The mission entitled “Regression and prediction of automobiles the usage of Ma-chine Learning” is an automobile dataset analysis. Regression evaluation a set of statistical strategies for estimating the relationships between an established variable and impartial var-iable. Data evaluation is carried out the use of panda’s library bundle in python. The subject is decided by way of a set of education documents. Readily accessible dataset from Kaggle internet site is used for facts analysis. In order to assemble a regression model, computing device gaining knowledge of algorithm is used Random Forest regression mannequin is de-signed and implemented. Dataset is break up as cut up as 70% for education and 30% for testing. The mannequin is nicely skilled and based totally on the training. The data are ex-amined and accuracy is calculated. Each and each information evaluation are stated and tabulated for in addition identification. Random wooded area is a Supervised Machine Learning Algorithm that is used extensively in Classification and Regression problems. It builds selection bushes on unique samples and takes their majority vote for classification and common in case of regression. Predicting the rate of used automobiles is one of the big and fascinating areas of analysis. As an extended demand in the second-hand vehicle mar-ket, the commercial enterprise for each shoppers and marketers has increased. For depend-able and correct prediction, it requires specialist expertise about the area due to the fact of the fee of the vehicles established on many vital factors. Decision Tree is one of the most frequently used, sensible techniques for supervised learning. Voting Regressor is an ensem-ble meta-estimator that suits a number of base regressors, every on the complete dataset to common the character predictions to shape a last prediction