Hi,

I was trying to run your code on my machine with Phyton 3.7 and at step Multinomial Naive Bayes¶ i get the following error

ValueError Traceback (most recent call last)
in
4 for i in range(-1000,1000,50):
5 clf1 = MultinomialNB(alpha=i)
----> 6 clf1.fit(X_train,y_train)
7 clf1.fit(X_train_2,y_train)
8 scores = cross_val_score(clf1, X_train, y_train, cv=10)

c:\users\johnm\appdata\local\programs\python\python37\lib\site-packages\sklearn\naive_bayes.py in fit(self, X, y, sample_weight)
609 dtype=np.float64)
610 self._count(X, Y)
--> 611 alpha = self._check_alpha()
612 self._update_feature_log_prob(alpha)
613 self._update_class_log_prior(class_prior=class_prior)

c:\users\johnm\appdata\local\programs\python\python37\lib\site-packages\sklearn\naive_bayes.py in check_alpha(self)
471 if np.min(self.alpha) < 0:
472 raise ValueError('Smoothing parameter alpha = %.1e. '
--> 473 'alpha should be > 0.' % np.min(self.alpha))
474 if isinstance(self.alpha, np.ndarray):
475 if not self.alpha.shape[0] == self.feature_count.shape[1]:

ValueError: Smoothing parameter alpha = -1.0e+03. alpha should be > 0.

It would Appear that the MultinomialNB function cannot accept a negative alpha value, how did you manage to run the code with a negative Alpha

hii broo..i cannot get proper table.team column. the column shows all true values instead of each team name what should i do?? Plzz help me. i attached screenshot below.

AT

In [38]:
#Extract necessary features from the data file
feature_table = df.iloc[:,:23]
print(table)

#Full Time Result(FTR), Home Shots on Target(HST), Away Shots on Target(AST), Home Corners(HC), Away Corners(AC)
feature_table = feature_table[['HomeTeam','AwayTeam','FTR','HST','AST','HC','AC']]
print(feature_table)
#Home Attacking Strength(HAS), Home Defensive Strength(HDS), Away Attacking Strength(AAS), Away Defensive Strength(ADS)
f_HAS = []
f_HDS = []
f_AAS = []
f_ADS = []
for index,row in feature_table.iterrows():
f_HAS.append(table[table['Team'] == row['HomeTeam']]['HAS'].values[0])
f_HDS.append(table[table['Team'] == row['HomeTeam']]['HDS'].values[0])
f_AAS.append(table[table['Team'] == row['AwayTeam']]['AAS'].values[0])
f_ADS.append(table[table['Team'] == row['AwayTeam']]['ADS'].values[0])

feature_table['HAS'] = f_HAS
feature_table['HDS'] = f_HDS
feature_table['AAS'] = f_AAS
feature_table['ADS'] = f_ADS
feature_table

APPAER THIS ERROR AND I M NOT PROCEDED

IndexError Traceback (most recent call last)
in
10 f_ADS = []
11 for index,row in feature_table.iterrows():
---> 12 f_HAS.append(table_16[table_16['Team'] == row['HomeTeam']]['HAS'].values[0])
13 f_HDS.append(table_16[table_16['Team'] == row['HomeTeam']]['HDS'].values[0])
14 f_AAS.append(table_16[table_16['Team'] == row['AwayTeam']]['AAS'].values[0])

I applied the random forest algorithm on merged_dataset.csv. Out of 6080 rows, I used 80% rows for training and the remaining 20% for testing. I found that the trained model predicted target with 100% accuracy. I take attribute FTR as a target.

CODE :

`from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.ensemble import RandomForestClassifier

dataframe = pd.read_csv('./dataset/Merged_dataset.csv')
print(dataframe.head())

df = dataframe.apply(LabelEncoder().fit_transform)
print(df.head())

target = np.array(df['FTR'])
features= df.drop(['id','FTR','FTAG','FTHG'], axis = 1)
features = np.array(features)

#Split the data into training and testing sets
train_features, test_features, train_labels, test_labels = train_test_split(features, target, test_size = 0.20, random_state = 42)

model = RandomForestClassifier()
model.fit(train_features, train_labels)

predicted_labels = model.predict(test_features)

print("actual Test labels")
print(test_labels)
print("")
print("predicted test labels")
print(predicted_labels)

#calculate accuracy
count = 0
totalCount = len(predicted_labels)
for i in range(len(test_labels)):
if(predicted_labels[i] == test_labels[i]):
count = count+1

print("Accuracy : "+str((count/totalCount)*100)+" %")
`

OUTPUT :

isn't it too unreal to have 100% percent accuracy? If I applied Logistic Regression then model's accuracy is 68%

will you correct what is wrong in my code? or what is the concept that I am missing while training my model?

Who can explain prediction codes and normalization?