[ML]📊1. Auto-MPG 데이터 - 단순 회귀 분석하기(Simple Linear Regression)

 

 

 

 

 

 

 

[ML]📊1. Auto-MPG 데이터 - 단순 회귀 분석하기(Simple Linear Regression) 

 

 

 

 

 

Auto-MPG 데이터셋 소개

This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original".

"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)

 

Auto-MPG 단순 회귀 분석(Simple Linear Regression)¶

 

기본 라이브러리 불러오기¶

In [25]:

import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns

In [26]:

# 오류 메세지 안뜨게
import warnings
warnings.filterwarnings(action='ignore')

 

데이터 준비하기¶

 

데이터 불러오기¶

In [8]:

df= pd.read_csv('auto-mpg.csv', header=None)
df.head()

Out[8]:

	0	1	2	3	4	5	6	7	8
0	18.0	8	307.0	130.0	3504.0	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693.0	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150.0	3436.0	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150.0	3433.0	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140.0	3449.0	10.5	70	1	ford torino

 

🍭여기서 잠깐! 원래 알고있는 mpg 데이터 셋가 뭔가 다르지않나??

seaborn에서 제공하는 mpg 데이터셋과 비교해보자면!! => origin 국가별 라벨인코딩이 되어있는 상태이고, 열도 빠져있는 상태임!

In [7]:

sns.load_dataset("mpg").head()

Out[7]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	usa	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	usa	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	usa	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	usa	ford torino

In [9]:

sns.load_dataset("mpg").columns

Out[9]:

Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',
       'acceleration', 'model_year', 'origin', 'name'],
      dtype='object')

 

==> 일단 회귀분석을 돌리는 데 집중해야하니까, 갖고 있는 csv의 열 이름만 채워보쟈!

 

데이터 준비¶

In [10]:

# 열 설정하기
df.columns = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',
       'acceleration', 'model_year', 'origin', 'name']

 

데이터 살펴보기¶

In [11]:

df.head()

Out[11]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504.0	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693.0	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150.0	3436.0	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150.0	3433.0	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140.0	3449.0	10.5	70	1	ford torino

 

• mpg : 연비(miles per gallon)
• cylinders : 실린더수
• displacement : 배기량
• horsepower: 출력
• weight : 차 무게
• acceleration : 가속능력
• model year : 출시년도
• origin : 제조국 1(USA), 2(EU), 3(JPN)
• name : 모델명

In [13]:

df.shape

Out[13]:

(398, 9)

In [14]:

df.info()

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   mpg           398 non-null    float64
 1   cylinders     398 non-null    int64  
 2   displacement  398 non-null    float64
 3   horsepower    398 non-null    object 
 4   weight        398 non-null    float64
 5   acceleration  398 non-null    float64
 6   model_year    398 non-null    int64  
 7   origin        398 non-null    int64  
 8   name          398 non-null    object 
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB

 

🪄point
horsepower가 문자열로 되어있어서 바꿔줘야할 듯!

In [17]:

df.isnull().sum()

Out[17]:

mpg             0
cylinders       0
displacement    0
horsepower      0
weight          0
acceleration    0
model_year      0
origin          0
name            0
dtype: int64

 

결측치가 정말 없는 건지, 각 열의 unique값 확인하기

In [22]:

for i in df.columns:
    print(df[i].unique())
    print()

 

[18.  15.  16.  17.  14.  24.  22.  21.  27.  26.  25.  10.  11.   9.
 28.  19.  12.  13.  23.  30.  31.  35.  20.  29.  32.  33.  17.5 15.5
 14.5 22.5 24.5 18.5 29.5 26.5 16.5 31.5 36.  25.5 33.5 20.5 30.5 21.5
 43.1 36.1 32.8 39.4 19.9 19.4 20.2 19.2 25.1 20.6 20.8 18.6 18.1 17.7
 27.5 27.2 30.9 21.1 23.2 23.8 23.9 20.3 21.6 16.2 19.8 22.3 17.6 18.2
 16.9 31.9 34.1 35.7 27.4 25.4 34.2 34.5 31.8 37.3 28.4 28.8 26.8 41.5
 38.1 32.1 37.2 26.4 24.3 19.1 34.3 29.8 31.3 37.  32.2 46.6 27.9 40.8
 44.3 43.4 36.4 44.6 40.9 33.8 32.7 23.7 23.6 32.4 26.6 25.8 23.5 39.1
 39.  35.1 32.3 37.7 34.7 34.4 29.9 33.7 32.9 31.6 28.1 30.7 24.2 22.4
 34.  38.  44. ]

[8 4 6 3 5]

[307.  350.  318.  304.  302.  429.  454.  440.  455.  390.  383.  340.
 400.  113.  198.  199.  200.   97.  110.  107.  104.  121.  360.  140.
  98.  232.  225.  250.  351.  258.  122.  116.   79.   88.   71.   72.
  91.   97.5  70.  120.   96.  108.  155.   68.  114.  156.   76.   83.
  90.  231.  262.  134.  119.  171.  115.  101.  305.   85.  130.  168.
 111.  260.  151.  146.   80.   78.  105.  131.  163.   89.  267.   86.
 183.  141.  173.  135.   81.  100.  145.  112.  181.  144. ]

['130.0' '165.0' '150.0' '140.0' '198.0' '220.0' '215.0' '225.0' '190.0'
 '170.0' '160.0' '95.00' '97.00' '85.00' '88.00' '46.00' '87.00' '90.00'
 '113.0' '200.0' '210.0' '193.0' '?' '100.0' '105.0' '175.0' '153.0'
 '180.0' '110.0' '72.00' '86.00' '70.00' '76.00' '65.00' '69.00' '60.00'
 '80.00' '54.00' '208.0' '155.0' '112.0' '92.00' '145.0' '137.0' '158.0'
 '167.0' '94.00' '107.0' '230.0' '49.00' '75.00' '91.00' '122.0' '67.00'
 '83.00' '78.00' '52.00' '61.00' '93.00' '148.0' '129.0' '96.00' '71.00'
 '98.00' '115.0' '53.00' '81.00' '79.00' '120.0' '152.0' '102.0' '108.0'
 '68.00' '58.00' '149.0' '89.00' '63.00' '48.00' '66.00' '139.0' '103.0'
 '125.0' '133.0' '138.0' '135.0' '142.0' '77.00' '62.00' '132.0' '84.00'
 '64.00' '74.00' '116.0' '82.00']

[3504. 3693. 3436. 3433. 3449. 4341. 4354. 4312. 4425. 3850. 3563. 3609.
 3761. 3086. 2372. 2833. 2774. 2587. 2130. 1835. 2672. 2430. 2375. 2234.
 2648. 4615. 4376. 4382. 4732. 2264. 2228. 2046. 2634. 3439. 3329. 3302.
 3288. 4209. 4464. 4154. 4096. 4955. 4746. 5140. 2962. 2408. 3282. 3139.
 2220. 2123. 2074. 2065. 1773. 1613. 1834. 1955. 2278. 2126. 2254. 2226.
 4274. 4385. 4135. 4129. 3672. 4633. 4502. 4456. 4422. 2330. 3892. 4098.
 4294. 4077. 2933. 2511. 2979. 2189. 2395. 2288. 2506. 2164. 2100. 4100.
 3988. 4042. 3777. 4952. 4363. 4237. 4735. 4951. 3821. 3121. 3278. 2945.
 3021. 2904. 1950. 4997. 4906. 4654. 4499. 2789. 2279. 2401. 2379. 2124.
 2310. 2472. 2265. 4082. 4278. 1867. 2158. 2582. 2868. 3399. 2660. 2807.
 3664. 3102. 2875. 2901. 3336. 2451. 1836. 2542. 3781. 3632. 3613. 4141.
 4699. 4457. 4638. 4257. 2219. 1963. 2300. 1649. 2003. 2125. 2108. 2246.
 2489. 2391. 2000. 3264. 3459. 3432. 3158. 4668. 4440. 4498. 4657. 3907.
 3897. 3730. 3785. 3039. 3221. 3169. 2171. 2639. 2914. 2592. 2702. 2223.
 2545. 2984. 1937. 3211. 2694. 2957. 2671. 1795. 2464. 2572. 2255. 2202.
 4215. 4190. 3962. 3233. 3353. 3012. 3085. 2035. 3651. 3574. 3645. 3193.
 1825. 1990. 2155. 2565. 3150. 3940. 3270. 2930. 3820. 4380. 4055. 3870.
 3755. 2045. 1945. 3880. 4060. 4140. 4295. 3520. 3425. 3630. 3525. 4220.
 4165. 4325. 4335. 1940. 2740. 2755. 2051. 2075. 1985. 2190. 2815. 2600.
 2720. 1800. 2070. 3365. 3735. 3570. 3535. 3155. 2965. 3430. 3210. 3380.
 3070. 3620. 3410. 3445. 3205. 4080. 2560. 2230. 2515. 2745. 2855. 2405.
 2830. 3140. 2795. 2135. 3245. 2990. 2890. 3265. 3360. 3840. 3725. 3955.
 3830. 4360. 4054. 3605. 1925. 1975. 1915. 2670. 3530. 3900. 3190. 3420.
 2200. 2150. 2020. 2595. 2700. 2556. 2144. 1968. 2120. 2019. 2678. 2870.
 3003. 3381. 2188. 2711. 2434. 2110. 2800. 2085. 2335. 2950. 3250. 1850.
 2145. 1845. 2910. 2420. 2500. 2905. 2290. 2490. 2635. 2620. 2725. 2385.
 1755. 1875. 1760. 2050. 2215. 2380. 2320. 2210. 2350. 2615. 3230. 3160.
 2900. 3415. 3060. 3465. 2605. 2640. 2575. 2525. 2735. 2865. 3035. 1980.
 2025. 1970. 2160. 2205. 2245. 1965. 1995. 3015. 2585. 2835. 2665. 2370.
 2790. 2295. 2625.]

[12.  11.5 11.  10.5 10.   9.   8.5  8.   9.5 15.  15.5 16.  14.5 20.5
 17.5 12.5 14.  13.5 18.5 19.  13.  19.5 18.  17.  23.5 16.5 21.  16.9
 14.9 17.7 15.3 13.9 12.8 15.4 17.6 22.2 22.1 14.2 17.4 16.2 17.8 12.2
 16.4 13.6 15.7 13.2 21.9 16.7 12.1 14.8 18.6 16.8 13.7 11.1 11.4 18.2
 15.8 15.9 14.1 21.5 14.4 19.4 19.2 17.2 18.7 15.1 13.4 11.2 14.7 16.6
 17.3 15.2 14.3 20.1 24.8 11.3 12.9 18.8 18.1 17.9 21.7 23.7 19.9 21.8
 13.8 12.6 16.1 20.7 18.3 20.4 19.6 17.1 15.6 24.6 11.6]

[70 71 72 73 74 75 76 77 78 79 80 81 82]

[1 3 2]

['chevrolet chevelle malibu' 'buick skylark 320' 'plymouth satellite'
 'amc rebel sst' 'ford torino' 'ford galaxie 500' 'chevrolet impala'
 'plymouth fury iii' 'pontiac catalina' 'amc ambassador dpl'
 'dodge challenger se' "plymouth 'cuda 340" 'chevrolet monte carlo'
 'buick estate wagon (sw)' 'toyota corona mark ii' 'plymouth duster'
 'amc hornet' 'ford maverick' 'datsun pl510'
 'volkswagen 1131 deluxe sedan' 'peugeot 504' 'audi 100 ls' 'saab 99e'
 'bmw 2002' 'amc gremlin' 'ford f250' 'chevy c20' 'dodge d200' 'hi 1200d'
 'chevrolet vega 2300' 'toyota corona' 'ford pinto'
 'plymouth satellite custom' 'ford torino 500' 'amc matador'
 'pontiac catalina brougham' 'dodge monaco (sw)'
 'ford country squire (sw)' 'pontiac safari (sw)'
 'amc hornet sportabout (sw)' 'chevrolet vega (sw)' 'pontiac firebird'
 'ford mustang' 'mercury capri 2000' 'opel 1900' 'peugeot 304' 'fiat 124b'
 'toyota corolla 1200' 'datsun 1200' 'volkswagen model 111'
 'plymouth cricket' 'toyota corona hardtop' 'dodge colt hardtop'
 'volkswagen type 3' 'chevrolet vega' 'ford pinto runabout'
 'amc ambassador sst' 'mercury marquis' 'buick lesabre custom'
 'oldsmobile delta 88 royale' 'chrysler newport royal' 'mazda rx2 coupe'
 'amc matador (sw)' 'chevrolet chevelle concours (sw)'
 'ford gran torino (sw)' 'plymouth satellite custom (sw)'
 'volvo 145e (sw)' 'volkswagen 411 (sw)' 'peugeot 504 (sw)'
 'renault 12 (sw)' 'ford pinto (sw)' 'datsun 510 (sw)'
 'toyouta corona mark ii (sw)' 'dodge colt (sw)'
 'toyota corolla 1600 (sw)' 'buick century 350' 'chevrolet malibu'
 'ford gran torino' 'dodge coronet custom' 'mercury marquis brougham'
 'chevrolet caprice classic' 'ford ltd' 'plymouth fury gran sedan'
 'chrysler new yorker brougham' 'buick electra 225 custom'
 'amc ambassador brougham' 'plymouth valiant' 'chevrolet nova custom'
 'volkswagen super beetle' 'ford country' 'plymouth custom suburb'
 'oldsmobile vista cruiser' 'toyota carina' 'datsun 610' 'maxda rx3'
 'mercury capri v6' 'fiat 124 sport coupe' 'chevrolet monte carlo s'
 'pontiac grand prix' 'fiat 128' 'opel manta' 'audi 100ls' 'volvo 144ea'
 'dodge dart custom' 'saab 99le' 'toyota mark ii' 'oldsmobile omega'
 'chevrolet nova' 'datsun b210' 'chevrolet chevelle malibu classic'
 'plymouth satellite sebring' 'buick century luxus (sw)'
 'dodge coronet custom (sw)' 'audi fox' 'volkswagen dasher' 'datsun 710'
 'dodge colt' 'fiat 124 tc' 'honda civic' 'subaru' 'fiat x1.9'
 'plymouth valiant custom' 'mercury monarch' 'chevrolet bel air'
 'plymouth grand fury' 'buick century' 'chevroelt chevelle malibu'
 'plymouth fury' 'buick skyhawk' 'chevrolet monza 2+2' 'ford mustang ii'
 'toyota corolla' 'pontiac astro' 'volkswagen rabbit' 'amc pacer'
 'volvo 244dl' 'honda civic cvcc' 'fiat 131' 'capri ii' 'renault 12tl'
 'dodge coronet brougham' 'chevrolet chevette' 'chevrolet woody'
 'vw rabbit' 'dodge aspen se' 'ford granada ghia' 'pontiac ventura sj'
 'amc pacer d/l' 'datsun b-210' 'volvo 245' 'plymouth volare premier v8'
 'mercedes-benz 280s' 'cadillac seville' 'chevy c10' 'ford f108'
 'dodge d100' 'honda accord cvcc' 'buick opel isuzu deluxe'
 'renault 5 gtl' 'plymouth arrow gs' 'datsun f-10 hatchback'
 'oldsmobile cutlass supreme' 'dodge monaco brougham'
 'mercury cougar brougham' 'chevrolet concours' 'buick skylark'
 'plymouth volare custom' 'ford granada' 'pontiac grand prix lj'
 'chevrolet monte carlo landau' 'chrysler cordoba' 'ford thunderbird'
 'volkswagen rabbit custom' 'pontiac sunbird coupe'
 'toyota corolla liftback' 'ford mustang ii 2+2' 'dodge colt m/m'
 'subaru dl' 'datsun 810' 'bmw 320i' 'mazda rx-4'
 'volkswagen rabbit custom diesel' 'ford fiesta' 'mazda glc deluxe'
 'datsun b210 gx' 'oldsmobile cutlass salon brougham' 'dodge diplomat'
 'mercury monarch ghia' 'pontiac phoenix lj' 'ford fairmont (auto)'
 'ford fairmont (man)' 'plymouth volare' 'amc concord'
 'buick century special' 'mercury zephyr' 'dodge aspen' 'amc concord d/l'
 'buick regal sport coupe (turbo)' 'ford futura' 'dodge magnum xe'
 'datsun 510' 'dodge omni' 'toyota celica gt liftback' 'plymouth sapporo'
 'oldsmobile starfire sx' 'datsun 200-sx' 'audi 5000' 'volvo 264gl'
 'saab 99gle' 'peugeot 604sl' 'volkswagen scirocco' 'honda accord lx'
 'pontiac lemans v6' 'mercury zephyr 6' 'ford fairmont 4'
 'amc concord dl 6' 'dodge aspen 6' 'ford ltd landau'
 'mercury grand marquis' 'dodge st. regis' 'chevrolet malibu classic (sw)'
 'chrysler lebaron town @ country (sw)' 'vw rabbit custom'
 'maxda glc deluxe' 'dodge colt hatchback custom' 'amc spirit dl'
 'mercedes benz 300d' 'cadillac eldorado' 'plymouth horizon'
 'plymouth horizon tc3' 'datsun 210' 'fiat strada custom'
 'buick skylark limited' 'chevrolet citation' 'oldsmobile omega brougham'
 'pontiac phoenix' 'toyota corolla tercel' 'datsun 310' 'ford fairmont'
 'audi 4000' 'toyota corona liftback' 'mazda 626' 'datsun 510 hatchback'
 'mazda glc' 'vw rabbit c (diesel)' 'vw dasher (diesel)'
 'audi 5000s (diesel)' 'mercedes-benz 240d' 'honda civic 1500 gl'
 'renault lecar deluxe' 'vokswagen rabbit' 'datsun 280-zx' 'mazda rx-7 gs'
 'triumph tr7 coupe' 'ford mustang cobra' 'honda accord'
 'plymouth reliant' 'dodge aries wagon (sw)' 'toyota starlet'
 'plymouth champ' 'honda civic 1300' 'datsun 210 mpg' 'toyota tercel'
 'mazda glc 4' 'plymouth horizon 4' 'ford escort 4w' 'ford escort 2h'
 'volkswagen jetta' 'renault 18i' 'honda prelude' 'datsun 200sx'
 'peugeot 505s turbo diesel' 'volvo diesel' 'toyota cressida'
 'datsun 810 maxima' 'oldsmobile cutlass ls' 'ford granada gl'
 'chrysler lebaron salon' 'chevrolet cavalier' 'chevrolet cavalier wagon'
 'chevrolet cavalier 2-door' 'pontiac j2000 se hatchback' 'dodge aries se'
 'ford fairmont futura' 'amc concord dl' 'volkswagen rabbit l'
 'mazda glc custom l' 'mazda glc custom' 'plymouth horizon miser'
 'mercury lynx l' 'nissan stanza xe' 'honda civic (auto)' 'datsun 310 gx'
 'buick century limited' 'oldsmobile cutlass ciera (diesel)'
 'chrysler lebaron medallion' 'ford granada l' 'toyota celica gt'
 'dodge charger 2.2' 'chevrolet camaro' 'ford mustang gl' 'vw pickup'
 'dodge rampage' 'ford ranger' 'chevy s-10']

 

🪄point
결측치가 물음표? 로 되어있음. 대치해줘야함!

In [30]:

for i in df.columns:
    if "?"in df[i].unique():
        print(i,"에는 '?' 결측치가 포함되어 있어요!")
        print("===")
print("검사 끄읏-!")

 

horsepower 에는 '?' 결측치가 포함되어 있어요!
===
검사 끄읏-!

 

데이터 전처리¶

 

결측치 처리하기¶

==> 평균으로 바꾸거나, 삭제하거나

In [35]:

# ? 를 결측치로 바꾸고 개수 확인하기
df.horsepower.replace("?",np.nan, inplace=True)
df.horsepower.isnull().sum()

Out[35]:

6

 

평균으로 바꿀까?¶

In [49]:

tmp_horsepower = df.horsepower.dropna(axis=0)

In [58]:

df.shape

Out[58]:

(398, 9)

In [50]:

tmp_horsepower.shape

# 398 ->392로 바뀜을 확인

Out[50]:

(392,)

In [57]:

tmp_horsepower.astype("float").describe().to_frame()

Out[57]:

	horsepower
count	392.000000
mean	104.469388
std	38.491160
min	46.000000
25%	75.000000
50%	93.500000
75%	126.000000
max	230.000000

In [43]:

sns.distplot(df[['horsepower']])

Out[43]:

<AxesSubplot:ylabel='Density'>

 

 

삭제 할까?¶

In [48]:

# 결측치의 퍼센트는??

( df.horsepower.isnull().sum() / df.shape[0] ) *100

Out[48]:

1.507537688442211

 

결측치 처리하자!¶

In [63]:

df.dropna(subset=["horsepower"], axis=0,inplace=True)

In [64]:

df.shape

Out[64]:

(392, 9)

 

object를 float로 바꾸기¶

In [65]:

df.horsepower.dtype

Out[65]:

dtype('O')

In [66]:

df["horsepower"] = df.horsepower.astype("float")

In [67]:

df.horsepower.dtype

Out[67]:

dtype('float64')

 

독립변수(x), 종속변수(y) 정해보기¶

 

독립변수(x) : 실린더 수cylinders, 마력horsepower, 차 무게weight
종속변수(y) : 연비mpg

In [68]:

ndf = df[['mpg', 'cylinders', 'horsepower', 'weight']]
ndf.head()

Out[68]:

	mpg	cylinders	horsepower	weight
0	18.0	8	130.0	3504.0
1	15.0	8	165.0	3693.0
2	18.0	8	150.0	3436.0
3	16.0	8	150.0	3433.0
4	17.0	8	140.0	3449.0

 

시각화 - 상관관계 파악하기¶

In [69]:

# pairplot
sns.pairplot(ndf)

Out[69]:

<seaborn.axisgrid.PairGrid at 0x1fb01eac6d0>

 

In [102]:

# jointplot
fig = plt.figure(figsize=(9, 3))   
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)

plt.suptitle("<regplot> weight / horsepower / cylinders ")

sns.regplot(data=ndf, x="weight", y="mpg", ax=ax1)
sns.regplot(data=ndf, x="horsepower", y="mpg", ax=ax2)
sns.regplot(data=ndf, x="cylinders", y="mpg", ax=ax3)

Out[102]:

<AxesSubplot:xlabel='cylinders', ylabel='mpg'>

 

 

Train / Test 데이터 분할하기¶

 

위 상관관계 그래프를 통해 독립변수x는 weight와 horsepower, 종속변수y는 mpg

In [91]:

X= ndf[["weight","horsepower"]]
y=ndf['mpg']

In [92]:

from sklearn.model_selection import train_test_split

In [93]:

X_train, X_test, y_train, y_test = train_test_split(X,               #독립 변수 
                                                    y,               #종속 변수
                                                    test_size=0.3,   #검증 30%
                                                    random_state=42) #랜덤 추출 값 

In [94]:

print('train data 개수: ', len(X_train))
print('test data 개수: ', len(X_test))

 

train data 개수:  274
test data 개수:  118

 

모형 학습¶

In [95]:

from sklearn.linear_model import LinearRegression
lr= LinearRegression()

In [96]:

# train 데이터로 모형학습

lr.fit(X_train, y_train)

Out[96]:

LinearRegression()

 

score¶

In [97]:

# 결정계수
lr.score(X_test, y_test)

Out[97]:

0.650337010312882

In [99]:

# weight와 horsepower
print('기울기: ', lr.coef_)

 

기울기:  [-0.00564316 -0.0603262 ]

In [100]:

print('y절편', lr.intercept_)

 

y절편 46.81799993061944

 

시각화 - 예측정도¶

In [104]:

# 예측값 y_hat 
y_hat = lr.predict(X)

plt.figure(figsize=(10, 5))

ax1 = sns.kdeplot(y, label="y")
ax2 = sns.kdeplot(y_hat, label="y_hat", ax=ax1)

plt.legend()

Out[104]:

<matplotlib.legend.Legend at 0x1fb06a915b0>

 

 

kdeplot을 통하여 주황색 예측값이 오른쪽으로 뾰족하게, 실제 값과 반대쪽으로 뾰족함. 좀더 오차를 줄일 필요가 있다!