AppleQ - Apple Quality Prediction using ML
AppleIQ is a machine learning project designed to classify apple quality based on key features such as size, color, and texture. This project provides valuable insights for farmers, distributors, and quality control teams, helping to ensure only premium-quality apples reach the market.
Ideal for students, researchers, and agricultural professionals, AppleIQ offers a practical ML classification project focusing on real-world applications in agriculture.
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Introducing
AppleQ - Apple Quality Prediction using ML
AppleQ is a machine learning project designed to classify apple quality based on features such as size, color, and texture.
This project provides valuable insights for farmers, distributors, and retailers to ensure only the highest quality apples reach the market.
- Advanced Classification Models
Uses Random Forest, SVM, and K-Nearest Neighbors for accurate apple quality classification.
- Comprehensive Quality Analysis
Evaluates features like color, size, weight, and surface texture for quality grading.
- User-Friendly Web Interface
Includes a Streamlit-based frontend for seamless interaction and real-time predictions.
Everything You Need to Get Started
Leverage Scikit-Learn, Pandas, and NumPy to preprocess apple data and train classification models.
Evaluate model performance using metrics like accuracy, precision, and F1 score.
Deploy using Streamlit or Flask for real-time quality assessment.
Who Can Benefit from AppleIQ?
- ML Students and Enthusiasts – Add a quality control project to your portfolio.
- Farmers and Distributors – Use predictive models to optimize harvest and quality selection.
- Agribusinesses – Streamline quality assessment with ML-driven predictions.
- Researchers – Explore the impact of quality control in agricultural markets.
Data Preprocessing
- Load and clean the apple quality dataset, handling missing values and outliers.
- Extract relevant features such as color, size, and texture.
- Standardize the data to enhance model performance.
Model Training
- Train models using Random Forest, SVM, and K-Nearest Neighbors for classification.
- Perform hyperparameter tuning to optimize model accuracy.
Prediction and Evaluation
- Predict apple quality based on input features like size and texture.
- Evaluate performance using metrics like accuracy, precision, and F1 score.
Why Choose AppleIQ?
Gain access to the complete source code and documentation.
Start immediately with pre-trained models and ready-to-use datasets.
Follow step-by-step instructions for setup, training, and deployment.
Engage with a user-friendly interface for real-time quality assessment.
Explore AppleIQ in Action!
Try the live demo to see apple quality predictions based on real-world data, helping streamline quality control in agriculture.
What you get!
A turnkey solution for your project requirement. Buy and get everything you need to complete your project including support.
- Dataset
- Training File
- Trained ML Model – Pickle file
- Deployment – Streamlit .py file
- Libraries & Requirement folder
- Project Documentation
- Free 1-on-1 Training
- Installation Support