<owner>/<dataset-name> (use "kaggle datasets list" to show options), or <owner>/<dataset-name>/<version-number> for a specific version optional arguments: -h, --help show this help message and exit -v, --csv Print results in CSV format (if not set then print in table format) ...
私下共享Dataset,不组队; 一台机器或一个IP登陆多个账号; 别人帮我提交代码或CSV; 非作弊行为 跑公开Notebook,提交; 使用其他人公开的Dataset,提交; 所有Kaggle官网能看到的Notebook和Dataset,都是公开的,都可以使用,都不算作弊。 Kaggle
Step 4. Download the Dataset Once you’ve found a dataset that suits your research needs and complies with its licensing terms, you can download it directly from Kaggle. Most datasets are available in common formats like CSV or JSON. Click the “Download” button to save the dataset to your...
预测数据集(用于提交)full_dataset=CustomDataset(train_csv,root_folder,transform=train_transforms)predict_dataset=CustomDataset(test_csv,root_folder,transform=test_transforms,is_test=True)# 拆分训练数据集 一部分用来训练,另一部分用来验证train_size=int(0.9*len(full_dataset))test_size=len(full_dataset)...
csv-files opendata democracy countries kaggle open-data women gender-equality kaggle-dataset women-empowerment kaggle-notebook suffrage Updated Apr 24, 2024 Load more… Improve this page Add a description, image, and links to the kaggle-notebook topic page so that developers can more easily...
X_sub = data_all.loc[data_sub.index][feature] #提取测试数据特征y_sub = votingC.predict(X_sub) #使用模型预测数据标签result = pd.DataFrame({'PassengerId':data_sub.index,'Survived':y_sub})result.to_csv(r'D:\[DataSet]\1_Titanic\submission.csv', index=False)...
# data = pd.read_csv("../input/riiid-test-answer-prediction/train.csv")Pandas介绍 Pandas是最常用的数据集读取方法,也是Kaggle的默认方法。Pandas功能丰富、使用灵活,可以很好的读取和处理数据。 使用pandas读取大型数据集的挑战之一是其保守性,同时推断数据集列的数据类型会导致pandas dataframe占用大量非必要内存...
_car_free_v1.csv") lm_ali_4 = pd.read_csv("/kaggle/input/llm-dataset/gen_llm_exploring_venus_v1.csv") lm_ali_5 = pd.read_csv("/kaggle/input/llm-dataset/gen_llm_face_on_mars_v1.csv") lm_ali_6 = pd.read_csv("/kaggle/input/llm-dataset/gen_llm_driveless_cars_v1.csv") ...
Essentially, instantiate aKaggleDatasetsobject, and from it search datasets, see their metadata, download the data (automatically caching it in well organized folders), and all from an interface that looks like a humble dict withowner/datasetkeys, and that's the coolest bit. ...
read_csv('../raw/calendar.csv') TARGET = 'sales' # Our main target END_TRAIN = 1941 # Last day in train set MAIN_INDEX = ['id','d'] # We can identify item by these columns ii.创建初始DataFrame 代码首先通过"melt"操作将 train_df 从宽格式转换为长格式,创建了一个名为 grid_df 的...