housing price

最新推荐文章于 2025-03-14 16:36:18 发布

u010016056

最新推荐文章于 2025-03-14 16:36:18 发布

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本文链接：https://blog.youkuaiyun.com/u010016056/article/details/80601536

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该博客探讨了房价预测问题的数据预处理过程，包括数据读取、特征工程、缺失值处理、数据可视化和数据划分。使用StratifiedShuffleSplit进行分层抽样，并通过Scikit-Learn库进行了数值特征的标准化、分类特征的独热编码。接着，建立了线性回归和决策树模型，计算了预测误差，如RMSE和MAE。最后，进行了交叉验证以评估决策树模型的性能，并使用GridSearchCV进行参数调优。

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

###################Get data#######################

house_data = pd.read_csv("./housing.csv")

# Divide by 1.5 to limit the number of income categories
house_data["income_cat"] = np.ceil(house_data["median_income"] / 1.5)
# Label those above 5 as 5
house_data["income_cat"].where(house_data["income_cat"] < 5, 5.0, inplace=True)

from sklearn.model_selection import StratifiedShuffleSplit

split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(house_data, house_data["income_cat"]):
    strat_train_set = house_data.loc[train_index]
    strat_test_set = house_data.loc[test_index]

# from sklearn.model_selection import train_test_split
# train_set, test_set = train_test_split(house_data, test_size=0.2, random_state=16)

strat_train_set = strat_train_set.drop("income_cat",axis =1)
strat_test_set = strat_test_set.drop("income_cat",axis =1)
house_data = strat_train_set.copy()


###############Visualize########################

house_data.hist(bins=50, figsize=(15, 10))
house_data.plot(kind="scatter", x="longitude", y="latitude", alpha=0.1)
house_data.plot(kind="scatter", x="longitude", y="latitude", alpha=0.3,
                s=house_data["population"] / 100, label="population",
                c=house_data["median_house_value"], cmap=plt.get_cmap("jet"), colorbar=True,
                )
plt.legend()
#plt.show()

corr_matrix = house_data.corr()
a = corr_matrix["median_house_value"].sort_values(ascending=False)

house_data["rooms_per_households"] = house_data["total_rooms"] / house_data["households"]
house_data["bedrooms_per_room"] = house_data["total_bedrooms"] / house_data["total_rooms"]
house_data["population_per_households"] = house_data["population"] / house_data["households"]

#print(house_data.info())

corr_matrix = house_data.corr()
b = corr_matrix["median_house_value"].sort_values(ascending=False)
#print(b)


###################Prepare data####################

house_data = strat_train_set.drop("median_house_value", axis

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