零基础入门数据挖掘-特征工程

1.采用箱线图删除异常值

2.特征构造

• data['creatDate'] - data['regDate']  构造汽车使用时间特征
• regionCode -> city 根据邮编构造城市特征
• 根据汽车品牌构造销售统计量特征

3.归一化

  power log归一化

from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
data['power'] = np.log(data['power'] + 1) 
data['power'] = ((data['power'] - np.min(data['power'])) / (np.max(data['power']) - np.min(data['power'])))
data['power'].plot.hist()

  kilometer归一化

4.特征筛选

利用相关性筛选特征

data_numeric = data[['power', 'kilometer', 'brand_amount', 'brand_price_average', 
                     'brand_price_max', 'brand_price_median']]
correlation = data_numeric.corr()

f, ax = plt.subplots(figsize = (7, 7))
plt.title('Correlation of Numeric Features with Price', y=1, size=16)
sns.heatmap(correlation, square=True, vmax=0.8)