#importing basic libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


data=pd.read_csv('Iris (1).csv')

x=data.iloc[:,1:5].values
y=data.iloc[:,5].values

from sklearn.model_selection import train_test_split as tts
x_train,x_test,y_train,y_test=tts(x,y,test_size=0.3,random_state=50)

from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
x_train=sc.fit_transform(x_train)
x_test=sc.transform(x_test)

from sklearn.ensemble import RandomForestClassifier as RFC
classifier=RFC()
classifier.fit(x_train,y_train)

import pickle
pickle.dump(classifier,open('model.pkl','wb'))
aced28fee3b6bae6358aa705dd05ccf75208fd5d
<mask token>

<mask token>
classifier.fit(x_train, y_train)
<mask token>
pickle.dump(classifier, open('model.pkl', 'wb'))

<mask token>
data = pd.read_csv('Iris (1).csv')
x = data.iloc[:, 1:5].values
y = data.iloc[:, 5].values
<mask token>
x_train, x_test, y_train, y_test = tts(x, y, test_size=0.3, random_state=50)
<mask token>
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
<mask token>
classifier = RFC()
classifier.fit(x_train, y_train)
<mask token>
pickle.dump(classifier, open('model.pkl', 'wb'))

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('Iris (1).csv')
x = data.iloc[:, 1:5].values
y = data.iloc[:, 5].values
from sklearn.model_selection import train_test_split as tts
x_train, x_test, y_train, y_test = tts(x, y, test_size=0.3, random_state=50)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.ensemble import RandomForestClassifier as RFC
classifier = RFC()
classifier.fit(x_train, y_train)
import pickle
pickle.dump(classifier, open('model.pkl', 'wb'))

#importing basic libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


data=pd.read_csv('Iris (1).csv')

x=data.iloc[:,1:5].values
y=data.iloc[:,5].values

from sklearn.model_selection import train_test_split as tts
x_train,x_test,y_train,y_test=tts(x,y,test_size=0.3,random_state=50)

from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
x_train=sc.fit_transform(x_train)
x_test=sc.transform(x_test)

from sklearn.ensemble import RandomForestClassifier as RFC
classifier=RFC()
classifier.fit(x_train,y_train)

import pickle
pickle.dump(classifier,open('model.pkl','wb'))