Недавно я работал с набором данных в python и получил неожиданную ошибку. ошибка была: ValueError: could not convert string to float. На самом деле в наборе данных также были текстовые данные, которые я конвертировал в целое число с помощью LabelEncoder. Но когда я иду в обучающую часть, где я подгоняю модель, я получаю эту ошибку, которая не имеет смысла.
код:
import sklearn
from sklearn import model_selection
from sklearn import linear_model
from sklearn import preprocessing
import pandas as pd
import pickle
import numpy as np
data = pd.read_csv("house_train.csv")
data = data.fillna(value=0)
dataX_train = data.drop(["SalePrice"], axis = 1)
dataX_test = data.SalePrice
le = preprocessing.LabelEncoder()
dataX_train.MSZoning = le.fit_transform(list(data["MSZoning"]))
dataX_train.Street = le.fit_transform(list(data["Street"]))
dataX_train.Alley = le.fit_transform(list(data["Alley"]))
dataX_train.LotShape = le.fit_transform(list(data["LotShape"]))
dataX_train.LandContour = le.fit_transform(list(data["LandContour"]))
dataX_train.Utilities = le.fit_transform(list(data["Utilities"]))
dataX_train.LotConfig = le.fit_transform(list(data["LotConfig"]))
dataX_train.LandSlope = le.fit_transform(list(data["LandSlope"]))
dataX_train.Neighborhood = le.fit_transform(list(data["Neighborhood"]))
dataX_train.Condition1 = le.fit_transform(list(data["Condition1"]))
dataX_train.Condition2 = le.fit_transform(list(data["Condition2"]))
dataX_train.BldgType = le.fit_transform(list(data["BldgType"]))
dataX_train.HouseStyle = le.fit_transform(list(data["HouseStyle"]))
dataX_train.RoofStyle = le.fit_transform(list(data["RoofStyle"]))
dataX_train.RoofMatl = le.fit_transform(list(data["RoofMatl"]))
dataX_train.Exterior1st = le.fit_transform(list(data["Exterior1st"]))
dataX_train.Exterior2nd = le.fit_transform(list(data["Exterior2nd"]))
dataX_train.MasVnrType = le.fit_transform(list(data["MasVnrType"]))
dataX_train.ExterQual = le.fit_transform(list(data["ExterQual"]))
dataX_train.ExterCond = le.fit_transform(list(data["ExterCond"]))
dataX_train.Foundation = le.fit_transform(list(data["Foundation"]))
dataX_train.BsmtQual = le.fit_transform(list(data["BsmtQual"]))
dataX_train.BsmtExposure = le.fit_transform(list(data["BsmtExposure"]))
dataX_train.BsmtFinType1 = le.fit_transform(list(data["BsmtFinType1"]))
dataX_train.BsmtFinType2 = le.fit_transform(list(data["BsmtFinType2"]))
dataX_train.Heating = le.fit_transform(list(data["Heating"]))
dataX_train.HeatingQC = le.fit_transform(list(data["HeatingQC"]))
dataX_train.CentralAir = le.fit_transform(list(data["CentralAir"]))
dataX_train.Electrical = le.fit_transform(list(data["Electrical"]))
dataX_train.KitchenQual = le.fit_transform(list(data["KitchenQual"]))
dataX_train.Functional = le.fit_transform(list(data["Functional"]))
dataX_train.FireplaceQu = le.fit_transform(list(data["FireplaceQu"]))
dataX_train.GarageType = le.fit_transform(list(data["GarageType"]))
dataX_train.GarageFinish = le.fit_transform(list(data["GarageFinish"]))
dataX_train.GarageQual = le.fit_transform(list(data["GarageQual"]))
dataX_train.GarageCond = le.fit_transform(list(data["GarageCond"]))
dataX_train.PavedDrive = le.fit_transform(list(data["PavedDrive"]))
dataX_train.PoolQC = le.fit_transform(list(data["PoolQC"]))
dataX_train.Fence = le.fit_transform(list(data["Fence"]))
dataX_train.MiscFeature = le.fit_transform(list(data["MiscFeature"]))
dataX_train.SaleType = le.fit_transform(list(data["SaleType"]))
dataX_train.SaleCondition = le.fit_transform(list(data["SaleCondition"]))
best = 0
x_train, x_test, y_train, y_test = model_selection.train_test_split(dataX_train, dataX_test,
test_size = 0.2)
clf = linear_model.LinearRegression()
clf.fit(x_train, y_train)
acc = clf.score(x_test, y_test)
if acc > best:
best = acc
with open("housingmodel.pickle", "wb") as f:
pickle.dump(clf , f)
print(acc)