做网站需要掌握的软件,制作官网的公司推荐,天津制作网站,wordpress登陆可见大家好#xff0c;我是带我去滑雪#xff0c;每天教你一个小技巧#xff01; 房价数据集通常包含各种各样的特征#xff0c;如房屋面积、地理位置、建造年份等。通过对数据进行处理和分析#xff0c;可以更好地理解这些特征之间的关系#xff0c;以及它们对房价的影响程度… 大家好我是带我去滑雪每天教你一个小技巧 房价数据集通常包含各种各样的特征如房屋面积、地理位置、建造年份等。通过对数据进行处理和分析可以更好地理解这些特征之间的关系以及它们对房价的影响程度。这有助于确定哪些特征是最重要的从而更有针对性地制定房地产策略。本次使用波士顿房价数据集boston_housing_data.csv该数据集有城镇人均犯罪率CRIM、住宅用地所占比例ZN、城镇中非住宅用地所占比例INDUS等共计13个特征变量响应变量为社区房价中位数MEDV。实现对房价数据进行可视化和统计分析如绘制直方图、密度图、箱线图以及查看各个散点图的分布最后使用支持向量机和KNN等几种机器学习方法进行学习。下面开始实战。
1导入相关模块
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from numpy import arange
from matplotlib import pyplot
from pandas import read_csv
from pandas import set_option
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import ElasticNet
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.metrics import mean_squared_error
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression
2导入数据并进行可视化分析
def testHouse():data pd.read_csv(house_data.csv)set_option(display.column_space, 120)print(data.shape)print(data.isnull().any().sum())prices data[MEDV]features data.drop(MEDV, axis1)# 直方图data.hist(sharexFalse, shareyFalse, xlabelsize1, ylabelsize1)pyplot.show()# 密度图data.plot(kinddensity, subplotsTrue, layout(4, 4), sharexFalse, fontsize1)pyplot.show()# 箱线图data.plot(kindbox, subplotsTrue, layout(4, 4), sharexFalse, shareyFalse, fontsize8)pyplot.show()# 查看各个特征的散点分布scatter_matrix(data, alpha0.7, figsize(10, 10), diagonalkde)pyplot.show()# Heatmap
testHouse()
结果展示
绘制房价数据的直方图 绘制房价数据的密度图 绘制 房价数据的箱线图 查看房价数据各个特征的散点分布 3使用支持向量机和KNN等机器学习方法学习
def featureSelection():data pd.read_csv(house_data.csv)x data[[CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX,PTRATIO, B, LSTAT]]# print(x.head())y data[MEDV]from sklearn.feature_selection import SelectKBestSelectKBest SelectKBest(f_regression, k3)bestFeature SelectKBest.fit_transform(x, y)SelectKBest.get_support(indicesFalse)# print(SelectKBest.transform(x))print(x.columns[SelectKBest.get_support(indicesFalse)])features data[[RM, PTRATIO, LSTAT]].copy()from sklearn.preprocessing import MinMaxScalerscaler MinMaxScaler()for feature in features.columns:features.loc[:, 标准化 feature] scaler.fit_transform(features[[feature]])# 散点可视化查看特征归一化后的数据font {family: SimHei}x_train, x_test, y_train, y_test train_test_split(features[[标准化RM, 标准化PTRATIO, 标准化LSTAT]], y,test_size0.3, random_state33)import warningswarnings.filterwarnings(actionignore, modulescipy, message^internal gelsd) #过滤告警lr LinearRegression()lr_predict cross_val_predict(lr, x_train, y_train, cv5)lr_score cross_val_score(lr, x_train, y_train, cv5)lr_meanscore lr_score.mean()#SVRfrom sklearn.svm import SVRlinear_svr SVR(kernel linear)linear_svr_predict cross_val_predict(linear_svr, x_train, y_train, cv5)linear_svr_score cross_val_score(linear_svr, x_train, y_train, cv5)linear_svr_meanscore linear_svr_score.mean()poly_svr SVR(kernel poly)poly_svr_predict cross_val_predict(poly_svr, x_train, y_train, cv5)poly_svr_score cross_val_score(poly_svr, x_train, y_train, cv5)poly_svr_meanscore poly_svr_score.mean()rbf_svr SVR(kernel rbf)rbf_svr_predict cross_val_predict(rbf_svr, x_train, y_train, cv5)rbf_svr_score cross_val_score(rbf_svr, x_train, y_train, cv5)rbf_svr_meanscore rbf_svr_score.mean()knn KNeighborsRegressor(2, weightsuniform)knn_predict cross_val_predict(knn, x_train, y_train, cv5)knn_score cross_val_score(knn, x_train, y_train, cv5)knn_meanscore knn_score.mean()dtr DecisionTreeRegressor(max_depth4)dtr_predict cross_val_predict(dtr, x_train, y_train, cv5)dtr_score cross_val_score(dtr, x_train, y_train, cv5)dtr_meanscore dtr_score.mean()evaluating {lr: lr_score,linear_svr: linear_svr_score,poly_svr: poly_svr_score,rbf_svr: rbf_svr_score,knn: knn_score,dtr: dtr_score}evaluating pd.DataFrame(evaluating)print(evaluating)def main():
if __name__ __main__:main()
输出结果 Index([RM, PTRATIO, LSTAT], dtypeobject)lr linear_svr poly_svr rbf_svr knn dtr
0 0.738899 0.632970 0.866308 0.758355 0.806363 0.787402
1 0.755418 0.618558 0.865458 0.772783 0.888141 0.871562
2 0.433104 0.386320 0.569238 0.529242 0.590950 0.545247
3 0.604445 0.554785 0.723299 0.740388 0.728388 0.583349
4 0.793609 0.611882 0.805474 0.736040 0.863620 0.824755 需要数据集的家人们可以去百度网盘永久有效获取
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