讀取csv

df_train = pd.read_csv('./data/' + 'titanic_train.csv')
df_test = pd.read_csv('./data/' + 'titanic_test.csv')

取出目標欄位

Y_train = df_train['Survived']

df_train = df_train.drop(['Survived'] , axis=1) # 移除欄位

取出非特徵欄位

ids = df_test['PassengerId']

df_train = df_train.drop(['PassengerId'] , axis=1) # 移除欄位
df_test = df_test.drop(['PassengerId'] , axis=1) # 移除欄位

合併df_train、df_test

df = pd.concat([df_train,df_test])
df.head()

填補缺失值
編碼
歸一化

LEncoder = LabelEncoder()
MMEncoder = MinMaxScaler()
for c in df.columns:
    df[c] = df[c].fillna(-1) # 填補缺失值
    if df[c].dtype == 'object':
        print(c)
        df[c] = LEncoder.fit_transform(list(df[c].values)) # 編碼
    df[c] = MMEncoder.fit_transform(df[c].values.reshape(-1, 1)) # 歸一化
df.head()

取得X_train、y_train

train_num = Y_train.shape[0]
X_train = df[:train_num]
X_test = df[train_num:]

訓練模型

model = GradientBoostingClassifier()
model.fit(X_train, Y_train)

列出特徵重要性

importance = pd.Series(data=model.feature_importances_, index=X_train.columns)
importance = importance.sort_values(ascending=False)
print(importance)

預測X_test

pred = model.predict(X_test)
sub = pd.DataFrame({'PassengerId': ids, 'Survived': pred})
sub.head()

儲存預測數據到csv檔

sub.to_csv('titanic_baseline.csv', index=False)