Good And Bad Classification Of Paneer
Description
Project Title: Good and Bad Classification of Paneer Data Overview: This dataset is designed for the binary classification problem of distinguishing between good-quality and bad-quality paneer samples. It contains a total of 1,000 paneer samples, evenly split between 500 good-quality and 500 bad-quality samples. The dataset is structured to support the development and evaluation of machine learning models aimed at automating the quality assessment of paneer based on a set of relevant features. Data Structure: Each record in the dataset corresponds to a specific paneer sample, with both good and bad classifications labeled accordingly. The data contains the following features, which were selected based on their relevance to determining paneer quality: Sensory Features: Texture: Quantitative or categorical assessment of the paneer's texture, ranging from soft to firm. Color: A measure of the paneer’s color, quantified using a color scale or RGB values. Odor: Odor intensity or specific notes that can indicate spoilage or freshness, captured using a scoring system or categorical labels. Taste: Panel scores or taste descriptors to indicate sweetness, sourness, bitterness, or other taste profiles. Physical Features: Moisture Content (%): Measured moisture level, which can affect the paneer’s texture and shelf life. pH Level: The acidity or alkalinity of the paneer sample, influencing spoilage and taste. Fat Content (%): Fat percentage, which may vary between good and bad samples due to spoilage or production methods. Ash Content (%): Indicative of mineral content, ash level can also serve as a marker for quality degradation. Chemical Composition: Protein Content (%): Protein level, which can indicate the nutritional value and potential spoilage. Lactose Content (%): Lactose levels, which can affect taste and freshness. Free Fatty Acids (FFA %): Elevated levels of FFAs can suggest spoilage or poor-quality paneer. Thiobarbituric Acid Reactive Substances (TBARS): Used to measure lipid oxidation, which indicates rancidity in paneer samples. Microbial Features: Total Plate Count (TPC): Indicates the microbial load, with higher counts correlating with spoilage. Yeast and Mold Count: Common in bad samples, high counts can signal contamination or spoilage. Coliform Count: An indicator of potential contamination, common in bad-quality paneer samples. Target Variable: Class: Binary label indicating whether the sample is classified as good or bad (1 for good, 0 for bad). Data Collection Process: The paneer samples were collected from various sources, including both controlled environments (e.g., dairy production units) and consumer environments (e.g., retail outlets). Sensory analysis was conducted by trained panelists, while physical, chemical, and microbial tests were performed using standard laboratory methods. Use Cases: Supervised Machine Learning Models: The dataset can be used to train models such as logistic regression, support vector machines