This repository contains a deep learning solution for predicting the quality of apples based on various features and attributes. The goal is to develop a robust model that can assist in determining the quality of apples in an automated and efficient manner.
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Deep Learning Models: Implementations of state-of-the-art deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze and predict apple quality.
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Data Preprocessing: Robust data preprocessing pipelines to handle cleaning, normalization, and feature extraction from raw apple quality datasets.
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Model Evaluation: Comprehensive evaluation metrics and tools to assess the performance of the trained models, including accuracy, precision, recall, and F1 score.
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Visualization: Visualization scripts for displaying model training/validation curves, confusion matrices, and other relevant visualizations to gain insights into the model's behavior.
Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.
- Clone the repository:
git clone https://github.com/your-username/apple-quality-prediction.git