I am new to machine learning and this community too. So please pardon me if i make any mistake while putting up this question.
I am trying this https://www.kaggle.com/doaaalsenani/usa-cers-dataset problem from kaggle where i am trying to predict price of cars based on various parameters.
And I am not sure which algorithm to apply for this type of problem as it is having both categorical and numeric data but then also i tried to apply linear regression to it on price feature as price is dependent on all other features but after converting all categorical features such as color , model , brand to one hot encoding and applying feature scaling to all it gave me 2.4 mean squared error which is terribly bad , may be i am using irrelevant or too many features but i don't feel straight forward linear regression is good choice.
I completed a course on machine learning where when i applied linear regression all data was in numeric form but not in this case and i know linear regression is not a good choice for this problem or may be i need to make some modifications to it but i don't know what to do else.
Can anybody suggest me what should i begin with problems like this ? What algorithms are used or what kind of modifications can be used. I am open to any suggestions as i really don't have a path for solving this type of problem.
And in my computation i dropped vin , lot , Unnamed:0 columns.
Please help me with my issues.
EDIT AFTER TRYING RANDOM FOREST AS THAT WORKED:
Why did linear regression behave so poorly here ? Is there any way I can determine before applying linear aggression if it should be applied or not , other than covariance/correlation matrix as that cannot be much useful with this case because it is having categorical data too.