In [1]:

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

50_Startups.csv 데이터를 읽으세요.¶

각각의 피쳐를 분석하여, 어떤 신생 회사의 데이터가 있으면, 그 회사가 얼마의 수익을 낼 지 예측합니다. (투자를 해야 할지 말아야 할지)¶

In [3]:

df = pd.read_csv("../data/50_Startups.csv")

In [4]:

df

Out[4]:

	R&D Spend	Administration	Marketing Spend	State	Profit
0	165349.20	136897.80	471784.10	New York	192261.83
1	162597.70	151377.59	443898.53	California	191792.06
2	153441.51	101145.55	407934.54	Florida	191050.39
3	144372.41	118671.85	383199.62	New York	182901.99
4	142107.34	91391.77	366168.42	Florida	166187.94
5	131876.90	99814.71	362861.36	New York	156991.12
6	134615.46	147198.87	127716.82	California	156122.51
7	130298.13	145530.06	323876.68	Florida	155752.60
8	120542.52	148718.95	311613.29	New York	152211.77
9	123334.88	108679.17	304981.62	California	149759.96
10	101913.08	110594.11	229160.95	Florida	146121.95
11	100671.96	91790.61	249744.55	California	144259.40
12	93863.75	127320.38	249839.44	Florida	141585.52
13	91992.39	135495.07	252664.93	California	134307.35
14	119943.24	156547.42	256512.92	Florida	132602.65
15	114523.61	122616.84	261776.23	New York	129917.04
16	78013.11	121597.55	264346.06	California	126992.93
17	94657.16	145077.58	282574.31	New York	125370.37
18	91749.16	114175.79	294919.57	Florida	124266.90
19	86419.70	153514.11	0.00	New York	122776.86
20	76253.86	113867.30	298664.47	California	118474.03
21	78389.47	153773.43	299737.29	New York	111313.02
22	73994.56	122782.75	303319.26	Florida	110352.25
23	67532.53	105751.03	304768.73	Florida	108733.99
24	77044.01	99281.34	140574.81	New York	108552.04
25	64664.71	139553.16	137962.62	California	107404.34
26	75328.87	144135.98	134050.07	Florida	105733.54
27	72107.60	127864.55	353183.81	New York	105008.31
28	66051.52	182645.56	118148.20	Florida	103282.38
29	65605.48	153032.06	107138.38	New York	101004.64
30	61994.48	115641.28	91131.24	Florida	99937.59
31	61136.38	152701.92	88218.23	New York	97483.56
32	63408.86	129219.61	46085.25	California	97427.84
33	55493.95	103057.49	214634.81	Florida	96778.92
34	46426.07	157693.92	210797.67	California	96712.80
35	46014.02	85047.44	205517.64	New York	96479.51
36	28663.76	127056.21	201126.82	Florida	90708.19
37	44069.95	51283.14	197029.42	California	89949.14
38	20229.59	65947.93	185265.10	New York	81229.06
39	38558.51	82982.09	174999.30	California	81005.76
40	28754.33	118546.05	172795.67	California	78239.91
41	27892.92	84710.77	164470.71	Florida	77798.83
42	23640.93	96189.63	148001.11	California	71498.49
43	15505.73	127382.30	35534.17	New York	69758.98
44	22177.74	154806.14	28334.72	California	65200.33
45	1000.23	124153.04	1903.93	New York	64926.08
46	1315.46	115816.21	297114.46	Florida	49490.75
47	0.00	135426.92	0.00	California	42559.73
48	542.05	51743.15	0.00	New York	35673.41
49	0.00	116983.80	45173.06	California	14681.40

In [5]:

df.describe()

Out[5]:

	R&D Spend	Administration	Marketing Spend	Profit
count	50.000000	50.000000	50.000000	50.000000
mean	73721.615600	121344.639600	211025.097800	112012.639200
std	45902.256482	28017.802755	122290.310726	40306.180338
min	0.000000	51283.140000	0.000000	14681.400000
25%	39936.370000	103730.875000	129300.132500	90138.902500
50%	73051.080000	122699.795000	212716.240000	107978.190000
75%	101602.800000	144842.180000	299469.085000	139765.977500
max	165349.200000	182645.560000	471784.100000	192261.830000

In [6]:

df.isna().sum()

Out[6]:

R&D Spend          0
Administration     0
Marketing Spend    0
State              0
Profit             0
dtype: int64

In [12]:

X = df.iloc[:, :-2+1 ]

In [7]:

y = df["Profit"]

In [13]:

Out[13]:

0     192261.83
1     191792.06
2     191050.39
3     182901.99
4     166187.94
5     156991.12
6     156122.51
7     155752.60
8     152211.77
9     149759.96
10    146121.95
11    144259.40
12    141585.52
13    134307.35
14    132602.65
15    129917.04
16    126992.93
17    125370.37
18    124266.90
19    122776.86
20    118474.03
21    111313.02
22    110352.25
23    108733.99
24    108552.04
25    107404.34
26    105733.54
27    105008.31
28    103282.38
29    101004.64
30     99937.59
31     97483.56
32     97427.84
33     96778.92
34     96712.80
35     96479.51
36     90708.19
37     89949.14
38     81229.06
39     81005.76
40     78239.91
41     77798.83
42     71498.49
43     69758.98
44     65200.33
45     64926.08
46     49490.75
47     42559.73
48     35673.41
49     14681.40
Name: Profit, dtype: float64

In [14]:

Out[14]:

	R&D Spend	Administration	Marketing Spend	State
0	165349.20	136897.80	471784.10	New York
1	162597.70	151377.59	443898.53	California
2	153441.51	101145.55	407934.54	Florida
3	144372.41	118671.85	383199.62	New York
4	142107.34	91391.77	366168.42	Florida
5	131876.90	99814.71	362861.36	New York
6	134615.46	147198.87	127716.82	California
7	130298.13	145530.06	323876.68	Florida
8	120542.52	148718.95	311613.29	New York
9	123334.88	108679.17	304981.62	California
10	101913.08	110594.11	229160.95	Florida
11	100671.96	91790.61	249744.55	California
12	93863.75	127320.38	249839.44	Florida
13	91992.39	135495.07	252664.93	California
14	119943.24	156547.42	256512.92	Florida
15	114523.61	122616.84	261776.23	New York
16	78013.11	121597.55	264346.06	California
17	94657.16	145077.58	282574.31	New York
18	91749.16	114175.79	294919.57	Florida
19	86419.70	153514.11	0.00	New York
20	76253.86	113867.30	298664.47	California
21	78389.47	153773.43	299737.29	New York
22	73994.56	122782.75	303319.26	Florida
23	67532.53	105751.03	304768.73	Florida
24	77044.01	99281.34	140574.81	New York
25	64664.71	139553.16	137962.62	California
26	75328.87	144135.98	134050.07	Florida
27	72107.60	127864.55	353183.81	New York
28	66051.52	182645.56	118148.20	Florida
29	65605.48	153032.06	107138.38	New York
30	61994.48	115641.28	91131.24	Florida
31	61136.38	152701.92	88218.23	New York
32	63408.86	129219.61	46085.25	California
33	55493.95	103057.49	214634.81	Florida
34	46426.07	157693.92	210797.67	California
35	46014.02	85047.44	205517.64	New York
36	28663.76	127056.21	201126.82	Florida
37	44069.95	51283.14	197029.42	California
38	20229.59	65947.93	185265.10	New York
39	38558.51	82982.09	174999.30	California
40	28754.33	118546.05	172795.67	California
41	27892.92	84710.77	164470.71	Florida
42	23640.93	96189.63	148001.11	California
43	15505.73	127382.30	35534.17	New York
44	22177.74	154806.14	28334.72	California
45	1000.23	124153.04	1903.93	New York
46	1315.46	115816.21	297114.46	Florida
47	0.00	135426.92	0.00	California
48	542.05	51743.15	0.00	New York
49	0.00	116983.80	45173.06	California

In [18]:

X["State"].nunique()

Out[18]:

In [22]:

sorted(X["State"].unique())

Out[22]:

['California', 'Florida', 'New York']

In [23]:

from sklearn.preprocessing import OneHotEncoder

In [24]:

from sklearn.compose import ColumnTransformer

In [28]:

Out[28]:

	R&D Spend	Administration	Marketing Spend	State
0	165349.20	136897.80	471784.10	New York
1	162597.70	151377.59	443898.53	California
2	153441.51	101145.55	407934.54	Florida
3	144372.41	118671.85	383199.62	New York
4	142107.34	91391.77	366168.42	Florida
5	131876.90	99814.71	362861.36	New York
6	134615.46	147198.87	127716.82	California
7	130298.13	145530.06	323876.68	Florida
8	120542.52	148718.95	311613.29	New York
9	123334.88	108679.17	304981.62	California
10	101913.08	110594.11	229160.95	Florida
11	100671.96	91790.61	249744.55	California
12	93863.75	127320.38	249839.44	Florida
13	91992.39	135495.07	252664.93	California
14	119943.24	156547.42	256512.92	Florida
15	114523.61	122616.84	261776.23	New York
16	78013.11	121597.55	264346.06	California
17	94657.16	145077.58	282574.31	New York
18	91749.16	114175.79	294919.57	Florida
19	86419.70	153514.11	0.00	New York
20	76253.86	113867.30	298664.47	California
21	78389.47	153773.43	299737.29	New York
22	73994.56	122782.75	303319.26	Florida
23	67532.53	105751.03	304768.73	Florida
24	77044.01	99281.34	140574.81	New York
25	64664.71	139553.16	137962.62	California
26	75328.87	144135.98	134050.07	Florida
27	72107.60	127864.55	353183.81	New York
28	66051.52	182645.56	118148.20	Florida
29	65605.48	153032.06	107138.38	New York
30	61994.48	115641.28	91131.24	Florida
31	61136.38	152701.92	88218.23	New York
32	63408.86	129219.61	46085.25	California
33	55493.95	103057.49	214634.81	Florida
34	46426.07	157693.92	210797.67	California
35	46014.02	85047.44	205517.64	New York
36	28663.76	127056.21	201126.82	Florida
37	44069.95	51283.14	197029.42	California
38	20229.59	65947.93	185265.10	New York
39	38558.51	82982.09	174999.30	California
40	28754.33	118546.05	172795.67	California
41	27892.92	84710.77	164470.71	Florida
42	23640.93	96189.63	148001.11	California
43	15505.73	127382.30	35534.17	New York
44	22177.74	154806.14	28334.72	California
45	1000.23	124153.04	1903.93	New York
46	1315.46	115816.21	297114.46	Florida
47	0.00	135426.92	0.00	California
48	542.05	51743.15	0.00	New York
49	0.00	116983.80	45173.06	California

In [29]:

ct = ColumnTransformer( [ ("encoder", OneHotEncoder(), [3]) ], remainder = 'passthrough')

In [30]:

ct.fit_transform( X )

Out[30]:

array([[0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.6534920e+05,
        1.3689780e+05, 4.7178410e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.6259770e+05,
        1.5137759e+05, 4.4389853e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.5344151e+05,
        1.0114555e+05, 4.0793454e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.4437241e+05,
        1.1867185e+05, 3.8319962e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.4210734e+05,
        9.1391770e+04, 3.6616842e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.3187690e+05,
        9.9814710e+04, 3.6286136e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.3461546e+05,
        1.4719887e+05, 1.2771682e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.3029813e+05,
        1.4553006e+05, 3.2387668e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.2054252e+05,
        1.4871895e+05, 3.1161329e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.2333488e+05,
        1.0867917e+05, 3.0498162e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.0191308e+05,
        1.1059411e+05, 2.2916095e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0067196e+05,
        9.1790610e+04, 2.4974455e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 9.3863750e+04,
        1.2732038e+05, 2.4983944e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 9.1992390e+04,
        1.3549507e+05, 2.5266493e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.1994324e+05,
        1.5654742e+05, 2.5651292e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.1452361e+05,
        1.2261684e+05, 2.6177623e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 7.8013110e+04,
        1.2159755e+05, 2.6434606e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 9.4657160e+04,
        1.4507758e+05, 2.8257431e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 9.1749160e+04,
        1.1417579e+05, 2.9491957e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 8.6419700e+04,
        1.5351411e+05, 0.0000000e+00],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 7.6253860e+04,
        1.1386730e+05, 2.9866447e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.8389470e+04,
        1.5377343e+05, 2.9973729e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 7.3994560e+04,
        1.2278275e+05, 3.0331926e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.7532530e+04,
        1.0575103e+05, 3.0476873e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.7044010e+04,
        9.9281340e+04, 1.4057481e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.4664710e+04,
        1.3955316e+05, 1.3796262e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 7.5328870e+04,
        1.4413598e+05, 1.3405007e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.2107600e+04,
        1.2786455e+05, 3.5318381e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.6051520e+04,
        1.8264556e+05, 1.1814820e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.5605480e+04,
        1.5303206e+05, 1.0713838e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.1994480e+04,
        1.1564128e+05, 9.1131240e+04],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.1136380e+04,
        1.5270192e+05, 8.8218230e+04],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.3408860e+04,
        1.2921961e+05, 4.6085250e+04],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 5.5493950e+04,
        1.0305749e+05, 2.1463481e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.6426070e+04,
        1.5769392e+05, 2.1079767e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 4.6014020e+04,
        8.5047440e+04, 2.0551764e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.8663760e+04,
        1.2705621e+05, 2.0112682e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.4069950e+04,
        5.1283140e+04, 1.9702942e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 2.0229590e+04,
        6.5947930e+04, 1.8526510e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.8558510e+04,
        8.2982090e+04, 1.7499930e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.8754330e+04,
        1.1854605e+05, 1.7279567e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.7892920e+04,
        8.4710770e+04, 1.6447071e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.3640930e+04,
        9.6189630e+04, 1.4800111e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.5505730e+04,
        1.2738230e+05, 3.5534170e+04],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.2177740e+04,
        1.5480614e+05, 2.8334720e+04],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.0002300e+03,
        1.2415304e+05, 1.9039300e+03],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.3154600e+03,
        1.1581621e+05, 2.9711446e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        1.3542692e+05, 0.0000000e+00],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 5.4205000e+02,
        5.1743150e+04, 0.0000000e+00],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
        1.1698380e+05, 4.5173060e+04]])

In [31]:

X.head(3)

Out[31]:

	R&D Spend	Administration	Marketing Spend	State
0	165349.20	136897.80	471784.10	New York
1	162597.70	151377.59	443898.53	California
2	153441.51	101145.55	407934.54	Florida

In [32]:

X = ct.fit_transform( X )

In [34]:

from sklearn.model_selection import train_test_split

In [35]:

train_test_split(X , y, test_size=0.2, random_state=1)

Out[35]:

[array([[1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.3408860e+04,
         1.2921961e+05, 4.6085250e+04],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 3.8558510e+04,
         8.2982090e+04, 1.7499930e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.8389470e+04,
         1.5377343e+05, 2.9973729e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.8663760e+04,
         1.2705621e+05, 2.0112682e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 8.6419700e+04,
         1.5351411e+05, 0.0000000e+00],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.3640930e+04,
         9.6189630e+04, 1.4800111e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
         1.1698380e+05, 4.5173060e+04],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 7.5328870e+04,
         1.4413598e+05, 1.3405007e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 7.3994560e+04,
         1.2278275e+05, 3.0331926e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 9.1992390e+04,
         1.3549507e+05, 2.5266493e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.7892920e+04,
         8.4710770e+04, 1.6447071e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 9.4657160e+04,
         1.4507758e+05, 2.8257431e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.0002300e+03,
         1.2415304e+05, 1.9039300e+03],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.7044010e+04,
         9.9281340e+04, 1.4057481e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.7532530e+04,
         1.0575103e+05, 3.0476873e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.4210734e+05,
         9.1391770e+04, 3.6616842e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 5.5493950e+04,
         1.0305749e+05, 2.1463481e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.1994324e+05,
         1.5654742e+05, 2.5651292e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.1994480e+04,
         1.1564128e+05, 9.1131240e+04],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.0191308e+05,
         1.1059411e+05, 2.2916095e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 6.6051520e+04,
         1.8264556e+05, 1.1814820e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.2177740e+04,
         1.5480614e+05, 2.8334720e+04],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.6426070e+04,
         1.5769392e+05, 2.1079767e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 9.1749160e+04,
         1.1417579e+05, 2.9491957e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 7.6253860e+04,
         1.1386730e+05, 2.9866447e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.4664710e+04,
         1.3955316e+05, 1.3796262e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.3461546e+05,
         1.4719887e+05, 1.2771682e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.3029813e+05,
         1.4553006e+05, 3.2387668e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
         1.3542692e+05, 0.0000000e+00],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.6259770e+05,
         1.5137759e+05, 4.4389853e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 7.8013110e+04,
         1.2159755e+05, 2.6434606e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.6534920e+05,
         1.3689780e+05, 4.7178410e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.1452361e+05,
         1.2261684e+05, 2.6177623e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.3187690e+05,
         9.9814710e+04, 3.6286136e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0067196e+05,
         9.1790610e+04, 2.4974455e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.2333488e+05,
         1.0867917e+05, 3.0498162e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.2054252e+05,
         1.4871895e+05, 3.1161329e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 9.3863750e+04,
         1.2732038e+05, 2.4983944e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.5505730e+04,
         1.2738230e+05, 3.5534170e+04],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 4.4069950e+04,
         5.1283140e+04, 1.9702942e+05]]),
 array([[0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.2107600e+04,
         1.2786455e+05, 3.5318381e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 4.6014020e+04,
         8.5047440e+04, 2.0551764e+05],
        [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.8754330e+04,
         1.1854605e+05, 1.7279567e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 2.0229590e+04,
         6.5947930e+04, 1.8526510e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.5344151e+05,
         1.0114555e+05, 4.0793454e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.4437241e+05,
         1.1867185e+05, 3.8319962e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 5.4205000e+02,
         5.1743150e+04, 0.0000000e+00],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.5605480e+04,
         1.5303206e+05, 1.0713838e+05],
        [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.3154600e+03,
         1.1581621e+05, 2.9711446e+05],
        [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.1136380e+04,
         1.5270192e+05, 8.8218230e+04]]),
 32     97427.84
 39     81005.76
 21    111313.02
 36     90708.19
 19    122776.86
 42     71498.49
 49     14681.40
 26    105733.54
 22    110352.25
 13    134307.35
 41     77798.83
 17    125370.37
 45     64926.08
 24    108552.04
 23    108733.99
 4     166187.94
 33     96778.92
 14    132602.65
 30     99937.59
 10    146121.95
 28    103282.38
 44     65200.33
 34     96712.80
 18    124266.90
 20    118474.03
 25    107404.34
 6     156122.51
 7     155752.60
 47     42559.73
 1     191792.06
 16    126992.93
 0     192261.83
 15    129917.04
 5     156991.12
 11    144259.40
 9     149759.96
 8     152211.77
 12    141585.52
 43     69758.98
 37     89949.14
 Name: Profit, dtype: float64,
 27    105008.31
 35     96479.51
 40     78239.91
 38     81229.06
 2     191050.39
 3     182901.99
 48     35673.41
 29    101004.64
 46     49490.75
 31     97483.56
 Name: Profit, dtype: float64]

In [36]:

X_train, X_test, y_train, y_test = train_test_split(X , y, test_size=0.2, random_state=1)

In [ ]:

In [38]:

from sklearn.linear_model import LinearRegression

In [39]:

regressor = LinearRegression()

In [40]:

regressor.fit(X_train,y_train)

Out[40]:

LinearRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

In [45]:

X_test.shape

Out[45]:

(10, 6)

In [47]:

y_pred = regressor.predict(X_test)

In [48]:

y_pred

Out[48]:

array([114664.41715867,  90593.1553162 ,  75692.84151574,  70221.88679651,
       179790.25514874, 171576.92018521,  49753.58752029, 102276.65888936,
        58649.37795761,  98272.02561131])

In [49]:

y_test

Out[49]:

27    105008.31
35     96479.51
40     78239.91
38     81229.06
2     191050.39
3     182901.99
48     35673.41
29    101004.64
46     49490.75
31     97483.56
Name: Profit, dtype: float64

In [50]:

y_test-y_pred

Out[50]:

27    -9656.107159
35     5886.354684
40     2547.068484
38    11007.173203
2     11260.134851
3     11325.069815
48   -14080.177520
29    -1272.018889
46    -9158.627958
31     -788.465611
Name: Profit, dtype: float64

In [52]:

error = y_test-y_pred

In [54]:

(error ** 2).mean()

Out[54]:

79495441.50407246

In [62]:

plt.plot(y_test.values)
plt.plot(y_pred)
plt.legend(["real","pred"])
plt.savefig("chart1.jpg")
plt.show()

In [ ]:

In [63]:

# 만든 인공지능을, 서비스로 배포(deploy) 하는것이 중요하다.

In [ ]:

In [64]:

# 실제 예측을 해보자.

In [ ]:

# 운영비는 15만달러, 마케팅비는 40만달러, 연구개발비는 13만달러이고,
# 회사는 Florida 에 있다.
# 이회사는 얼마의 수익을 얻을 수 있을까 예측???

In [69]:

df.head(2)

Out[69]:

	R&D Spend	Administration	Marketing Spend	State	Profit
0	165349.2	136897.80	471784.10	New York	192261.83
1	162597.7	151377.59	443898.53	California	191792.06

In [73]:

X_test

Out[73]:

array([[0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 7.2107600e+04,
        1.2786455e+05, 3.5318381e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 4.6014020e+04,
        8.5047440e+04, 2.0551764e+05],
       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, 2.8754330e+04,
        1.1854605e+05, 1.7279567e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 2.0229590e+04,
        6.5947930e+04, 1.8526510e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.5344151e+05,
        1.0114555e+05, 4.0793454e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 1.4437241e+05,
        1.1867185e+05, 3.8319962e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 5.4205000e+02,
        5.1743150e+04, 0.0000000e+00],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.5605480e+04,
        1.5303206e+05, 1.0713838e+05],
       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.3154600e+03,
        1.1581621e+05, 2.9711446e+05],
       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, 6.1136380e+04,
        1.5270192e+05, 8.8218230e+04]])

In [72]:

new_data = np.array([130000, 150000, 400000, "Florida"]) 

In [77]:

new_data = new_data.reshape(1,4)  # 트랜스폼은 2차원이기 때문에 , 1행4열 2차원으로 변경

In [80]:

new_data = ct.transform(new_data).astype(float) # fit은 교육시킬때만 쓴다.

C:\Users\5-10\Anaconda3\envs\YH\lib\site-packages\sklearn\base.py:450: UserWarning: X does not have valid feature names, but OneHotEncoder was fitted with feature names
  warnings.warn(

In [81]:

new_data

Out[81]:

array([[0.0e+00, 1.0e+00, 0.0e+00, 1.3e+05, 1.5e+05, 4.0e+05]])

In [82]:

regressor.predict(new_data) # 예측한 결과값

Out[82]:

array([160947.68743064])

In [ ]:

In [84]:

regressor.coef_  # X에 붙어있는 값 y= ax1 + bx2 + cx3 + dx4 + fx5 + hx6 + g 

Out[84]:

array([-2.85177769e+02,  2.97560876e+02, -1.23831070e+01,  7.74342081e-01,
       -9.44369585e-03,  2.89183133e-02])

In [ ]:

regressor.intercept_   # 상수항

Machine [supervised{Classification(Logisticregression)}] (0)	2022.12.02
Machine 예측 모델 실습, 배포를 위한 저장 (0)	2022.12.01
Machine [supervised{Prediction(Linear Regression)}] (0)	2022.12.01
Machine preprocessing, Feature Scaling, Dataset Training & Test (0)	2022.12.01
Machine 원핫 인코딩 (One Hot Encoding) (0)	2022.12.01

Gemini & Ocean

Machine Multiple Linear Regression

50_Startups.csv 데이터를 읽으세요.¶

각각의 피쳐를 분석하여, 어떤 신생 회사의 데이터가 있으면, 그 회사가 얼마의 수익을 낼 지 예측합니다. (투자를 해야 할지 말아야 할지)¶

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Machine Multiple Linear Regression

50_Startups.csv 데이터를 읽으세요.¶

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