https://github.com/poti1/cereal-ml
Cereal Calorie Predictor. A PyTorch neural network that predicts cereal calorie content from nutritional ingredients. Built as a hands-on ML exercise covering the full pipeline: data preprocessing, model training, evaluation, and prediction.
https://github.com/poti1/cereal-ml
deeplearning deeplearning-ai neural-network python python3 pytorch
Last synced: 20 days ago
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Cereal Calorie Predictor. A PyTorch neural network that predicts cereal calorie content from nutritional ingredients. Built as a hands-on ML exercise covering the full pipeline: data preprocessing, model training, evaluation, and prediction.
- Host: GitHub
- URL: https://github.com/poti1/cereal-ml
- Owner: poti1
- Created: 2026-05-07T12:10:20.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2026-05-07T12:44:09.000Z (about 1 month ago)
- Last Synced: 2026-05-07T14:33:04.698Z (about 1 month ago)
- Topics: deeplearning, deeplearning-ai, neural-network, python, python3, pytorch
- Language: Python
- Homepage:
- Size: 468 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# cereal-ml
Simple PyTorch-based calorie prediction using cereal nutrition data.
Predict cereal calories from nutritional features using:
- CLI commands
- Python API calls
- Trained PyTorch models
---
## Features
- Train calorie prediction model from cereal nutrition data
- Save/load PyTorch models
- CLI predictions
- Python API predictions
- Loss plotting
- MAE evaluation
- Simple custom feature parser
---
## Installation
Clone the repo:
```bash
git clone https://github.com/poti1/cereal-ml.git
cd cereal-ml
```
Install dependencies:
```bash
pip install -r requirements.txt
```
---
## Training
Train the model:
```bash
./cereal.py --train
```
Example output:
Example training metrics:
```text
Epoch 1/10 loss: 104.8351 val_loss: 111.3147
...
Epoch 10/10 loss: 25.8951 val_loss: 27.3140
mae: 28.50
```
---
## CLI Prediction
Run predictions directly from the command line:
```bash
./cereal.py --data 'sugars=3, protein=5'
```
Example:
---
## Python API Usage
Use the predictor directly in Python:
```python
import cereal
cereal.predict('fat=10, sodium=20, carbo=15')
```
Example:
---
## Example Predictions
| Input | Predicted Calories |
|---|---|
| sugars=3, protein=5 | 71 |
| fat=10, sodium=20, carbo=15 | 268 |
---
## Plotting Loss
Generate a training loss plot:
```bash
./cereal.py --plot
```
This creates:
```text
loss.png
```
---
## Model Output
The trained model is saved as:
```text
cereal.pt
```
---
## Tech Stack
- Python
- PyTorch
- pandas
- matplotlib
- scikit-learn
---
## Goals
This project was built to practice:
- ML training pipelines
- Regression models
- Feature parsing
- Model serialization
- CLI + Python interfaces
- End-to-end ML workflows
---
## License
MIT