3. 아이리스 데이터 셋

 

 

1. Iris DataSet
데이터 셋: 특정한 작업을 위해 데이터를 관련성 있게 모아놓은 것
사이킷런 데이터셋 페이지 (https://scikit-learn.org/stable/api/sklearn.datasets.html#module-sklearn.datasets)

sklearn.datasets

 

from sklearn.datasets import load_iris

iris = load_iris()
iris

print(iris['DESCR'])

 

- sepal length in cm: 꽃받침의 길이
- sepal width in cm: 꽃받침의 너비
- petal length in cm: 꽃잎의 길이
- petal width in cm: 꽃잎의 너비

 

data = iris['data'] # 독립변수
data

 

 

feature_names = iris['feature_names']
feature_names

import pandas as pd

df_iris = pd.DataFrame(data, columns=feature_names)
df_iris

target = iris['target']
target

target.shape

 

# 파생변수로 옆에 붙이기
df_iris['target'] = target
df_iris

 

from sklearn.model_selection import train_test_split

# train_test_split(독립변수, 종속변수, 테스트사이즈, 시드값 ...)
X_train, X_test, y_train, y_test = train_test_split(df_iris.drop('target', axis=1), df_iris['target'], test_size=0.2, random_state=2023)

X_train.shape, X_test.shape

y_train.shape, y_test.shape

 

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

svc = SVC()

svc.fit(X_train, y_train)

y_pred = svc.predict(X_test)
y_pred

 

# 6.2, 2.1, 4.1, 1.5
y_pred = svc.predict([[6.2, 2.1, 4.1, 1.5]])
y_pred
# array([1]) 일 것 같다고 예측해 줌