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범주형
- 설명변수 => 목표변수
- 목표변수가 범주형인 경우 한값에 분류하여 예측
- 질병진단, 스펨메일필터링
- knn k nearest neighbors
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
pd.set_option('display.max_columns', 15)
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 class 891 non-null category
9 who 891 non-null object
10 adult_male 891 non-null bool
11 deck 203 non-null category
12 embark_town 889 non-null object
13 alive 891 non-null object
14 alone 891 non-null bool
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.6+ KB
rdf = df.drop(['deck', 'embark_town'], axis = 1)
rdf.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 class 891 non-null category
9 who 891 non-null object
10 adult_male 891 non-null bool
11 alive 891 non-null object
12 alone 891 non-null bool
dtypes: bool(2), category(1), float64(2), int64(4), object(4)
memory usage: 72.4+ KB
rdf = rdf.dropna(subset=['age'], axis = 0)
rdf.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 714 entries, 0 to 890
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 714 non-null int64
1 pclass 714 non-null int64
2 sex 714 non-null object
3 age 714 non-null float64
4 sibsp 714 non-null int64
5 parch 714 non-null int64
6 fare 714 non-null float64
7 embarked 712 non-null object
8 class 714 non-null category
9 who 714 non-null object
10 adult_male 714 non-null bool
11 alive 714 non-null object
12 alone 714 non-null bool
dtypes: bool(2), category(1), float64(2), int64(4), object(4)
memory usage: 63.6+ KB
most_freq = rdf['embarked'].value_counts(dropna=True).idxmax()
# rdf.groupby('embarked')['embarked'].count().idxmax()
rdf['embarked'].fillna(most_freq, inplace = True)
rdf.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 714 entries, 0 to 890
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 714 non-null int64
1 pclass 714 non-null int64
2 sex 714 non-null object
3 age 714 non-null float64
4 sibsp 714 non-null int64
5 parch 714 non-null int64
6 fare 714 non-null float64
7 embarked 714 non-null object
8 class 714 non-null category
9 who 714 non-null object
10 adult_male 714 non-null bool
11 alive 714 non-null object
12 alone 714 non-null bool
dtypes: bool(2), category(1), float64(2), int64(4), object(4)
memory usage: 63.6+ KB
ndf = rdf[['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'embarked']]
ndf.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 714 entries, 0 to 890
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 714 non-null int64
1 pclass 714 non-null int64
2 sex 714 non-null object
3 age 714 non-null float64
4 sibsp 714 non-null int64
5 parch 714 non-null int64
6 embarked 714 non-null object
dtypes: float64(1), int64(4), object(2)
memory usage: 44.6+ KB
ndf.describe()
survived pclass age sibsp parch
count 714.000000 714.000000 714.000000 714.000000 714.000000
mean 0.406162 2.236695 29.699118 0.512605 0.431373
std 0.491460 0.838250 14.526497 0.929783 0.853289
min 0.000000 1.000000 0.420000 0.000000 0.000000
25% 0.000000 1.000000 20.125000 0.000000 0.000000
50% 0.000000 2.000000 28.000000 0.000000 0.000000
75% 1.000000 3.000000 38.000000 1.000000 1.000000
max 1.000000 3.000000 80.000000 5.000000 6.000000# 원핫인코딩 # 범주형데이터를 모형이 인식할 수 있게 숫자형으로 변환
oh_set = pd.get_dummies(ndf['sex'])
oh_set.head()
female male
0 0 1
1 1 0
2 1 0
3 1 0
4 0 1
ndf = pd.concat([ndf, oh_set], axis = 1)
ndf.head()
survived pclass sex age sibsp parch embarked female male
0 0 3 male 22.0 1 0 S 0 1
1 1 1 female 38.0 1 0 C 1 0
2 1 3 female 26.0 0 0 S 1 0
3 1 1 female 35.0 1 0 S 1 0
4 0 3 male 35.0 0 0 S 0 1
oh_embarked = pd.get_dummies(ndf['embarked'], prefix = 'town')
ndf = pd.concat([ndf, oh_embarked], axis = 1)
ndf
survived pclass sex age sibsp parch embarked female male town_C town_Q town_S
0 0 3 male 22.0 1 0 S 0 1 0 0 1
1 1 1 female 38.0 1 0 C 1 0 1 0 0
2 1 3 female 26.0 0 0 S 1 0 0 0 1
3 1 1 female 35.0 1 0 S 1 0 0 0 1
4 0 3 male 35.0 0 0 S 0 1 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ...
885 0 3 female 39.0 0 5 Q 1 0 0 1 0
886 0 2 male 27.0 0 0 S 0 1 0 0 1
887 1 1 female 19.0 0 0 S 1 0 0 0 1
889 1 1 male 26.0 0 0 C 0 1 1 0 0
890 0 3 male 32.0 0 0 Q 0 1 0 1 0
714 rows × 12 columns
x =ndf[['pclass','age','sibsp','parch','female','male','town_C','town_Q','town_S']]
y = ndf['survived']
x.head()
pclass age sibsp parch female male town_C town_Q town_S
0 3 22.0 1 0 0 1 0 0 1
1 1 38.0 1 0 1 0 1 0 0
2 3 26.0 0 0 1 0 0 0 1
3 1 35.0 1 0 1 0 0 0 1
4 3 35.0 0 0 0 1 0 0 1# 설명변수 데이터 정규화
# 분석시 데이터 값의 크기에 따라서 분석의 결과에 영향
# 나이 범위가 크기 때문에 정규화를 통해 모든 속성변수들의 값을 기준단위로 변경
from sklearn import preprocessing
import numpy as np
preprocessing.StandardScaler().fit(x).transform(x)
array([[ 0.91123237, -0.53037664, 0.52457013, ..., -0.47180795,
-0.20203051, 0.53307848],
[-1.47636364, 0.57183099, 0.52457013, ..., 2.11950647,
-0.20203051, -1.87589641],
[ 0.91123237, -0.25482473, -0.55170307, ..., -0.47180795,
-0.20203051, 0.53307848],
...,
[-1.47636364, -0.73704057, -0.55170307, ..., -0.47180795,
-0.20203051, 0.53307848],
[-1.47636364, -0.25482473, -0.55170307, ..., 2.11950647,
-0.20203051, -1.87589641],
[ 0.91123237, 0.15850313, -0.55170307, ..., -0.47180795,
4.94974747, -1.87589641]])
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state=10)
x_train.shape
# (499, 9)
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
y_hat = knn.predict(x_test)
print(y_hat[0:10])
print(y_test[0:10])
[0 0 1 0 0 1 0 1 0 0]
728 0
555 0
426 1
278 0
617 0
751 1
576 1
679 1
567 0
117 0
Name: survived, dtype: int64
# 성능평가
from sklearn import metrics
knn_matrix = metrics.confusion_matrix(y_test, y_hat)
print(knn_matrix)
# [[111 14]
# [ 29 61]]precision 정확도
- 예측 true 실제 true인 tp의 비율
- 정확도가 높은 것은 예측 T 실제 F fn이 작은 경우
Recall 재현율
- 실제값이 true 인 분석대상중 True로 예측한 비율
- 재현율의 높은 것은 fn 오류가 낮다
F1 score
- 정확도와 재현율이 조화 평균을 계산한 값
- 모형의 예측력을 평가 지표
knn_report = metrics.classification_report(y_test, y_hat)
print(knn_report)
precision recall f1-score support
0 0.79 0.89 0.84 125
1 0.81 0.68 0.74 90
accuracy 0.80 215
macro avg 0.80 0.78 0.79 215
weighted avg 0.80 0.80 0.80 215accuracy : 정확도 macro avg : 단순평균
weighted avg : 가중평균, 표본의 갯수로 가중평균
# svm support vector machine
from sklearn import svm
# kernel = 'rbf' 적용
# 커널 : 벡터공간으로 매핑함수
# rbf = radial basis function
# linear
# polynimial
# sigmoid
svm_model = svm.SVC(kernel='rbf')
svm_model.fit(x_train, y_train)
y_hat = svm_model.predict(x_test)
print(y_hat[0:10])
# [0 0 0 0 0 1 0 0 0 0]
from sklearn import metrics
svm_matrix = metrics.confusion_matrix(y_test, y_hat)
print(svm_matrix)
# [[118 7]
# [ 79 11]]
svm_report = metrics.classification_report(y_test, y_hat)
print(svm_report)
precision recall f1-score support
0 0.60 0.94 0.73 125
1 0.61 0.12 0.20 90
accuracy 0.60 215
macro avg 0.61 0.53 0.47 215
weighted avg 0.60 0.60 0.51 215
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