Good and Bad classification of Cooked Maggi
Description
*Data Description: Good and Bad Classification of Cooked Maggi* This dataset is designed for binary classification, distinguishing between *good* and *bad* samples of cooked Maggi. It consists of *1,000 total samples*, with an equal distribution: - *500 Good Samples* – Representing well-cooked Maggi that meets desirable quality standards. - *500 Bad Samples* – Representing improperly cooked Maggi, which may include undercooked, overcooked, burnt, or soggy variations. ### *Dataset Attributes* Each sample is characterized by various features, which may include: #### *1. Image Data (if applicable)* - High-resolution images of cooked Maggi for visual classification. - Images may capture different angles, lighting conditions, and variations in texture. #### *2. Physical & Sensory Features* - *Texture*: Softness, hardness, or stickiness. - *Color*: Golden-brown (good) vs. dark (burnt) or pale (undercooked). - *Moisture Content*: Dry, well-balanced, or excessively watery. - *Clumpiness*: Well-separated strands vs. sticky/mushy consistency. #### *3. Ingredient & Cooking Factors* - *Water Ratio*: Proper vs. excess/insufficient water used. - *Cooking Time*: Optimal vs. undercooked/overcooked. - *Seasoning Distribution*: Evenly mixed vs. uneven. - *Burnt Residue Presence*: Charred vs. clean. #### *4. Label (Target Variable)* - *0 = Bad Maggi* - *1 = Good Maggi* ### *Potential Applications* - Machine learning model training for *automated quality assessment*. - Developing AI-driven *food quality monitoring systems*. - Enhancing *food industry automation* with real-time detection. This dataset is suitable for *computer vision-based classification* (if images are used) or *sensor-based analysis* for food quality control.