entrepreneurial-linear-regression

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: 0.94
  • MSE: 0.74

🗂️ Files

• readiness_model.pkl – Trained regression model
• readiness_scaler.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.pkl")
scaler = joblib.load("readiness_scaler.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}")