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https://github.com/felixojiambo/ethniccuisines_ml

The Ethnic Cuisines is machine learning project built to provide personalized dining recommendations. The system analyzes social media trends, user reviews, and individual preferences to suggest ethnic cuisines. It incorporates various machine learning features to ensure accurate, up-to-date, and relevant recommendations.
https://github.com/felixojiambo/ethniccuisines_ml

Last synced: about 2 months ago
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Host: GitHub
URL: https://github.com/felixojiambo/ethniccuisines_ml
Owner: felixojiambo
Created: 2024-07-11T11:45:00.000Z (6 months ago)
Default Branch: main
Last Pushed: 2024-07-13T06:41:09.000Z (6 months ago)
Last Synced: 2024-07-13T14:16:54.437Z (6 months ago)
Language: Python
Homepage:
Size: 42 KB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

## Project Overview

## Machine Learning Features and Implementation Plan

### 1. Trend Analysis Algorithms
**Goal**: Identify popular trends in ethnic dining from social media.

**Steps**:
- **Data Collection**: Scrape data from social media platforms using APIs.
- **Preprocessing**: Clean and preprocess text data (tokenization, stop word removal, stemming/lemmatization).
- **Algorithm**: Implement NLP techniques like topic modeling (LDA) to find trending topics.
- **Evaluation**: Use metrics like coherence score to evaluate topic models.

### 2. Review Sentiment Analysis
**Goal**: Assess the sentiment of user reviews.

**Steps**:
- **Data Collection**: Aggregate reviews from various platforms.
- **Preprocessing**: Clean text data and convert it to a suitable format for analysis.
- **Algorithm**: Use pre-trained models (e.g., BERT) or train a custom sentiment analysis model.
- **Evaluation**: Use accuracy, precision, recall, and F1-score to evaluate the sentiment model.

### 3. User Preference Learning
**Goal**: Continuously learn and adapt to individual user tastes.

**Steps**:
- **Data Collection**: Gather user interaction data (clicks, likes, searches).
- **Preprocessing**: Normalize and structure user interaction data.
- **Algorithm**: Collaborative filtering or content-based filtering algorithms.
- **Evaluation**: Use metrics like RMSE (Root Mean Square Error) or precision/recall at k.

### 4. Cuisine Recommendation Engine
**Goal**: Suggest cuisines based on user behavior and trends.

**Steps**:
- **Data Collection**: Compile user history and social media trends.
- **Preprocessing**: Feature extraction and normalization.
- **Algorithm**: Hybrid recommendation systems combining collaborative and content-based filtering.
- **Evaluation**: Measure performance using A/B testing and user satisfaction surveys.

### 5. Image Recognition for Shared Photos
**Goal**: Analyze user photos to identify dishes and restaurants.

**Steps**:
- **Data Collection**: Collect labeled datasets of food images.
- **Preprocessing**: Image augmentation and normalization.
- **Algorithm**: Use convolutional neural networks (CNNs) for image recognition.
- **Evaluation**: Accuracy, precision, recall, and F1-score.

### 6. Behavioral Pattern Recognition
**Goal**: Identify patterns in user selections and interactions.

**Steps**:
- **Data Collection**: Aggregate user interaction data.
- **Preprocessing**: Normalize and structure the data.
- **Algorithm**: Clustering algorithms (e.g., K-means) and sequential pattern mining.
- **Evaluation**: Silhouette score for clustering and pattern evaluation metrics.

### 7. Natural Language Processing for Reviews
**Goal**: Process and understand user reviews.

**Steps**:
- **Data Collection**: Aggregate user reviews.
- **Preprocessing**: Text cleaning, tokenization, and feature extraction (TF-IDF, word embeddings).
- **Algorithm**: NLP models like RNNs or Transformers.
- **Evaluation**: Use metrics like BLEU, ROUGE for text understanding and summarization.

### 8. Adaptive Filtering Mechanisms
**Goal**: Adjust filters based on learned user preferences.

**Steps**:
- **Data Collection**: Monitor user interactions with filters.
- **Preprocessing**: Structure the data for analysis.
- **Algorithm**: Adaptive algorithms that learn and adjust in real-time.
- **Evaluation**: User satisfaction and engagement metrics.

### 9. Automated Content Tagging
**Goal**: Tag and categorize user-generated content for easier navigation.

**Steps**:
- **Data Collection**: Aggregate user-generated content.
- **Preprocessing**: Text and image preprocessing.
- **Algorithm**: Use NLP and image recognition models.
- **Evaluation**: Accuracy and precision of tagging.

### 10. Predictive Analytics for Dining Preferences
**Goal**: Predict future user preferences based on past behavior.

**Steps**:
- **Data Collection**: Gather historical user data.
- **Preprocessing**: Feature extraction and normalization.
- **Algorithm**: Time series analysis or regression models.
- **Evaluation**: RMSE or mean absolute error (MAE).

### 11. Customized Notification Algorithms
**Goal**: Tailor notifications to individual users.

**Steps**:
- **Data Collection**: Monitor user interaction with notifications.
- **Preprocessing**: Normalize and structure the data.
- **Algorithm**: Use reinforcement learning to optimize notifications.
- **Evaluation**: User engagement and response rates.

### 12. Cultural and Regional Analysis
**Goal**: Adapt recommendations to cultural and regional dining preferences.

**Steps**:
- **Data Collection**: Collect regional dining data.
- **Preprocessing**: Normalize and categorize data based on regions.
- **Algorithm**: Cluster analysis and region-specific models.
- **Evaluation**: User satisfaction and engagement metrics.

### 13. Real-Time Data Processing
**Goal**: Quickly process new data for immediate recommendations.

**Steps**:
- **Data Collection**: Real-time data collection systems.
- **Preprocessing**: Stream processing and real-time feature extraction.
- **Algorithm**: Use real-time processing frameworks like Apache Kafka and Spark Streaming.
- **Evaluation**: Latency and throughput metrics.

### 14. Anomaly Detection in User Behavior
**Goal**: Identify and address unusual user activities.

**Steps**:
- **Data Collection**: Monitor user activities.
- **Preprocessing**: Feature extraction and normalization.
- **Algorithm**: Anomaly detection algorithms like Isolation Forest or Autoencoders.
- **Evaluation**: Precision, recall, and F1-score for anomaly detection.

### 15. Automated Restaurant Rating Updates
**Goal**: Regularly update restaurant ratings based on new data.

**Steps**:
- **Data Collection**: Aggregate review and rating data.
- **Preprocessing**: Normalize and preprocess data.
- **Algorithm**: Update algorithms using batch processing or real-time updates.
- **Evaluation**: Accuracy and consistency of ratings.

## Implementation Plan

### Phase 1: Data Collection and Preprocessing
1. **Set up data scraping tools** for social media and review platforms.
2. **Collect initial datasets** for training models (text, images, user interaction data).
3. **Preprocess data**: cleaning, normalization, feature extraction.

### Phase 2: Model Development and Training
1. **Develop models** for each machine learning feature.
2. **Train models** using collected datasets.
3. **Evaluate models** using appropriate metrics.

### Phase 3: Integration and Deployment
1. **Integrate models** into the backend system.
2. **Set up real-time data processing** frameworks.
3. **Deploy models** to cloud-based infrastructure.
4. **Implement continuous monitoring** and updates for models.

### Phase 4: Testing and Optimization
1. **Conduct extensive testing** to ensure models perform well in real-world scenarios.
2. **Optimize models** based on user feedback and performance metrics.
3. **Implement A/B testing** for recommendations and notifications.

### Phase 5: Maintenance and Updates
1. **Regularly update datasets** and retrain models.
2. **Monitor system performance** and address any issues.
3. **Continuously improve algorithms** based on new data and technologies.

## Getting Started

To get started with the project:

1. **Clone the repository**: `git clone `
2. **Install dependencies**: `pip install -r requirements.txt`
3. **Set up environment variables** for API keys and database connections.
4. **Run data collection scripts** to gather initial datasets.
5. **Train initial models** using provided training scripts.
6. **Deploy the system** to your preferred cloud platform.

## Contributing

We welcome contributions! Please read our [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact

For any questions or suggestions, please contact us at [[email protected]](mailto:[email protected]).