Dataset

This dataset was created by Sofia Ashraf

Released under Apache 2.0

Contents

This project focuses on exploring and analyzing the most popular datasets available on Kaggle. By delving into these datasets, we aim to identify key trends, understand user preferences, and highlight the topics that drive engagement within the data science and machine learning communities

Also there are interesting charts and analytics in the attached notebook

Dataset

This dataset was created by Deandre Daniels

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Context

A fictional dataset for exploratory data analysis (EDA) and to test simple prediction models.

This toy dataset features 150000 rows and 6 columns.

Columns

Note: All data is fictional. The data has been generated so that their distributions are convenient for statistical analysis.

Number: A simple index number for each row

City: The location of a person (Dallas, New York City, Los Angeles, Mountain View, Boston, Washington D.C., San Diego and Austin)

Gender: Gender of a person (Male or Female)

Age: The age of a person (Ranging from 25 to 65 years)

Income: Annual income of a person (Ranging from -674 to 177175)

Illness: Is the person Ill? (Yes or No)

Acknowledgements

Stock photo by Mika Baumeister on Unsplash.

Machine Learning Tutorials - Example Projects - AI

I am sharing my 28 Machine Learning, Deep Learning (Artificial Intelligence - AI) projects with their data, software and outputs on Kaggle for educational purposes as open source. It appeals to people who want to work in this field, have 0 Machine Learning knowledge, have Intermediate Machine Learning knowledge, specialize in this field (Attracts to all levels). The deep learning projects in it are for advanced level, so I recommend you to start your studies from the Machine Learning section. You can check your own outputs along with the outputs in it. I am happy to share 28 educational projects with the whole world through Kaggle. Knowledge is free and better when shared!

Algorithms used in it:

1) Nearest Neighbor
2) Naive Bayes
3) Decision Trees
4) Linear Regression
5) Support Vector Machines (SVM)
6) Neural Networks
7) K-means clustering

Kind regards, Emirhan BULUT

You can use the links below for communication. If you have any questions or comments, feel free to let me know!

LinkedIn: https://www.linkedin.com/in/artificialintelligencebulut/ Email: emirhan@novosteer.com

Emirhan BULUT. (2022). Machine Learning Tutorials - Example Projects - AI [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4361310

This dataset contains information about housing sales in Nashville, TN such as property, owner, sales, and tax information. The SQL queries I created for Data Cleaning can be found here.

Software Project Management Tool Recommendation System:

This dataset contains Details about the Software Project Management tools selected by 90 students according to their preferences and convenience.

Using This Dataset many analytics can be covered i.e There are 4 Batches among 90 students, so analytics of the best Software Project Management tool in each batch.

Similarly, the best tool in all the batches, which student & how many have chosen the same tool and many more!

Future Plans Further, I will Incorporate other features of the tools like the Expense of the tool and other unique features the tool has.

And Recommendation System will expect that the user will select the features they want in their tool, so based upon the selected features the recommendation system will recommend the best tool for the user.

Happy Learning PAM

Dataset

This dataset was created by vamsi kamatham

Contents

Explore the world of data visualization with this Power BI dataset containing HR Analytics and Sales Analytics datasets. Gain insights, create impactful reports, and craft engaging dashboards using real-world data from HR and sales domains. Sharpen your Power BI skills and uncover valuable data-driven insights with this powerful dataset. Happy analyzing!

Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.

The link for the Excel project to download can be found on GitHub here. It includes the raw data, Pivot Tables, and an interactive dashboard with Pivot Charts and Slicers. The project also includes business questions and the formulas I used to answer. The image below is included for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2F61e460b5f6a1fa73cfaaa33aa8107bd5%2FBusinessQuestions.png?generation=1686190703261971&alt=media" alt=""> The link for the Tableau adjusted dashboard can be found here.

A screenshot of the interactive Excel dashboard is also included below for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2Fe581f1fce8afc732f7823904da9e4cce%2FScooter%20Dashboard%20Image.png?generation=1686190815608343&alt=media" alt="">

Description This dataset is designed for whole life cycle management of civil engineering projects, integrating Building Information Modeling (BIM) and Artificial Intelligence (AI). It includes comprehensive project data covering cost, schedule, structural health, environmental conditions, resource allocation, safety risks, and drone-based monitoring.

Key Features Project Metadata: ID, type (bridge, road, building, etc.), location, and timeline. Financial Data: Planned vs. actual cost, cost overruns. Scheduling Data: Planned vs. actual duration, schedule deviation. Structural Health Monitoring: Vibration levels, crack width, load-bearing capacity. Environmental Factors: Temperature, humidity, air quality, weather conditions. Resource & Safety Management: Material usage, labor hours, equipment utilization, accident records. Drone-Based Monitoring: Image analysis scores, anomaly detection, completion percentage. Target Variable: Risk Level (Low, Medium, High) based on cost, schedule, safety, and structural health. Use Cases Predictive Modeling: Train AI models to forecast project risks and optimize decision-making. BIM & AI Integration: Leverage real-time IoT and drone data for smart construction management. Risk Assessment: Identify early signs of cost overruns, delays, and structural failures. Automation & Efficiency: Develop automated maintenance and safety monitoring frameworks

Tabular dataset for data analysis and machine learning practice. The dataset is about the market and is usable for Power BI practice and data science.

Dataset

This dataset was created by NIYIBIGIRA Geredi

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Dataset

This dataset was created by MYTHILI KB

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Bike sales dataset analysis

This project was done to analyze sales data: to identify trends, top-selling products, and revenue metrics for business decision-making. I did this project offered by MeriSKILL, to learn more and be exposed to real-world projects and challenges that will provide me with valuable industry experience and help me develop my data analytical skills.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20837845%2Fe3561db319392bf9cc8b7d3fcc7ed94d%2F2019%20Sales%20dashboard.png?generation=1717273572595587&alt=media" alt=""> More on this project is on Medium

Dataset

This dataset was created by Shreyak Silwal

Released under Other (specified in description)

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لينك شرح المشروع علي اليوتيوب https://user264629.psee.ly/69ch9f

Dataset

This dataset was created by Albert Alfred

Released under Other (specified in description)

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Dataset

This dataset was created by Jerraldo1705

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