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Synthetic Flower Characteristics
Synthetic Data Generation
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About
Three popular flower types: roses, shoeblack plants, and hibiscus. It includes various attributes important for understanding and predicting their characteristics, such as species, size, fragrance, and height. This makes it a valuable resource for botanical studies, horticultural planning, and the training of machine learning models. The dataset is synthetic, created using the Faker library, and designed to simulate realistic data. While the data points do not represent real-world measurements, they are structured to align with typical characteristics observed in these flower species, making it an excellent tool for educational purposes, prototyping machine learning models, and conducting preliminary data analysis.
Columns
- species: The type of flower, which can be "rose," "shoeblack plant," or "hibiscus."
- size: The size category of the flower, either "small," "medium," or "large," depending on the species.
- fragrance: Describes the intensity of the flower's fragrance, categorised as "strong," "mild," or "none."
- height_cm: The flower's height in centimetres, presented as a numerical value rounded to two decimal places.
Distribution
The dataset is provided in a CSV file named
flower_dataset.csv, with a file size of 280.74 kB. It contains 4 distinct columns. Each column holds 10,000 valid entries, providing a consistent structure for analysis.Usage
This dataset is ideal for a range of applications, including:
- Botanical research and studies.
- Horticultural planning and understanding flower attributes.
- Training and testing machine learning models.
- Educational exercises for data science and biology students.
- Prototyping new data analysis techniques and models.
- Performing preliminary data analysis without needing real-world data collection.
Coverage
As a synthetic dataset, it does not have a specific geographic or time-range coverage. The data points do not correspond to actual measurements but are designed to simulate characteristics of roses, shoeblack plants, and hibiscus. There are no demographic scopes, as the data pertains solely to flowers.
License
CC0: Public Domain
Who Can Use It
- Botanists: To study simulated flower characteristics and attributes.
- Horticulturists: For planning and understanding potential flower variations.
- Data Scientists and Machine Learning Engineers: To train and prototype models for classification or prediction tasks related to flower attributes.
- Educators and Students: As a teaching aid for data analysis, statistics, and machine learning concepts.
Dataset Name Suggestions
- Floral Attributes Simulation
- Synthetic Flower Characteristics
- Roses, Shoeblack, Hibiscus Dataset
- Botanical Simulation Data
- Fictional Flower Metrics
Attributes
Original Data Source: Synthetic Flower Characteristics
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