---
license: mit
language:
- en
tags:
- sentiment-analysis
- imdb
- scikit-learn
- mlp
- text-classification
pipeline_tag: text-classification
---

# IMDB Sentiment MLP

This model is a course project for IMDB movie-review sentiment classification.

It uses a TF-IDF text representation followed by a small `scikit-learn` MLP neural network.

## Metrics

- Accuracy: 82.00%
- Train samples: 400
- Test samples: 100
- Dataset: `imdb_top_500.csv`
- Labels: `0 = negative`, `1 = positive`

## Files

- `model.joblib`: full scikit-learn pipeline
- `vectorizer.joblib`: standalone TF-IDF vectorizer
- `metrics.json`: training and evaluation metrics

## Example

```python
import joblib

model = joblib.load("model.joblib")
prediction = model.predict(["This movie is great and deeply moving."])[0]
print("positive" if prediction == 1 else "negative")
```