The goal here is to develop an application which can predict the price of a refurbished car based on the variables provided in the dataset. You are required to build a regression model to predict the price of a given car.
Automobiles, Sales and Marketing
There is a huge demand for refurbished cars in the Indian Market today. As sales of new cars have slowed down in the recent past, the refurbished car market has continued to grow over the past year and is larger than the new car market now.
Consider this: In 2018-19, the sales of refurbished cars which were previously owned by someone has increased steadily, and it is currently estimated to be approximately 1.3 times the sales of new cars. There are multiple reasons for this shift. However, the key reason is the increase in total cost of ownership of new cars which includes taxes, insurances.
There is a slowdown in new car sales and that could mean that the demand is shifting towards the pre-owned market.
Data has been split into two groups and provided in the module:
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Training set : The training set is used to build your machine learning model. For the training set, we provide the price of a car (also known as the variable price) for each instance.
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Test set : The test set should be used to see how well your model performs on unseen data. For the test set, it is your job to predict the price of the car (price) for each instance.
RMSE - root mean squared error The measure of accuracy will be RMSE. The predicted price for each car in the test dataset will be compared with the actual price to calculate the RMSE value of the entire prediction. The lower the RMSE value, the better the model will be.