二手车价格预测Baseline (二）特征与标签构建

• Step3 特征与标签构建

1. 提取数据类型特征列名

numerical_cols = Train_data.select_dtypes(exclude = 'object').columns
print(numerical_cols)

categorical_cols = Train_data.select_dtypes(include = 'object').columns
print(categorical_cols)

2. 构建训练和测试样本

##选择特征列
feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','creatDate','price','model','brand','regionCode','seller']]
feature_cols = [col for col in feature_cols if 'Type' not in col]

## 提前特征列，标签列构造训练样本和测试样本
X_data = Train_data[feature_cols]
Y_data = Train_data['price']

X_test = TestA_data[feature_cols]

print('X train shape:',X_data.shape)
print('X test shape:',X_test.shape)

## 定义了一个统计函数，方便后续信息统计
def Sta_inf(data):
　print('_min',np.min(data))
　print('_max:',np.max(data))
　print('_mean',np.mean(data))
　 print('_ptp',np.ptp(data))
　print('_std',np.std(data))
　print('_var',np.var(data))

3. 统计标签的基本分布信息

print('Sta of label:')
Sta_inf(Y_data)

## 绘制标签的统计图，查看标签分布
plt.hist(Y_data)
plt.show()
plt.close()

4. 缺省值用-1填补

X_data = X_data.fillna(-1)
X_test = X_test.fillna(-1)