data = [train_df, test_df] for dataset in data: dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].mean()) dataset['Fare'] = dataset['Fare'].astype(int) dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0 dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <...
Datasets Explore, analyze, and share quality data.Learn moreabout data types, creating, and collaborating. addNew Dataset search filter_listFilters All datasetsComputer ScienceEducationClassificationComputer VisionNLPData VisualizationPre-Trained Model...
api.dataset_download_files(‘dataset_owner/dataset_name’) Step 5. Understand the Data Before diving into your research, take the time to understand the dataset thoroughly. Review any documentation or metadata provided with the dataset to gain insights into its structure, variables, and any preproc...
and custom objective funciton" and "post-processing".Adversarial validation to estimate difference between distributions of training and test datasetpseudo-labeling: to label some test datasetHyperparameter search with Halving (HalvingGridSearchCV, HalvingRandomSearchCV)Bayesian optimization using scikit-optimi...
dataset/gen_llm_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_...
When usingGithub, you can useKaggleas a convenient place to storeDatasetandNotebook(Free!) It also has the advantage of being able to connectDatasetdirectly toNotebook. There is a capacity limit of up to 20GB perpublic Datasetand up to 20GB total forall private Dataset. ...
kaggle datasets download -d raddar/amex-data-integer-dtypes-parquet-format Note: This might take a while as you can see the file is approx 4GB in size Voila…. you will see your dataset will be downloaded (as a zip file) in your current working directory onto your ...
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. ...
Our problem requires us to predict the sale price of houses – a regression problem. So, the first model that we will be fitting to our dataset is a linear regression model. But the skewness in our target feature poses a problem for a linear model because some values will have an asymmet...
c. viewingData.py -> codes to travel into the dataset Tasks of this week: -Kiven: Get the one-hot matrix for each cell in 'before' and 'after' column using the dictionary provided, as well its label(class). And store them into a numpy file. ...