DATA_HUB['kaggle_house_train'] = ( #@save DATA_URL + 'kaggle_house_pred_train.csv', '585e9cc93e70b39160e7921475f9bcd7d31219ce') DATA_HUB['kaggle_house_test'] = ( #@save DATA_URL + 'kaggle_house_pred_test.csv', 'fa19780a7b011d9b009e8bff8e99922a8ee2eb90') 当然,在下载后使...
We can make new features from existing data in the dataset to capture some trends in the data that might not be explicit. This makes the already existing data more useful. For example, adding a new feature that indicates the total square feet of the house is important as a house with a ...
flatten()) del model, state, prediction gc.collect() torch.cuda.empty_cache() # predictions1 = np.mean(predictions, axis=0) # fea_df = pd.DataFrame(predictions).T # fea_df.columns = [f"{CFG.model.split('/')[-1]}_fold{fold}" for fold in CFG.trn_fold] # del test_dataset, ...
近年来,不论是传统行业还是互联网行业,都面临着用户流失问题。一般在银行、电话服务公司、互联网公司、保险等公司,经常使用客户流失分析和客户流失率作为他们的关键性业务指标之一。
In the context of feature engineering for prediction,you could think of an unsupervised algorithm as a "feature discovery" technique. Clustering 聚类,简单理解,就是将“距离足够近”的点归为一类。(此处的距离不一定是几何上的距离,需要看标准是什么) ...
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()],...
pythonanalysiskagglekaggle-titanickaggle-competitiondata-analysiskaggle-scriptskaggle-house-priceskaggle-datasetkaggle-solutionkaggle-notebookskaggle-kernelkaggle-kernelskaggle-notebookkaggle-knowledge UpdatedOct 24, 2024 Jupyter Notebook Kaggle Computer Vision answers ...
특히Titanic: Machine Learning from Disaster,House Prices: Advanced Regression Techniques,Digit Recognizer이 3가지 Competition은 머신러닝에 입문한 분들에게 가장 많이 추천되고 도움이 되는 Competition들입니다. ...
对于LightGBM解决回归问题,我们用Kaggle比赛中回归问题:House Prices: Advanced Regression Techniques,地址:https://www.kaggle.com/c/house-prices-advanced-regression-techniques 来进行实例讲解。 该房价预测的训练数据集中一共有列,第一列是Id,最后一列是label,中间列是特征。这列特征中,有列是分类型变量,列是整...
) DATA_HUB['kaggle_house_test'] = ( #@save DATA_URL + 'kaggle_house_pred_test.csv...