---
license: mit
tags:
- recommendation-system
- collaborative-filtering
- matrix-factorization
- movie-recommendations
- movielens
- machine-learning
library_name: scikit-learn
---

# DataSynthis_ML_JobTask

A powerful movie recommendation system using collaborative filtering and matrix factorization techniques on the MovieLens 100k dataset.

## Model Description

This model provides personalized movie recommendations using two state-of-the-art algorithms:

- **Collaborative Filtering (CF)**: Item-based similarity using cosine similarity
- **Matrix Factorization (SVD)**: Singular Value Decomposition for dimensionality reduction

## Dataset

- **MovieLens 100k**: 100,000 ratings from 943 users on 1,682 movies
- **User ID Range**: 1-943
- **Movie Count**: 1,682 unique movies
- **Rating Scale**: 1-5 stars

## Usage

### Python

```python
from model import predict

# Get recommendations using SVD (default)
recommendations = predict(user_id=1, n_recommendations=10, method="svd")

# Get recommendations using collaborative filtering
recommendations = predict(user_id=1, n_recommendations=10, method="cf")

print(recommendations)
```

### Parameters

- **user_id** (int): User ID between 1-943 (required)
- **n_recommendations** (int): Number of recommendations between 1-20 (default: 10)
- **method** (str): "svd" for matrix factorization or "cf" for collaborative filtering (default: "svd")

### Output

Returns a list of dictionaries with movie recommendations:

```json
[
  {
    "movie_id": 50,
    "title": "Star Wars (1977)",
    "predicted_rating": 4.5
  },
  {
    "movie_id": 181,
    "title": "Return of the Jedi (1983)",
    "predicted_rating": 4.3
  }
]
```

## Model Performance

- **SVD Method**: Fast predictions with good accuracy using 20 components
- **Collaborative Filtering**: More interpretable, based on item similarity
- **Cold Start Handling**: Graceful error handling for unknown users

## Technical Details

- **Framework**: Scikit-learn
- **Algorithms**: TruncatedSVD, Cosine Similarity
- **Data Processing**: Pandas for efficient matrix operations
- **Memory Efficient**: Optimized for large-scale recommendation tasks

## Installation

```bash
pip install pandas numpy scikit-learn
```

## Training

The model is pre-trained on the MovieLens 100k dataset. To retrain:

```python
from model import MovieRecommender

model = MovieRecommender()
model.load_data()
model.train()
model.save_model("movie_recommender.pkl")
```

## Citation

```bibtex
@misc{datasynthis_ml_jobtask,
  title={DataSynthis ML JobTask: Movie Recommendation System},
  author={tasdid25},
  year={2025},
  url={https://huggingface.co/tasdid25/DataSynthis_ML_JobTask}
}
```

## License

MIT License - see LICENSE file for details.