README.md

metadata

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

{
  "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

{
  "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

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

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.