Pandas 라이브러리 IRIS 데이터 셋 실습해보기¶

학습 내용¶

  • scikit-learn를 활용한 머신러닝 모델 구축 실습

01 데이터 준비¶

In [20]:
import pandas as pd
import seaborn as sns
import numpy as np

print(pd.__version__)
iris = sns.load_dataset("iris")
iris

2.0.3
Out[20]:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
... ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica
146 6.3 2.5 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica

150 rows × 5 columns

02 라이브러리 불러오기¶

In [21]:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

03 모델 구축 및 예측, 평가¶

특징 선택 및 데이터 처리¶

In [22]:
# 데이터 입력과 출력으로 나누기 
X = iris.iloc[:, 0:4]   # 1열~4열
y = iris.iloc[:, 4]     # 5열 - species

X.shape, y.shape
Out[22]:
((150, 4), (150,))
In [23]:
y
Out[23]:
0         setosa
1         setosa
2         setosa
3         setosa
4         setosa
         ...    
145    virginica
146    virginica
147    virginica
148    virginica
149    virginica
Name: species, Length: 150, dtype: object
In [24]:
# 데이터를 학습용과 검증용으로 분류 
# test 30%, train 70% 로 분할
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3)

모델 선택 및 학습¶

In [25]:
# 모델 선택 - 앙상블
model = RandomForestClassifier(max_depth=5, n_estimators=10)
model.fit(X_train, y_train)
Out[25]:
RandomForestClassifier(max_depth=5, n_estimators=10)
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RandomForestClassifier(max_depth=5, n_estimators=10)
In [26]:
# 학습한 모델로 예측 수행
y_pred = model.predict(X_test)
print( len(y_pred) )
print( y_pred[0:10] )

45
['versicolor' 'versicolor' 'virginica' 'versicolor' 'setosa' 'versicolor'
 'setosa' 'versicolor' 'versicolor' 'setosa']
In [27]:
df_iris = pd.DataFrame(list(zip(y_pred, y_test)), columns=['pred_val', 'actual'])
df_iris.head()
Out[27]:
pred_val actual
0 versicolor versicolor
1 versicolor versicolor
2 virginica virginica
3 versicolor versicolor
4 setosa setosa
In [28]:
df_iris.loc[ df_iris['pred_val'] == df_iris['actual'], 'correct' ] = 1
df_iris.head(10)
Out[28]:
pred_val actual correct
0 versicolor versicolor 1.0
1 versicolor versicolor 1.0
2 virginica virginica 1.0
3 versicolor versicolor 1.0
4 setosa setosa 1.0
5 versicolor versicolor 1.0
6 setosa setosa 1.0
7 versicolor versicolor 1.0
8 versicolor versicolor 1.0
9 setosa setosa 1.0
In [29]:
df_iris.correct.value_counts()
Out[29]:
correct
1.0    40
Name: count, dtype: int64
In [30]:
np.mean( df_iris['correct'] )
Out[30]:
1.0
In [ ]: