Read Time21 Minute, 12 Second
Note: This is the first try of solving this kind of problem and needs improvement as the accuracy is very low. If you know these techniques, I would appreciate the help. Now let’s just see how I tried predicting a football match result using machine learning.
I tried multiple algorithms, and the SVM using Radial Basis Function kernel was the best for accuracy. [Which still sucks though.]
Now, let’s just see what I tried to do. You can find the code with and without BoW on GitHub.
Predicting Football Match Results Using Machine Learning
- Downloaded the dataset from kaggle. The dataset is only about Liverpool FC matches from 1993 to 2018. [dataset by Rogelio Camargo)
- Then opened the ‘csv’ file using pandas.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset=pd.read_csv('C:\\Users\\shubh\\Downloads\\liverpoolfcstats.csv')
3. The file will look like this.
Unnamed: 0 Date HomeTeam AwayTeam FTHG FTAG FTR HTHG \ 0 4 14/08/93 Liverpool Sheffield Weds 2 0 H NaN 1 20 18/08/93 QPR Liverpool 1 3 A NaN 2 33 22/08/93 Swindon Liverpool 0 5 A NaN 3 40 25/08/93 Liverpool Tottenham 1 2 A NaN 4 49 28/08/93 Liverpool Leeds 2 0 H NaN HTAG HTR Season 0 NaN NaN 1993-94 1 NaN NaN 1993-94 2 NaN NaN 1993-94 3 NaN NaN 1993-94 4 NaN NaN 1993-94
4. Took only Home Team and Away team columns and combined them for making a vector for each row.
X=dataset.iloc[:,2:4]
for a in X:
a.replace(" ","")
X['Match']=X['HomeTeam']+X['AwayTeam']
X2=X.iloc[:,2]
print(X2)
5. X2 will look like this.
0 LiverpoolSheffield Weds
1 QPRLiverpool
2 SwindonLiverpool
3 LiverpoolTottenham
4 LiverpoolLeeds
...
953 LiverpoolBournemouth
954 West BromLiverpool
955 LiverpoolStoke
956 ChelseaLiverpool
957 LiverpoolBrighton
Name: Match, Length: 958, dtype: object
6. Tried to one hot encode X2, but later did label encoding
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() X2 = le.fit_transform(X2) print(X2) X2 = X2.reshape(len(X2),1)
The X2 will look like this.
[60 82 90 66 46 14 28 17 13 24 55 73 61 69 77 25 85 57 65 91 84 71 21 49 80 48 78 86 22 38 4 41 37 0 74 59 96 45 52 94 53 1 15 24 86 69 74 77 60 25 4 71 21 82 54 37 17 66 14 39 13 23 48 22 53 45 41 79 57 85 52 38 49 91 61 46 0 73 47 78 96 1 94 28 60 22 91 57 96 28 30 74 38 86 48 77 41 94 76 61 6 49 24 13 54 85 46 1 66 82 4 25 71 37 79 52 14 69 17 51 0 73 76 24 63 14 61 23 37 94 74 40 4 22 41 71 91 60 51 54 77 47 86 13 69 25 16 46 28 1 52 79 0 38 88 17 49 66 96 85 96 47 4 22 60 86 25 94 37 17 40 6 66 26 0 49 15 38 46 77 71 23 52 28 61 85 41 1 30 91 2 74 39 14 13 69 24 16 86 24 77 38 94 36 74 37 17 54 23 40 46 1 28 91 96 60 76 52 0 61 14 51 12 69 13 16 41 79 22 25 47 4 66 49 85 71 85 67 76 22 24 49 23 41 1 37 86 69 32 40 88 94 60 38 77 71 91 92 51 46 0 74 63 25 16 52 14 66 96 17 13 47 61 8 32 0 86 25 48 94 63 13 16 47 41 22 38 91 77 36 45 74 24 76 61 1 51 73 69 88 23 40 49 21 46 17 66 14 8 52 37 12 69 6 25 17 66 77 46 23 12 49 4 63 16 51 42 13 24 1 94 30 86 0 61 74 47 22 21 41 18 52 76 37 36 88 40 91 28 45 1 61 4 52 27 6 68 73 37 22 66 69 75 63 18 49 12 88 41 28 0 77 25 86 24 94 50 3 30 91 46 74 42 17 36 93 48 13 37 1 66 17 4 47 12 24 81 46 18 49 76 27 77 61 30 73 13 25 91 97 41 6 48 22 86 56 72 23 28 0 36 42 74 51 3 52 91 48 6 68 74 53 13 18 36 4 27 39 76 24 1 17 56 52 93 61 37 78 49 86 12 42 3 77 28 41 30 73 66 81 15 51 0 25 76 63 91 49 3 37 28 18 69 1 56 73 88 70 51 52 17 68 6 66 74 27 13 12 95 24 48 36 0 42 77 41 93 30 4 94 25 81 84 69 17 13 52 66 6 28 74 25 58 0 76 48 56 95 42 12 67 4 91 30 92 37 94 41 77 59 49 1 24 83 73 51 70 81 18 36 1 37 88 40 81 27 95 66 17 24 4 42 77 30 83 49 56 16 73 70 76 25 94 63 13 51 6 69 52 58 74 41 0 28 18 3 48 91 88 51 1 49 62 17 73 70 13 56 91 68 6 42 69 4 44 0 30 77 87 41 95 37 81 48 76 63 74 25 18 28 24 20 52 94 93 66 91 62 25 6 34 94 44 13 88 49 18 27 48 17 4 24 70 81 72 1 87 66 97 30 41 0 73 28 95 56 74 63 3 42 69 10 37 20 24 73 68 3 74 63 29 17 28 6 37 95 87 69 91 25 77 72 30 4 5 41 97 42 62 13 70 94 49 88 93 48 0 27 52 18 66 1 63 0 30 87 91 72 17 49 53 93 64 13 48 18 57 1 95 28 52 73 62 6 97 66 74 24 88 41 82 70 77 25 4 68 78 42 37 89 93 48 24 88 49 78 62 58 17 52 13 70 89 91 61 94 25 42 87 82 63 74 53 0 73 68 64 95 66 86 1 69 83 37 77 41 18 57 62 1 49 89 61 88 39 77 68 0 42 17 20 53 69 91 35 73 13 44 87 25 41 93 24 18 64 86 74 11 63 66 94 48 78 37 15 52 61 73 91 25 94 41 68 82 44 77 37 15 62 23 63 74 24 10 64 47 88 1 69 17 66 86 48 34 89 49 0 52 93 20 57 13 39 87 87 31 0 69 74 53 25 17 91 61 13 39 73 64 77 68 92 47 88 94 24 49 78 23 63 1 48 15 86 66 62 7 41 52 89 67 37 93 0 10 91 47 13 44 89 49 68 15 67 86 63 7 69 76 17 62 48 88 74 64 37 20 66 23 24 34 73 41 31 87 93 39 92 61 94 51 92 39 24 73 34 23 77 49 91 43 94 61 37 87 9 41 68 7 0 64 47 10 48 89 19 66 86 69 52 74 67 15 17 31 93 62 13 33]
7. Took 5th column from the dataset as dependent as we have to predict the result.
y=dataset.iloc[:,6].values print(y)
8. It will look like this
['H' 'A' 'A' 'A' 'H' 'H' 'A' 'H' 'H' 'D' 'H' 'D' 'H' 'H' 'H' 'H' 'H' 'H' 'D' 'D' 'D' 'D' 'A' 'D' 'A' 'H' 'D' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'A' 'D' 'H' 'A' 'A' 'A' 'H' 'A' 'H' 'A' 'D' 'H' 'D' 'H' 'H' 'H' 'H' 'A' 'H' 'H' 'H' 'H' 'D' 'D' 'D' 'D' 'A' 'H' 'A' 'H' 'A' 'D' 'D' 'D' 'A' 'H' 'A' 'H' 'D' 'H' 'A' 'A' 'H' 'H' 'A' 'D' 'H' 'H' 'H' 'H' 'H' 'A' 'H' 'H' 'H' 'H' 'D' 'D' 'A' 'H' 'H' 'A' 'D' 'H' 'D' 'A' 'H' 'H' 'D' 'H' 'D' 'H' 'A' 'D' 'A' 'A' 'H' 'D' 'H' 'H' 'H' 'H' 'H' 'D' 'H' 'D' 'D' 'D' 'H' 'D' 'A' 'H' 'A' 'H' 'A' 'H' 'H' 'H' 'A' 'D' 'D' 'A' 'A' 'H' 'H' 'D' 'D' 'A' 'H' 'D' 'H' 'A' 'H' 'D' 'H' 'H' 'D' 'A' 'A' 'A' 'D' 'A' 'H' 'H' 'D' 'D' 'A' 'D' 'A' 'H' 'D' 'H' 'H' 'H' 'H' 'H' 'D' 'H' 'A' 'A' 'A' 'A' 'H' 'H' 'A' 'H' 'D' 'H' 'D' 'A' 'D' 'D' 'H' 'H' 'D' 'A' 'D' 'H' 'D' 'H' 'H' 'H' 'H' 'A' 'D' 'A' 'H' 'H' 'D' 'H' 'D' 'D' 'H' 'H' 'A' 'A' 'A' 'H' 'H' 'H' 'H' 'A' 'H' 'D' 'H' 'H' 'H' 'H' 'D' 'H' 'H' 'H' 'D' 'D' 'A' 'A' 'A' 'H' 'D' 'H' 'H' 'A' 'A' 'H' 'A' 'H' 'A' 'D' 'A' 'D' 'H' 'D' 'H' 'H' 'H' 'A' 'H' 'H' 'H' 'D' 'H' 'H' 'A' 'D' 'H' 'A' 'D' 'D' 'D' 'A' 'H' 'A' 'H' 'A' 'D' 'H' 'A' 'D' 'H' 'H' 'H' 'D' 'H' 'H' 'D' 'D' 'H' 'A' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'A' 'A' 'H' 'H' 'H' 'A' 'D' 'D' 'H' 'D' 'H' 'D' 'H' 'D' 'A' 'A' 'H' 'A' 'A' 'H' 'D' 'A' 'H' 'H' 'A' 'A' 'H' 'A' 'D' 'A' 'A' 'H' 'D' 'H' 'A' 'H' 'D' 'H' 'A' 'A' 'D' 'D' 'H' 'D' 'D' 'A' 'H' 'A' 'A' 'D' 'A' 'H' 'A' 'H' 'H' 'A' 'H' 'H' 'H' 'H' 'A' 'H' 'D' 'D' 'D' 'A' 'H' 'A' 'H' 'A' 'H' 'H' 'H' 'D' 'H' 'A' 'H' 'H' 'D' 'D' 'D' 'H' 'D' 'A' 'D' 'A' 'D' 'H' 'H' 'A' 'H' 'H' 'H' 'A' 'H' 'A' 'A' 'H' 'A' 'D' 'D' 'A' 'A' 'H' 'H' 'A' 'H' 'H' 'A' 'A' 'D' 'H' 'D' 'A' 'H' 'D' 'A' 'H' 'H' 'D' 'D' 'D' 'H' 'D' 'H' 'H' 'H' 'D' 'H' 'H' 'A' 'D' 'A' 'H' 'A' 'D' 'D' 'H' 'H' 'H' 'H' 'H' 'H' 'A' 'H' 'D' 'A' 'H' 'H' 'H' 'D' 'H' 'D' 'H' 'A' 'H' 'A' 'A' 'A' 'H' 'A' 'H' 'H' 'H' 'D' 'H' 'H' 'H' 'D' 'A' 'H' 'D' 'H' 'H' 'D' 'H' 'D' 'D' 'D' 'A' 'H' 'H' 'H' 'A' 'H' 'A' 'A' 'H' 'H' 'H' 'A' 'H' 'D' 'H' 'H' 'D' 'H' 'H' 'A' 'H' 'H' 'D' 'H' 'H' 'A' 'H' 'A' 'H' 'A' 'A' 'H' 'A' 'D' 'H' 'H' 'H' 'H' 'H' 'H' 'D' 'H' 'H' 'H' 'H' 'D' 'H' 'D' 'A' 'H' 'A' 'H' 'H' 'A' 'H' 'A' 'H' 'A' 'D' 'H' 'H' 'A' 'D' 'H' 'A' 'D' 'H' 'H' 'H' 'H' 'D' 'A' 'D' 'A' 'H' 'D' 'D' 'A' 'D' 'A' 'D' 'D' 'H' 'A' 'H' 'H' 'A' 'H' 'A' 'D' 'D' 'D' 'D' 'H' 'H' 'D' 'H' 'A' 'H' 'H' 'H' 'H' 'H' 'D' 'H' 'A' 'D' 'H' 'A' 'A' 'H' 'D' 'H' 'D' 'A' 'A' 'H' 'A' 'H' 'H' 'H' 'A' 'D' 'D' 'A' 'D' 'D' 'H' 'A' 'D' 'D' 'D' 'H' 'A' 'D' 'H' 'H' 'A' 'H' 'A' 'H' 'D' 'A' 'H' 'A' 'A' 'H' 'H' 'H' 'A' 'A' 'H' 'A' 'H' 'H' 'H' 'H' 'H' 'D' 'D' 'A' 'D' 'A' 'H' 'H' 'H' 'A' 'D' 'H' 'D' 'H' 'H' 'H' 'D' 'H' 'H' 'H' 'H' 'H' 'D' 'D' 'H' 'A' 'A' 'D' 'D' 'H' 'H' 'D' 'H' 'D' 'A' 'H' 'H' 'A' 'H' 'D' 'H' 'H' 'H' 'H' 'H' 'A' 'H' 'H' 'H' 'D' 'A' 'H' 'H' 'A' 'D' 'H' 'H' 'A' 'H' 'H' 'D' 'H' 'H' 'A' 'A' 'H' 'D' 'A' 'H' 'H' 'H' 'H' 'A' 'D' 'D' 'A' 'D' 'A' 'D' 'H' 'H' 'A' 'D' 'D' 'H' 'H' 'D' 'H' 'A' 'D' 'H' 'A' 'H' 'H' 'H' 'A' 'H' 'D' 'A' 'A' 'A' 'A' 'H' 'H' 'H' 'D' 'A' 'D' 'A' 'A' 'D' 'H' 'D' 'D' 'D' 'H' 'D' 'H' 'H' 'A' 'A' 'H' 'H' 'A' 'H' 'H' 'H' 'D' 'D' 'A' 'H' 'A' 'H' 'H' 'A' 'D' 'D' 'D' 'A' 'D' 'A' 'H' 'H' 'A' 'H' 'D' 'A' 'A' 'H' 'D' 'H' 'H' 'H' 'D' 'H' 'H' 'H' 'A' 'H' 'H' 'H' 'H' 'A' 'D' 'H' 'D' 'H' 'A' 'H' 'A' 'A' 'A' 'H' 'H' 'A' 'H' 'A' 'A' 'D' 'H' 'H' 'H' 'A' 'A' 'H' 'D' 'H' 'A' 'D' 'H' 'A' 'H' 'H' 'A' 'D' 'H' 'D' 'A' 'H' 'D' 'A' 'A' 'H' 'D' 'H' 'A' 'H' 'H' 'A' 'A' 'H' 'H' 'D' 'H' 'H' 'D' 'A' 'H' 'A' 'H' 'D' 'A' 'H' 'D' 'H' 'D' 'D' 'D' 'A' 'A' 'A' 'H' 'H' 'D' 'H' 'H' 'A' 'H' 'D' 'A' 'A' 'H' 'D' 'A' 'H' 'A' 'H' 'D' 'H' 'A' 'H' 'D' 'H' 'H' 'D' 'D' 'A' 'H' 'D' 'H' 'A' 'H' 'A' 'D' 'H' 'A' 'H' 'D' 'H' 'H' 'D' 'A' 'A' 'H' 'H' 'D' 'D' 'A' 'D' 'H' 'H' 'H' 'H' 'H' 'D' 'H' 'D' 'A' 'A' 'A' 'A' 'D' 'A' 'H' 'D' 'H' 'H' 'H' 'D' 'A' 'D' 'D' 'H' 'H' 'A' 'H' 'D' 'A' 'A' 'D' 'D' 'A' 'D' 'H' 'H' 'A' 'H' 'H' 'A' 'D' 'A' 'H' 'H' 'H' 'H' 'A' 'D' 'H' 'D' 'D' 'H' 'H']
9. After label encoding X2, did the same for y.
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y = le.fit_transform(y) print(y) y = y.reshape(len(y),1)
Now y will look like this
[2 0 0 0 2 2 0 2 2 1 2 1 2 2 2 2 2 2 1 1 1 1 0 1 0 2 1 2 2 2 2 2 2 2 2 0 1 2 0 0 0 2 0 2 0 1 2 1 2 2 2 2 0 2 2 2 2 1 1 1 1 0 2 0 2 0 1 1 1 0 2 0 2 1 2 0 0 2 2 0 1 2 2 2 2 2 0 2 2 2 2 1 1 0 2 2 0 1 2 1 0 2 2 1 2 1 2 0 1 0 0 2 1 2 2 2 2 2 1 2 1 1 1 2 1 0 2 0 2 0 2 2 2 0 1 1 0 0 2 2 1 1 0 2 1 2 0 2 1 2 2 1 0 0 0 1 0 2 2 1 1 0 1 0 2 1 2 2 2 2 2 1 2 0 0 0 0 2 2 0 2 1 2 1 0 1 1 2 2 1 0 1 2 1 2 2 2 2 0 1 0 2 2 1 2 1 1 2 2 0 0 0 2 2 2 2 0 2 1 2 2 2 2 1 2 2 2 1 1 0 0 0 2 1 2 2 0 0 2 0 2 0 1 0 1 2 1 2 2 2 0 2 2 2 1 2 2 0 1 2 0 1 1 1 0 2 0 2 0 1 2 0 1 2 2 2 1 2 2 1 1 2 0 2 2 2 2 2 2 2 0 0 2 2 2 0 1 1 2 1 2 1 2 1 0 0 2 0 0 2 1 0 2 2 0 0 2 0 1 0 0 2 1 2 0 2 1 2 0 0 1 1 2 1 1 0 2 0 0 1 0 2 0 2 2 0 2 2 2 2 0 2 1 1 1 0 2 0 2 0 2 2 2 1 2 0 2 2 1 1 1 2 1 0 1 0 1 2 2 0 2 2 2 0 2 0 0 2 0 1 1 0 0 2 2 0 2 2 0 0 1 2 1 0 2 1 0 2 2 1 1 1 2 1 2 2 2 1 2 2 0 1 0 2 0 1 1 2 2 2 2 2 2 0 2 1 0 2 2 2 1 2 1 2 0 2 0 0 0 2 0 2 2 2 1 2 2 2 1 0 2 1 2 2 1 2 1 1 1 0 2 2 2 0 2 0 0 2 2 2 0 2 1 2 2 1 2 2 0 2 2 1 2 2 0 2 0 2 0 0 2 0 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 0 2 0 2 2 0 2 0 2 0 1 2 2 0 1 2 0 1 2 2 2 2 1 0 1 0 2 1 1 0 1 0 1 1 2 0 2 2 0 2 0 1 1 1 1 2 2 1 2 0 2 2 2 2 2 1 2 0 1 2 0 0 2 1 2 1 0 0 2 0 2 2 2 0 1 1 0 1 1 2 0 1 1 1 2 0 1 2 2 0 2 0 2 1 0 2 0 0 2 2 2 0 0 2 0 2 2 2 2 2 1 1 0 1 0 2 2 2 0 1 2 1 2 2 2 1 2 2 2 2 2 1 1 2 0 0 1 1 2 2 1 2 1 0 2 2 0 2 1 2 2 2 2 2 0 2 2 2 1 0 2 2 0 1 2 2 0 2 2 1 2 2 0 0 2 1 0 2 2 2 2 0 1 1 0 1 0 1 2 2 0 1 1 2 2 1 2 0 1 2 0 2 2 2 0 2 1 0 0 0 0 2 2 2 1 0 1 0 0 1 2 1 1 1 2 1 2 2 0 0 2 2 0 2 2 2 1 1 0 2 0 2 2 0 1 1 1 0 1 0 2 2 0 2 1 0 0 2 1 2 2 2 1 2 2 2 0 2 2 2 2 0 1 2 1 2 0 2 0 0 0 2 2 0 2 0 0 1 2 2 2 0 0 2 1 2 0 1 2 0 2 2 0 1 2 1 0 2 1 0 0 2 1 2 0 2 2 0 0 2 2 1 2 2 1 0 2 0 2 1 0 2 1 2 1 1 1 0 0 0 2 2 1 2 2 0 2 1 0 0 2 1 0 2 0 2 1 2 0 2 1 2 2 1 1 0 2 1 2 0 2 0 1 2 0 2 1 2 2 1 0 0 2 2 1 1 0 1 2 2 2 2 2 1 2 1 0 0 0 0 1 0 2 1 2 2 2 1 0 1 1 2 2 0 2 1 0 0 1 1 0 1 2 2 0 2 2 0 1 0 2 2 2 2 0 1 2 1 1 2 2]
10. Splitting the data into training and test sets.
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X2, y, test_size = 1/3, random_state = 0)
11. Did standard scaling.
from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) print(X_train) print(X_test)
12. Training SVM
from sklearn.svm import SVC classifier = SVC(kernel = 'rbf', random_state = 0) classifier.fit(X_train, y_train)
13. Predicting on New Data
I=input('Enter match as LiverpoolLeeds')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
I = le.fit_transform([I])
I = I.reshape(len(I),1)
print(sc.transform(I))
print(classifier.predict(sc.transform(I)))
Output will look like this
Enter match as LiverpoolLeedsLiverpoolLeeds [[-1.70802806]] [0]
14. Printing out predicted values and actual test values.
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))
The output will be:
[[2 2] [0 0] [2 2] [0 0] [2 2] [2 2] [0 2] [0 0] [2 2] [0 1] [2 2] [2 2] [2 0] [0 2] [2 0] [2 2] [0 2] [2 2] [2 1] [0 1] [2 0] [2 2] [2 2] [2 0] [0 1] [2 0] [0 0] [2 1] [2 2] [2 2] [2 2] [2 0] [2 2] [2 2] [2 0] [2 1] [2 2] [0 1] [0 0] [2 2] [2 0] [2 1] [2 2] [2 0] [0 0] [2 2] [2 2] [2 2] [2 2] [2 2] [0 0] [2 0] [2 0] [0 2] [2 2] [0 0] [0 2] [2 2] [0 2] [2 2] [2 2] [0 0] [2 2] [0 0] [2 2] [2 0] [0 1] [2 2] [2 2] [2 2] [0 0] [2 0] [2 0] [2 1] [2 0] [2 0] [0 0] [0 1] [0 1] [2 1] [2 2] [0 0] [0 2] [2 1] [2 2] [2 1] [2 2] [0 0] [0 2] [2 0] [2 2] [0 1] [2 1] [2 1] [2 2] [2 1] [2 2] [2 1] [0 0] [2 2] [2 2] [2 2] [2 1] [2 2] [2 1] [2 0] [2 1] [2 2] [0 2] [2 2] [2 0] [2 2] [2 2] [2 2] [2 1] [2 0] [2 1] [2 0] [0 1] [0 1] [0 1] [0 2] [2 2] [2 1] [0 2] [2 1] [0 0] [2 0] [2 0] [2 2] [0 1] [2 1] [2 2] [2 1] [2 1] [2 2] [2 0] [2 1] [2 1] [2 0] [2 2] [2 2] [2 2] [0 1] [2 1] [2 1] [2 2] [2 1] [0 1] [2 2] [0 1] [2 2] [2 0] [2 1] [2 2] [2 1] [2 2] [0 0] [2 2] [0 2] [0 1] [2 0] [2 2] [2 2] [2 2] [0 0] [2 0] [2 2] [0 0] [2 0] [2 2] [2 2] [2 1] [2 2] [2 0] [2 2] [2 2] [2 2] [0 2] [0 0] [2 0] [2 2] [2 1] [2 1] [0 0] [2 1] [0 1] [2 2] [2 1] [0 0] [2 2] [2 1] [2 0] [0 2] [2 2] [2 0] [0 0] [2 1] [2 1] [2 2] [2 1] [2 0] [0 0] [2 2] [0 0] [0 0] [0 0] [2 2] [2 2] [2 2] [2 0] [2 2] [2 0] [2 0] [0 0] [2 2] [2 0] [0 2] [2 0] [2 2] [2 0] [2 2] [2 2] [0 0] [2 1] [2 0] [2 0] [2 2] [2 1] [2 2] [2 1] [2 0] [2 0] [2 0] [0 0] [2 2] [2 1] [2 2] [2 2] [0 2] [0 1] [0 2] [2 2] [2 2] [2 2] [2 2] [0 0] [2 2] [2 2] [0 2] [2 2] [2 1] [2 2] [2 2] [2 2] [0 0] [2 2] [2 2] [0 0] [0 0] [2 0] [2 0] [0 2] [2 0] [2 1] [0 0] [0 2] [2 0] [2 2] [0 0] [2 2] [0 1] [2 1] [0 1] [2 2] [2 2] [2 1] [0 2] [0 2] [2 1] [2 1] [0 1] [2 2] [0 1] [2 2] [0 0] [2 2] [2 2] [2 0] [2 2] [0 2] [2 2] [2 2] [2 2] [2 2] [0 0] [2 2] [2 2] [2 1] [2 1] [0 2] [2 2] [2 2] [2 1] [2 0] [2 2] [2 1] [2 1] [2 2] [2 2] [2 1] [2 0] [2 1] [2 2] [2 2] [2 0] [0 1] [2 2] [2 2] [2 0]]
15. Now, to find the accuracy.
y_pred = classifier.predict(X_test) from sklearn.metrics import confusion_matrix, accuracy_score cm = confusion_matrix(y_test, y_pred) print(cm) accuracy_score(y_test, y_pred)
15. Accuracy did suck, to be honest at only 51.25% and one of the reasons might be the encoding technique. Still, compared with other algorithms, this was better with the Radial Basis Function kernel. Even using the other kernels gave less accuracy.
If you have read this article, ‘predicting a football match result using machine learning’, comment on what we must do to improve accuracy, and how.
You might also like: How Machine Learning (ML) is Used in Cyber Security
Follow us: Twitter, Instagram to stay updated every minute.
Image: Pexels