Support Vector Machine (SVM) Code

Support Vector Machien Algorithm Python Code

Data Perparation Code

The Train Dataset

The Test Dataset

Support Vector Machine Code

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

playerTrainDF = pd.read_csv("SVMTrainDF.csv")
playerTestDF = pd.read_csv("SVMTestDF.csv")

playerTestLabel = playerTestDF['performance']

playerTestDF = playerTestDF.drop(['performance'],axis = 1)
playerTrainDF_nolabel = playerTrainDF.drop(['performance'],axis = 1)
playerTrainLabel = playerTrainDF['performance']

dropcols = ['season','teamName','playerName','position']
playerTrainDF_nolabel_quant = playerTrainDF_nolabel.drop(dropcols,axis = 1)
playerTestDF_quant = playerTestDF.drop(dropcols,axis=1)

############################# Kernel: Linear ###########################################

svm_model = LinearSVC(C=1.0,random_state=42)
svm_model.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
prediction = svm_model.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel, prediction))

svm_model2 = LinearSVC(C=1.0,random_state=42)
svm_model2.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
prediction2 = svm_model2.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel, prediction2))

svm_model3 = LinearSVC(C=50,random_state=42)
svm_model3.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
prediction3 = svm_model3.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel, prediction3))

linear_cnf_matrix = confusion_matrix(playerTestLabel,prediction)
linear_cnf_matrix2 = confusion_matrix(playerTestLabel,prediction2)
linear_cnf_matrix3 = confusion_matrix(playerTestLabel,prediction3)

fig, axes = plt.subplots(2, 2, figsize=(15, 15))

labels = ['Good','Normal']
sns.heatmap(linear_cnf_matrix, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[0,0])
axes[0,0].set_title("LinearSVC Confusion Matrix C=0.01",fontsize=20)
axes[0,0].set_xlabel("Actual", fontsize=15)
axes[0,0].set_ylabel("Predicted", fontsize=15)

sns.heatmap(linear_cnf_matrix2, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[0,1])
axes[0,1].set_title("LinearSVC Confusion Matrix C=1.0",fontsize=20)
axes[0,1].set_xlabel("Actual", fontsize=15)
axes[0,1].set_ylabel("Predicted", fontsize=15)

sns.heatmap(linear_cnf_matrix3, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[1,0])
axes[1,0].set_title("LinearSVC Confusion Matrix C=50",fontsize=20)
axes[1,0].set_xlabel("Actual", fontsize=15)
axes[1,0].set_ylabel("Predicted", fontsize=15)


############################ Kernel: RBF #########################################

from sklearn.svm import SVC
svm_rbf_model = SVC(C=0.01, kernel='rbf',verbose=False)
svm_rbf_model.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
rbf_prediction = svm_rbf_model.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel,rbf_prediction))

svm_rbf_model2 = SVC(C=0.015, kernel='rbf',verbose=False)
svm_rbf_model2.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
rbf_prediction2 = svm_rbf_model2.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel,rbf_prediction2))

svm_rbf_model3 = SVC(C=30, kernel='rbf',verbose=False)
svm_rbf_model3.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
rbf_prediction3 = svm_rbf_model3.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel,rbf_prediction3))

rbf_cnf_matrix = confusion_matrix(playerTestLabel,rbf_prediction)
rbf_cnf_matrix2 = confusion_matrix(playerTestLabel,rbf_prediction2)
rbf_cnf_matrix3 = confusion_matrix(playerTestLabel,rbf_prediction3)


fig, axes = plt.subplots(2, 2, figsize=(15, 15))

labels = ['Good','Normal']
sns.heatmap(rbf_cnf_matrix, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[0,0])
axes[0,0].set_title("rbfSVC Confusion Matrix C=0.01",fontsize=20)
axes[0,0].set_xlabel("Actual", fontsize=15)
axes[0,0].set_ylabel("Predicted", fontsize=15)

sns.heatmap(rbf_cnf_matrix2, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[0,1])
axes[0,1].set_title("rbfSVC Confusion Matrix C=0.015",fontsize=20)
axes[0,1].set_xlabel("Actual", fontsize=15)
axes[0,1].set_ylabel("Predicted", fontsize=15)

sns.heatmap(rbf_cnf_matrix3, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[1,0])
axes[1,0].set_title("rbfSVC Confusion Matrix C=30",fontsize=20)
axes[1,0].set_xlabel("Actual", fontsize=15)
axes[1,0].set_ylabel("Predicted", fontsize=15)

######################################## Kernel: Polynomial ############################################

svm_poly_model = SVC(C=0.01,kernel='poly',gamma="scale",verbose=False)
svm_poly_model.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
poly_prediction = svm_poly_model.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel,poly_prediction))

svm_poly_model2 = SVC(C=0.05, kernel='poly',gamma="scale", verbose=False)
svm_poly_model2.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
poly_prediction2 = svm_poly_model2.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel,poly_prediction2))

svm_poly_model3 = SVC(C=30,kernel='poly',gamma="scale", verbose=False)
svm_poly_model3.fit(playerTrainDF_nolabel_quant,playerTrainLabel)
poly_prediction3 = svm_poly_model3.predict(playerTestDF_quant)
print(accuracy_score(playerTestLabel,poly_prediction3))

poly_cnf_matrix = confusion_matrix(playerTestLabel,poly_prediction)
poly_cnf_matrix2 = confusion_matrix(playerTestLabel,poly_prediction2)
poly_cnf_matrix3 = confusion_matrix(playerTestLabel,poly_prediction3)


fig, axes = plt.subplots(2, 2, figsize=(15, 15))

labels = ['Good','Normal']
sns.heatmap(poly_cnf_matrix, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[0,0])
axes[0,0].set_title("PolySVC Confusion Matrix C=0.01",fontsize=20)
axes[0,0].set_xlabel("Actual", fontsize=15)
axes[0,0].set_ylabel("Predicted", fontsize=15)

sns.heatmap(poly_cnf_matrix2, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[0,1])
axes[0,1].set_title("PolySVC Confusion Matrix C=0.05",fontsize=20)
axes[0,1].set_xlabel("Actual", fontsize=15)
axes[0,1].set_ylabel("Predicted", fontsize=15)

sns.heatmap(poly_cnf_matrix3, annot=True,cmap='Blues',xticklabels=labels, yticklabels=labels, cbar=False,ax=axes[1,0])
axes[1,0].set_title("PolySVC Confusion Matrix C=30",fontsize=20)
axes[1,0].set_xlabel("Actual", fontsize=15)
axes[1,0].set_ylabel("Predicted", fontsize=15)

######################################### SVM Visualization ##########################################

pca = PCA(n_components=2)
X = pca.fit_transform(playerTrainDF_nolabel_quant)
Y = pca.fit_transform(playerTestDF_quant)

linear_model = LinearSVC(C=20)
linear_model.fit(X, playerTrainLabel)
linear_prediction = linear_model.predict(Y)
print(accuracy_score(playerTestLabel, linear_prediction))


def plot_svm(model, X, y):
    plt.figure(figsize=(10, 6))

    plt.scatter(X[:, 0], X[:, 1], c=playerTrainLabel.map({'Good': 'blue', 'Normal': 'red'}), s=30, edgecolors='k')
    ax = plt.gca()
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
    xx = np.linspace(xlim[0], xlim[1], 100)
    yy = np.linspace(ylim[0], ylim[1], 100)
    YY, XX = np.meshgrid(yy, xx)
    xy = np.vstack([XX.ravel(), YY.ravel()]).T
    Z = model.decision_function(xy).reshape(XX.shape)

    # Decision boundary
    ax.contour(XX, YY, Z, colors='green', levels=[-1, 0, 1], alpha=0.5, linestyles=['-', '--', '-'])

    plt.title("SVM Viusalization Example (Linear)")
    plt.xlabel("Rating")
    plt.ylabel("Key Pass")
    plt.show()


plot_svm(linear_model, X, playerTrainLabel.values)

########################### Midfielder predictions result show in dataset ############################
temp = pd.DataFrame(poly_prediction3)
temp = temp.rename(columns={0:'label'})
temp3 = playerTestDF.reset_index()
temp3 = temp3.drop(['index'],axis=1)
merged_df = pd.merge(temp3, temp, left_index=True, right_index=True)
merged_df

club = pd.read_csv("Clubs_DF.csv")
club = club[['season','teamName','PTS']]
club = club.drop_duplicates()
merged_df = pd.merge(merged_df, club, on=['season','teamName'],how='left')
temp4 = merged_df.sort_values(by=['season', 'PTS'], ascending=[True, False])
temp4 = temp4[['season','teamName','playerName','label','PTS']]