Hot answers tagged

5

How to fetch Kaggle data from python code? Install kaggle package C:\Users\TalgatHafiz> pip install kaggle login to your Kaggle account click on the icon in the upper right corner -> My Account Scroll down to API section Click "Create New API Token" "kaggle.json" file is created and saved locally Create ".kaggle" dir C:...


3

OpenML has a gallery of different use case examples, including browsing and downloading datasets through python, and running benchmarks: https://openml.github.io/openml-python/master/examples/index.html When you want to benchmark new algorithms, this is the gist: import openml from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import ...


3

Here is some script for "openml" collection of datasets. Hopefully one can provide something similar for other databases. #see docs: https://docs.openml.org/Python-guide/ !pip install openml import openml import numpy as np import pandas as pd import time # Get information on all collection of openml datasets: datalist = openml.datasets....


2

In your case you have 3 options for training your model: Make a class for each card number, so 13 classes. Make a class for 1, 2, 3, king, queen, jack and one bucket class for "other", so 7 classes. Make a class for 1, 2, 3, king, queen, and jack, so 6 classes. And whenever the model is not confident of any, assume it is "other". Best ...


2

Normalizing so that "all the observations have the same importance" is kinda ambiguous and ill-defined. In any case, it would be strongly advised to avoid re-inventing the wheel, and use one of the several scalers available out there (e.g. in the sklearn.preprocessing module). Here is an example using MinMaxScaler, which will re-scale your data in [...


1

Apparently the paper introducing the dataset mentions a list of "online appendices" (table 1 p 363) which seems to contain details about the categories. However I wasn't able to find these appendices in the additional material. [edit] Since it appears that there is no existing source for the original list of topics, I think your best bet is to ...


1

You probably should conduct a missing values analysis to see what is the percentage of missing per column (figure below, from dataprep package) Decide a threshold according to which you may want to completely drop a column or not (depending on how your analysis or model treats nans as well) For the columns that are not dropped, you should impute the missing ...


Only top voted, non community-wiki answers of a minimum length are eligible