Dataset

This dataset was created by Regi

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Dataset

This dataset was created by Fatih Öztürk

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Dataset

This dataset was created by Fatih Ozturk

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This is the classic Titanic Dataset provided in the Kaggle Competition K Kernel and then cleaned in one of the most popular Kernels there. Please see the Kernel titled, "A Data Science Framework: To Achieve 99% Accuracy" for a great lesson in data science. This Kernel gives a great explanaton of the thinking behind the of this data cleaning as well as a very professional demonstration of the technologies and skills to do so. It then continues to provide an overview of many ML techniques and it is copiously and meticulously documented with many useful citations.

Of course, data cleaning is an essential skill in data science but I wanted to use this data for a study of other machine learning techniques. So, I found and used this set of data that is well known and cleaned to a benchmark accepted by many.

Dataset

This dataset was created by deeplearner

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Context

The data is from: https://github.com/opencv/opencv/tree/master/data/haarcascades, included here for easy usage with OpenCV from Kaggle Kernels.

Content

Haar Cascades for Face, Profile, Eyes, Smile, Upper Body Detection.

Acknowledgements

The copyright belongs to the authors as mentioned in each file. Data source is from: https://github.com/opencv/opencv/tree/master/data/haarcascades

Inspiration

Use this data to extract persons face, eyes, smile, profile face, upper body from both still images and videos.

Dataset

This dataset was created by Zafar

Released under CC0: Public Domain

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Prediction files

Dataset

This dataset was created by YuanXu

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Dataset

This dataset was created by Darien Schettler

Released under CC0: Public Domain

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Dataset

This dataset was created by luyaxin

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Dataset

This dataset was created by Maksim Filin

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Dataset

This dataset was created by My Ho63

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Dataset

This dataset was created by Ogün Can Kaya

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Dataset

This dataset was created by Vijayabhaskar J

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Context

This dataset contains submissions (and scores) obtained from well performed kernels published in https://www.kaggle.com/c/cat-in-the-dat competition.

Links are in 'kernels.csv' file.

Regards to great authors!

Dataset

This dataset was created by HowYouDoin'

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Pictures for my kernel https://www.kaggle.com/hocop1/unet-character-detector

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SGEMM GPU kernel performance Dataset

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The data set is of SGEMM GPU kernel performance which consists of 14 features and 241600 records. This data set measures the running time of a matrix-matrix product A*B = C, where all matrices have size 2048 x 2048, using a parameterizable SGEMM GPU kernel with 261400 possible parameter combinations. Out of 14 features, the first 10 are ordinal and can only take up to 4 different powers of two values, and the 4 last variables are binary.

Acknowledgements

https://archive.ics.uci.edu/ml/datasets/SGEMM+GPU+kernel+performance

Context

Experiment to apply same strategy from Beluga's Keras dataset with PyTorch models. This dataset has the weights for several of the models included in PyTorch. To use these weights they need to be copied when the kernel runs, like in this example.

Content

PyTorch models included:

• Inception-V3

• ResNet18

• ResNet50

Acknowledgements

Beluga's Keras dataset PyTorch

This dataset was used for the Winter 2018 Kaggle University Club Hackathon and is now publicly available. See Acknowledgments section for citation and licensing. Note: The types of data and recommendation based solutions provided by the contestants are purely for NLP learning purposes. They are not suitable for a real world drug recommendations solutions.

Welcome to the Kaggle University Club Hackathon!

If you are interested in joining Kaggle University Club, please e-mail Jessica Li at lijessica@google.com

This Hackathon is open to all undergraduate, master, and PhD students who are part of the Kaggle University Club program. The Hackathon provides students with a chance to build capacity via hands-on ML, learn from one another, and engage in a self-defined project that is meaningful to their careers.

Teams must register via Google Form to be eligible for the Hackathon. The Hackathon starts on Monday, November 12, 2018 and ends on Monday, December 10, 2018. Teams have one month to work on a team submission. Teams must do all work within the Kernel editor and set Kernel(s) to public at all times.

Prompt

The freestyle format of hackathons has time and again stimulated groundbreaking and innovative data insights and technologies. The Kaggle University Club Hackathon recreates this environment virtually on our platform. We challenge you to build a meaningful project around the UCI Machine Learning - Drug Review Dataset. Teams are free to let their creativity run and propose methods to analyze this dataset and form interesting machine learning models.

Machine learning has permeated nearly all fields and disciplines of study. One hot topic is using natural language processing and sentiment analysis to identify, extract, and make use of subjective information. The UCI ML Drug Review dataset provides patient reviews on specific drugs along with related conditions and a 10-star patient rating system reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. This data was published in a study on sentiment analysis of drug experience over multiple facets, ex. sentiments learned on specific aspects such as effectiveness and side effects (see the acknowledgments section to learn more).

The sky's the limit here in terms of what your team can do! Teams are free to add supplementary datasets in conjunction with the drug review dataset in their Kernel. Discussion is highly encouraged within the forum and Slack so everyone can learn from their peers.

Here are just a couple ideas as to what you could do with the data:

• Classification: Can you predict the patient's condition based on the review?
• Regression: Can you predict the rating of the drug based on the review?
• Sentiment analysis: What elements of a review make it more helpful to others? Which patients tend to have more negative reviews? Can you determine if a review is positive, neutral, or negative?
• Data visualizations: What kind of drugs are there? What sorts of conditions do these patients have?

Top Submissions

There is no one correct answer to this Hackathon, and teams are free to define the direction of their own project. That being said, there are certain core elements generally found across all outstanding Kernels on the Kaggle platform. The best Kernels are:

1. Complex: How many domains of analysis and topics does this Kernel cover? Does it attempt machine learning methods? Does the Kernel offer a variety of unique analyses and interesting conclusions or solutions?
2. Original: What is the subject matter of this Kernel? Does it have a well-defined and interesting project scope, narrative or problem? Could the results make an impact? Is it thought provoking?
3. Approachable: How easy is it to understand this Kernel? Are all thought processes clear? Is the code clean, with useful comments? Are visualizations and processes articulated and self-explanatory?

Teams with top submissions have a chance to receive exclusive Kaggle University Club swag and be featured on our official blog and across social media.

IMPORTANT: Teams must set all Kernels to public at all times. This is so we can track each team's progression, but more importantly it encourages collaboration, productive discussion, and healthy inspiration to all teams. It is not so that teams can simply copycat good ideas. If a team's Kernel isn't their own organic work, it will not be considered a top submission. Teams must come up with a project on their own.

Submission Styling

The final Kernel submission for the Hackathon must contain the following information:

• All team members added as collaborators to the Kernel
• Somewhere at the top of your Kernel, find a space to put down all team member names, university name, club name, and team name (as specified whe...