2 분 소요

예측 모델 실습

auto-mpg.csv 데이터를 통해, mpg (mile per gallern, 자동차 연비) 를 예측하는 모델을 만드세요.

컬럼 정보 :

MPG (miles per gallon),

cylinders,

engine displacement (cu. inches),

horsepower,

vehicle weight (lbs.),

time to accelerate from O to 60 mph (sec.),

model year (modulo 100),

origin of car (1. American, 2. European,3. Japanese).

Also provided are the car labels (types)

Missing data values are marked by series of question marks.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


df = pd.read_csv('auto-mpg.csv')

df
mpg cyl displ hp weight accel yr origin name
0 18.0 8 307.0 130 3504 12.0 70 1 chevrolet chevelle malibu
1 15.0 8 350.0 165 3693 11.5 70 1 buick skylark 320
2 18.0 8 318.0 150 3436 11.0 70 1 plymouth satellite
3 16.0 8 304.0 150 3433 12.0 70 1 amc rebel sst
4 17.0 8 302.0 140 3449 10.5 70 1 ford torino
... ... ... ... ... ... ... ... ... ...
387 27.0 4 140.0 86 2790 15.6 82 1 ford mustang gl
388 44.0 4 97.0 52 2130 24.6 82 2 vw pickup
389 32.0 4 135.0 84 2295 11.6 82 1 dodge rampage
390 28.0 4 120.0 79 2625 18.6 82 1 ford ranger
391 31.0 4 119.0 82 2720 19.4 82 1 chevy s-10

392 rows × 9 columns

df.drop('name',axis=1,inplace=True)

# 데이터에 Nan 값 찾기.
df.isna().sum()

mpg       0
cyl       0
displ     0
hp        0
weight    0
accel     0
yr        0
origin    0
dtype: int64

# 데이터 X와 y로 나누기

df.head()
mpg cyl displ hp weight accel yr origin
0 18.0 8 307.0 130 3504 12.0 70 1
1 15.0 8 350.0 165 3693 11.5 70 1
2 18.0 8 318.0 150 3436 11.0 70 1
3 16.0 8 304.0 150 3433 12.0 70 1
4 17.0 8 302.0 140 3449 10.5 70 1 
X = df.iloc[:,1:]

y = df['mpg']

# origin 은 카테고리컬 데이터이므로 갯수 확인 후 원핫 인코딩.

sorted(X['origin'].unique())

[1, 2, 3]

X = pd.get_dummies(X,columns=['origin'])

# 학습용과 테스트 용으로 분리

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=10)

## 리니어 리그레이션으로 모델링

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()

# 인공지능 학습

regressor.fit(X_train,y_train)

LinearRegression()

# 인공지능 테스트

y_pred = regressor.predict(X_test)

error = y_test - y_pred

# MSE 구하기
(error**2).mean()

12.782792965646852

# 차트로 그리기

plt.plot(y_test.values)
plt.plot(y_pred)
plt.legend(['Real','Pred'])

plt.show()

12

# 방정식의 계수
regressor.coef_

array([-0.83960353,  0.03032018, -0.03061675, -0.00644172,  0.00434557,
        0.75847227, -1.83056491,  0.54183524,  1.28872967])

# 방정식의 상수항
regressor.intercept_

-12.3134923524076

댓글남기기

참고