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...
特征探索:https://www.kaggle.com/elcorazonzhao/house-prices-model
cmap="YlGnBu",cbar=True,annot=True,square=True,fmt='.2f',annot_kws={'size':10},yticklabels=cols.values,xticklabels=cols.values)plt.show()k=10#number of variables for heatmapcols=corrmat.nlargest(k,'SalePrice')['SalePrice'].index...
%matplotlib inlineimportnumpyasnpimportpandasaspdimporttorchfromtorchimportnnfromd2limporttorchasd2lfromtorch.utilsimportdatafrommatplotlibimportpyplotasplt#超参数调整区域batch_size=32learning_rate=0.001weight_decay=0num_epochs=200k=5# 我这里就直接读入了train_data=pd.read_csv('../data/kaggle_house_pred_...
Kaggle: House Prices: Advanced Regression Techniques notebook来自https://www.kaggle.com/neviadomski/how-to-get-to-top-25-with-simple-model-sklearn 思路流程: 1.导入数据,查看数据结构和缺失值情况 重点在于查看缺失值情况的写法: NAs = pd.concat([train.isnull().sum(), test.isnull().sum()],...
dataset - 原始数据文件,包括以下四个文件 train.csv test.csv data_description.txt sample_submission.csv EDA.ipynb - 特征工程代码 House_Price_Forecast_Model.ipynb - 模型预测代码 test_params.ipynb - 模型调参代码 https://www.kaggle.com/c/house-prices-advanced-regression-techniques ...
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...
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...
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 number of bedrooms or a white-picket fence. ...
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,对变量进行汇总,分析,作图 3,初步探索性分析,有聚类,因子,主成分分析 ...