Dataset Description: 200 Agile Software Projects Overview This dataset contains records of 200 Agile software development projects. It includes various performance metrics related to Agile methodologies, measuring their effectiveness in project success, risk mitigation, time efficiency, and cost savings. The dataset is designed for analysis of AI-driven automation in Agile software teams.

Dataset Variables Agile Effectiveness (Likert scale: 2 to 5)

1. Measures how well Agile methodologies enhance project management processes. Risk Mitigation (Likert scale: 2 to 5)

2. Captures the effectiveness of Agile in identifying and reducing risks throughout the project lifecycle. Management Satisfaction (Likert scale: 2 to 5)

3. Represents how satisfied management is with the outcomes of Agile-implemented projects. Supply Chain Improvement (Likert scale: 2 to 5)

4. Evaluates the impact of Agile practices on optimizing supply chain processes. Time Efficiency (Likert scale: 2 to 5)

5. Measures improvements in time management within Agile projects. Cost Savings (%) (Range: 10% to 48%)

6. Quantifies the percentage of cost savings achieved due to Agile methodologies. Project Success (Binary: 0 = Failure, 1 = Success)

Indicates whether the project was considered successful. Usage This dataset is useful for: ✅ Evaluating the impact of AI automation on Agile workflows. ✅ Understanding factors contributing to Agile project success. ✅ Analyzing cost savings and efficiency improvements in Agile teams. ✅ Building machine learning models to predict project success based on Agile metrics.

Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records, and the treatment of chronic diseases.

MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.

The Process:

MedCamp employees / volunteers reach out to people and drive registrations.
During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.

Other things to note:

Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
For a few camps, there was hardware failure, so some information about date and time of registration is lost.
MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides  
information about several health issues through various awareness stalls.

Favorable outcome:

For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
You need to predict the chances (probability) of having a favourable outcome.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train
Test data is for all camps conducted on or after 1st April 2006.

Acknowledgements

Credits to AV

Inspiration

To share with the data science community to jump start their journey in Healthcare Analytics

Alvaro Basily Kaggle Dataset

## Overview

Alvaro Basily Kaggle Dataset is a dataset for object detection tasks - it contains Damaged Roads annotations for 3,321 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).

The BDG2 open data set consists of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. This subset includes data from 2,380 meters from 1,448 buildings that were used in the GEPIII, a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

Car Damages Kaggle

## Overview

Car Damages Kaggle is a dataset for instance segmentation tasks - it contains Car Damages annotations for 814 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).

Resistors Kaggle

## Overview

Resistors Kaggle is a dataset for object detection tasks - it contains Resistor annotations for 1,000 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).

Gun Kaggle

## Overview

Gun Kaggle is a dataset for object detection tasks - it contains Gun Danger annotations for 2,988 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).

DoctorP (doctorp.org) is a multifunctional platform for plant disease detection, designed for use with agricultural and ornamental crops. The platform provides various interfaces, including mobile applications for iOS and Android, a Telegram bot, and an API for seamless integration with external services. Users and services can upload photos of diseased plants to receive predictions and treatment recommendations.

DoctorP supports an extensive range of disease classification models. This dataset features a reduced-scale (128x128) collection of real-life images, comprising over 4,000 samples across 68 classes of plant diseases, pests, and their effects.

Researchers are encouraged to utilize this dataset for scientific tasks, with proper citation of the corresponding research:

Uzhinskiy, A. Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain. Biology 2025, 14, 99. https://doi.org/10.3390/biology14010099

Uzhinskiy, A.; Ososkov, G.; Goncharov, P.; Nechaevskiy, A.; Smetanin, A. Oneshot Learning with Triplet Loss for Vegetation Classification Tasks. Comput. Opt. 2021, 45, 608–614

For suggestions on improving the app, reach out to info@doctorp.org

PCB Boards Kaggle Merged Dataset

## Overview

PCB Boards Kaggle Merged Dataset is a dataset for object detection tasks - it contains PCB Boards annotations for 8,125 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).

Damaged Roads Alvaro Basily Kaggle

## Overview

Damaged Roads Alvaro Basily Kaggle is a dataset for object detection tasks - it contains Damaged Roads annotations for 3,321 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).

Fireplace Kaggle

## Overview

Fireplace Kaggle is a dataset for object detection tasks - it contains Fireplace annotations for 720 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).

Yt+kaggle

## Overview

Yt+kaggle is a dataset for object detection tasks - it contains Yt annotations for 8,332 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).

Analysis of ‘ 👨‍💻 Top Starred Open Source Projects on GitHub’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/top-starred-open-source-projects-on-githube on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

GitHub is the leader in hosting open source projects. For those who are not familiar with open source projects, a group of developers share and contribute to common code to develop software. Example open source projects include, Chromium (which makes Google Chrome), WordPress, and Hadoop. Open source projects are said to have disrupted the software industry (2008 Kansas Keynote).

Methodology

For this study, I crawled the leader in hosting open source projects, GitHub.com and extracted a list of the top starred open source projects. On GitHub, a user may choose the star a repository representing that they “like” the project. For each project, I gathered the repository username or Organization the project resided in, the repository name, a description, the last updated date, the language of the project, the number of stars, any tags, and finally the url of the project.

Source

The micro-research study using this dataset can be found at The Concept Center

This dataset was created by Chase Willden and contains around 1000 samples along with Language, Last Update Date, technical information and other features such as: - Username - Url - and more.

How to use this dataset

• Analyze Number Of Stars in relation to Repository Name
• Study the influence of Description on Tags
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Kaggle Cup

## Overview

Kaggle Cup is a dataset for classification tasks - it contains Disease annotations for 1,200 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).

The Bike Purchasing Dataset I cleaned, filtered, and visualized examined bike purchases made by customers. The dataset included details of the customers, including marital status, gender, income, age, commute distance, region and whether or not if they made a bike purchase.

Here is a link to the data source on Github: https://github.com/AlexTheAnalyst/Excel-Tutorial/blob/main/Excel%20Project%20Dataset.xlsx

"Movie Recommendation on the IMDB Dataset: A Journey into Machine Learning" is an exciting project focused on leveraging the IMDB Dataset for developing an advanced movie recommendation system. This project aims to explore the vast potential of machine learning techniques in providing personalized movie recommendations to users.

The IMDB Dataset, comprising a wealth of movie information including genres, ratings, and user reviews, serves as the foundation for this project. By harnessing the power of machine learning algorithms and data analysis, the project seeks to build a recommendation system that can accurately suggest movies tailored to each individual's preferences.

Context

This comes from Society for Science's Abstract Search.

This project is also hosted on GitHub

Content

This contains the projects of every international science fair participant.

Data includes: - Project Title - Category - Abstract - Awards Won - Region - School

Acknowledgements

Because this comes from a web scrape, all of the data belongs to Science for Society.

Inspiration

I want someone to do a meta science fair project. Just the thought of doing a science fair project about science fair is incredibly cool.

Dataset

This dataset was created by Jude albaiti

Contents

Dataset

This dataset was created by Thejeswini.V

Contents

In this project, we aim to analyze and gain insights into the performance of students based on various factors that influence their academic achievements. We have collected data related to students' demographic information, family background, and their exam scores in different subjects.

**********Key Objectives:*********

1. Performance Evaluation: Evaluate and understand the academic performance of students by analyzing their scores in various subjects.

2. Identifying Underlying Factors: Investigate factors that might contribute to variations in student performance, such as parental education, family size, and student attendance.

3. Visualizing Insights: Create data visualizations to present the findings effectively and intuitively.

Dataset Details:

• The dataset used in this analysis contains information about students, including their age, gender, parental education, lunch type, and test scores in subjects like mathematics, reading, and writing.

Analysis Highlights:

• We will perform a comprehensive analysis of the dataset, including data cleaning, exploration, and visualization to gain insights into various aspects of student performance.

• By employing statistical methods and machine learning techniques, we will determine the significant factors that affect student performance.

Why This Matters:

Understanding the factors that influence student performance is crucial for educators, policymakers, and parents. This analysis can help in making informed decisions to improve educational outcomes and provide support where it is most needed.

Acknowledgments:

We would like to express our gratitude to [mention any data sources or collaborators] for making this dataset available.

Please Note:

This project is meant for educational and analytical purposes. The dataset used is fictitious and does not represent any specific educational institution or individuals.