Publishing your first dataset on Kaggle

While we want to work on a data science and machine learning problem, it is nice when we find out that a dataset that is suitable for solving our desired problem is already available and ready to use on a platform like Kaggle. It makes our life much easier. Collecting data can be sometimes a difficult and slow process. Data is the new gold. By making our datasets public and by promoting an open source thinking among data science and machine learning practitioners we can accelerate the progress that is done in this field. A good place to do so is Kaggle. It is for data scientists what Github is for software developers. If we happen to have collected an interesting dataset dataset, it is good practice to publish it on Kaggle, so that others can use it too. And by doing so, we can increase our reputation on Kaggle, and this may help us in getting a job in the field; this is another benefit of publishing datasets on Kaggle.

Let us get started.


Now, assuming you already have a dataset that you can publish, the first thing you need to do is to create the dataset entry. From your Kaggle homepage, go to the “Data” tab from the left panel:

Image for post
Next, click on “New Dataset” to create your dataset entry:

Image for post

Now, a dialog like this opens where you can give your dataset a name, edit its URL and upload the files:

Image for post

If your dataset is large you can upload an archive and Kaggle will automatically decompress it so that when someone that visits its page, he/she can see individual files in it.

Note the “private” icon in the bottom-right corner of the dialog. When you create a dataset, it is made by default private; so that only you and people you specify can access it. This is the preferred way to create it, and after you add extra information and make sure everything is OK, you make it public. You can also create it directly as public by toggling that private/public button in the dialog.

As an example, I will upload a dataset with Medium articles scraped using Python and Beautiful Soup. If you are interested to see how I collected this data, you can read my previous post here.

#kaggle #data-science #open-source #data #open-data #data analysis

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Publishing your first dataset on Kaggle

Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems

According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.

Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.

To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.

https://twitter.com/asapp/status/1397928363923177472

The dataset is currently available on GitHub.

#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets

Publishing your first dataset on Kaggle

While we want to work on a data science and machine learning problem, it is nice when we find out that a dataset that is suitable for solving our desired problem is already available and ready to use on a platform like Kaggle. It makes our life much easier. Collecting data can be sometimes a difficult and slow process. Data is the new gold. By making our datasets public and by promoting an open source thinking among data science and machine learning practitioners we can accelerate the progress that is done in this field. A good place to do so is Kaggle. It is for data scientists what Github is for software developers. If we happen to have collected an interesting dataset dataset, it is good practice to publish it on Kaggle, so that others can use it too. And by doing so, we can increase our reputation on Kaggle, and this may help us in getting a job in the field; this is another benefit of publishing datasets on Kaggle.

Let us get started.


Now, assuming you already have a dataset that you can publish, the first thing you need to do is to create the dataset entry. From your Kaggle homepage, go to the “Data” tab from the left panel:

Image for post
Next, click on “New Dataset” to create your dataset entry:

Image for post

Now, a dialog like this opens where you can give your dataset a name, edit its URL and upload the files:

Image for post

If your dataset is large you can upload an archive and Kaggle will automatically decompress it so that when someone that visits its page, he/she can see individual files in it.

Note the “private” icon in the bottom-right corner of the dialog. When you create a dataset, it is made by default private; so that only you and people you specify can access it. This is the preferred way to create it, and after you add extra information and make sure everything is OK, you make it public. You can also create it directly as public by toggling that private/public button in the dialog.

As an example, I will upload a dataset with Medium articles scraped using Python and Beautiful Soup. If you are interested to see how I collected this data, you can read my previous post here.

#kaggle #data-science #open-source #data #open-data #data analysis

Dejah  Reinger

Dejah Reinger

1599921480

API-First, Mobile-First, Design-First... How Do I Know Where to Start?

Dear Frustrated,

I understand your frustration and I have some good news and bad news.

Bad News First (First joke!)
  • Stick around another 5-10 years and there will be plenty more firsts to add to your collection!
  • Definitions of these Firsts can vary from expert to expert.
  • You cannot just pick a single first and run with it. No first is an island. You will probably end up using a lot of these…

Good News

While there are a lot of different “first” methodologies out there, some are very similar and have just matured just as our technology stack has.

Here is the first stack I recommend looking at when you are starting a new project:

1. Design-First (Big Picture)

Know the high-level, big-picture view of what you are building. Define the problem you are solving and the requirements to solve it. Are you going to need a Mobile app? Website? Something else?

Have the foresight to realize that whatever you think you will need, it will change in the future. I am not saying design for every possible outcome but use wisdom and listen to your experts.

2. API First

API First means you think of APIs as being in the center of your little universe. APIs run the world and they are the core to every (well, almost every) technical product you put on a user’s phone, computer, watch, tv, etc. If you break this first, you will find yourself in a world of hurt.

Part of this First is the knowledge that you better focus on your API first, before you start looking at your web page, mobile app, etc. If you try to build your mobile app first and then go back and try to create an API that matches the particular needs of that one app, the above world of hurt applies.

Not only this but having a working API will make design/implementation of your mobile app or website MUCH easier!

Another important point to remember. There will most likely be another client that needs what this API is handing out so take that into consideration as well.

3. API Design First and Code-First

I’ve grouped these next two together. Now I know I am going to take a lot of flak for this but hear me out.

Code-First

I agree that you should always design your API first and not just dig into building it, However, code is a legitimate design tool, in the right hands. Not everyone wants to use some WYSIWYG tool that may or may not take add eons to your learning curve and timetable. Good Architects (and I mean GOOD!) can design out an API in a fraction of the time it takes to use some API design tools. I am NOT saying everyone should do this but don’t rule out Code-First because it has the word “Code” in it.

You have to know where to stop though.

Designing your API with code means you are doing design-only. You still have to work with the technical and non-technical members of your team to ensure that your API solves your business problem and is the best solution. If you can’t translate your code-design into some visual format that everyone can see and understand, DON’T use code.

#devops #integration #code first #design first #api first #api

Riiid Announces $100,000 Kaggle Competition Using EdNet- World’s Largest Education Dataset

Riiid Labs has announced the launch of the first-ever global Artificial Intelligence Education (AIEd) Challenge, created to accelerate innovation in education by building a better and more equitable learning model for students around the world.

Read more: https://analyticsindiamag.com/riiid-announces-100000-kaggle-competition-using-ednet-worlds-largest-education-dataset/

#edtech #kaggle #competition #artificial-intelligence #dataset #machine-learning

Submitted Solution for Kaggle COVID-19 Open Research Dataset Challenge (CORD-19)

This post describes the solution that was submitted for the Kaggle CORD-19 competition.
Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals and the opinions of this article should not be interpreted as professional advice.

#kaggle-competition #nlp #covid19 #kaggle #azure-search #azure