README.md

---
title: Kickstarter Success Prediction
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: "3.9"
app_file: app.py
pinned: false
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# Kickstarter Success Prediction API

This API predicts the success probability of Kickstarter campaigns using machine learning. 

## API Usage

### Endpoint: `/predict`

Send a POST request with your campaign data in JSON format.

### Input Format

```json
{
  "raw_description": "Detailed project description...",
  "raw_blurb": "Short project summary...",
  "raw_risks": "Project risks and challenges...",
  "raw_category": "Technology",
  "raw_subcategory": "Gadgets",
  "raw_country": "Canada",
  "description_length": 557,
  "funding_goal": 58000,
  "image_count": 8,
  "video_count": 3,
  "campaign_duration": 90,
  "previous_projects_count": 5,
  "previous_success_rate": 0.4,
  "previous_pledged": 18745.33,
  "previous_funding_goal": 23564.99
}
```

The system will use the provided numerical values directly if they exist in the input. If any numerical fields are missing, they will be calculated during preprocessing.

### Output Format

```json
{
  "success_probability": 0.7532,
  "predicted_outcome": "Success",
  "shap_values": {
    "funding_goal": -0.8991450071334839,
    "description_embedding": -0.04273056983947754,
    "subcategory_embedding": 0.011444330215454102,
    "previous_funding_goal": -0.008600413799285889,
    "video_count": 0.0037734508514404297,
    ...
  },
  "longformer_embedding": [0.0213, -0.0124, 0.0342, ..., 0.0547]
}
```

- `success_probability`: A value between 0 and 1 representing the likelihood of project success
- `predicted_outcome`: "Success" if probability ≥ 0.5, otherwise "Failure"
- `shap_values`: Contribution of each feature to the prediction (positive values increase success probability, negative values decrease it)
- `longformer_embedding`: The 768-dimensional vector generated by the Longformer model representing the semantic content of the project description (useful for further analysis or clustering)

## Example Usage with Python

```python
import requests
import json
import numpy as np

# API endpoint
api_url = "https://huggingface.co/spaces/angusfung/kickstarter-success-prediction/predict"

# Load your campaign data
campaign_data = {
  "raw_description": "Introducing the AquaGo...",
  "raw_blurb": "AquaGo is a smart, eco-friendly portable water purifier...",
  "raw_risks": "Bringing a product to market involves...",
  "raw_subcategory": "Gadgets",
  "raw_category": "Technology",
  "raw_country": "Canada",
  "funding_goal": 2000,
  "image_count": 8,
  "video_count": 3
}

# Make prediction request
response = requests.post(api_url, json=campaign_data)

# Print results
if response.status_code == 200:
    result = response.json()
    print(f"Success Probability: {result['success_probability']:.2f}")
    print(f"Predicted Outcome: {result['predicted_outcome']}")
    print("\nTop 5 SHAP Values (Feature Importance):")
    for i, (feature, value) in enumerate(list(result['shap_values'].items())[:5]):
        print(f"{feature}: {value:.4f}")
    
    # Access the longformer embedding if needed
    if 'longformer_embedding' in result:
        embedding = np.array(result['longformer_embedding'])
        print(f"\nLongformer Embedding Shape: {embedding.shape}")
else:
    print(f"Error: {response.status_code}")
    print(response.text)
```

## Example Usage with cURL

```bash
curl -X POST "https://huggingface.co/spaces/angusfung/kickstarter-success-prediction/predict" \
     -H "Content-Type: application/json" \
     -d @campaign.json
```

Where `campaign.json` contains your campaign data in the format described above.