Created
September 12, 2018 17:08
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Kaggle Titanic by SVM
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import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
ds = pd.read_csv('train.csv') | |
# Ageの欠損値を Mr, Mrs, etc ごとの中央値で埋める | |
noage = ds[ ds['Age'].isna() ] | |
honorifics = ['Mr', 'Mrs', 'Ms', 'Miss', 'Master', 'Rev', 'Dr'] | |
for h in honorifics: | |
regexp = fr".*\b{h}\..*" | |
tn1 = ds[ ds['Name'].str.match(regexp) ] | |
median = tn1['Age'].median() | |
#print(f"{h}: {len(tn1)}, {median}") | |
hoe = noage[ noage['Name'].str.match(regexp) ] | |
ds['Age'] = ds['Age'].fillna( hoe['Age'].fillna(median) ) | |
# Sex カラムを One Hot Encode する(male カラムは除外) | |
ds['female'] = pd.get_dummies(ds['Sex'])['female'].values | |
# Sex, Age カラムのみ学習に使う | |
X = ds.loc[:, ['female', 'Age']] | |
# Survived カラム | |
y = ds.loc[:, ['Survived']] | |
# 標準化 | |
from sklearn.preprocessing import StandardScaler | |
sc_X = StandardScaler() | |
X = sc_X.fit_transform(X) | |
# 訓練データとテストデータを分割 | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) | |
# Support Vector Machine | |
from sklearn.svm import SVC | |
svc = SVC() | |
svc.fit(X_train, y_train) | |
y_pred = svc.predict(X_test) | |
svc.score(X_test, y_test) | |
# 0.7988826815642458 | |
from sklearn.metrics import classification_report | |
print(classification_report(y_test, y_pred)) | |
# precision recall f1-score support | |
# | |
# 0 0.84 0.84 0.84 110 | |
# 1 0.74 0.74 0.74 69 | |
# | |
# avg / total 0.80 0.80 0.80 179 |
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