Apple Quality Prediction Model

[Varunshiyam]

Is your feature request related to a problem? Please describe.

In the agricultural sector, the quality assessment of apples is often a labor-intensive task requiring expertise and time. Automating this process through predictive modeling can enhance efficiency and consistency in determining apple quality, which is critical for market standards. This project addresses the problem of automatically predicting apple quality based on physical and chemical attributes, thereby facilitating better quality control in the fruit supply chain.

Describe the solution you'd like

The "Apple Quality Prediction Model" aims to classify apples into quality categories ("good" or "bad") based on a set of measured attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, and acidity. The dataset used in this project contains various records with these attributes, with a target quality label for each sample. By applying supervised learning techniques, multiple machine learning models are trained and evaluated, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Network, and XGBoost. The project's goal is to select the model that provides the highest accuracy and reliability for apple quality prediction.

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[github-actions[bot]]

Hello @Varunshiyam! Your issue #789 has been closed. Thank you for your contribution!