https://github.com/chandkund/recipe-recommendations
The Recipe Recommendations project leverages natural language processing (NLP) techniques to process recipe descriptions and classify them into categories like desserts, main courses, and more. The system provides personalized recommendations by analyzing the text input and suggesting relevant recipes.
https://github.com/chandkund/recipe-recommendations
embeddings matplotlib nlp numpy pandas softmax tensorflow
Last synced: 3 months ago
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The Recipe Recommendations project leverages natural language processing (NLP) techniques to process recipe descriptions and classify them into categories like desserts, main courses, and more. The system provides personalized recommendations by analyzing the text input and suggesting relevant recipes.
- Host: GitHub
- URL: https://github.com/chandkund/recipe-recommendations
- Owner: chandkund
- License: mit
- Created: 2024-09-27T20:08:49.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-13T18:45:00.000Z (over 1 year ago)
- Last Synced: 2025-03-08T05:13:09.309Z (over 1 year ago)
- Topics: embeddings, matplotlib, nlp, numpy, pandas, softmax, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 132 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Recipe Recommendations
This project builds a machine learning model to recommend and classify recipes based on their descriptions using a deep learning approach with LSTM layers.
## Project Overview
The Recipe Recommendations project leverages natural language processing (NLP) techniques to process recipe descriptions and classify them into categories like desserts, main courses, and more. The system provides personalized recommendations by analyzing the text input and suggesting relevant recipes.
The deep learning model uses LSTM layers to capture the context of the recipe descriptions, and it is trained on labeled data to classify the recipes into various categories.
## Datasets
The dataset consists of the following columns:
1. **Describe**: Textual descriptions of the recipes.
2. **C_Type**: The category or type of the recipe (e.g., Dessert, Main Course). This is used as the target for classification.
## Installation
To run this project, you will need to install the necessary dependencies:
```bash
pip install pandas numpy tensorflow matplotlib
```
## Model Architecture
The model is built using the following layers:
- **Embedding Layer**: Converts words into vector representations.
- **Bidirectional LSTM**: Two layers to capture forward and backward context.
- **Dropout Layers**: To prevent overfitting.
- **Dense Layers**: Fully connected layers for classification.
- **Softmax Layer**: The final activation layer for multi-class classification.
## How to Use
1. Clone the repository:
```bash
git clone https://github.com/chandkund/Recipe-Recommendations.git
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Run the model training:
```python
python Recipe Recommendations.ipynb
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.