preprocessing

最新推荐文章于 2024-01-09 00:24:34 发布

u010016056

最新推荐文章于 2024-01-09 00:24:34 发布

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本文链接：https://blog.youkuaiyun.com/u010016056/article/details/80275153

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本文通过使用Pandas、Matplotlib等库对房价数据集进行读取、清洗及可视化展示，包括直方图绘制、散点图绘制、相关性分析等步骤，并对缺失值进行了填充处理。

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

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

# import pandas.plotting.scatter_matrix as scatter_matrix
# attributes = ["median_house_value","median_income","total_rooms","housing_median_age"]
# scatter_matrix(house_data[attributes],figsize = (10,6))

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


from sklearn.preprocessing import  Imputer

imputer = Imputer(strategy="median")
house_num = house_data.drop("ocean_proximity",axis=1)
imputer.fit(house_num)
X = imputer.transform(house_num)
house_tr = pd.DataFrame(X,columns=house_num.columns,index=list(house_data.index.values))
print(house_tr.info())

house_cat = house_data["ocean_proximity"]
a = house_cat.value_counts()
print(a)

house_cat_encoded,house_cat_categories = house_cat.factorize()
print(house_cat_encoded)
print(house_cat_encoded.shape)

from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
house_cat_onehot = encoder.fit_transform(house_cat_encoded.reshape(-1,1))
print(house_cat_onehot)
print(house_cat_onehot.shape)

from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
house_cat_onehot1 = encoder.fit_transform(house_cat)
print(house_cat_onehot1)
print(house_cat_onehot1.shape)

from sklearn.preprocessing import StandardScaler
std_scaler = StandardScaler()
std = std_scaler.fit_transform(house_tr)
print(std)