# How to predict custom value after using linear regression?

I'm new to machine learning, and I'm currently practicing by playing around with datasets that I find on Kaggle. Currently I'm trying to predict the price of an Audi, based on the model, mileage and manufacturing year, using a slighly modified version of this set (only columns I use are model, mileage, price and year).

I have the following code written down which makes use of linear regression.

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

cars=pd.get_dummies(df['model'],prefix='car')

new_df=pd.concat([df,cars],axis='columns')
new_df=new_df.drop(['model'],axis='columns')

x_data=new_df.drop(['price'],axis='columns')
y_data=new_df['price']

model=LinearRegression()
model.fit(x_data,y_data)


This gives me a meagre model.score of 0.8290666609212749.

To test for a custom car that I find on one of the many used car websites, I do the following:

#Audi A3, 42100 mileage, 2016 built
model.predict([[42100,2016,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])


This works, but as you can see it's a huge hassle to work with because of all the dummy variables. Is there a way of making this simpler?