Context

A While back I was going through the Discussion section of Kaggle here. and whilst looking at the discussions with various number of upvotes, a question was seeded inside my brain that would eventually stay there rent free for about 2 weeks. "What makes a good discussion" or specifically is there a conceivable pattern that can explain which discussion would be upvoted more often than others.

And such is the bug of curiosity that I have to feed, I ended up scrapping top 2000(Based on Upvotes) Discussion to analyze and play with.

This dataset contains any information I thought could factor into popularity of a Discussion from the point of view of both the users and algorithms of kaggle.

If there is anyone out there with the same question lingering in their mind, this data is for you.

Content

The dataset contains information from the discussion itself like title, number of comments, elapsed time, upvotes as well as information about the author of the said discussion like author tier, medals, author discussion tier.

What the dataset doesn't have is the actual text of the discussion, That is because, The aim is to find things that attracts the reader to open a discussion rather than liking it, at least for now. Incase you want to try something with the full text, I have included the links to scrapped discussion you can just scrape the full text yourself and add it in for further analyses.

Format: CSV; Kaggle_Getting_Started_Discussion_2k.csv

Columns:

1. Author_Name: Name of the Author of the discussion\
2. Title: Title of the discussion
3. Elapsed_Time: Time elapsed from when the discussion was published
4. Num_comments: Number of comments on the discussion
5. Number_of_Upvotes: Number of upvotes
6. Author_Tier: The highest tier of the author
7. Author_Discussion_Tier: The Discussion tier of the author
8. Num_Followers: Number of followers of the author
9. Discussion_Golds: Discussion gold medal gained by the author
10. Discussion_Silvers: Discussion silver medal gained by the author
11. Discussion_Bronze: Discussion bronze medal gained by the author
12. Current_Discussion_Rank: The current discussion rank of the author
13. Highest_Discussion_Rank: The highest discussion rank of the author
14. Discussion_link: Link to the discussion

Inspiration

Lets find what makes the kaggle discussion forum tick.

Dataset Summary

Natural Language Processing with Disaster Tweets: https://www.kaggle.com/competitions/nlp-getting-started/data This particular challenge is perfect for data scientists looking to get started with Natural Language Processing. The competition dataset is not too big, and even if you don’t have much personal computing power, you can do all of the work in our free, no-setup, Jupyter Notebooks environment called Kaggle Notebooks.

Columns

id - a unique identifier for each tweet… See the full description on the dataset page: https://huggingface.co/datasets/gdwangh/kaggle-nlp-getting-start.

Dataset

This dataset was created by Togae Noh

Contents

Dataset

This dataset was created by DanB

Contents

Kaggle

## Overview

Kaggle is a dataset for object detection tasks - it contains K annotations for 779 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Kaggle Annotation

## Overview

Kaggle Annotation is a dataset for object detection tasks - it contains Objects annotations for 965 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

Kaggle Fish Detection

## Overview

Kaggle Fish Detection is a dataset for instance segmentation tasks - it contains Fish annotations for 2,208 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Kaggle Dataset Conversion

## Overview

Kaggle Dataset Conversion is a dataset for object detection tasks - it contains Cars Swimminpool annotations for 3,744 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Iranian Plate From Kaggle

## Overview

Iranian Plate From Kaggle is a dataset for object detection tasks - it contains Plate REgl annotations for 433 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Kaggle Wheat Detection Dataset

## Overview

Kaggle Wheat Detection Dataset is a dataset for object detection tasks - it contains Wheat annotations for 3,373 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

If you need a practice dataset to improve your skills then use this dataset. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2681031%2F9dfecc01e0d719b732e69389b592de91%2Fpython%20panda2.jpg?generation=1599246568164860&alt=media" alt="">

I created this dataset for my notebook Getting started with Dictionary and Pandas. To help people improve their dictionary and panda skills. https://www.kaggle.com/brendan45774/getting-started-with-dictionary-and-pandas

KAGGLE

## Overview

KAGGLE is a dataset for object detection tasks - it contains Vest Helmet annotations for 2,696 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Mask Kaggle

## Overview

Mask Kaggle is a dataset for object detection tasks - it contains Faces annotations for 848 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Tomato Kaggle

## Overview

Tomato Kaggle is a dataset for object detection tasks - it contains Tomatoes annotations for 895 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Shwetank Chaudhary

Contents

Daun Jagung (kaggle)

## Overview

Daun Jagung (kaggle) is a dataset for object detection tasks - it contains Objects DoCx annotations for 2,690 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Potholes Kaggle

## Overview

Potholes Kaggle is a dataset for object detection tasks - it contains Pothole annotations for 647 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Labeled Licence Plates Dataset Kaggle

## Overview

Labeled Licence Plates Dataset  Kaggle is a dataset for object detection tasks - it contains Number Plate annotations for 702 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Dataset

This dataset was created by Maxim Kosarev

Released under Apache 2.0

Contents