Sports Betting SVM in Python

The .py file and the .csv file needed to run the SVM model code can be downloaded below.

The code for creating and visualizing the results of the SVM model can be found below. A quick note:

The data must be split into training and testing sets before the SVM model is created
- This is done using train_test_split()
The SVM model can be created using different kernels
- The following model is made using the poly kernel

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 22 13:18:52 2023

@author: casey
"""

## LOAD LIBRARIES
# Set seed for reproducibility
import random; random.seed(53)
import pandas as pd

# Import all we need from sklearn
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn import svm
from sklearn.model_selection import cross_validate

# Import visualization
import scikitplot as skplt
import matplotlib.pyplot as plt
import seaborn as sns

# --------------------------------------------------------------------------------------- #
## LOAD DATA

svm_df = pd.read_csv('C:/Users/casey/OneDrive/Documents/MSDS_Courses/Spring_2023/Machine_Learning/SVM/prepped_data/gbg_svm_py.csv')

# --------------------------------------------------------------------------------------- #
## CREATE TRAINING AND TESTING SETS

# X will contain all variables except the labels (the labels are the last column 'total_result')
X = svm_df.iloc[:,:-1]
# y will contain the labels (the labels are the last column 'total_result')
y = svm_df.iloc[:,-1:]

# split the data vectors randomly into 80% train and 20% test
# X_train contains the quantitative variables for the training set
# X_test contains the quantitative variables for the testing set
# y_train contains the labels for training set
# y_test contains the lables for the testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

# --------------------------------------------------------------------------------------- #
## TRAIN SVM MODEL

# change kernel to achieve different accuracies (linear, sigmoid, rbf, poly)
# C is the regularization parameter, must be positive, default is 1.0
# for poly, need to specify degree, default is 3
# SVM_Classifier = svm.SVC(kernel='poly', degree=2)
# for a list of all parameters go the following documentation
# https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
model = svm.SVC(kernel='rbf', C=1.0)
svm_classifier = model.fit(X_train, y_train)

# --------------------------------------------------------------------------------------- #
## EVALUATE MODEL

y_pred = svm_classifier.predict(X_test)
svm_accuracy_score = accuracy_score(y_test, y_pred)
svm_results = classification_report(y_test, y_pred)
print('SVM Accuracy Score: ', svm_accuracy_score)
print(confusion_matrix(y_test, y_pred))
print(svm_results)

# ------------------------------------------------------------------------------------- #
## PLOT CONFUSION MATRIX

fig = plt.figure(figsize=(15,6))

ax1 = fig.add_subplot(121)
skplt.metrics.plot_confusion_matrix(y_pred, y_test,
                                    title="Confusion Matrix for Term Frequency Vectorizer",
                                    cmap="Oranges",
                                    ax=ax1)