One of the main areas of research in machine learning is the prediction of the price of used cars. It is based on finance and the marketing domain. It is a major research topic in machine learning because the price of a car depends on many factors. Some of the factors that contribute a lot to the price of a car are:
Brand Year Selling price Present price Fuel type Km Driven any many more
If one ignores the brand of the car, a car manufacturer primarily fixes the price of a car based on the features it can offer a customer. Later, the brand may raise the price depending on its goodwill, but the most important factors are what features a car gives you to add value to your life. So, in the section below, I will walk you through the task of training a car price prediction model with machine learning using the Python programming language.
1.The dataset I’m using here to train a car price prediction model was downloaded from Kaggle. It contains data about all the main features that contribute to the price of a used car.
2.There are 9 columns in this dataset, so it is very important to check whether or not this dataset contains null values before going any further:
3.So this dataset doesn’t have any null values, now let’s look at some of the other important insights to get an idea of what kind of data we’re dealing with:
4.I will use the random forest regression algorithm to train a car price prediction model. So let’s split the data into training and test sets and use the random forest regression algorithm to train the model:
So this is how you can train a machine learning model for the task of predicting car prices by using the Python programming language. It is a major research topic in machine learning because the price of a used car depends on many factors.