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Apple Quality Classification Data
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About
Explore the intricate botanical and chemical metrics defining fruit quality with this dataset provided by an American agriculture company. This collection enables the analysis of determining factors such as sweetness, crunchiness, and acidity to classify apples into quality grades. Ideal for machine learning classification tasks, the data supports the development of predictive models to distinguish between 'good' and 'bad' produce based on quantitative features, offering valuable insights for agricultural technology and food science research.
Columns
- A_id: Unique identifier for tracking each individual apple.
- Size: Quantitative measurement of the apple's dimensions.
- Weight: Mass of the apple.
- Sweetness: Numerical score representing the taste profile regarding sugar content.
- Crunchiness: Texture metric indicating the firmness and crispness of the fruit.
- Juiciness: Measurement of the apple's moisture content.
- Ripeness: Indicator of the fruit's maturity level.
- Acidity: Numerical value representing the tartness or acid content.
- Quality: Categorical classification of the apple as either 'good' or 'bad'.
Distribution
- Format: CSV (Comma Separated Values)
- Size: 387.65 kB
- Structure: 9 Columns
- Records: 4,000 Valid Entries (balanced distribution with 50% 'good' and 50% 'bad' labels)
Usage
- Machine Learning Classification: Training binary classification models (Support Vector Machines, Random Forests) to predict fruit quality.
- Agricultural Analysis: Studying correlations between physical attributes (size, weight) and chemical properties (acidity, sweetness).
- Data Visualisation: creating scatter plots and histograms to understand feature distributions in food quality control.
- Educational Projects: Serves as a clean, balanced dataset for students learning data preprocessing and supervised learning techniques.
Coverage
- Geographic Scope: Sourced from an American agriculture company.
- Subject Scope: Covers various physical and chemical attributes of apples.
- Demographic/Entity: Individual fruit specimens evaluated for quality control.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For testing classification algorithms and feature engineering.
- Agricultural Technologists: For automating quality control processes.
- Food Scientists: For analysing the relationship between acidity, ripeness, and perceived quality.
- Students and Educators: For teaching data science concepts using a balanced, real-world dataset.
Dataset Name Suggestions
- Apple Quality Classification Data
- Fruit Grade Attributes and Metrics
- American Agriculture Apple Dataset
- Apple Physicochemical Quality Features
Attributes
Original Data Source: Apple Quality Classification Data
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