CAR PREDICTION 1. Introduction In this blog report, introduction part includes a brief introduction of our target problem and its significance. The reason why we choose this topic here is that most people buy a car beyond their means and find it difficult to deal with it. This attempt was made to solve this. 1000 samples were obtained in the dataset. This dataset contains User ID, Gender, Age, AnnualSalary. It can be used to tell whether a person can afford a car based on his annual salary. Through this process, this prediction is done only based on everyone's AnnualSalary and their Age. 2. Dataset preparation We have found some dataset from Kaggle for car prediction. It has 1000 rows and 5 columns. There are number of features such as User ID, Gender, Age and AnnualSalary. In here, User ID, Age and AnnualSalary are int type. And Gender is string type. The table below displays the dataset ...