Insurance Premium Prediction – IT and Computer Engineering Guide
1. Project Overview
Objective: Build a predictive model to estimate insurance
premiums based on customer details and risk factors.
Scope: Enable insurance companies to set premiums accurately by analyzing
customer risk profiles.
2. Prerequisites
Knowledge: Understanding of regression techniques, data
preprocessing, and model evaluation metrics.
Tools: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and optionally
XGBoost or LightGBM.
Data: A dataset containing customer details (e.g., age, gender, driving record,
claims history) and insurance premiums.
3. Project Workflow
- Data Collection: Gather data on customer demographics, risk factors, and premiums.
- Data Preprocessing: Clean data, handle missing values, encode categorical variables, and scale numerical features.
- Exploratory Data Analysis: Identify patterns and correlations between factors and premiums.
- Feature Engineering: Create new features to enhance model performance.
- Model Training: Train regression models such as Linear Regression, Random Forest, or Gradient Boosting.
- Evaluation: Assess model accuracy using metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
- Deployment: Deploy the model via a web application or API for use by insurance companies.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
Step 2: Load and Preprocess Data
# Load dataset
data = pd.read_csv('insurance_data.csv')
# Handle missing values
data.fillna(data.median(), inplace=True)
# Encode categorical variables
data = pd.get_dummies(data, columns=['Gender', 'Region'], drop_first=True)
# Scale numeric features
scaler = StandardScaler()
data[['Age', 'Annual_Income', 'Claim_History']] =
scaler.fit_transform(data[['Age', 'Annual_Income', 'Claim_History']])
Step 3: Train-Test Split
# Define features and target
X = data.drop(columns=['Insurance_Premium'])
y = data['Insurance_Premium']
# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
Step 4: Train the Model
# Train a Gradient Boosting Regressor
model = GradientBoostingRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
Step 5: Evaluate the Model
# Make predictions
y_pred = model.predict(X_test)
# Evaluate performance
print('MAE:', mean_absolute_error(y_test, y_pred))
print('RMSE:', np.sqrt(mean_squared_error(y_test, y_pred)))
print('R² Score:', r2_score(y_test, y_pred))
Step 6: Visualize Results
# Plot actual vs. predicted premiums
plt.scatter(y_test, y_pred, alpha=0.7)
plt.xlabel('Actual Premiums')
plt.ylabel('Predicted Premiums')
plt.title('Actual vs Predicted Premiums')
plt.show()
5. Results and Insights
Analyze the model's performance metrics and highlight factors with the greatest impact on insurance premiums.
6. Challenges and Mitigation
Imbalanced Data: Ensure balanced representation of high- and
low-risk profiles.
Overfitting: Use cross-validation and regularization to prevent overfitting.
7. Future Enhancements
Integrate additional factors like geographic risk and claim
frequency.
Implement a recommendation system for personalized insurance packages.
8. Conclusion
The Insurance Premium Prediction project utilizes machine learning to accurately predict premiums, benefiting both insurers and customers.