Good And Bad Classification Of Laddu
Description
The project "Good and Bad Classification of Laddu" involves a dataset containing 1,000 samples of Laddus, evenly split between two classes: 500 labeled as "Good" and 500 labeled as "Bad." The primary objective of the dataset is to build and evaluate machine learning models that can accurately classify a given Laddu as either good or bad based on various features. Dataset Overview: Total Samples: 1,000 Class Distribution: Good Laddus: 500 samples Bad Laddus: 500 samples Features: The dataset contains multiple features that represent the physical, chemical, and sensory properties of each Laddu sample. These features may include but are not limited to: Physical Characteristics: Size, weight, color, and texture of the Laddu. These features are often determined through sensory evaluations and could include measures such as smoothness, roundness, firmness, and visual appeal. Chemical Composition: Nutrient values such as moisture content, fat percentage, sugar levels, and the presence of any additives or preservatives. Sensory Features: Attributes such as taste, aroma, and overall palatability, which can be subjectively scored by experts or consumers on a rating scale. Defect Indicators: Features that explicitly signal the presence of undesirable characteristics like improper cooking, spoilage, contamination, or ingredient imbalance. Data Structure: Each row in the dataset represents one Laddu sample, and the columns correspond to the extracted features that describe the sample. The final column will contain the label (either "Good" or "Bad") corresponding to the quality classification of the Laddu. Feature Columns: F1, F2, ..., Fn (representing various features) Label Column: Class (0 for Bad, 1 for Good) Project Goals: The primary goal of this project is to train a classification model that can differentiate between good and bad Laddus based on the given features. The project will involve the following key tasks: Data Preprocessing: Handling missing values, normalizing features, and possibly performing feature engineering to derive new, informative variables. Exploratory Data Analysis (EDA): Understanding the distribution of features for good and bad samples, identifying key differences, and visualizing trends or patterns. Model Building: Using machine learning algorithms like Decision Trees, Support Vector Machines, or Neural Networks to build a classification model. Model Evaluation: Assessing the model's performance using metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Cross-validation may also be used to validate the model's robustness. Optimization: Hyperparameter tuning to improve the model's performance, possibly using grid search or random search techniques. Challenges: Class Balance: Since the dataset is balanced, model performance will not be skewed by imbalanced data. However, ensuring that both classes are well represented in the training and testing sets will be important. Feature Selectio