In [19]:
#imports
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
In [20]:
data = pd.read_csv('car_price.csv')
data.head(10)
Out[20]:
car_ID symboling CarName fueltype aspiration doornumber carbody drivewheel enginelocation wheelbase ... enginesize fuelsystem boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
0 1 3 alfa-romero giulia gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 13495.000
1 2 3 alfa-romero stelvio gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 16500.000
2 3 1 alfa-romero Quadrifoglio gas std two hatchback rwd front 94.5 ... 152 mpfi 2.68 3.47 9.0 154 5000 19 26 16500.000
3 4 2 audi 100 ls gas std four sedan fwd front 99.8 ... 109 mpfi 3.19 3.40 10.0 102 5500 24 30 13950.000
4 5 2 audi 100ls gas std four sedan 4wd front 99.4 ... 136 mpfi 3.19 3.40 8.0 115 5500 18 22 17450.000
5 6 2 audi fox gas std two sedan fwd front 99.8 ... 136 mpfi 3.19 3.40 8.5 110 5500 19 25 15250.000
6 7 1 audi 100ls gas std four sedan fwd front 105.8 ... 136 mpfi 3.19 3.40 8.5 110 5500 19 25 17710.000
7 8 1 audi 5000 gas std four wagon fwd front 105.8 ... 136 mpfi 3.19 3.40 8.5 110 5500 19 25 18920.000
8 9 1 audi 4000 gas turbo four sedan fwd front 105.8 ... 131 mpfi 3.13 3.40 8.3 140 5500 17 20 23875.000
9 10 0 audi 5000s (diesel) gas turbo two hatchback 4wd front 99.5 ... 131 mpfi 3.13 3.40 7.0 160 5500 16 22 17859.167

10 rows × 26 columns

1. 探索数据特性¶

In [21]:
print("Rows: ",data.shape[0])
print("Columns: ",data.shape[1])

Rows:  205
Columns:  26
In [22]:
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
In [23]:
data.isna().sum()
# 没有空值
Out[23]:
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
In [24]:
data.duplicated().sum()
# 没有重复值
Out[24]:
0
In [25]:
data.groupby("CarName").sum(numeric_only=True)
Out[25]:
car_ID symboling wheelbase carlength carwidth carheight curbweight enginesize boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
CarName
Nissan versa 90 1 94.5 165.3 63.8 54.5 1889 97 3.15 3.29 9.4 69 5200 31 37 5499.0
alfa-romero Quadrifoglio 3 1 94.5 171.2 65.5 52.4 2823 152 2.68 3.47 9.0 154 5000 19 26 16500.0
alfa-romero giulia 1 3 88.6 168.8 64.1 48.8 2548 130 3.47 2.68 9.0 111 5000 21 27 13495.0
alfa-romero stelvio 2 3 88.6 168.8 64.1 48.8 2548 130 3.47 2.68 9.0 111 5000 21 27 16500.0
audi 100 ls 4 2 99.8 176.6 66.2 54.3 2337 109 3.19 3.40 10.0 102 5500 24 30 13950.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
volvo 246 204 -1 109.1 188.8 68.9 55.5 3217 145 3.01 3.40 23.0 106 4800 26 27 22470.0
volvo 264gl 404 -3 213.4 377.6 136.1 111.7 6107 271 7.40 6.30 17.0 276 10500 36 47 41045.0
volvo diesel 200 -1 104.3 188.8 67.2 57.5 3157 130 3.62 3.15 7.5 162 5100 17 22 18950.0
vw dasher 190 3 94.5 159.3 64.2 55.6 2254 109 3.19 3.40 8.5 90 5500 24 29 11595.0
vw rabbit 191 3 94.5 165.7 64.0 51.4 2221 109 3.19 3.40 8.5 90 5500 24 29 9980.0

147 rows × 16 columns

In [26]:
# 删除 CarName, CarID 因为它不会给回归任务增加太多价值
data = data.drop(['car_ID','CarName'],axis=1)
data.head(1)
Out[26]:
symboling fueltype aspiration doornumber carbody drivewheel enginelocation wheelbase carlength carwidth ... enginesize fuelsystem boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
0 3 gas std two convertible rwd front 88.6 168.8 64.1 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 13495.0

1 rows × 24 columns

In [27]:
sns.histplot(data=data, x="price")
Out[27]:
<Axes: xlabel='price', ylabel='Count'>
In [28]:
plt.figure(figsize=(15,7))
sns.heatmap(data.corr(numeric_only=True), annot=True)
plt.title("Data Correlation",size=15)
plt.show()
In [29]:
#燃料类型对价格的影响
sns.barplot(x="fueltype", y="price", data=data)
Out[29]:
<Axes: xlabel='fueltype', ylabel='price'>
In [30]:
#车型对价格的影响
sns.boxplot(x ="carbody", y ="price", data = data)
Out[30]:
<Axes: xlabel='carbody', ylabel='price'>
In [31]:
#门数对价格的影响
sns.boxplot(x ="doornumber", y ="price", data = data)
Out[31]:
<Axes: xlabel='doornumber', ylabel='price'>
In [32]:
#驱动器（FWD、RWD、AWD）对价格的影响
sns.boxplot(x ="drivewheel", y ="price", data = data)
Out[32]:
<Axes: xlabel='drivewheel', ylabel='price'>
In [33]:
#绘制热图中最相关属性之间的成对关系
columns=data[['wheelbase','carlength','carwidth','curbweight','price']]
sns.pairplot(columns)
plt.show()
#linear relationship
In [34]:
columns=data[['horsepower','citympg','highwaympg','price']]
sns.pairplot(columns)
plt.show()
#linear relationship

以下属性集具有线性关系：

1.轴距、车长、车宽、整备质量和价格（基本上是所有物理属性）

2.马力、城市英里数、高速公路英里数和价格（基本上是与车辆功率相关的所有属性）

2. 训练模型¶

In [35]:
encoder = LabelEncoder()
data['fueltype'] = encoder.fit_transform(data['fueltype'])
fueltype = {index : label for index, label in enumerate(encoder.classes_)}
data['aspiration'] = encoder.fit_transform(data['aspiration'])
aspiration = {index : label for index, label in enumerate(encoder.classes_)}
data['doornumber'] = encoder.fit_transform(data['doornumber'])
doornumber = {index : label for index, label in enumerate(encoder.classes_)}
data['carbody'] = encoder.fit_transform(data['carbody'])
carbody = {index : label for index, label in enumerate(encoder.classes_)}
data['drivewheel'] = encoder.fit_transform(data['drivewheel'])
drivewheel = {index : label for index, label in enumerate(encoder.classes_)}
data['enginelocation'] = encoder.fit_transform(data['enginelocation'])
enginelocation = {index : label for index, label in enumerate(encoder.classes_)}
data['fuelsystem'] = encoder.fit_transform(data['fuelsystem'])
fuelsystem = {index : label for index, label in enumerate(encoder.classes_)}
data['enginetype'] = encoder.fit_transform(data['enginetype'])
enginetype = {index : label for index, label in enumerate(encoder.classes_)}
data['cylindernumber'] = encoder.fit_transform(data['cylindernumber'])
cylindernumber = {index : label for index, label in enumerate(encoder.classes_)}
data['fuelsystem'] = encoder.fit_transform(data['fuelsystem'])
fuelsystem = {index : label for index, label in enumerate(encoder.classes_)}
In [36]:
x = data.drop('price', axis=1)
y = data['price']
In [37]:
scaler = MinMaxScaler(copy=True, feature_range=(0, 1))
X = scaler.fit_transform(x)
In [38]:
#train, test split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=30,random_state=0)
  1. 随机森林回归
In [39]:
rf = RandomForestRegressor(n_estimators=100,max_depth=5, random_state=33)
rf.fit(x_train, y_train)
Out[39]:
RandomForestRegressor(max_depth=5, random_state=33)
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RandomForestRegressor(max_depth=5, random_state=33)
In [40]:
print("Training r2_score: ",rf.score(x_train, y_train))
print("Testing r2_score: ",rf.score(x_test, y_test))

Training r2_score:  0.9753559007565417
Testing r2_score:  0.87367804775233
  1. 决策树回归
In [41]:
dt = DecisionTreeRegressor( max_depth=5,random_state=33)
dt.fit(x_train, y_train)
Out[41]:
DecisionTreeRegressor(max_depth=5, random_state=33)
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DecisionTreeRegressor(max_depth=5, random_state=33)
In [42]:
print('Training r2_score: ' , dt.score(x_train, y_train))
print('Testing r2_score: ' , dt.score(x_test, y_test))

Training r2_score:  0.9735394081185511
Testing r2_score:  0.8226507572837073
  1. 线性回归
In [47]:
def evaluate(model,x_train , y_train, x_test , y_test, y_predict):
    print(f'train r2_score:{r2_score(y_train, model.predict(x_train))}' )
    print(f'test r2_score : {r2_score(y_test, y_predict)}')
In [48]:
model = LinearRegression()
model.fit(x_train,y_train)
y_predict=model.predict(x_test)
evaluate(model,x_train , y_train, x_test , y_test, y_predict)

train r2_score:0.889157847638672
test r2_score : 0.7289860743863041