The main motive of my work is to check the  working of the machine learning techniques that anticipate  the cost price of old/used vehicles which were collected  through various sources. The forecasts are in view of  authentic information gathered from every day papers.  Various procedures like different logistic regression  analysis, k-closest neighbors and linear regression  analysis have been made use of to predict the forecasts.  The algorithms are best used to check the results and  also how good the accuracy can be taken into picture.  The results were best seen in k-closest neighbors and  were pretty decent in both logistic and linear regression  analysis. An apparently simple issue ended up being for sure  exceptionally troublesome to determine with high precision.  Every one of the three techniques gave tantamount results.  Later part of the work, we can make use of or utilize more  refined calculations to make the expectations by using  various other algorithms and attain higher precision.