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main.py
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import pandas as pd
import xgboost as xgb
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from IPython.display import display
from flask import Flask,render_template, request
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
app = Flask(__name__)
data = pd.read_csv('2021-2022.csv')
data= data.drop(['Div','Date','Time','Referee','AHh','AHCh'],axis=1)
data['HomeTeam'] = data['HomeTeam'].map({'Charlton':1,'Chelsea':2,'Coventry':3, 'Derby':4, 'Leeds':5, 'Leicester':6,'Liverpool':7, 'Sunderland':8, 'Tottenham':9, 'Man United':10, 'Arsenal':11,'Bradford':12, 'Ipswich':13, 'Middlesbrough':14, 'Everton':15, 'Man City':16,'Newcastle':17, 'Southampton':18, 'West Ham':19, 'Aston Villa':20, 'Bolton':21,
'Blackburn':22, 'Fulham':23, 'Birmingham':24, 'Middlesboro':25, 'West Brom':26,
'Portsmouth':27, 'Wolves':28, 'Norwich':29, 'Crystal Palace':30, 'Wigan':31,
'Reading':32, 'Sheffield United':33, 'Watford':34, 'Hull':35, 'Stoke':36,
'Burnley':37, 'Blackpool':38, 'QPR':39, 'Swansea':40, 'Cardiff':41, 'Bournemouth':42,
'Brighton':43, 'Huddersfield':44, 'Brentford':45}).astype('int')
data['AwayTeam'] = data['AwayTeam'].map({'Man City':1, 'West Ham':2, 'Middlesbrough':3, 'Southampton':4, 'Everton':5,
'Aston Villa':6, 'Bradford':7, 'Arsenal':8, 'Ipswich':9, 'Newcastle':10,
'Liverpool':11, 'Chelsea':12, 'Man United':13, 'Tottenham':14, 'Charlton':15,
'Sunderland':16, 'Derby':17, 'Coventry':18, 'Leicester':19, 'Leeds':20,
'Blackburn':21, 'Bolton':22, 'Fulham':23, 'West Brom':24, 'Middlesboro':25,
'Birmingham':26, 'Wolves':27, 'Portsmouth':28, 'Crystal Palace':29, 'Norwich':30,
'Wigan':31, 'Watford':32, 'Sheffield United':33, 'Reading':34, 'Stoke':35, 'Hull':36,
'Burnley':37, 'Blackpool':38, 'Swansea':39, 'QPR':40, 'Cardiff':41, 'Bournemouth':42,
'Huddersfield':43, 'Brighton':44, 'Brentford':45}).astype('int')
data['FTR'] = data['FTR'].map({'H':2,'A':1,'D':0}).astype('int')
data['FTR'].unique()
data1 = data.drop(['FTHG', 'FTAG', 'HTHG', 'HTAG', 'HTR'], axis=1)
data = data1
columns = ['HomeTeam', 'AwayTeam', 'AST','HST','HS','AS']
X = data[columns]
Y = data['FTR']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)
print(X.shape, X_train.shape, X_test.shape)
"""modelLogisticRegression = LogisticRegression(multi_class='multinomial', solver='lbfgs')
modelLogisticRegression.fit(X_train, Y_train)
X_train_prediction = modelLogisticRegression.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
print("Training Accuracy:",round(training_data_accuracy*100,2),'%')
X_test_prediction = modelLogisticRegression.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
print("Testing Accuracy:",round(test_data_accuracy*100,2),'%')"""
# Define the parameter grid for hyperparameter tuning
param_grid = {
'learning_rate': [0.1, 0.01, 0.001],
'max_depth': [3, 5, 7],
'n_estimators': [100, 200, 300]
}
# Create the XGBoost classifier
modelXGBClassifier = xgb.XGBClassifier()
# Perform grid search to find the best hyperparameters
grid_search = GridSearchCV(estimator=modelXGBClassifier, param_grid=param_grid, cv=5)
grid_search.fit(X_train, Y_train)
# Get the best model and its hyperparameters
best_model = grid_search.best_estimator_
best_params = grid_search.best_params_
# Print the best hyperparameters
print("Best Hyperparameters:", best_params)
# Evaluate the best model on the training data
X_train_prediction = best_model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
print("Training Accuracy:", round(training_data_accuracy * 100, 2), "%")
# Evaluate the best model on the test data
X_test_prediction = best_model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
print("Testing Accuracy:", round(test_data_accuracy * 100, 2), "%")
# Save the accuracy for later use
XGBTEST = round(test_data_accuracy * 100, 2)
rf = RandomForestClassifier(random_state=42)
rf.fit(X_train, Y_train)
X_train_prediction = rf.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
print("Training Accuracy:", round(training_data_accuracy * 100, 2), '%')
X_test_prediction = rf.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
print("Testing Accuracy:", round(test_data_accuracy * 100, 2), '%')
@app.route('//')
def homepage():
return render_template('HomePage.html')
@app.route('/home')
def homepage1():
return render_template('HomePage.html')
@app.route('/intro')
def intro():
return render_template("IntroPage.html")
@app.route("/", methods=['POST','GET'])
def intropage():
if request.method == 'POST':
home = int(request.form.get('HomeTeam'))
away = int(request.form.get('AwayTeam'))
input_data = (home, away, 0, 0, 0, 0)
#Logistic Regression
"""input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = modelLogisticRegression.predict_proba(input_data_reshaped)
print(prediction)
arr = np.array(prediction)
arr_perc = np.round(arr*100, 2)
arr_no_brackets = np.squeeze(arr_perc)"""
#XGBOOST
input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = grid_search.predict_proba(input_data_reshaped)
print(prediction)
arr = np.array(prediction)
arr_perc = np.round(arr * 100, 2)
arr_no_brackets = np.squeeze(arr_perc)
#Random Forest
input_data_as_numpy_array1 = np.asarray(input_data)
input_data_reshaped1 = input_data_as_numpy_array1.reshape(1, -1)
prediction1 = rf.predict_proba(input_data_reshaped1)
print(prediction1)
arr1 = np.array(prediction1)
arr_perc1 = np.round(arr1 * 100, 2)
arr_no_brackets1 = np.squeeze(arr_perc1)
if home == 1:
home = 'Charlton'
elif home == 2:
home = 'Chelsea'
elif home == 3:
home = 'Coventry'
elif home == 4:
home = 'Derby'
elif home == 5:
home = 'Leeds'
elif home == 6:
home = 'Leicester'
elif home == 7:
home = 'Liverpool'
elif home == 8:
home = 'Sunderland'
elif home == 9:
home = 'Tottenham'
elif home == 10:
home = 'Man United'
elif home == 11:
home = 'Arsenal'
elif home == 12:
home = 'Bradford'
elif home == 13:
home = 'Ipswich'
elif home == 14:
home = 'Middlesbrough'
elif home == 15:
home = 'Everton'
elif home == 16:
home = 'Man City'
elif home == 17:
home = 'Newcastle'
elif home == 18:
home = 'Southampton'
elif home == 19:
home = 'West Ham'
elif home == 20:
home = 'Aston Villa'
elif home == 21:
home = 'Bolton'
elif home == 22:
home = 'Blackburn'
elif home == 23:
home = 'Fulham'
elif home == 24:
home = 'Birmingham'
elif home == 25:
home = 'Middlesboro'
elif home == 26:
home = 'West Brom'
elif home == 27:
home = 'Portsmouth'
elif home == 28:
home = 'Wolves'
elif home == 29:
home = 'Norwich'
elif home == 30:
home = 'Crystal Palace'
elif home == 31:
home = 'Wigan'
elif home == 32:
home = 'Reading'
elif home == 33:
home = 'Sheffield United'
elif home == 34:
home = 'Watford'
elif home == 35:
home = 'Hull'
elif home == 36:
home = 'Stoke'
elif home == 37:
home = 'Burnley'
elif home == 38:
home = 'Blackpool'
elif home == 39:
home = 'QPR'
elif home == 40:
home = 'Swansea'
elif home == 41:
home = 'Cardiff'
elif home == 42:
home = 'Bournemouth'
elif home == 43:
home = 'Brighton'
elif home == 44:
home = 'Huddersfield'
elif home == 45:
home = 'Brentford'
else:
print('none')
#Away Team
if away == 1:
away = 'Man City'
elif away == 2:
away = 'West Ham'
elif away == 3:
away = 'Middlesbrough'
elif away == 4:
away = 'Southampton'
elif away == 5:
away = 'Everton'
elif away == 6:
away = 'Aston Villa'
elif away == 7:
away = 'Bradford'
elif away == 8:
away = 'Arsenal'
elif away == 9:
away = 'Ipswich'
elif away == 10:
away = 'Newcastle'
elif away == 11:
away = 'Liverpool'
elif away == 12:
away = 'Chelsea'
elif away == 13:
away = 'Man United'
elif away == 14:
away = 'Tottenham'
elif away == 15:
away = 'Charlton'
elif away == 16:
away = 'Sunderland'
elif away == 17:
away = 'Derby'
elif away == 18:
away = 'Coventry'
elif away == 19:
away = 'Leicester'
elif away == 20:
away = 'Leeds'
elif away == 21:
away = 'Blackburn'
elif away == 22:
away = 'Bolton'
elif away == 23:
away = 'Fulham'
elif away == 24:
away = 'West Brom'
elif away == 25:
away = 'Middlesboro'
elif away == 26:
away = 'Birmingham'
elif away == 27:
away = 'Wolves'
elif away == 28:
away = 'Portsmouth'
elif away == 29:
away = 'Crystal Palace'
elif away == 30:
away = 'Norwich'
elif away == 31:
away = 'Wigan'
elif away == 32:
away = 'Watford'
elif away == 33:
away = 'Sheffield United'
elif away == 34:
away = 'Reading'
elif away == 35:
away = 'Stoke'
elif away == 36:
away = 'Hull'
elif away == 37:
away = 'Burnley'
elif away == 38:
away = 'Blackpool'
elif away == 39:
away = 'Swansea'
elif away == 40:
away = 'QPR'
elif away == 41:
away = 'Cardiff'
elif away == 42:
away = 'Bournemouth'
elif away == 43:
away = 'Huddersfield'
elif away == 44:
away = 'Brighton'
elif away == 45:
away = 'Brentford'
else:
print('none')
return render_template('IntroPage.html', home=home, away=away, pr=arr_no_brackets, pr1=arr_no_brackets1)
else:
return render_template('IntroPage.html')
@app.route('/second')
def second():
return render_template("SecondPage.html")
@app.route("/he",methods=['GET','POST'])
def secondpage():
if request.method == 'POST':
home = int(request.form.get('HomeTeam'))
away = int(request.form.get('AwayTeam'))
Ast = int(request.form.get('Ast'))
Hst = int(request.form.get('Hst'))
Hs = int(request.form.get('Hs'))
As = int(request.form.get('As'))
input_data = (home, away, Ast, Hst, Hs, As)
"""# Logistic Regression
input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = modelLogisticRegression.predict_proba(input_data_reshaped)
print(prediction)
arr = np.array(prediction)
arr_perc = np.round(arr * 100, 2)
arr_no_brackets = np.squeeze(arr_perc)"""
#XGBOOST
input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = grid_search.predict_proba(input_data_reshaped)
print(prediction)
arr = np.array(prediction)
arr_perc = np.round(arr * 100, 2)
arr_no_brackets = np.squeeze(arr_perc)
# Random Forest
input_data_as_numpy_array1 = np.asarray(input_data)
input_data_reshaped1 = input_data_as_numpy_array1.reshape(1, -1)
prediction1 = rf.predict_proba(input_data_reshaped1)
print(prediction1)
arr1 = np.array(prediction1)
arr_perc1 = np.round(arr1 * 100, 2)
arr_no_brackets1 = np.squeeze(arr_perc1)
if home == 1:
home = 'Charlton'
elif home == 2:
home = 'Chelsea'
elif home == 3:
home = 'Coventry'
elif home == 4:
home = 'Derby'
elif home == 5:
home = 'Leeds'
elif home == 6:
home = 'Leicester'
elif home == 7:
home = 'Liverpool'
elif home == 8:
home = 'Sunderland'
elif home == 9:
home = 'Tottenham'
elif home == 10:
home = 'Man United'
elif home == 11:
home = 'Arsenal'
elif home == 12:
home = 'Bradford'
elif home == 13:
home = 'Ipswich'
elif home == 14:
home = 'Middlesbrough'
elif home == 15:
home = 'Everton'
elif home == 16:
home = 'Man City'
elif home == 17:
home = 'Newcastle'
elif home == 18:
home = 'Southampton'
elif home == 19:
home = 'West Ham'
elif home == 20:
home = 'Aston Villa'
elif home == 21:
home = 'Bolton'
elif home == 22:
home = 'Blackburn'
elif home == 23:
home = 'Fulham'
elif home == 24:
home = 'Birmingham'
elif home == 25:
home = 'Middlesboro'
elif home == 26:
home = 'West Brom'
elif home == 27:
home = 'Portsmouth'
elif home == 28:
home = 'Wolves'
elif home == 29:
home = 'Norwich'
elif home == 30:
home = 'Crystal Palace'
elif home == 31:
home = 'Wigan'
elif home == 32:
home = 'Reading'
elif home == 33:
home = 'Sheffield United'
elif home == 34:
home = 'Watford'
elif home == 35:
home = 'Hull'
elif home == 36:
home = 'Stoke'
elif home == 37:
home = 'Burnley'
elif home == 38:
home = 'Blackpool'
elif home == 39:
home = 'QPR'
elif home == 40:
home = 'Swansea'
elif home == 41:
home = 'Cardiff'
elif home == 42:
home = 'Bournemouth'
elif home == 43:
home = 'Brighton'
elif home == 44:
home = 'Huddersfield'
elif home == 45:
home = 'Brentford'
else:
print('none')
# Away Team
if away == 1:
away = 'Man City'
elif away == 2:
away = 'West Ham'
elif away == 3:
away = 'Middlesbrough'
elif away == 4:
away = 'Southampton'
elif away == 5:
away = 'Everton'
elif away == 6:
away = 'Aston Villa'
elif away == 7:
away = 'Bradford'
elif away == 8:
away = 'Arsenal'
elif away == 9:
away = 'Ipswich'
elif away == 10:
away = 'Newcastle'
elif away == 11:
away = 'Liverpool'
elif away == 12:
away = 'Chelsea'
elif away == 13:
away = 'Man United'
elif away == 14:
away = 'Tottenham'
elif away == 15:
away = 'Charlton'
elif away == 16:
away = 'Sunderland'
elif away == 17:
away = 'Derby'
elif away == 18:
away = 'Coventry'
elif away == 19:
away = 'Leicester'
elif away == 20:
away = 'Leeds'
elif away == 21:
away = 'Blackburn'
elif away == 22:
away = 'Bolton'
elif away == 23:
away = 'Fulham'
elif away == 24:
away = 'West Brom'
elif away == 25:
away = 'Middlesboro'
elif away == 26:
away = 'Birmingham'
elif away == 27:
away = 'Wolves'
elif away == 28:
away = 'Portsmouth'
elif away == 29:
away = 'Crystal Palace'
elif away == 30:
away = 'Norwich'
elif away == 31:
away = 'Wigan'
elif away == 32:
away = 'Watford'
elif away == 33:
away = 'Sheffield United'
elif away == 34:
away = 'Reading'
elif away == 35:
away = 'Stoke'
elif away == 36:
away = 'Hull'
elif away == 37:
away = 'Burnley'
elif away == 38:
away = 'Blackpool'
elif away == 39:
away = 'Swansea'
elif away == 40:
away = 'QPR'
elif away == 41:
away = 'Cardiff'
elif away == 42:
away = 'Bournemouth'
elif away == 43:
away = 'Huddersfield'
elif away == 44:
away = 'Brighton'
elif away == 45:
away = 'Brentford'
else:
print('none')
return render_template('SecondPage.html', home=home, away=away, pr=arr_no_brackets, pr1=arr_no_brackets1)
else:
return render_template("SecondPage.html")
if __name__ == "__main__":
app.run(debug=True)