously for learning
Supervised learning uses labeled data to learn input-output relationships. Unsupervised learning models data structure using unlabeled data. Reinforcement learning learns through trial and error
Linear Regression is a supervised learning technique for predicting continuous values. It finds linear relationships between target and one or more predictors. Simple Linear Regression uses one independent variable to predict one dependent variable
Multiple linear regression estimates relationships between two or more independent variables and one dependent variable. Assumes homogeneity of variance, independence of observations, normality, and linearity. Independent variables should not be highly correlated (r2 > ~0.6)