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https://github.com/chandkund/personalized-meal-recommendation
This project focuses on building a personalized meal recommendation system by predicting cuisines based on the ingredients used. Leveraging NLP and a deep learning model, this system helps users discover meal options tailored to their preferences.
https://github.com/chandkund/personalized-meal-recommendation
matplotlib nlp numpy pandas sklearn
Last synced: 18 days ago
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This project focuses on building a personalized meal recommendation system by predicting cuisines based on the ingredients used. Leveraging NLP and a deep learning model, this system helps users discover meal options tailored to their preferences.
- Host: GitHub
- URL: https://github.com/chandkund/personalized-meal-recommendation
- Owner: chandkund
- License: mit
- Created: 2024-09-27T19:26:21.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-02T17:41:07.000Z (19 days ago)
- Last Synced: 2025-02-02T18:29:20.227Z (19 days ago)
- Topics: matplotlib, nlp, numpy, pandas, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🍽️ Personalized Meal Recommendation System
This project focuses on building a personalized meal recommendation system by predicting cuisines based on the ingredients used. Leveraging NLP and a deep learning model, this system helps users discover meal options tailored to their preferences.
## Project Overview
In this project, we classify cuisines based on ingredients using an LSTM-based neural network. The model is trained to predict the cuisine type given a list of ingredients, making it a useful tool for personalized meal recommendations. The system can be extended to provide suggestions for meal planning and grocery shopping.
## Dataset Description
- **Ingredients:** The ingredients used in the recipe. This is a text-based feature that requires preprocessing.
- **Cuisine:** The cuisine type (e.g., Italian, Indian, Mexican). This is the target variable for classification.
Sample data:
| Description | Quantity | InvoiceDate | UnitPrice | CustomerID | Country |
|--------------------|----------|--------------------|-----------|------------|---------|
| WHITE METAL LANTERN| 6 | 12/1/2010 8:26 | 3.39 | 17850.0 | India |
| CREAM CUPID HANGER | 8 | 12/1/2010 8:26 | 2.75 | 17850.0 | India |
## Model Architecture
We use a deep learning model with the following architecture:
- **Embedding Layer:** Converts words into dense vector representations.
- **Bidirectional LSTM:** To capture dependencies in both forward and backward directions.
- **Dense Layer:** Fully connected layer for further processing.
- **Softmax Layer:** Output layer with softmax activation to classify cuisines.
```python
model = Sequential([
layers.Embedding(voc_size, 40),
layers.Bidirectional(layers.LSTM(100)),
layers.Dense(100, activation='relu'),
layers.Dense(len(le.classes_), activation='softmax')
])
```
## Preprocessing
- **Tokenization:** Breaking down the ingredients into individual tokens.
- **Lemmatization:** Reducing words to their base form.
- **Padding:** Ensuring that each sequence has a uniform length for input to the LSTM.
```python
from nltk.stem import WordNetLemmatizer
wnl = WordNetLemmatizer()
def transform_data(text):
# Lemmatization and tokenization process
pass
df['ingredients_transformed'] = df['ingredients'].apply(transform_data)
```
## Training and Evaluation
- **Optimizer:** Adam
- **Loss Function:** Categorical Crossentropy
- **Evaluation Metric:** Accuracy
```python
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=16, validation_data=(X_test, y_test))
```
## Future Enhancements
- Introduce a more advanced recommendation system based on user preferences.
- Integrate with grocery delivery APIs for a seamless shopping experience.
- Improve model accuracy by experimenting with different architectures.
## Setup Instructions
1. Clone the repository:
```bash
git clone https://github.com/chandkund/Personalized-Meal-Recommendation.git
cd Personalized-Meal-Recommendation
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the training script:
```bash
python Personalized meal.ipynb
```
## License
This project is licensed under the MIT License.
👨🍳 Happy Cooking