vaibhav-vyas/Social-Spam-CatBoost

Social Spam CatBoost Model

This is a Machine Learning model built using the CatBoost Classifier to automatically assess the validity of content moderation complaints. Based on anonymized tabular user activity and platform interaction data, it predicts whether a complaint will be recognized as valid or rejected.

Project Overview

• Task Type: Binary Classification (Target: is_valid -> 1 for Valid, 0 for Rejected)
• Model Architecture: CatBoost
• Primary Quality Metric: F1-Score

How to Use in Python

You can download and load this model directly from the Hugging Face Hub using the following Python script:

import huggingface_hub
from catboost import CatBoostClassifier
import pandas as pd

# 1. Download the model file from Hugging Face Hub
model_path = huggingface_hub.hf_hub_download(
    repo_id="vaibhav-vyas/Social-Spam-CatBoost", 
    filename="my_model.cbm"
)

# 2. Initialize and load the CatBoost model
model = CatBoostClassifier()
model.load_model(model_path)
print("Model successfully loaded from Hugging Face Hub!")

# 3. Run predictions on your new data
# new_data = pd.read_csv("your_test_data.csv")
# predictions = model.predict(new_data)

Project Ecosystem

• Live Demo Web App: Hugging Face Space Dashboard
• Source Code & Pipelines: GitHub Repository