Good And Bad Classification Of cauliflower curry
Description
Data Description for the Project: Good and Bad Classification of Cauliflower Curry This dataset is specifically curated for the binary classification task of determining whether a sample of cauliflower curry is "Good" or "Bad" based on its features. The dataset contains a balanced set of 1000 samples, with 500 labeled as "Good" and 500 labeled as "Bad." Each sample represents distinct characteristics of cauliflower curry, which were measured and recorded for the analysis. 1. Dataset Size: Total Samples: 1000 Good Samples: 500 Bad Samples: 500 2. Features of the Dataset: The features used for classification capture the sensory, chemical, and visual attributes of cauliflower curry, which are key indicators of quality. Sensory Features: Aroma Score (0 to 10): Indicates the freshness and pleasantness of the aroma. Taste Score (0 to 10): Evaluated based on flavor balance and seasoning. Texture Score (0 to 10): Measures firmness and consistency of the curry. Visual Features: Color Intensity (0 to 255): Captures the brightness and visual appeal of the curry. Presence of Spoiled Spots (Binary): 1 if visible signs of spoilage are present, 0 otherwise. Uniformity of Ingredients (0 to 10): Evaluates whether the ingredients are evenly distributed. Chemical Features: pH Value (2.5 to 7): Helps determine the acidity level. Moisture Content (%): Indicates water activity in the curry. Volatile Compounds Concentration (ppm): Measures compounds contributing to aroma degradation. 3. Target Variable: Quality Label (Binary): "Good" labeled as 1 "Bad" labeled as 0 4. Data Distribution: The dataset ensures a balanced distribution of classes, which helps avoid model biases during training and testing. Both good and bad samples have been selected to cover a wide range of variations, including different cooking methods, storage conditions, and ingredient compositions. 5. Data Collection Method: Samples were prepared and evaluated by expert food analysts. Sensory scores were assigned by a panel of trained testers, while chemical measurements were conducted using standard laboratory techniques. 6. Potential Applications: Automated quality inspection systems for food processing industries. Development of machine learning models for culinary quality assessment. Improved shelf-life prediction for cauliflower-based dishes. This dataset provides a robust foundation for building models capable of accurately classifying cauliflower curry samples based on their quality attributes.