dummy_ui

app.py

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+import numpy as np
+import pandas as pd
+import streamlit as st
+from sklearn.linear_model import LinearRegression
+
+# Generate synthetic data
+np.random.seed(42)
+n_samples = 500
+
+# Company attributes
+company_size = np.random.randint(50, 10000, n_samples)  # Number of employees
+industry_risk = np.random.choice([1, 2, 3, 4, 5], n_samples)  # Risk level (1: Low, 5: High)
+past_incidents = np.random.randint(0, 10, n_samples)  # Number of past cyber incidents
+security_measures = np.random.randint(1, 6, n_samples)  # Security rating (1: Poor, 5: Excellent)
+compliance = np.random.choice([0, 1], n_samples)  # 1 if compliant, 0 otherwise
+
+# Define premium based on attributes
+base_premium = 5000
+premium = (
+    base_premium + (company_size * 0.5) + (industry_risk * 2000) + (past_incidents * 1500)
+    - (security_measures * 1000) - (compliance * 3000) + np.random.normal(0, 2000, n_samples)
+)
+
+# Ensure minimum premium
+premium = np.clip(premium, 2000, None)
+
+# Create DataFrame
+data = pd.DataFrame({
+    "Company Size": company_size,
+    "Industry Risk": industry_risk,
+    "Past Incidents": past_incidents,
+    "Security Measures": security_measures,
+    "Compliance": compliance,
+    "Premium": premium
+})
+
+# Fit a simple regression model to understand impact of variables
+X = data[["Company Size", "Industry Risk", "Past Incidents", "Security Measures", "Compliance"]]
+y = data["Premium"]
+
+model = LinearRegression()
+model.fit(X, y)
+
+coefficients = pd.DataFrame({"Feature": X.columns, "Coefficient": model.coef_})
+
+# Streamlit UI
+st.title("Cyber Insurance Premium Estimator")
+
+company_size_input = st.number_input("Company Size (Number of Employees)", min_value=50, max_value=10000, value=500)
+industry_risk_input = st.selectbox("Industry Risk Level", [1, 2, 3, 4, 5])
+past_incidents_input = st.number_input("Past Cyber Incidents", min_value=0, max_value=10, value=2)
+security_measures_input = st.selectbox("Security Measures Rating", [1, 2, 3, 4, 5])
+compliance_input = st.selectbox("Compliance Status", [0, 1], format_func=lambda x: "Compliant" if x == 1 else "Non-Compliant")
+
+if st.button("Calculate Premium"):
+    input_data = np.array([[company_size_input, industry_risk_input, past_incidents_input, security_measures_input, compliance_input]])
+    predicted_premium = model.predict(input_data)[0]
+    st.subheader(f"Estimated Premium: ${predicted_premium:,.2f}")
+    
+    st.subheader("Feature Importance")
+    st.write(coefficients)