mjpsm / Idea-Difficulty-XGB

mjpsm/Idea-Difficulty-XGB

🧾 Overview

This model predicts the difficulty of a business idea as Low , Medium , or High .
It is part of the Entrepreneurial Readiness series of tabular classifiers (alongside Skill Level, Risk Tolerance, and Confidence).

The model was trained with XGBoost on a 2,000-row synthetic dataset of structured features that capture common difficulty drivers.

📥 Input Features

Target label:

• Low = Idea is relatively easy to execute
• Medium = Moderately challenging
• High = Difficult, requiring significant resources and expertise

📊 Performance

• Accuracy: 0.9733
• Macro F1: 0.9733
• Log Loss: 0.0584

Confusion Matrix (rows = true, cols = predicted):

🚀 Quickstart (load from the Hub)

# Load directly from: mjpsm/Idea-Difficulty-XGB
from huggingface_hub import hf_hub_download
from xgboost import XGBClassifier
import pandas as pd, json

REPO_ID = "mjpsm/Idea-Difficulty-XGB"
model_path = hf_hub_download(REPO_ID, "xgb_model.json")

clf = XGBClassifier()
clf.load_model(model_path)

# IMPORTANT: Use the same feature names/order as training
FEATURES = [
    "capital_required","technical_complexity","market_competition",
    "customer_acquisition_difficulty","regulatory_hurdles",
    "time_to_mvp_months","team_expertise_required","scalability_requirement"
]

row = pd.DataFrame([{
    "capital_required": 7,
    "technical_complexity": 9,
    "market_competition": 6,
    "customer_acquisition_difficulty": 8,
    "regulatory_hurdles": 7,
    "time_to_mvp_months": 18,
    "team_expertise_required": 5,
    "scalability_requirement": 9
}], columns=FEATURES)

pred_id = int(clf.predict(row)[0])

# If label_map.json is NOT uploaded, default to alphabetical LabelEncoder order:
CLASSES = ["High","Low","Medium"]  # update if you publish label_map.json
print("Predicted Idea Difficulty:", CLASSES[pred_id])

# OPTIONAL: If you later upload 'label_map.json', prefer this:
# lm_path = hf_hub_download(REPO_ID, "label_map.json")
# label_map = json.load(open(lm_path))
# inv_map = {v:k for k,v in label_map.items()}
# print("Predicted Idea Difficulty:", inv_map[pred_id])