Good And Bad Classification Of Hyacinth bean curry
Description
Data Description for Project: Good and Bad Classification of Hyacinth Bean Curry The dataset for this project contains samples of Hyacinth bean curry, categorized into two classes: good and bad. The primary objective is to classify the quality of the curry based on relevant features extracted from the dataset. Below is a detailed description of the data components: Dataset Overview: Total Samples: 1000 Good Curry Samples: 500 Bad Curry Samples: 500 Data Features: The dataset contains multiple features derived from sensory, chemical, and physical analyses of the curry samples, which may include but are not limited to: Sensory Attributes: Appearance (color intensity and uniformity) Aroma (freshness and off-flavors) Taste (bitterness, sweetness, or rancidity) Texture (smoothness or lumpiness) Physical Properties: Moisture Content Viscosity Particle Size Distribution Chemical Attributes: pH Level Acidity Fat Content Total Solids Data Distribution: Balanced dataset with an equal number of good and bad samples Labels: Binary classification 0: Bad sample 1: Good sample Data Collection Method: Samples were prepared under controlled conditions to ensure consistency. Sensory evaluation was conducted by trained panelists using a standard 5-point hedonic scale. Chemical and physical properties were determined using laboratory analytical techniques. Potential Data Processing Steps: Handling missing values (if any) Standardization or normalization of continuous variables Feature selection to identify the most relevant parameters for classification Encoding categorical sensory features Project Goal: The objective is to build a robust machine learning model capable of classifying the quality of Hyacinth bean curry into good or bad categories based on the extracted features, thereby assisting in quality control and product development processes.