---
language: en
license: mit
tags:
  - regression
  - linear-regression
  - readiness
  - entrepreneurship
  - sklearn
model-index:
  - name: Entrepreneurial Readiness Linear Regression Model
    results: []
---

# Entrepreneurial Readiness Linear Regression Model

This is a **linear regression model** trained to predict entrepreneurial readiness scores (from 0 to 10) based on financial, personal, and psychological factors like:

- Savings, income, expenses
- Risk level, confidence, sales skills
- Age, number of dependents
- Difficulty of business idea

## 🧪 Model Details

- **Algorithm**: Linear Regression
- **Library**: scikit-learn
- **Input**: 11 numerical features
- **Output**: Readiness score (0–10)
- **Evaluation**:
  - R² Score: 0.95
  - MSE: 0.59

## 🗂️ Files

- `readiness_model_v2.pkl` – Trained regression model
- `readiness_scaler_v2.pkl` – StandardScaler used during training

## 🚀 Usage Example

Use this example to load the model and make predictions:

    import joblib
    import pandas as pd

    model = joblib.load("readiness_model_v2.pkl")
    scaler = joblib.load("readiness_scaler_v2.pkl")

    input_data = pd.DataFrame([{
        "savings_amount": 10000,
        "monthly_income": 3000,
        "monthly_expenses": 2000,
        "monthly_entertainment": 300,
        "sales_skills": 6,
        "risk_level": 5,
        "age": 22,
        "dependents": 0,
        "assets": 2000,
        "confidence": 7,
        "difficulty_of_business_idea": 6
    }])

    scaled_input = scaler.transform(input_data)
    prediction = model.predict(scaled_input)
    print(f"Predicted Readiness: {prediction[0]:.2f}")