Цель этого проекта — разработать модель машинного обучения, которая может точно прогнозировать цены на автомобили на основе различных атрибутов. Мы будем использовать набор данных cars_price.csv, который содержит информацию о 206 автомобилях, включая их марку, модель, тип топлива, объем двигателя, мощность и другие характеристики.
Наша цель — разработать модель, которая может прогнозировать цены на автомобили с максимально возможной точностью. Мы будем использовать модель линейной регрессии для прогнозирования цен на автомобили. Почему линейная регрессия? Линейная регрессия может быть старым алгоритмом и самой базовой концепцией машинного обучения, но она все еще эффективна для построения моделей. Это алгоритм, используемый для прогнозирования значений, которые являются непрерывными по своей природе. Линейная регрессия стала более популярной, потому что это лучший алгоритм для начала, если вы новичок в машинном обучении.
Переходя к подходу проекта, начнем с импорта необходимых пакетов.
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)
import matplotlib.pyplot as plt
import matplotlib as mpl
from prettytable import PrettyTable
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
import seaborn as sns
Затем мы перейдем к загрузке данных, хранящихся на нашем локальном диске.
Загрузка данных
auto = pd.read_csv("C:/Users/Vishaal Grizzly/Downloads/cars_price.csv")Проверка размеров импортируемых данных
auto.shape(205, 26)
Проверка головы и хвоста данных
auto.head()
auto.tail()
Очистка данных
Замена ‘?’ на NAN
df = auto.replace('?',np.NAN)
df
df.describe()
Проверка нулевых значений, присутствующих в данных
df.isnull().sum()symboling 0 normalized-losses 41 make 0 fuel-type 0 aspiration 0 num-of-doors 2 body-style 0 drive-wheels 0 engine-location 0 wheel-base 0 length 0 width 0 height 0 curb-weight 0 engine-type 0 num-of-cylinders 0 engine-size 0 fuel-system 0 bore 4 stroke 4 compression-ratio 0 horsepower 2 peak-rpm 2 city-mpg 0 highway-mpg 0 price 4 dtype: int64
Проверка дубликатов в данных
print(df.loc[df.duplicated()].shape)(0, 26)df = df.drop_duplicates()df.shape(205, 26)
Список типов данных, присутствующих в каждом столбце
df.dtypessymboling int64 normalized-losses object make object fuel-type object aspiration object num-of-doors object body-style object drive-wheels object engine-location object wheel-base float64 length float64 width float64 height float64 curb-weight int64 engine-type object num-of-cylinders object engine-size int64 fuel-system object bore object stroke object compression-ratio float64 horsepower object peak-rpm object city-mpg int64 highway-mpg int64 price object dtype: object
Замена нулевых значений везде, где это необходимо
Работа со значениями в столбце нормализованных ошибок
n_l_data = df[df['normalized-losses']!= '?'] n_l_data['normalized-losses']0 NaN 1 NaN 2 NaN 3 164 4 164 ... 200 95 201 95 202 95 203 95 204 95 Name: normalized-losses, Length: 205, dtype: objectmean = n_l_data['normalized-losses'].astype(float).mean() df['normalized-losses'] = df['normalized-losses'].replace('?', mean).fillna(mean).astype(int) #df=df.drop(columns="normalized-losses")
Работа со значениями в столбце цены
price_data = df[df['price']!= '?'] price_data['price']0 13495 1 16500 2 16500 3 13950 4 17450 ... 200 16845 201 19045 202 21485 203 22470 204 22625 Name: price, Length: 205, dtype: objectmean = price_data['price'].astype(float).mean() df['price'] = df['price'].replace('?', mean).fillna(mean).astype(int)
Работа со значениями в колонке лошадиных сил
hp_data = df[df['horsepower'] != '?'] hp_data['price']0 13495 1 16500 2 16500 3 13950 4 17450 ... 200 16845 201 19045 202 21485 203 22470 204 22625 Name: price, Length: 205, dtype: int32mean = hp_data['price'].astype(int).mean() df['horsepower'] = df['horsepower'].replace('?', mean).fillna(mean).astype(int)
Работа со значениями в столбце пиковых оборотов
peak_rpm_data = df[df['peak-rpm'] != '?'] peak_rpm_data['peak-rpm']0 5000 1 5000 2 5000 3 5500 4 5500 ... 200 5400 201 5300 202 5500 203 4800 204 5400 Name: peak-rpm, Length: 205, dtype: objectmean = peak_rpm_data['peak-rpm'].astype(float).mean() df['price'] = df['price'].replace('?', mean).fillna(mean).astype(int)
Работа со значениями в столбце штрихов
stroke_data = df[df['stroke'] != '?'] stroke_data['stroke']0 2.68 1 2.68 2 3.47 3 3.4 4 3.4 ... 200 3.15 201 3.15 202 2.87 203 3.4 204 3.15 Name: stroke, Length: 205, dtype: objectmean = stroke_data['stroke'].astype(float).mean() df['stroke'] = df['stroke'].replace('?', mean).fillna(mean).astype(float)
Работа со значениями в столбце пиковых оборотов
peak_rpm_data = df[df['peak-rpm']!='?'] peak_rpm_data['peak-rpm']0 5000 1 5000 2 5000 3 5500 4 5500 ... 200 5400 201 5300 202 5500 203 4800 204 5400 Name: peak-rpm, Length: 205, dtype: objectmean = peak_rpm_data['peak-rpm'].astype(float).mean() df['peak-rpm'] = df['peak-rpm'].replace('?', mean).fillna(mean).astype(float)
Работа со значениями в столбце отверстия
bore_data = df[df['bore'] != '?'] bore_data['bore']0 3.47 1 3.47 2 2.68 3 3.19 4 3.19 ... 200 3.78 201 3.78 202 3.58 203 3.01 204 3.78 Name: bore, Length: 205, dtype: objectmean = bore_data['bore'].astype(float).mean() df['bore'] = df['bore'].replace('?', mean).fillna(mean).astype(float)
Работа со значениями в столбце количества дверей. Мы заменим отсутствующие значения на «четыре», так как большинство автомобилей, скорее всего, будут четырехдверными.
df['num-of-doors'] = df['num-of-doors'].replace('?', 'four')
df
df.describe<bound method NDFrame.describe of symboling normalized-losses make fuel-type aspiration \ 0 3 122 alfa-romero gas std 1 3 122 alfa-romero gas std 2 1 122 alfa-romero gas std 3 2 164 audi gas std 4 2 164 audi gas std .. ... ... ... ... ... 200 -1 95 volvo gas std 201 -1 95 volvo gas turbo 202 -1 95 volvo gas std 203 -1 95 volvo diesel turbo 204 -1 95 volvo gas turbo num-of-doors body-style drive-wheels engine-location wheel-base ... \ 0 two convertible rwd front 88.6 ... 1 two convertible rwd front 88.6 ... 2 two hatchback rwd front 94.5 ... 3 four sedan fwd front 99.8 ... 4 four sedan 4wd front 99.4 ... .. ... ... ... ... ... ... 200 four sedan rwd front 109.1 ... 201 four sedan rwd front 109.1 ... 202 four sedan rwd front 109.1 ... 203 four sedan rwd front 109.1 ... 204 four sedan rwd front 109.1 ... engine-size fuel-system bore stroke compression-ratio horsepower \ 0 130 mpfi 3.47 2.68 9.0 111 1 130 mpfi 3.47 2.68 9.0 111 2 152 mpfi 2.68 3.47 9.0 154 3 109 mpfi 3.19 3.40 10.0 102 4 136 mpfi 3.19 3.40 8.0 115 .. ... ... ... ... ... ... 200 141 mpfi 3.78 3.15 9.5 114 201 141 mpfi 3.78 3.15 8.7 160 202 173 mpfi 3.58 2.87 8.8 134 203 145 idi 3.01 3.40 23.0 106 204 141 mpfi 3.78 3.15 9.5 114 peak-rpm city-mpg highway-mpg price 0 5000.0 21 27 13495 1 5000.0 21 27 16500 2 5000.0 19 26 16500 3 5500.0 24 30 13950 4 5500.0 18 22 17450 .. ... ... ... ... 200 5400.0 23 28 16845 201 5300.0 19 25 19045 202 5500.0 18 23 21485 203 4800.0 26 27 22470 204 5400.0 19 25 22625 [205 rows x 26 columns]>
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 205 entries, 0 to 204
Data columns (total 26 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 symboling 205 non-null int64
1 normalized-losses 205 non-null int32
2 make 205 non-null object
3 fuel-type 205 non-null object
4 aspiration 205 non-null object
5 num-of-doors 203 non-null object
6 body-style 205 non-null object
7 drive-wheels 205 non-null object
8 engine-location 205 non-null object
9 wheel-base 205 non-null float64
10 length 205 non-null float64
11 width 205 non-null float64
12 height 205 non-null float64
13 curb-weight 205 non-null int64
14 engine-type 205 non-null object
15 num-of-cylinders 205 non-null object
16 engine-size 205 non-null int64
17 fuel-system 205 non-null object
18 bore 205 non-null float64
19 stroke 205 non-null float64
20 compression-ratio 205 non-null float64
21 horsepower 205 non-null int32
22 peak-rpm 205 non-null float64
23 city-mpg 205 non-null int64
24 highway-mpg 205 non-null int64
25 price 205 non-null int32
dtypes: float64(8), int32(3), int64(5), object(10)
memory usage: 40.8+ KB
Имеется 6 значений типов объектов. Мы можем заменить значения, присутствующие в столбцах, на «0» и «1».
#Fuel column df['fuel-type'].value_counts()gas 185 diesel 20 Name: fuel-type, dtype: int64df['fuel-type'] = df['fuel-type'].map({'diesel': 0, 'gas': 1})df['fuel-type'] = df ['fuel-type'].astype('int64')df['fuel-type'].value_counts()1 185 0 20 Name: fuel-type, dtype: int64#Aspiration column df['aspiration'].value_counts()std 168 turbo 37 Name: aspiration, dtype: int64df['aspiration'] = df['aspiration'].map({'turbo': 0, 'std':1})df['aspiration'] = df['aspiration'].astype('int64')df['aspiration'].value_counts()1 168 0 37 Name: aspiration, dtype: int64#Num-of-doors column df['num-of-doors'].value_counts()four 114 two 89 Name: num-of-doors, dtype: int64df['num-of-doors'].isnull()0 False 1 False 2 False 3 False 4 False ... 200 False 201 False 202 False 203 False 204 False Name: num-of-doors, Length: 205, dtype: booldf['num-of-doors'].unique()array(['two', 'four', nan], dtype=object)num_of_na = df['num-of-doors'].isna().sum()num_of_na2mask = df['num-of-doors'].isna()result = df[mask]result
df['num-of-doors'] = df['num-of-doors'].fillna('four')df['num-of-doors'] = df['num-of-doors'].map({'two': 0, 'four': 1})df['num-of-doors'] = df['num-of-doors'].fillna(0).astype('int64')df['num-of-doors'].value_counts()1 116 0 89 Name: num-of-doors, dtype: int64#engine-location column df['engine-location'].value_counts()front 202 rear 3 Name: engine-location, dtype: int64df['engine-location'] = df['engine-location'].map({'rear': 0, 'front': 1})#Peak-rpm column df['price'].value_counts()13207 4 8921 2 18150 2 8845 2 8495 2 .. 45400 1 16503 1 5389 1 6189 1 22625 1 Name: price, Length: 187, dtype: int64df['price'].dtypesdtype('int32')df['price'][0]13495#Body-style df=df.drop(columns='body-style')df.info()<class 'pandas.core.frame.DataFrame'> Int64Index: 205 entries, 0 to 204 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 symboling 205 non-null int64 1 normalized-losses 205 non-null int32 2 make 205 non-null object 3 fuel-type 205 non-null int64 4 aspiration 205 non-null int64 5 num-of-doors 205 non-null int64 6 drive-wheels 205 non-null object 7 engine-location 205 non-null int64 8 wheel-base 205 non-null float64 9 length 205 non-null float64 10 width 205 non-null float64 11 height 205 non-null float64 12 curb-weight 205 non-null int64 13 engine-type 205 non-null object 14 num-of-cylinders 205 non-null object 15 engine-size 205 non-null int64 16 fuel-system 205 non-null object 17 bore 205 non-null float64 18 stroke 205 non-null float64 19 compression-ratio 205 non-null float64 20 horsepower 205 non-null int32 21 peak-rpm 205 non-null float64 22 city-mpg 205 non-null int64 23 highway-mpg 205 non-null int64 24 price 205 non-null int32 dtypes: float64(8), int32(3), int64(9), object(5) memory usage: 47.3+ KB
Проверка количества уникальных значений в каждом столбце для выполнения одного горячего кодирования
for col in df: print(col, df[col].unique())symboling [ 3 1 2 0 -1 -2] normalized-losses [122 164 158 192 188 121 98 81 118 148 110 145 137 101 78 106 85 107 104 113 150 129 115 93 142 161 153 125 128 103 168 108 194 231 119 154 74 186 83 102 89 87 77 91 134 65 197 90 94 256 95] make ['alfa-romero' 'audi' 'bmw' 'chevrolet' 'dodge' 'honda' 'isuzu' 'jaguar' 'mazda' 'mercedes-benz' 'mercury' 'mitsubishi' 'nissan' 'peugot' 'plymouth' 'porsche' 'renault' 'saab' 'subaru' 'toyota' 'volkswagen' 'volvo'] fuel-type [1 0] aspiration [1 0] num-of-doors [0 1] drive-wheels ['rwd' 'fwd' '4wd'] engine-location [1 0] wheel-base [ 88.6 94.5 99.8 99.4 105.8 99.5 101.2 103.5 110. 88.4 93.7 103.3 95.9 86.6 96.5 94.3 96. 113. 102. 93.1 95.3 98.8 104.9 106.7 115.6 96.6 120.9 112. 102.7 93. 96.3 95.1 97.2 100.4 91.3 99.2 107.9 114.2 108. 89.5 98.4 96.1 99.1 93.3 97. 96.9 95.7 102.4 102.9 104.5 97.3 104.3 109.1] length [168.8 171.2 176.6 177.3 192.7 178.2 176.8 189. 193.8 197. 141.1 155.9 158.8 157.3 174.6 173.2 144.6 150. 163.4 157.1 167.5 175.4 169.1 170.7 172.6 199.6 191.7 159.1 166.8 169. 177.8 175. 190.9 187.5 202.6 180.3 208.1 199.2 178.4 173. 172.4 165.3 170.2 165.6 162.4 173.4 181.7 184.6 178.5 186.7 198.9 167.3 168.9 175.7 181.5 186.6 156.9 157.9 172. 173.5 173.6 158.7 169.7 166.3 168.7 176.2 175.6 183.5 187.8 171.7 159.3 165.7 180.2 183.1 188.8] width [64.1 65.5 66.2 66.4 66.3 71.4 67.9 64.8 66.9 70.9 60.3 63.6 63.8 64.6 63.9 64. 65.2 62.5 66. 61.8 69.6 70.6 64.2 65.7 66.5 66.1 70.3 71.7 70.5 72. 68. 64.4 65.4 68.4 68.3 65. 72.3 66.6 63.4 65.6 67.7 67.2 68.9 68.8] height [48.8 52.4 54.3 53.1 55.7 55.9 52. 53.7 56.3 53.2 50.8 50.6 59.8 50.2 52.6 54.5 58.3 53.3 54.1 51. 53.5 51.4 52.8 47.8 49.6 55.5 54.4 56.5 58.7 54.9 56.7 55.4 54.8 49.4 51.6 54.7 55.1 56.1 49.7 56. 50.5 55.2 52.5 53. 59.1 53.9 55.6 56.2 57.5] curb-weight [2548 2823 2337 2824 2507 2844 2954 3086 3053 2395 2710 2765 3055 3230 3380 3505 1488 1874 1909 1876 2128 1967 1989 2191 2535 2811 1713 1819 1837 1940 1956 2010 2024 2236 2289 2304 2372 2465 2293 2734 4066 3950 1890 1900 1905 1945 1950 2380 2385 2500 2410 2443 2425 2670 2700 3515 3750 3495 3770 3740 3685 3900 3715 2910 1918 1944 2004 2145 2370 2328 2833 2921 2926 2365 2405 2403 1889 2017 1938 1951 2028 1971 2037 2008 2324 2302 3095 3296 3060 3071 3139 3020 3197 3430 3075 3252 3285 3485 3130 2818 2778 2756 2800 3366 2579 2460 2658 2695 2707 2758 2808 2847 2050 2120 2240 2190 2340 2510 2290 2455 2420 2650 1985 2040 2015 2280 3110 2081 2109 2275 2094 2122 2140 2169 2204 2265 2300 2540 2536 2551 2679 2714 2975 2326 2480 2414 2458 2976 3016 3131 3151 2261 2209 2264 2212 2319 2254 2221 2661 2563 2912 3034 2935 3042 3045 3157 2952 3049 3012 3217 3062] engine-type ['dohc' 'ohcv' 'ohc' 'l' 'rotor' 'ohcf' 'dohcv'] num-of-cylinders ['four' 'six' 'five' 'three' 'twelve' 'two' 'eight'] engine-size [130 152 109 136 131 108 164 209 61 90 98 122 156 92 79 110 111 119 258 326 91 70 80 140 134 183 234 308 304 97 103 120 181 151 194 203 132 121 146 171 161 141 173 145] fuel-system ['mpfi' '2bbl' 'mfi' '1bbl' 'spfi' '4bbl' 'idi' 'spdi'] bore [3.47 2.68 3.19 3.13 3.5 3.31 3.62 2.91 3.03 2.97 3.34 3.6 2.92 3.15 3.43 3.63 3.54 3.08 3.32975124 3.39 3.76 3.58 3.46 3.8 3.78 3.17 3.35 3.59 2.99 3.33 3.7 3.61 3.94 3.74 2.54 3.05 3.27 3.24 3.01 ] stroke [2.68 3.47 3.4 2.8 3.19 3.39 3.03 3.11 3.23 3.46 3.9 3.41 3.07 3.58 4.17 2.76 3.15 3.25542289 3.16 3.64 3.1 3.35 3.12 3.86 3.29 3.27 3.52 2.19 3.21 2.9 2.07 2.36 2.64 3.08 3.5 3.54 2.87 ] compression-ratio [ 9. 10. 8. 8.5 8.3 7. 8.8 9.5 9.6 9.41 9.4 7.6 9.2 10.1 9.1 8.1 11.5 8.6 22.7 22. 21.5 7.5 21.9 7.8 8.4 21. 8.7 9.31 9.3 7.7 22.5 23. ] horsepower [ 111 154 102 115 110 140 160 101 121 182 48 70 68 88 145 58 76 60 86 100 78 90 176 262 135 84 64 120 72 123 155 184 175 116 69 55 97 152 200 95 142 143 207 288 13207 73 82 94 62 56 112 92 161 156 52 85 114 162 134 106] peak-rpm [5000. 5500. 5800. 4250. 5400. 5100. 4800. 6000. 4750. 4650. 4200. 4350. 4500. 5200. 4150. 5600. 5900. 5750. 5125.36945813 5250. 4900. 4400. 6600. 5300. ] city-mpg [21 19 24 18 17 16 23 20 15 47 38 37 31 49 30 27 25 13 26 36 22 14 45 28 32 35 34 29 33] highway-mpg [27 26 30 22 25 20 29 28 53 43 41 38 24 54 42 34 33 31 19 17 23 32 39 18 16 37 50 36 47 46] price [13495 16500 13950 17450 15250 17710 18920 23875 13207 16430 16925 20970 21105 24565 30760 41315 36880 5151 6295 6575 5572 6377 7957 6229 6692 7609 8558 8921 12964 6479 6855 5399 6529 7129 7295 7895 9095 8845 10295 12945 10345 6785 11048 32250 35550 36000 5195 6095 6795 6695 7395 10945 11845 13645 15645 8495 10595 10245 10795 11245 18280 18344 25552 28248 28176 31600 34184 35056 40960 45400 16503 5389 6189 6669 7689 9959 8499 12629 14869 14489 6989 8189 9279 5499 7099 6649 6849 7349 7299 7799 7499 7999 8249 8949 9549 13499 14399 17199 19699 18399 11900 13200 12440 13860 15580 16900 16695 17075 16630 17950 18150 12764 22018 32528 34028 37028 9295 9895 11850 12170 15040 15510 18620 5118 7053 7603 7126 7775 9960 9233 11259 7463 10198 8013 11694 5348 6338 6488 6918 7898 8778 6938 7198 7788 7738 8358 9258 8058 8238 9298 9538 8449 9639 9989 11199 11549 17669 8948 10698 9988 10898 11248 16558 15998 15690 15750 7975 7995 8195 9495 9995 11595 9980 13295 13845 12290 12940 13415 15985 16515 18420 18950 16845 19045 21485 22470 22625]one_hot_encoding = pd.get_dummies(df, columns = ['drive-wheels', 'engine-type','num-of-cylinders','fuel-system', 'make'])df_final = one_hot_encoding.reset_index(drop = True) df_final
df_final.info()<class 'pandas.core.frame.DataFrame'> RangeIndex: 205 entries, 0 to 204 Data columns (total 67 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 symboling 205 non-null int64 1 normalized-losses 205 non-null int32 2 fuel-type 205 non-null int64 3 aspiration 205 non-null int64 4 num-of-doors 205 non-null int64 5 engine-location 205 non-null int64 6 wheel-base 205 non-null float64 7 length 205 non-null float64 8 width 205 non-null float64 9 height 205 non-null float64 10 curb-weight 205 non-null int64 11 engine-size 205 non-null int64 12 bore 205 non-null float64 13 stroke 205 non-null float64 14 compression-ratio 205 non-null float64 15 horsepower 205 non-null int32 16 peak-rpm 205 non-null float64 17 city-mpg 205 non-null int64 18 highway-mpg 205 non-null int64 19 price 205 non-null int32 20 drive-wheels_4wd 205 non-null uint8 21 drive-wheels_fwd 205 non-null uint8 22 drive-wheels_rwd 205 non-null uint8 23 engine-type_dohc 205 non-null uint8 24 engine-type_dohcv 205 non-null uint8 25 engine-type_l 205 non-null uint8 26 engine-type_ohc 205 non-null uint8 27 engine-type_ohcf 205 non-null uint8 28 engine-type_ohcv 205 non-null uint8 29 engine-type_rotor 205 non-null uint8 30 num-of-cylinders_eight 205 non-null uint8 31 num-of-cylinders_five 205 non-null uint8 32 num-of-cylinders_four 205 non-null uint8 33 num-of-cylinders_six 205 non-null uint8 34 num-of-cylinders_three 205 non-null uint8 35 num-of-cylinders_twelve 205 non-null uint8 36 num-of-cylinders_two 205 non-null uint8 37 fuel-system_1bbl 205 non-null uint8 38 fuel-system_2bbl 205 non-null uint8 39 fuel-system_4bbl 205 non-null uint8 40 fuel-system_idi 205 non-null uint8 41 fuel-system_mfi 205 non-null uint8 42 fuel-system_mpfi 205 non-null uint8 43 fuel-system_spdi 205 non-null uint8 44 fuel-system_spfi 205 non-null uint8 45 make_alfa-romero 205 non-null uint8 46 make_audi 205 non-null uint8 47 make_bmw 205 non-null uint8 48 make_chevrolet 205 non-null uint8 49 make_dodge 205 non-null uint8 50 make_honda 205 non-null uint8 51 make_isuzu 205 non-null uint8 52 make_jaguar 205 non-null uint8 53 make_mazda 205 non-null uint8 54 make_mercedes-benz 205 non-null uint8 55 make_mercury 205 non-null uint8 56 make_mitsubishi 205 non-null uint8 57 make_nissan 205 non-null uint8 58 make_peugot 205 non-null uint8 59 make_plymouth 205 non-null uint8 60 make_porsche 205 non-null uint8 61 make_renault 205 non-null uint8 62 make_saab 205 non-null uint8 63 make_subaru 205 non-null uint8 64 make_toyota 205 non-null uint8 65 make_volkswagen 205 non-null uint8 66 make_volvo 205 non-null uint8 dtypes: float64(8), int32(3), int64(9), uint8(47) memory usage: 39.2 KB
Применение методов машинного обучения
from sklearn.model_selection import train_test_splitX = df_final.drop(columns = 'price') Y = df_final.priceprint (X.shape) print (Y.shape)(205, 66) (205,)#Scaling data X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42 )from sklearn.preprocessing import StandardScalersc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)print (X_train.shape) print (X_test.shape)(164, 66) (41, 66)#Building Linear Regression technique from sklearn.linear_model import LinearRegression Lin = LinearRegression() Lin.fit(X_train, Y_train)LinearRegression()Y_pred = Lin.predict(X_test)from sklearn.metrics import r2_scorer2_score(Y_test, Y_pred)-3.861645124236883e+22#Gradient boosting regressor from sklearn.ensemble import GradientBoostingRegressor gbr= GradientBoostingRegressor(random_state=0) gbr.fit(X_train,Y_train)GradientBoostingRegressor(random_state=0)Y_pred_gbr=gbr.predict(X_test) r2_score(Y_test,Y_pred_gbr)0.9241628097439228#random forest regressor from sklearn.ensemble import RandomForestRegressor regr = RandomForestRegressor(random_state=0) regr.fit(X_train, Y_train)RandomForestRegressor(random_state=0)Y_pred_rf= regr.predict(X_test) r2_score(Y_test,Y_pred_rf)0.94359287053183n_estimators = [5,20,50,100] # number of trees in the random forest max_features = [ 'sqrt'] # number of features in consideration at every split max_depth = [2,4,6,8,10,12] # maximum number of levels allowed in each decision tree min_samples_split = [2, 6, 10] # minimum sample number to split a node min_samples_leaf = [1, 3, 4] # minimum sample number that can be stored in a leaf random_grid = {'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf}from sklearn.model_selection import GridSearchCV grid_search = GridSearchCV(RandomForestRegressor(), param_grid=random_grid) grid_search.fit(X_train, Y_train) print(grid_search.best_estimator_)RandomForestRegressor(max_depth=12, max_features='sqrt', n_estimators=20)#random forest regressor from sklearn.ensemble import RandomForestRegressor regr = RandomForestRegressor(random_state=0,bootstrap=False, max_depth=6, max_features='sqrt',min_samples_split=6, n_estimators=20) regr.fit(X_train, Y_train) Y_pred_rf= regr.predict(X_test) r2_score(Y_test,Y_pred_rf)0.871234128824367
Случайный лесной регрессор
from sklearn.ensemble import RandomForestRegressor regr = RandomForestRegressor(random_state=0) regr.fit(X_train, Y_train) Y_pred_rf= regr.predict(X_test) r2_score(Y_test,Y_pred_rf)0.94359287053183
Импорт средней абсолютной ошибки и среднеквадратической ошибки из показателей sklearn
from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_errorprint('mae',mean_absolute_error(Y_test,Y_pred_rf)) print('mse',mean_squared_error(Y_test,Y_pred_rf)) print('r2',r2_score(Y_test,Y_pred_rf))mae 1407.029491869919 mse 4398181.163008079 r2 0.94359287053183
Последние мысли
Пройдя через кучу процессов, мы успешно построили и оценили модель линейной регрессии в python. Анализируя отчет, мы можем сделать вывод, что регрессор случайного леса дает наивысшую оценку R2 для набора данных об автомобилях, который мы использовали.
Вот ссылка на полный код на GitHub для лучшего понимания.
