random-forest-classification

July 12, 2024

1 Random Forest Classification

1.1 Importing the libraries
[1]: import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

1.2 Importing the dataset

[2]: dataset=pd.read_csv('/content/Social_Network_Adv.csv')
dataset

[2]: User ID Gender Age EstimatedSalary Purchased

0 15624510 Male 19 19000 0
1 15810944 Male 35 20000 0
2 15668575 Female 26 43000 0
3 15603246 Female 27 57000 0
4 15804002 Male 19 76000 0
.. … … … … …
395 15691863 Female 46 41000 1
396 15706071 Male 51 23000 1
397 15654296 Female 50 20000 1
398 15755018 Male 36 33000 0
399 15594041 Female 49 36000 1

[400 rows x 5 columns]

1.3 Process Categorical values

[3]: print(dataset.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 400 entries, 0 to 399
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----

1
 0 User ID 400 non-null int64
1 Gender 400 non-null object
2 Age 400 non-null int64
3 EstimatedSalary 400 non-null int64
4 Purchased 400 non-null int64
dtypes: int64(4), object(1)
memory usage: 15.8+ KB
None

[4]: dataset['Gender'] = dataset['Gender'].map({'Male': 0, 'Female': 1})

[5]: dataset

[5]: User ID Gender Age EstimatedSalary Purchased

0 15624510 0 19 19000 0
1 15810944 0 35 20000 0
2 15668575 1 26 43000 0
3 15603246 1 27 57000 0
4 15804002 0 19 76000 0
.. … … … … …
395 15691863 1 46 41000 1
396 15706071 0 51 23000 1
397 15654296 1 50 20000 1
398 15755018 0 36 33000 0
399 15594041 1 49 36000 1

[400 rows x 5 columns]

1.4 Find the correlation between the features

[6]: import seaborn as sns

[7]: correlation_matrix = dataset.corr()

print(correlation_matrix)

User ID Gender Age EstimatedSalary Purchased

User ID 1.000000 0.025249 -0.000721 0.071097 0.007120
Gender 0.025249 1.000000 0.073741 0.060435 0.042469
Age -0.000721 0.073741 1.000000 0.155238 0.622454
EstimatedSalary 0.071097 0.060435 0.155238 1.000000 0.362083
Purchased 0.007120 0.042469 0.622454 0.362083 1.000000

1.5 Splitting the dataset into the Training set and Test set
[8]: X = dataset.iloc[:, [2,3]].values
y = dataset.iloc[:, -1].values

2
[9]: from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25,␣
↪random_state = 0)

[10]: print(X_train)

[[ 44 39000]
[ 32 120000]
[ 38 50000]
[ 32 135000]
[ 52 21000]
[ 53 104000]
[ 39 42000]
[ 38 61000]
[ 36 50000]
[ 36 63000]
[ 35 25000]
[ 35 50000]
[ 42 73000]
[ 47 49000]
[ 59 29000]
[ 49 65000]
[ 45 131000]
[ 31 89000]
[ 46 82000]
[ 47 51000]
[ 26 15000]
[ 60 102000]
[ 38 112000]
[ 40 107000]
[ 42 53000]
[ 35 59000]
[ 48 41000]
[ 48 134000]
[ 38 113000]
[ 29 148000]
[ 26 15000]
[ 60 42000]
[ 24 19000]
[ 42 149000]
[ 46 96000]
[ 28 59000]
[ 39 96000]
[ 28 89000]
[ 41 72000]
[ 45 26000]
[ 33 69000]

3
[ 20 82000]
[ 31 74000]
[ 42 80000]
[ 35 72000]
[ 33 149000]
[ 40 71000]
[ 51 146000]
[ 46 79000]
[ 35 75000]
[ 38 51000]
[ 36 75000]
[ 37 78000]
[ 38 61000]
[ 60 108000]
[ 20 82000]
[ 57 74000]
[ 42 65000]
[ 26 80000]
[ 46 117000]
[ 35 61000]
[ 21 68000]
[ 28 44000]
[ 41 87000]
[ 37 33000]
[ 27 90000]
[ 39 42000]
[ 28 123000]
[ 31 118000]
[ 25 87000]
[ 35 71000]
[ 37 70000]
[ 35 39000]
[ 47 23000]
[ 35 147000]
[ 48 138000]
[ 26 86000]
[ 25 79000]
[ 52 138000]
[ 51 23000]
[ 35 60000]
[ 33 113000]
[ 30 107000]
[ 48 33000]
[ 41 80000]
[ 48 96000]
[ 31 18000]
[ 31 71000]
[ 43 129000]

4
[ 59 76000]
[ 18 44000]
[ 36 118000]
[ 42 90000]
[ 47 30000]
[ 26 43000]
[ 40 78000]
[ 46 59000]
[ 59 42000]
[ 46 74000]
[ 35 91000]
[ 28 59000]
[ 40 57000]
[ 59 143000]
[ 57 26000]
[ 52 38000]
[ 47 113000]
[ 53 143000]
[ 35 27000]
[ 58 101000]
[ 45 45000]
[ 23 82000]
[ 46 23000]
[ 42 65000]
[ 28 84000]
[ 38 59000]
[ 26 84000]
[ 29 28000]
[ 37 71000]
[ 22 55000]
[ 48 35000]
[ 49 28000]
[ 38 65000]
[ 27 17000]
[ 46 28000]
[ 48 141000]
[ 26 17000]
[ 35 97000]
[ 39 59000]
[ 24 27000]
[ 32 18000]
[ 46 88000]
[ 35 58000]
[ 56 60000]
[ 47 34000]
[ 40 72000]
[ 32 100000]
[ 19 21000]

5
[ 25 90000]
[ 35 88000]
[ 28 32000]
[ 50 20000]
[ 40 59000]
[ 50 44000]
[ 35 72000]
[ 40 142000]
[ 46 32000]
[ 39 71000]
[ 20 74000]
[ 29 75000]
[ 31 76000]
[ 47 25000]
[ 40 61000]
[ 34 112000]
[ 38 80000]
[ 42 75000]
[ 47 47000]
[ 39 75000]
[ 19 25000]
[ 37 80000]
[ 36 60000]
[ 41 52000]
[ 36 125000]
[ 48 29000]
[ 36 126000]
[ 51 134000]
[ 27 57000]
[ 38 71000]
[ 39 61000]
[ 22 27000]
[ 33 60000]
[ 48 74000]
[ 58 23000]
[ 53 72000]
[ 32 117000]
[ 54 70000]
[ 30 80000]
[ 58 95000]
[ 26 52000]
[ 45 79000]
[ 24 55000]
[ 40 75000]
[ 33 28000]
[ 44 139000]
[ 22 18000]
[ 33 51000]

6
[ 43 133000]
[ 24 32000]
[ 46 22000]
[ 35 55000]
[ 54 104000]
[ 48 119000]
[ 35 53000]
[ 37 144000]
[ 23 66000]
[ 37 137000]
[ 31 58000]
[ 33 41000]
[ 45 22000]
[ 30 15000]
[ 19 19000]
[ 49 74000]
[ 39 122000]
[ 35 73000]
[ 39 71000]
[ 24 23000]
[ 41 72000]
[ 29 83000]
[ 54 26000]
[ 35 44000]
[ 37 75000]
[ 29 47000]
[ 31 68000]
[ 42 54000]
[ 30 135000]
[ 52 114000]
[ 50 36000]
[ 56 133000]
[ 29 61000]
[ 30 89000]
[ 26 16000]
[ 33 31000]
[ 41 72000]
[ 36 33000]
[ 55 125000]
[ 48 131000]
[ 41 71000]
[ 30 62000]
[ 37 72000]
[ 41 63000]
[ 58 47000]
[ 30 116000]
[ 20 49000]
[ 37 74000]

7
[ 41 59000]
[ 49 89000]
[ 28 79000]
[ 53 82000]
[ 40 57000]
[ 60 34000]
[ 35 108000]
[ 21 72000]
[ 38 71000]
[ 39 106000]
[ 37 57000]
[ 26 72000]
[ 35 23000]
[ 54 108000]
[ 30 17000]
[ 39 134000]
[ 29 43000]
[ 33 43000]
[ 35 38000]
[ 41 45000]
[ 41 72000]
[ 39 134000]
[ 27 137000]
[ 21 16000]
[ 26 32000]
[ 31 66000]
[ 39 73000]
[ 41 79000]
[ 47 50000]
[ 41 30000]
[ 37 93000]
[ 60 46000]
[ 25 22000]
[ 28 37000]
[ 38 55000]
[ 36 54000]
[ 20 36000]
[ 56 104000]
[ 40 57000]
[ 42 108000]
[ 20 23000]
[ 40 65000]
[ 47 20000]
[ 18 86000]
[ 35 79000]
[ 57 33000]
[ 34 72000]
[ 49 39000]

8
 [ 27 31000]
[ 19 70000]
[ 39 79000]
[ 26 81000]
[ 25 80000]
[ 28 85000]
[ 55 39000]
[ 50 88000]
[ 49 88000]
[ 52 150000]
[ 35 65000]
[ 42 54000]
[ 34 43000]
[ 37 52000]
[ 48 30000]
[ 29 43000]
[ 36 52000]
[ 27 54000]
[ 26 118000]]

[11]: print(y_train)

[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1
0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1
1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0
1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0
0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0
0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0
0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0
0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1
0 0 0 0]

[12]: print(X_test)

[[ 30 87000]
[ 38 50000]
[ 35 75000]
[ 30 79000]
[ 35 50000]
[ 27 20000]
[ 31 15000]
[ 36 144000]
[ 18 68000]
[ 47 43000]
[ 30 49000]
[ 28 55000]
[ 37 55000]
[ 39 77000]

9
[ 20 86000]
[ 32 117000]
[ 37 77000]
[ 19 85000]
[ 55 130000]
[ 35 22000]
[ 35 47000]
[ 47 144000]
[ 41 51000]
[ 47 105000]
[ 23 28000]
[ 49 141000]
[ 28 87000]
[ 29 80000]
[ 37 62000]
[ 32 86000]
[ 21 88000]
[ 37 79000]
[ 57 60000]
[ 37 53000]
[ 24 58000]
[ 18 52000]
[ 22 81000]
[ 34 43000]
[ 31 34000]
[ 49 36000]
[ 27 88000]
[ 41 52000]
[ 27 84000]
[ 35 20000]
[ 43 112000]
[ 27 58000]
[ 37 80000]
[ 52 90000]
[ 26 30000]
[ 49 86000]
[ 57 122000]
[ 34 25000]
[ 35 57000]
[ 34 115000]
[ 59 88000]
[ 45 32000]
[ 29 83000]
[ 26 80000]
[ 49 28000]
[ 23 20000]
[ 32 18000]
[ 60 42000]

10
 [ 19 76000]
[ 36 99000]
[ 19 26000]
[ 60 83000]
[ 24 89000]
[ 27 58000]
[ 40 47000]
[ 42 70000]
[ 32 150000]
[ 35 77000]
[ 22 63000]
[ 45 22000]
[ 27 89000]
[ 18 82000]
[ 42 79000]
[ 40 60000]
[ 53 34000]
[ 47 107000]
[ 58 144000]
[ 59 83000]
[ 24 55000]
[ 26 35000]
[ 58 38000]
[ 42 80000]
[ 40 75000]
[ 59 130000]
[ 46 41000]
[ 41 60000]
[ 42 64000]
[ 37 146000]
[ 23 48000]
[ 25 33000]
[ 24 84000]
[ 27 96000]
[ 23 63000]
[ 48 33000]
[ 48 90000]
[ 42 104000]]

[13]: print(y_test)

[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0
0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1
0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]

11
 1.6 Feature Scaling
[14]: from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

[15]: print(X_train)

[[ 0.58164944 -0.88670699]
[-0.60673761 1.46173768]
[-0.01254409 -0.5677824 ]
[-0.60673761 1.89663484]
[ 1.37390747 -1.40858358]
[ 1.47293972 0.99784738]
[ 0.08648817 -0.79972756]
[-0.01254409 -0.24885782]
[-0.21060859 -0.5677824 ]
[-0.21060859 -0.19087153]
[-0.30964085 -1.29261101]
[-0.30964085 -0.5677824 ]
[ 0.38358493 0.09905991]
[ 0.8787462 -0.59677555]
[ 2.06713324 -1.17663843]
[ 1.07681071 -0.13288524]
[ 0.68068169 1.78066227]
[-0.70576986 0.56295021]
[ 0.77971394 0.35999821]
[ 0.8787462 -0.53878926]
[-1.20093113 -1.58254245]
[ 2.1661655 0.93986109]
[-0.01254409 1.22979253]
[ 0.18552042 1.08482681]
[ 0.38358493 -0.48080297]
[-0.30964085 -0.30684411]
[ 0.97777845 -0.8287207 ]
[ 0.97777845 1.8676417 ]
[-0.01254409 1.25878567]
[-0.90383437 2.27354572]
[-1.20093113 -1.58254245]
[ 2.1661655 -0.79972756]
[-1.39899564 -1.46656987]
[ 0.38358493 2.30253886]
[ 0.77971394 0.76590222]
[-1.00286662 -0.30684411]
[ 0.08648817 0.76590222]
[-1.00286662 0.56295021]
[ 0.28455268 0.07006676]

12
[ 0.68068169 -1.26361786]
[-0.50770535 -0.01691267]
[-1.79512465 0.35999821]
[-0.70576986 0.12805305]
[ 0.38358493 0.30201192]
[-0.30964085 0.07006676]
[-0.50770535 2.30253886]
[ 0.18552042 0.04107362]
[ 1.27487521 2.21555943]
[ 0.77971394 0.27301877]
[-0.30964085 0.1570462 ]
[-0.01254409 -0.53878926]
[-0.21060859 0.1570462 ]
[-0.11157634 0.24402563]
[-0.01254409 -0.24885782]
[ 2.1661655 1.11381995]
[-1.79512465 0.35999821]
[ 1.86906873 0.12805305]
[ 0.38358493 -0.13288524]
[-1.20093113 0.30201192]
[ 0.77971394 1.37475825]
[-0.30964085 -0.24885782]
[-1.6960924 -0.04590581]
[-1.00286662 -0.74174127]
[ 0.28455268 0.50496393]
[-0.11157634 -1.06066585]
[-1.10189888 0.59194336]
[ 0.08648817 -0.79972756]
[-1.00286662 1.54871711]
[-0.70576986 1.40375139]
[-1.29996338 0.50496393]
[-0.30964085 0.04107362]
[-0.11157634 0.01208048]
[-0.30964085 -0.88670699]
[ 0.8787462 -1.3505973 ]
[-0.30964085 2.24455257]
[ 0.97777845 1.98361427]
[-1.20093113 0.47597078]
[-1.29996338 0.27301877]
[ 1.37390747 1.98361427]
[ 1.27487521 -1.3505973 ]
[-0.30964085 -0.27785096]
[-0.50770535 1.25878567]
[-0.80480212 1.08482681]
[ 0.97777845 -1.06066585]
[ 0.28455268 0.30201192]
[ 0.97777845 0.76590222]
[-0.70576986 -1.49556302]

13
[-0.70576986 0.04107362]
[ 0.48261718 1.72267598]
[ 2.06713324 0.18603934]
[-1.99318916 -0.74174127]
[-0.21060859 1.40375139]
[ 0.38358493 0.59194336]
[ 0.8787462 -1.14764529]
[-1.20093113 -0.77073441]
[ 0.18552042 0.24402563]
[ 0.77971394 -0.30684411]
[ 2.06713324 -0.79972756]
[ 0.77971394 0.12805305]
[-0.30964085 0.6209365 ]
[-1.00286662 -0.30684411]
[ 0.18552042 -0.3648304 ]
[ 2.06713324 2.12857999]
[ 1.86906873 -1.26361786]
[ 1.37390747 -0.91570013]
[ 0.8787462 1.25878567]
[ 1.47293972 2.12857999]
[-0.30964085 -1.23462472]
[ 1.96810099 0.91086794]
[ 0.68068169 -0.71274813]
[-1.49802789 0.35999821]
[ 0.77971394 -1.3505973 ]
[ 0.38358493 -0.13288524]
[-1.00286662 0.41798449]
[-0.01254409 -0.30684411]
[-1.20093113 0.41798449]
[-0.90383437 -1.20563157]
[-0.11157634 0.04107362]
[-1.59706014 -0.42281668]
[ 0.97777845 -1.00267957]
[ 1.07681071 -1.20563157]
[-0.01254409 -0.13288524]
[-1.10189888 -1.52455616]
[ 0.77971394 -1.20563157]
[ 0.97777845 2.07059371]
[-1.20093113 -1.52455616]
[-0.30964085 0.79489537]
[ 0.08648817 -0.30684411]
[-1.39899564 -1.23462472]
[-0.60673761 -1.49556302]
[ 0.77971394 0.53395707]
[-0.30964085 -0.33583725]
[ 1.77003648 -0.27785096]
[ 0.8787462 -1.03167271]
[ 0.18552042 0.07006676]

14
[-0.60673761 0.8818748 ]
[-1.89415691 -1.40858358]
[-1.29996338 0.59194336]
[-0.30964085 0.53395707]
[-1.00286662 -1.089659 ]
[ 1.17584296 -1.43757673]
[ 0.18552042 -0.30684411]
[ 1.17584296 -0.74174127]
[-0.30964085 0.07006676]
[ 0.18552042 2.09958685]
[ 0.77971394 -1.089659 ]
[ 0.08648817 0.04107362]
[-1.79512465 0.12805305]
[-0.90383437 0.1570462 ]
[-0.70576986 0.18603934]
[ 0.8787462 -1.29261101]
[ 0.18552042 -0.24885782]
[-0.4086731 1.22979253]
[-0.01254409 0.30201192]
[ 0.38358493 0.1570462 ]
[ 0.8787462 -0.65476184]
[ 0.08648817 0.1570462 ]
[-1.89415691 -1.29261101]
[-0.11157634 0.30201192]
[-0.21060859 -0.27785096]
[ 0.28455268 -0.50979612]
[-0.21060859 1.6067034 ]
[ 0.97777845 -1.17663843]
[-0.21060859 1.63569655]
[ 1.27487521 1.8676417 ]
[-1.10189888 -0.3648304 ]
[-0.01254409 0.04107362]
[ 0.08648817 -0.24885782]
[-1.59706014 -1.23462472]
[-0.50770535 -0.27785096]
[ 0.97777845 0.12805305]
[ 1.96810099 -1.3505973 ]
[ 1.47293972 0.07006676]
[-0.60673761 1.37475825]
[ 1.57197197 0.01208048]
[-0.80480212 0.30201192]
[ 1.96810099 0.73690908]
[-1.20093113 -0.50979612]
[ 0.68068169 0.27301877]
[-1.39899564 -0.42281668]
[ 0.18552042 0.1570462 ]
[-0.50770535 -1.20563157]
[ 0.58164944 2.01260742]

15
[-1.59706014 -1.49556302]
[-0.50770535 -0.53878926]
[ 0.48261718 1.83864855]
[-1.39899564 -1.089659 ]
[ 0.77971394 -1.37959044]
[-0.30964085 -0.42281668]
[ 1.57197197 0.99784738]
[ 0.97777845 1.43274454]
[-0.30964085 -0.48080297]
[-0.11157634 2.15757314]
[-1.49802789 -0.1038921 ]
[-0.11157634 1.95462113]
[-0.70576986 -0.33583725]
[-0.50770535 -0.8287207 ]
[ 0.68068169 -1.37959044]
[-0.80480212 -1.58254245]
[-1.89415691 -1.46656987]
[ 1.07681071 0.12805305]
[ 0.08648817 1.51972397]
[-0.30964085 0.09905991]
[ 0.08648817 0.04107362]
[-1.39899564 -1.3505973 ]
[ 0.28455268 0.07006676]
[-0.90383437 0.38899135]
[ 1.57197197 -1.26361786]
[-0.30964085 -0.74174127]
[-0.11157634 0.1570462 ]
[-0.90383437 -0.65476184]
[-0.70576986 -0.04590581]
[ 0.38358493 -0.45180983]
[-0.80480212 1.89663484]
[ 1.37390747 1.28777882]
[ 1.17584296 -0.97368642]
[ 1.77003648 1.83864855]
[-0.90383437 -0.24885782]
[-0.80480212 0.56295021]
[-1.20093113 -1.5535493 ]
[-0.50770535 -1.11865214]
[ 0.28455268 0.07006676]
[-0.21060859 -1.06066585]
[ 1.67100423 1.6067034 ]
[ 0.97777845 1.78066227]
[ 0.28455268 0.04107362]
[-0.80480212 -0.21986468]
[-0.11157634 0.07006676]
[ 0.28455268 -0.19087153]
[ 1.96810099 -0.65476184]
[-0.80480212 1.3457651 ]

16
[-1.79512465 -0.59677555]
[-0.11157634 0.12805305]
[ 0.28455268 -0.30684411]
[ 1.07681071 0.56295021]
[-1.00286662 0.27301877]
[ 1.47293972 0.35999821]
[ 0.18552042 -0.3648304 ]
[ 2.1661655 -1.03167271]
[-0.30964085 1.11381995]
[-1.6960924 0.07006676]
[-0.01254409 0.04107362]
[ 0.08648817 1.05583366]
[-0.11157634 -0.3648304 ]
[-1.20093113 0.07006676]
[-0.30964085 -1.3505973 ]
[ 1.57197197 1.11381995]
[-0.80480212 -1.52455616]
[ 0.08648817 1.8676417 ]
[-0.90383437 -0.77073441]
[-0.50770535 -0.77073441]
[-0.30964085 -0.91570013]
[ 0.28455268 -0.71274813]
[ 0.28455268 0.07006676]
[ 0.08648817 1.8676417 ]
[-1.10189888 1.95462113]
[-1.6960924 -1.5535493 ]
[-1.20093113 -1.089659 ]
[-0.70576986 -0.1038921 ]
[ 0.08648817 0.09905991]
[ 0.28455268 0.27301877]
[ 0.8787462 -0.5677824 ]
[ 0.28455268 -1.14764529]
[-0.11157634 0.67892279]
[ 2.1661655 -0.68375498]
[-1.29996338 -1.37959044]
[-1.00286662 -0.94469328]
[-0.01254409 -0.42281668]
[-0.21060859 -0.45180983]
[-1.79512465 -0.97368642]
[ 1.77003648 0.99784738]
[ 0.18552042 -0.3648304 ]
[ 0.38358493 1.11381995]
[-1.79512465 -1.3505973 ]
[ 0.18552042 -0.13288524]
[ 0.8787462 -1.43757673]
[-1.99318916 0.47597078]
[-0.30964085 0.27301877]
[ 1.86906873 -1.06066585]

17
 [-0.4086731 0.07006676]
[ 1.07681071 -0.88670699]
[-1.10189888 -1.11865214]
[-1.89415691 0.01208048]
[ 0.08648817 0.27301877]
[-1.20093113 0.33100506]
[-1.29996338 0.30201192]
[-1.00286662 0.44697764]
[ 1.67100423 -0.88670699]
[ 1.17584296 0.53395707]
[ 1.07681071 0.53395707]
[ 1.37390747 2.331532 ]
[-0.30964085 -0.13288524]
[ 0.38358493 -0.45180983]
[-0.4086731 -0.77073441]
[-0.11157634 -0.50979612]
[ 0.97777845 -1.14764529]
[-0.90383437 -0.77073441]
[-0.21060859 -0.50979612]
[-1.10189888 -0.45180983]
[-1.20093113 1.40375139]]

[16]: print(X_test)

[[-0.80480212 0.50496393]
[-0.01254409 -0.5677824 ]
[-0.30964085 0.1570462 ]
[-0.80480212 0.27301877]
[-0.30964085 -0.5677824 ]
[-1.10189888 -1.43757673]
[-0.70576986 -1.58254245]
[-0.21060859 2.15757314]
[-1.99318916 -0.04590581]
[ 0.8787462 -0.77073441]
[-0.80480212 -0.59677555]
[-1.00286662 -0.42281668]
[-0.11157634 -0.42281668]
[ 0.08648817 0.21503249]
[-1.79512465 0.47597078]
[-0.60673761 1.37475825]
[-0.11157634 0.21503249]
[-1.89415691 0.44697764]
[ 1.67100423 1.75166912]
[-0.30964085 -1.37959044]
[-0.30964085 -0.65476184]
[ 0.8787462 2.15757314]
[ 0.28455268 -0.53878926]
[ 0.8787462 1.02684052]

18
[-1.49802789 -1.20563157]
[ 1.07681071 2.07059371]
[-1.00286662 0.50496393]
[-0.90383437 0.30201192]
[-0.11157634 -0.21986468]
[-0.60673761 0.47597078]
[-1.6960924 0.53395707]
[-0.11157634 0.27301877]
[ 1.86906873 -0.27785096]
[-0.11157634 -0.48080297]
[-1.39899564 -0.33583725]
[-1.99318916 -0.50979612]
[-1.59706014 0.33100506]
[-0.4086731 -0.77073441]
[-0.70576986 -1.03167271]
[ 1.07681071 -0.97368642]
[-1.10189888 0.53395707]
[ 0.28455268 -0.50979612]
[-1.10189888 0.41798449]
[-0.30964085 -1.43757673]
[ 0.48261718 1.22979253]
[-1.10189888 -0.33583725]
[-0.11157634 0.30201192]
[ 1.37390747 0.59194336]
[-1.20093113 -1.14764529]
[ 1.07681071 0.47597078]
[ 1.86906873 1.51972397]
[-0.4086731 -1.29261101]
[-0.30964085 -0.3648304 ]
[-0.4086731 1.31677196]
[ 2.06713324 0.53395707]
[ 0.68068169 -1.089659 ]
[-0.90383437 0.38899135]
[-1.20093113 0.30201192]
[ 1.07681071 -1.20563157]
[-1.49802789 -1.43757673]
[-0.60673761 -1.49556302]
[ 2.1661655 -0.79972756]
[-1.89415691 0.18603934]
[-0.21060859 0.85288166]
[-1.89415691 -1.26361786]
[ 2.1661655 0.38899135]
[-1.39899564 0.56295021]
[-1.10189888 -0.33583725]
[ 0.18552042 -0.65476184]
[ 0.38358493 0.01208048]
[-0.60673761 2.331532 ]
[-0.30964085 0.21503249]

19
 [-1.59706014 -0.19087153]
[ 0.68068169 -1.37959044]
[-1.10189888 0.56295021]
[-1.99318916 0.35999821]
[ 0.38358493 0.27301877]
[ 0.18552042 -0.27785096]
[ 1.47293972 -1.03167271]
[ 0.8787462 1.08482681]
[ 1.96810099 2.15757314]
[ 2.06713324 0.38899135]
[-1.39899564 -0.42281668]
[-1.20093113 -1.00267957]
[ 1.96810099 -0.91570013]
[ 0.38358493 0.30201192]
[ 0.18552042 0.1570462 ]
[ 2.06713324 1.75166912]
[ 0.77971394 -0.8287207 ]
[ 0.28455268 -0.27785096]
[ 0.38358493 -0.16187839]
[-0.11157634 2.21555943]
[-1.49802789 -0.62576869]
[-1.29996338 -1.06066585]
[-1.39899564 0.41798449]
[-1.10189888 0.76590222]
[-1.49802789 -0.19087153]
[ 0.97777845 -1.06066585]
[ 0.97777845 0.59194336]
[ 0.38358493 0.99784738]]

1.7 Training the Random Forest Classification model on the Training set
[17]: from sklearn.ensemble import RandomForestClassifier
'''n_estimators:This specifies the number of decision trees in the random␣
↪forest'''

#criterion=entropy uses info gain as criteria

classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy',␣
↪random_state = 0)

classifier.fit(X_train, y_train)

[17]: RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)

1.8 Predicting a new result

[18]: print(classifier.predict(sc.transform([[30,87000]])))

[0]

20
 1.9 Predicting the Test set results
[19]: y_pred = classifier.predict(X_test)
'''
This function concatenates the predicted values (y_pred) and the actual values␣
↪(y_test) into a single array along the columns (axis=1).

It reshapes both y_pred and y_test to ensure they have the same shape before␣
↪concatenation.'''

print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.
↪reshape(len(y_test),1)),1))

[[0 0]
[0 0]
[0 0]
[0 0]
[0 0]
[0 0]
[0 0]
[1 1]
[0 0]
[1 0]
[0 0]
[0 0]
[0 0]
[0 0]
[0 0]
[1 0]
[1 0]
[0 0]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[1 1]
[0 0]
[0 1]
[0 0]
[0 0]
[0 0]
[0 0]
[0 0]
[0 1]
[1 1]
[0 0]
[0 0]
[0 0]

21
[0 0]
[0 0]
[0 0]
[1 1]
[0 0]
[0 0]
[0 0]
[0 0]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[1 1]
[1 1]
[0 0]
[0 0]
[1 0]
[1 1]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[1 1]
[0 0]
[1 1]
[0 0]
[0 0]
[0 0]
[0 0]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[0 0]
[0 0]
[0 0]
[1 1]
[1 1]
[1 1]
[1 0]
[0 0]
[0 0]

22
 [1 1]
[0 1]
[0 0]
[1 1]
[1 1]
[0 0]
[0 0]
[1 1]
[0 0]
[0 0]
[0 0]
[0 1]
[0 0]
[1 1]
[1 1]
[1 1]]

1.10 Making the Confusion Matrix

[20]: from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)

[[63 5]
[ 4 28]]

[20]: 0.91

1.11 confusion matrix

[21]: import seaborn as sns
import matplotlib.pyplot as plt
# Plot confusion matrix
ax = plt.axes()
df_cm = cm

sns.heatmap(df_cm, annot=True, annot_kws={"size": 30}, fmt='d',cmap="Blues",␣

↪xticklabels=['no','yes'], yticklabels=['no','yes'],ax = ax )

ax.set_title('Confusion Matrix')
plt.show()

23
[21]:

24