Update README.md

README.md

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 This dataset consists of 30 individual flowers. For each flower, five physical features were recorded: flower_diameter_cm (the width of the bloom at its widest point), petal_length_cm (average length of a petal), petal_width_cm (average width of a petal), petal_count (number of petals on the bloom), and stem_height_cm (approximate stem height from the soil line). The target variable is the color of the pedals, assigned categorically as Red, Yellow, Orange, Purple, Pink, or White. This small, structured dataset mimics the famous Iris dataset but with local flowers.
 
 To increase the size of the dataset and provide variety for modeling, the original 30 rows were expanded to 300 synthetic samples using a stratified bootstrap with controlled noise. Specifically, rows were sampled with replacement within each color class to maintain the original class distribution. For each numeric feature, small random perturbations (Gaussian noise scaled to 10 % of the feature’s standard deviation) were added to simulate natural measurement variability. Values were then clamped to plausible ranges derived from the original data’s 1st–99th percentiles to avoid impossible numbers (like negative lengths), and integer-like features such as petal_count were rounded back to whole numbers. Categorical features were left unchanged so that each synthetic row retained a realistic label. This procedure produced a larger, more diverse dataset that still reflects realistic physical characteristics of the observed flowers.
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+### Dataset Intended Use
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+To practice AI ML
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+### Dataset Out of Scope
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+To predict flower properties.
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+### Personal and Sensitive Information
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+This dataset does not contain personal or sensitive information.
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 - **Curated by:** Scotty McGee
+- **Language:** English