Introduction to Python for insurance risk assessment in Deepnote

Insurance risk assessment involves evaluating the potential risk of insuring an individual or entity. This guide will walk you through using Python in Deepnote notebooks to perform an insurance risk assessment.

Setting up your environment

First, you need to set up your Deepnote environment. Make sure you have access to Deepnote and create a new project. Install the necessary libraries:

!pip install pandas numpy scikit-learn matplotlib

Data loading and exploration

Load your insurance dataset. You can use a publicly available dataset or your own data. Here's an example using a fictional dataset.

import pandas as pd

# Load the dataset
df = pd.read_csv('/work/insurance_dataset.csv')

# Display the first few rows of the dataset
df.head()

Data preprocessing

Preprocessing is crucial for building an accurate risk assessment model. This includes handling missing values, encoding categorical variables, and scaling numerical features.

# Handle missing values
df = df.dropna()

# Encode categorical variables
df = pd.get_dummies(df, drop_first=True)

# Scale numerical features
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
numerical_features = ['age', 'bmi', 'income']
df[numerical_features] = scaler.fit_transform(df[numerical_features])

Risk assessment models

We'll use Logistic Regression, Random Forest, and XGBoost for risk assessment. Split the data into training and testing sets.

from sklearn.model_selection import train_test_split

# Define the features and target variable
X = df.drop('risk', axis=1)
y = df['risk']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic Regression
from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression()
logreg.fit(X_train, y_train)

# Random Forest
from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier()
rf.fit(X_train, y_train)

# XGBoost
from xgboost import XGBClassifier

xgb = XGBClassifier()
xgb.fit(X_train, y_train)

Evaluation metrics

Evaluate the models using accuracy, precision, recall, and F1 score.

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Predictions
logreg_pred = logreg.predict(X_test)
rf_pred = rf.predict(X_test)
xgb_pred = xgb.predict(X_test)

# Evaluation
def evaluate_model(y_test, y_pred):
    print(f'Accuracy: {accuracy_score(y_test, y_pred)}')
    print(f'Precision: {precision_score(y_test, y_pred)}')
    print(f'Recall: {recall_score(y_test, y_pred)}')
    print(f'F1 Score: {f1_score(y_test, y_pred)}')

print("Logistic Regression Performance:")
evaluate_model(y_test, logreg_pred)

print("\\\\nRandom Forest Performance:")
evaluate_model(y_test, rf_pred)

print("\\\\nXGBoost Performance:")
evaluate_model(y_test, xgb_pred)

Visualization

Visualize the feature importance for a better understanding of the models.

import matplotlib.pyplot as plt

# Random Forest feature importance
feature_importance = rf.feature_importances_
features = X.columns

plt.figure(figsize=(10, 6))
plt.barh(features, feature_importance)
plt.xlabel('Importance')
plt.ylabel('Features')
plt.title('Random Forest Feature Importance')
plt.show()

Conclusion

This guide provided an introduction to using Python for insurance risk assessment in Deepnote notebooks. You learned how to load and preprocess data, build and evaluate models, and visualize feature importance.

Deepnote's collaborative environment and powerful computational resources make it an excellent choice for data science projects. Keep exploring and refining your models to improve risk assessment accuracy.

Feel free to reach out if you have any questions or need further assistance!