[试图建立回归模型,但是遇到了我无法解决的问题。用谷歌搜索并阅读有关它的所有内容,但没有用。具有此数据框:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 334195 entries, 0 to 334194
Data columns (total 12 columns):
type 334195 non-null int64
zipcode 334195 non-null int64
sqft 334195 non-null float64
lotsize 334195 non-null float64
beds 334195 non-null float64
baths 334195 non-null float64
year 334195 non-null float64
s_num 334195 non-null int64
s_rate 334195 non-null float64
s_dist 334195 non-null float64
crimes 334195 non-null float64
target 334195 non-null float64
dtypes: float64(9), int64(3)
正在尝试这样做:
data = pd.read_csv('data_prep_sale')
df = pd.DataFrame(data)
x = df.drop(['target'],axis=1)
y = pd.Series(df['target'])
trimmed_feature_names = []
for i in range(x.shape[1]):
correlation = np.corrcoef(x.iloc[:,i],y)[0,1]
if abs(correlation) > 0.5:
feature_name = x.columns[i]
print(feature_name, correlation)
trimmed_feature_names.append(feature_name)
并继续获得所有x的矩阵:
array([[ 1., nan],
[nan, nan]])
这是一个数据样本:
type zipcode sqft lotsize beds baths year s_num s_rate s_dist crimes target
4 28387 2900.0 0.0 4.0 3.5 2019.0 8 5.20 5.54 6.0 144.137931
4 99216 1947.0 5828.0 3.0 3.0 2019.0 3 4.00 1.33 3.1 159.219312
3 90049 3000.0 8626.0 3.0 2.0 1967.0 3 6.67 1.96 4.4 965.000000
1 75205 6457.0 8220.0 5.0 8.0 2006.0 4 9.25 0.75 4.6 370.915286
Link to the complete data file
请,请帮助我!需要任何想法!
根据上传的文件,在inf
列中有一些target
值...就像第43、283、372等行中的值。因此,要解决此问题,您必须删除所有inf
行。此外,还有一种更好的方法来查找target
与其他功能之间的相关性。两者都显示在以下代码中:
import numpy as np
import pandas as pd
data = pd.read_csv('data_prep_sale.csv')
df = pd.DataFrame(data)
# remove any (inf, -inf, nan) values
df = df.replace([np.inf, -np.inf], np.nan).dropna()
# find the correlation between target other features
print(df.corr()["target"])
从相关输出中可以看到,所有值都远低于0.5
。