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Magical Remedy Binary Classification
Data Science and Analytics
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About
Explaining data detailing the results of a historical effort to cure a gravely ill princess. The records document the varying amounts of volatile and scarce magical ingredients gathered by local villagers. A master alchemist, utilising the new magical art of Data Science and Machine Learning, compiled this information to determine the successful combination required to save the royal daughter. This dataset represents a beginner-friendly binary classification task designed to predict if a specific mixture of ingredients will result in a cure, noting that the kingdom has only one critical chance left. The data is highly usable, featuring balanced samples, entirely numerical columns, and zero missing values, although users are warned that some features may not be helpful for prediction.
Columns
The dataset consists of 14 columns, primarily detailing the quantities of mythical substances used in various attempts:
- Phoenix Feather: Quantity of Phoenix Feather utilised (Mean 15.4, Max 42.1).
- Unicorn Horn: Quantity of Unicorn Horn utilised (Mean 10.9, Max 34.1).
- Dragon's Blood: Quantity of Dragon's Blood utilised (Mean 16.1, Max 40.8).
- Mermaid Tears: Quantity of Mermaid Tears utilised (Mean 13.6, Max 35.8).
- Fairy Dust: Quantity of Fairy Dust utilised (Mean 15.1, Max 40.4).
- Goblin Toes: Quantity of Goblin Toes utilised (Mean 14.2, Max 37.8).
- Witch's Brew: Quantity of Witch's Brew utilised (Mean 12.3, Max 37.3).
- Griffin Claw: Quantity of Griffin Claw utilised (Mean 14.9, Max 39.4).
- Troll Hair: Quantity of Troll Hair utilised (Mean 16.9, Max 44.8).
- Kraken Ink: Quantity of Kraken Ink utilised (Mean 14.9, Max 39.5).
- Minotaur Horn: Quantity of Minotaur Horn utilised (Mean 10.9, Max 33.7).
- Basilisk Scale: Quantity of Basilisk Scale utilised (Mean 15.4, Max 42.0).
- Chimera Fang: Quantity of Chimera Fang utilised (Mean 12.1, Max 37.8).
- Cured (Target Variable): A binary outcome variable (0 or 1).
Distribution
The data file, typically available in CSV format, contains 2,338 valid records across 14 columns. The entire structure is numerical. Data quality is excellent: all records are 100% valid with zero mismatched or missing values. The target variable, 'Cured', is highly balanced, with 1,177 instances of class 0 and 1,161 instances of class 1, making it well-suited for classification benchmarking.
Usage
This dataset is ideal for fundamental machine learning exercises. It serves as an excellent resource for:
- Developing and evaluating binary classification models.
- Practising essential feature engineering techniques, especially concerning identifying and managing features that may not contribute positively to the prediction.
- Introduction to Python programming in a data science context.
- Benchmarking model performance on inherently balanced classification problems.
Coverage
The data is contextualised within a fairy tale narrative set in a far-off land. Since the focus is exclusively on the relationship between the measured quantities of mythical ingredients and the resulting curative outcome, the dataset does not contain explicit geographical, temporal, or demographic bounds.
License
CC0: Public Domain
Who Can Use It
- Students and Data Science Beginners: Learning core binary classification concepts and initial model training pipelines.
- Machine Learning Educators: Providing a clean, compelling, narrative-driven dataset for instructional purposes.
- ML Engineers: Testing feature selection strategies and validating algorithm performance on clean, balanced inputs.
Dataset Name Suggestions
- The Princess Cure Dataset
- Magical Remedy Binary Classification
- Alchemist Ingredient Classifier
- Mythical Ingredients Prediction Data
Attributes
Original Data Source: Magical Remedy Binary Classification
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