The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives and many more. Car price prediction is one of the major research areas in machine learning. So if you want to learn how to train a car price prediction model then this article is for you. In this article, I will take you through how to train a car price prediction model with machine learning using Python.
Car Price Prediction with Machine Learning
One of the main areas of research in machine learning is the prediction of the price of 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
- Model
- Horsepower
- Mileage
- Safety Features
- GPS and 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.
Car Price Prediction Model using Python
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 car. So let’s start this task by importing the necessary Python libraries and the dataset:
car_ID symboling CarName ... citympg highwaympg price 0 1 3 alfa-romero giulia ... 21 27 13495.0 1 2 3 alfa-romero stelvio ... 21 27 16500.0 2 3 1 alfa-romero Quadrifoglio ... 19 26 16500.0 3 4 2 audi 100 ls ... 24 30 13950.0 4 5 2 audi 100ls ... 18 22 17450.0 [5 rows x 26 columns]
There are 26 columns in this dataset, so it is very important to check whether or not this dataset contains null values before going any further:
data.isnull().sum()
car_ID 0 symboling 0 CarName 0 fueltype 0 aspiration 0 doornumber 0 carbody 0 drivewheel 0 enginelocation 0 wheelbase 0 carlength 0 carwidth 0 carheight 0 curbweight 0 enginetype 0 cylindernumber 0 enginesize 0 fuelsystem 0 boreratio 0 stroke 0 compressionratio 0 horsepower 0 peakrpm 0 citympg 0 highwaympg 0 price 0 dtype: int64
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:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 205 entries, 0 to 204 Data columns (total 26 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 car_ID 205 non-null int64 1 symboling 205 non-null int64 2 CarName 205 non-null object 3 fueltype 205 non-null object 4 aspiration 205 non-null object 5 doornumber 205 non-null object 6 carbody 205 non-null object 7 drivewheel 205 non-null object 8 enginelocation 205 non-null object 9 wheelbase 205 non-null float64 10 carlength 205 non-null float64 11 carwidth 205 non-null float64 12 carheight 205 non-null float64 13 curbweight 205 non-null int64 14 enginetype 205 non-null object 15 cylindernumber 205 non-null object 16 enginesize 205 non-null int64 17 fuelsystem 205 non-null object 18 boreratio 205 non-null float64 19 stroke 205 non-null float64 20 compressionratio 205 non-null float64 21 horsepower 205 non-null int64 22 peakrpm 205 non-null int64 23 citympg 205 non-null int64 24 highwaympg 205 non-null int64 25 price 205 non-null float64 dtypes: float64(8), int64(8), object(10) memory usage: 41.8+ KB
print(data.describe())
car_ID symboling wheelbase ... citympg highwaympg price count 205.000000 205.000000 205.000000 ... 205.000000 205.000000 205.000000 mean 103.000000 0.834146 98.756585 ... 25.219512 30.751220 13276.710571 std 59.322565 1.245307 6.021776 ... 6.542142 6.886443 7988.852332 min 1.000000 -2.000000 86.600000 ... 13.000000 16.000000 5118.000000 25% 52.000000 0.000000 94.500000 ... 19.000000 25.000000 7788.000000 50% 103.000000 1.000000 97.000000 ... 24.000000 30.000000 10295.000000 75% 154.000000 2.000000 102.400000 ... 30.000000 34.000000 16503.000000 max 205.000000 3.000000 120.900000 ... 49.000000 54.000000 45400.000000 [8 rows x 16 columns]
data.CarName.unique()
array(['alfa-romero giulia', 'alfa-romero stelvio',
'alfa-romero Quadrifoglio', 'audi 100 ls', 'audi 100ls',
'audi fox', 'audi 5000', 'audi 4000', 'audi 5000s (diesel)',
'bmw 320i', 'bmw x1', 'bmw x3', 'bmw z4', 'bmw x4', 'bmw x5',
'chevrolet impala', 'chevrolet monte carlo', 'chevrolet vega 2300',
'dodge rampage', 'dodge challenger se', 'dodge d200',
'dodge monaco (sw)', 'dodge colt hardtop', 'dodge colt (sw)',
'dodge coronet custom', 'dodge dart custom',
'dodge coronet custom (sw)', 'honda civic', 'honda civic cvcc',
'honda accord cvcc', 'honda accord lx', 'honda civic 1500 gl',
'honda accord', 'honda civic 1300', 'honda prelude',
'honda civic (auto)', 'isuzu MU-X', 'isuzu D-Max ',
'isuzu D-Max V-Cross', 'jaguar xj', 'jaguar xf', 'jaguar xk',
'maxda rx3', 'maxda glc deluxe', 'mazda rx2 coupe', 'mazda rx-4',
'mazda glc deluxe', 'mazda 626', 'mazda glc', 'mazda rx-7 gs',
'mazda glc 4', 'mazda glc custom l', 'mazda glc custom',
'buick electra 225 custom', 'buick century luxus (sw)',
'buick century', 'buick skyhawk', 'buick opel isuzu deluxe',
'buick skylark', 'buick century special',
'buick regal sport coupe (turbo)', 'mercury cougar',
'mitsubishi mirage', 'mitsubishi lancer', 'mitsubishi outlander',
'mitsubishi g4', 'mitsubishi mirage g4', 'mitsubishi montero',
'mitsubishi pajero', 'Nissan versa', 'nissan gt-r', 'nissan rogue',
'nissan latio', 'nissan titan', 'nissan leaf', 'nissan juke',
'nissan note', 'nissan clipper', 'nissan nv200', 'nissan dayz',
'nissan fuga', 'nissan otti', 'nissan teana', 'nissan kicks',
'peugeot 504', 'peugeot 304', 'peugeot 504 (sw)', 'peugeot 604sl',
'peugeot 505s turbo diesel', 'plymouth fury iii',
'plymouth cricket', 'plymouth satellite custom (sw)',
'plymouth fury gran sedan', 'plymouth valiant', 'plymouth duster',
'porsche macan', 'porcshce panamera', 'porsche cayenne',
'porsche boxter', 'renault 12tl', 'renault 5 gtl', 'saab 99e',
'saab 99le', 'saab 99gle', 'subaru', 'subaru dl', 'subaru brz',
'subaru baja', 'subaru r1', 'subaru r2', 'subaru trezia',
'subaru tribeca', 'toyota corona mark ii', 'toyota corona',
'toyota corolla 1200', 'toyota corona hardtop',
'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii',
'toyota corolla', 'toyota corolla liftback',
'toyota celica gt liftback', 'toyota corolla tercel',
'toyota corona liftback', 'toyota starlet', 'toyota tercel',
'toyota cressida', 'toyota celica gt', 'toyouta tercel',
'vokswagen rabbit', 'volkswagen 1131 deluxe sedan',
'volkswagen model 111', 'volkswagen type 3', 'volkswagen 411 (sw)',
'volkswagen super beetle', 'volkswagen dasher', 'vw dasher',
'vw rabbit', 'volkswagen rabbit', 'volkswagen rabbit custom',
'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245',
'volvo 264gl', 'volvo diesel', 'volvo 246'], dtype=object)
The price column in this dataset is supposed to be the column whose values we need to predict. So let’s see the distribution of the values of the price column:
Now let’s have a look at the correlation among all the features of this dataset:
print(data.corr())
car_ID symboling ... highwaympg price car_ID 1.000000 -0.151621 ... 0.011255 -0.109093 symboling -0.151621 1.000000 ... 0.034606 -0.079978 wheelbase 0.129729 -0.531954 ... -0.544082 0.577816 carlength 0.170636 -0.357612 ... -0.704662 0.682920 carwidth 0.052387 -0.232919 ... -0.677218 0.759325 carheight 0.255960 -0.541038 ... -0.107358 0.119336 curbweight 0.071962 -0.227691 ... -0.797465 0.835305 enginesize -0.033930 -0.105790 ... -0.677470 0.874145 boreratio 0.260064 -0.130051 ... -0.587012 0.553173 stroke -0.160824 -0.008735 ... -0.043931 0.079443 compressionratio 0.150276 -0.178515 ... 0.265201 0.067984 horsepower -0.015006 0.070873 ... -0.770544 0.808139 peakrpm -0.203789 0.273606 ... -0.054275 -0.085267 citympg 0.015940 -0.035823 ... 0.971337 -0.685751 highwaympg 0.011255 0.034606 ... 1.000000 -0.697599 price -0.109093 -0.079978 ... -0.697599 1.000000 [16 rows x 16 columns]
Training a Car Price Prediction Model
I will use the decision tree regression algorithm to train a car price prediction model. So let’s split the data into training and test sets and use the decision tree regression algorithm to train the model:
1.0
The model gives 100% accuracy on the test set, which is excellent.
Summary
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 car depends on many factors. I hope you liked this article on the task of training a model for predicting car prices with machine learning. Feel free to ask your valuable questions in the comments section below.
