DATA SCIENCE

[rohitsekar1996]

''' IRIS DATASET '''

required libraries

import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import accuracy_score

read the dataset

data = pd.read_csv('Iris.csv') print(data.head())

print('\n\nColumn Names\n\n') print(data.columns)

#label encode the target variable encode = LabelEncoder() data.Species = encode.fit_transform(data.Species)

print(data.head())

train-test-split

train , test = train_test_split(data,test_size=0.2,random_state=0)

print('shape of training data : ',train.shape) print('shape of testing data',test.shape)

seperate the target and independent variable

train_x = train.drop(columns=['Species'],axis=1) train_y = train['Species']

test_x = test.drop(columns=['Species'],axis=1) test_y = test['Species']

create the object of the model

model = LogisticRegression()

model.fit(train_x,train_y)

predict = model.predict(test_x)

print('Predicted Values on Test Data',encode.inverse_transform(predict))

print('\n\nAccuracy Score on test data : \n\n') print(accuracy_score(test_y,predict))