Case Study: Price Forecasting For Used Cars

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TITLE: Price forecasting for used Cars.
NAME(S): Rishabh Rana 1459503

ABSTRACT
The number of used vehicle market continues to grow in every part of world including New Zealand, with an increase of 15% in the sales of used as well as imported used cars than last year.

The task is to create a system software that allows us to predict the price for cars based on several attributes such as car’s condition, engine, current market value, background details, mileage, model, and make year, etc. The work is how to make machine so that it can learn those attributes and generates the price of car using them? To solve this, I opt Machine learning to help me out. It will help to forecast the price based on different features. I will be using supervised …show more content…

It depend on several factors usually like age of the car, make, model, origin, mileage, and horsepower. Other factors are type of fuel it works on. acceleration, interior, volume of cylinders, number of doors, breaking system, transmission type, its color and weight, etc. Machine learning systems automatically learn programs from Data. It follows a combination three components: - a) Representation b) Evaluation c) Optimization.

According to this project, we want to forecast the price for cars and to predict it we need to provide inputs to forecast the target, this process include Supervised Technique. As in supervised learning it takes a known set of input object and known responses to the desired object, and seeks to build a forecasting model that produce specific predictions for the response to new object.

Supervised Learning is further divided into two categories: - first one is classification and second one is regression. Classification for those responses that just have a few known values, like 'true ' or 'false '. But Regression are for those responses that are of real number, such as miles per litre for a particular …show more content…

But we cannot use simple linear regression in this case because our project input include multiple features like model, make year, brand name, transmission, mileage, doors and type, etc. to train our data value therefore our focus will be on using the multiple linear regression for this project.
We are using Multiple Linear Regression defined as a model that “the relationship between two or more variables by fitting a linear equation to observed data. Every value of the independent variable x is associated with a value of the dependent variable y”. So this definition directly relates our above methods for handling data inputs therefore this stats that our model is fit to our application. The most important goals of multiple regression analysis are to i) Describe ii) Predict iii)

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