House Prices analysis Using R--浅析 通过Kaggle 平台,真的能有效的提高自学效率。 首先是数据来源: TheAmes Housing dataset(kaggle)was compiled by Dean De Cock for use in data science education.(载自 Kaggle 平台) 拿到数据我要做的有: 1,查看缺失值并极大可能的消减缺失值对分析的影响 2,对变量进行...
Kaggle入门:House Prices Prediction 1、Data Description Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But thisdatasetproves that much more influences price negotiations than the numb...
In addition to these data, we validated our algorithm on the KAGGLE house dataset, which covers a wide range of features. The results of the hybrid algorithm were compared using multiple linear regression, Lasso, ridge regression, Support Vector Regression (SVR), AdaBoost, decision tree, random...
#Kaggle: House Prices: Advanced Regression Techniquesimportnumpy as npimportpandas as pdimportmatplotlib.pyplot as pltimportseaborn as snsfromsklearnimportensemble, linear_model, treefromsklearn.model_selectionimporttrain_test_split, cross_val_scorefromsklearn.metricsimportmean_squared_error, r2_scorefrom...
kaggle 竞赛入门 导入常用的数据分析以及模型的库 数据处理 Data fields 去除异常值 处理缺失值 分析 Utilities Exploratory Data Analysis Correlation matrix BsmtQual BsmtCond BsmtExplosure Bsm
The housing dataset was loaded via Colab. The dataset is from Kaggle:https://www.kaggle.com/datasets/muhammadbinimran/housing-price-prediction-data(also please see housing_price_dataset.csv attached). Basic data analysis was performed to identify the shape of data, get column names, find missing...
kaggle_house dataset
read_csv('E:\kaggleDataset\\train(1).csv') figure=plt.figure() sns.pairplot(x_vars=['OverallQual','GrLivArea','YearBuilt','TotalBsmtSF'],y_vars=['SalePrice'],data=train_data,dropna=True) plt.show() 通过散点图的方式可以观察到一些可疑的异常值 对于GrLivArea与SalePrice的关系图,有两...
最终,使用StackingModel在SubmitDataset上Score=0.12948。 七,小优化 在数据处理之前,进行异常值处理(EllipticEnvelope): def remove_outlier(x): lst = [] lst1 = list(x.columns) lst1.remove('Id') lst1.remove('MSSubClass') lst1.remove('OverallQual') ...
importosfordirname,_,filenamesinos.walk('/kaggle/input'):forfilenameinfilenames:print(os.path.join(dirname,filename)) # 使用jupyter notebook如果缺少库一般可以直接使用如下的形式安装!pip install seaborn!pip install sklearn!pip install xgboostimportwarningsimportnumpyasnpimportpandasaspdimportseabornass...