0.Data Preprocessing

0.1 Importing the libraries 

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

0.2 Importing the dataset 

dataset = pd.read_csv('50_Startups.csv')
dataset
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

0.3 Check if any null value 

dataset.isna().sum()

R&D Spend          0
Administration     0
Marketing Spend    0
State              0
Profit             0
dtype: int64
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 5 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   R&D Spend        50 non-null     float64
 1   Administration   50 non-null     float64
 2   Marketing Spend  50 non-null     float64
 3   State            50 non-null     object 
 4   Profit           50 non-null     float64
dtypes: float64(4), object(1)
memory usage: 2.1+ KB

0.4 Split into X & y 

X = dataset.drop('Profit', axis=1)
X
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 
y = dataset['Profit']
y

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

0.5 Encoding categorical data 

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
 
categorical_feature = ["State"]
one_hot = OneHotEncoder()
transformer = ColumnTransformer([("one_hot",
                                  one_hot,
                                  categorical_feature)],
                                 remainder="passthrough")

transformed_X = transformer.fit_transform(X)

pd.DataFrame(transformed_X).head()
0 1 2 3 4 5
0 0.0 0.0 1.0 165349.20 136897.80 471784.10
1 1.0 0.0 0.0 162597.70 151377.59 443898.53
2 0.0 1.0 0.0 153441.51 101145.55 407934.54
3 0.0 0.0 1.0 144372.41 118671.85 383199.62
4 0.0 1.0 0.0 142107.34 91391.77 366168.42

0.6 Splitting the dataset into the Training set and Test set 

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(transformed_X, y, test_size = 0.25, random_state = 2509)

1. Training the Multiple Linear Regression model on the Training set 

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

LinearRegression()

1.1 Score 

regressor.score(X_test,y_test)

0.9840064291741994

2. Predicting the Test set results 

y_pred = regressor.predict(X_test)

d = {'y_pred': y_pred, 'y_test': y_test}

2.1 Compare Predicted results 

pd.DataFrame(d)
y_pred y_test
32 98884.371543 97427.84
33 100047.235184 96778.92
47 47766.247901 42559.73
9 154976.558305 149759.96
37 91129.087779 89949.14
8 151755.926389 152211.77
23 112436.195860 108733.99
24 113375.898676 108552.04
17 130706.106786 125370.37
1 189141.730655 191792.06
39 85217.422839 81005.76
22 116952.737156 110352.25
46 60343.602070 49490.75