• M. Aditya Vamsi
• K. Raja Rishita
• M. Amin Qurishi
• N. Siva Sandeep
• K. Raghu Ram

Abstract: - Car makers are continually streamlining their cycles to increment eco-friendliness, in light of the fact that the vehicle business has been developing for more than two centuries, and fuel costs are persistently expanding. Clients are turning out to be more specific about highlights, so car creators are continually changing their cycles to increment eco-friendliness. In any case, imagine a scenario where there was a precise assessor for a vehicle's MPG (Miles per Gallon) or the fuel utilization indicator in light of a few known details. By having a more positive vehicle that is likewise more proficient, you could beat rivals on the lookout, increment interest for the item and put more creation into the market. Utilizing Machine Learning, we are currently planning the expectation models and lessening the Error values for cars that have been made in the beyond couple of year. Here we will use the datasets available to machine learning practitioners to create a model to predict fuel efficiency of various kinds of vehicles across various periods. As part of the model, we will include descriptions of many different cars from different periods. These descriptions will include things such as cylinders, displacement, horsepower, and weight. The ML method is appropriate for this type of analysis since it can be applied to learn patterns in data and construct models from them. Besides that, deep learning concepts will be implemented to create other models. Based on the analysis, it will be seen whether which model will produce less error with better efficiency.

Machine Learning, Fuel Efficiency Prediction, Random Forest, Decision tree, KNN, Linear Regression.

" Fuel Efficiency Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.f353-f362, April-2022, Available :http://www.jetir.org/papers/JETIR2204545.pdf

" Fuel Efficiency Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppf353-f362, April-2022, Available at : http://www.jetir.org/papers/JETIR2204545.pdf