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Join date: May 13, 2022

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Kaggle Notebooks is a new data exploration tool that integrates with Kaggle. Kaggle is a global platform that supports competitions, where people run machine learning code. Kaggle Notebooks is free and open-source and supports Kaggle’s Notebook format. We’re launching Kaggle Notebooks on Google Cloud Platform, with an initial set of notebooks that are available at no cost. We’re also starting with data from Kaggle competitions that are designed to require machine learning techniques, such as the Kaggle Competition: Predicting Medical Conditions. For example, how can we classify dogs with cancers? Is there a way to classify illegal searches? We’ve released Kaggle Notebooks, the notebook engine that runs on Kaggle Notebooks, open source to the world. As of now, the engine supports notebooks that can import data from Google Cloud, or we can use a notebook to start fresh. This is a preview, we expect to evolve the tool and improve our documentation, so expect some rough edges. This is a preview, we expect to evolve the tool and improve our documentation, so expect some rough edges. If you have any feedback or have any questions, please let us know! Introduction | The Kaggle Competition: Predicting Medical Conditions Machine learning is an approach to predictive analytics in which computers learn from data to make predictions about the future. In this challenge, you’ll use data that includes diagnostic information and also test results from oncology patients to develop models to predict the most common medical conditions. Data & Preprocessing | Data Sources What data are we using? We’re working with the full data from the Kaggle Competition: Predicting Medical Conditions and also some of the cleaned data provided by the Kaggle Competition Editor. Note: This challenge is designed to use the data that you get in the Kaggle Competition Editor. If you want to download the full data, the competition publishes the data as JSONs with unique filenames. We’re going to preprocess the data as described below, and then run some data exploration and development work with this preprocessed data. In this case, we’re going to import all the JSON files as HDF5 files. If you don’t have these files, you can download the full data from the Kaggle competition in JSON format. Before importing data, we need to

 

 

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