我的代码出了问题,不知何故它有错误类型的问题,我想我需要对数据进行一些清理。但我不知道,这种情况能做些什么呢?对于数据清理,我尝试了一些函数,如value_count,但没有帮助。你觉得这里有什么问题?
file_name='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv'
df=pd.read_csv(file_name)
features =["floors", "waterfront","lat" ,"bedrooms" ,"sqft_basement" ,"view" ,"bathrooms","sqft_living15","sqft_above","grade","sqft_living"]
Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]
y=df['price']
pipe=Pipeline(Input)
print(pipe)
features=features.astype(float)
pipe.fit(features,y)
ypipe=pipe.predict(features)
ypipe[0:10]发布于 2022-01-27 12:36:35
您需要使用features列表从dataframe中选择列。features是一个没有astype属性的字符串列表。所以,代码就会变成这样。
df[features]=df[features].astype(float)
pipe.fit(df[features],y)
ypipe=pipe.predict(df[features])https://stackoverflow.com/questions/70878366
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