pokemon-price-predictor

Pokémon TCG Price Predictor

This repository contains a PyTorch model trained to analyze Pokemon card features to identify cards with potential for significant price increases.

This model is the backend for the PokePrice Gradio Demo.

Model Description

The model is a simple Multi-Layer Perceptron (MLP) implemented in PyTorch. It takes various features of a Pokémon card as input—such as its rarity, type, and historical price data—and outputs a single logit. A sigmoid function can be applied to this logit to get a probability score for the price rising.

• Model type: Tabular Binary Classification
• Architecture: PricePredictor (MLP)
• Framework: PyTorch
• Training Data: A custom dataset derived from the PokemonTCG/pokemon-tcg-data repository, augmented with pricing history.

How to Use

To use this model, you will need torch, scikit-learn, pandas, and huggingface_hub. You can download the model artifacts directly from the Hub.

First, ensure you have network.py (which defines the model class) in your working directory.

import torch
import joblib
import json
import pandas as pd
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

# Make sure you have network.py in the same directory
from network import PricePredictor

REPO_ID = "your-username/pokemon-price-predictor"
MODEL_FILENAME = "model.safetensors"
CONFIG_FILENAME = "config.json"
SCALER_FILENAME = "scaler.pkl"

print("Downloading model files from the Hub...")
model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
config_path = hf_hub_download(repo_id=REPO_ID, filename=CONFIG_FILENAME)
scaler_path = hf_hub_download(repo_id=REPO_ID, filename=SCALER_FILENAME)
print("Downloads complete.")

with open(config_path, "r") as f:
    config = json.load(f)

feature_columns = config["feature_columns"]
input_size = config["input_size"]

model = PricePredictor(input_size=input_size)
model.load_state_dict(load_file(model_path))
model.eval()

scaler = joblib.load(scaler_path)

data_to_predict = {
    'rawPrice': [10.0], 'gradedPriceTen': [100.0], 'gradedPriceNine': [50.0],
}

input_df = pd.DataFrame(data_to_predict)
missing_cols = set(feature_columns) - set(input_df.columns)
for c in missing_cols:
    input_df[c] = 0.0 
input_df = input_df[feature_columns]


input_scaled = scaler.transform(input_df.values)
input_tensor = torch.tensor(input_scaled, dtype=torch.float32)

with torch.no_grad():
    logits = model(input_tensor)
    probability = torch.sigmoid(logits).item()

print(f"\nPrediction for the input card:")
print(f"  - Probability of 30% price rise in 6 months: {probability:.4f}")

if probability > 0.5:
    print("  - Prediction: Price WILL LIKELY rise.")
else:
    print("  - Prediction: Price WILL LIKELY NOT rise.")

Model Explainability

To understand the model's decisions, SHAP (SHapley Additive exPlanations) values were computed.

Global Feature Importance

This plot shows the average impact of each feature on the model's output magnitude. Features at the top are most influential.

Limitations and Bias

• The model is trained on historical data and may not predict future trends accurately, especially in a volatile market.
• The definition of "price rise" is fixed at 30% over 6 months. The model is not trained for other thresholds or timeframes.
• The dataset may have inherent biases related to card popularity, set releases, or data collection artifacts.

Author

Callum Anderson