Clash Royale CLAN TAG

Logistic Regression (tried to implement) but keep getting too accurate model

So I've tried to follow the below tutorial and keep getting a 99% accurate algorithm, but I seriously doubt this. i'm trying to predict risk of injury which is highlighted by IntTot below.

https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8

Input: Note the column headings are not correct below, first column is index.

TotClear BLD_HEIGHT INITIAL_CALL_HOUR TOTAL_NUM_PERSONNEL InjTot TotClearS TOTAL_NUM_PERSONNELS year ALARM_TO_FD
141677 8.316667 0 17 14 0.0 (7.333, 9.683] (7.0, 594.0] 2011 05
314976 21.483333 0 9 4 0.0 (21.0, 28.45] (-0.001, 4.0] 2013 03
215834 5.666667 0 23 4 0.0 NaN NaN 2012 03
318900 13.966667 0 23 20 0.0 (11.867, 14.167] (7.0, 594.0] 2013 01
468452 4.050000 0 5 4 0.0 (-58.834, 7.333] (-0.001, 4.0] NaN 03
338749 4.916667 0 18 18 0.0 (-58.834, 7.333] (7.0, 594.0] 2013 05
263937 5.833333 0 9 17 0.0 NaN NaN 2012 05
167954 142.833333 0 16 13 0.0 NaN NaN 2012 05
712047 8.900000 0 21 4 0.0 NaN NaN 2016 01
143231 28.883333 0 17 4 0.0 (28.45, 4321.35] (-0.001, 4.0]


So at first I clean the data, I try to get it into where a feature value is represented by a binary column. That is what the for loop does. It takes the columns I ahve interest in, extracts values and sets binary columns. I go from 10 ish columns to 144 columns. I first have to take InjTot and convert to a boolean. I will perform recursive feature extraction to obtain relevant columns for analysis.

train['event_type'] = pd.Categorical(train['event_type']).codes

cat_vars=['TotClearS','TOTAL_NUM_PERSONNELS','year','ALARM_TO_FD','event_type']

for var in cat_vars:
cat_list='var'+'_'+var
cat_list = pd.get_dummies(train[var], prefix=var)
data1=train.join(cat_list)
train=data1

data_vars=train.columns.values.tolist()
to_keep=[i for i in data_vars if i not in cat_vars]

train['InjInt'] = train['InjTot'].apply(lambda x: 1 if x >0 else 0 )
train = train.drop(['TotClearS','InjTot','TOTAL_NUM_PERSONNELS','year','ALARM_TO_FD','event_type','BLD_HEIGHT', 'INITIAL_CALL_HOUR','TOTAL_NUM_PERSONNEL'],axis=1)
y = train['InjInt']
X=[i for i in train if i not in y]


Here perform the regression with the matrix of train whose columns of X I have interest in. I do not include the target column in my definition of X (InjInt)

logreg = LogisticRegression()

rfe = RFE(logreg, 15)
rfe = rfe.fit(train[X], y)

X_train, X_test, y_train, y_test = train_test_split(train[X], y, test_size=0.3, random_state=0)
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

y_pred = logreg.predict(X_test)
print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))


Now I followed the tutorial for the most part just not sure why this is returning 99% accuracy unless I completely missed the boat or have an error in my analysis...

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