#boxplot YearBuilt/salepricevar='YearBuilt'data=pd.concat([df_train['SalePrice'],df_train[var]],axis=1)f,ax=plt.subplots(figsize=(16,8))fig=sns.boxplot(x=var,y="SalePrice",data=data,palette=sns.color_palette("hls",8))fig.axis(ymin=0,ymax=800000)plt.xticks(rotation=90)(array(...
Kaggle-数据分析竞赛:House Price Prediction官网链接 参赛情况 参赛时间 2020-05 最终结果(均方根误差RMSE) 0.115 竞赛排名 前10% 项目python代码notebook https://www.kaggle.com/yzh094/my-house-p
一、数据背景项目数据来源于kaggle,为House Prices Prediction.这是一份用于回归预测的数据集。其目的是利用数据集中的特征数据,来预测房屋的销售价格(SalePrice)。评判规则为均方根误差… 励志小葵 Python数据分析-房价的影响因素图解 Ofter...发表于Ofter... 2分钟学会Weka的线性回归模型来预测房价 魔法纽扣发表于伪...
Kaggle竞赛 —— 房价预测 (House Prices) 完整代码见kaggle kernel或Github 比赛页面:https://www.kaggle.com/c/house-prices-advanced-regression-techniques 这个比赛总的情况就是给你79个特征然后根据这些预测房价 (SalePrice),这其中既有离散型也有连续性特征,而且存在大量的缺失值。不过好在比赛方提供了data_des...
在kaggle上找了另外一个大佬的Solution,和我的差不多,但是别人的够专业。 回归问题还是要看看label的分布是什么: sns.set_style("white")sns.set_color_codes(palette='deep')f,ax=plt.subplots(figsize=(8,7))sns.distplot(train['SalePrice'],color='b')ax.xaxis.grid(False)ax.set(ylabel='Frequency...
Kaggle 竞赛——房价预测（HousePrices ）完整代码见 或 这个⽐赛总的情况就是给你79个特征然后根据这些预测房价 (SalePrice)，这其中既有离散型也有连续性特征，⽽且存在⼤量的缺失值。不过好在⽐赛⽅提供了data_description.txt 这个⽂件，⾥⾯对各个特征的含义进⾏了描述，理解了其中内容后对于...
Kaggle: House Price Prediction Data Processing and Feature Extration Approchs Trial 1: Droped 'Id'. One hot encoded all none neumerical features. Replace all none neumerical features Nan with 'None' and one hot encoded them. Filled all neumerical data Nan with means of the column. Schewe...
House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric...
如上可以看出有38个数值型特征,本项目需计算各特征与目标值“SalePrice”之间的逻辑关系,这属于监督学习中的回归问题,可以使用线性回归或者岭回归进一步分析。 运用可视化方法观察几个特征与目标值的线性关系: importseabornassnsg=sns.pairplot(x_vars=['OverallQual','GrLivArea','YearBuilt','TotalBsmtSF'],y_va...
sns.distplot(train['SalePrice'],fit=norm)# Get the fitted parameters used by the function(mu,sigma)=norm.fit(train['SalePrice'])print('\nmu = {:.2f} and sigma = {:.2f}\n'.format(mu,sigma))#Now plot the distributionplt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$...