import numpy as np
import pandas as pd
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import warnings
import pickle
warnings.filterwarnings("ignore")

data=pd.read_csv("Forest_fire.csv")
data=np.array(data)

X=data[1:, 1:-1]
y=data[1:, -1]
y=y.astype('int')
X=X.astype('int')
#print(X,y)

X_train,X_test,y_train,y_test=train_test_split(X , y, test_size=0.3,random_state=0)
log_reg=LogisticRegression()

log_reg.fit(X_train,y_train)

inputt=[int(x) for x in "45 32 60".split(' ')]
final=[np.array(inputt)]

b=log_reg.predict_proba(final)
print(b)

pickle.dump(log_reg,open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))

