Regression with an Insurance Dataset

1. Problem Statement

Imagine that you are an important member of the actuarial staff of one of the country's largest insurance companies. Every year the company reevaluates pricing to ensure that they are able to meet obligations, cover expenses, and generate a profit. You are tasked with exploring a dataset which includes everything from demographics and history to coverage options. Your goal is to create a predictive model that can accurately predict insurance premiums.

You will need to carefully analyze all of the available features to build a model that is both as fair and as accurate as possible. If you are successful in making accurate predictions, the company can continue to offer competitive prices and ensure they have the resources needed to provide excellent services. It will also allow them to more fairly price premiums, avoiding excessively high premiums for certain groups.

Goal: The goal of this project is to build a regression model that can accurately predict insurance premiums.

2. Data Description

The dataset for this competition (both train and test) was generated from a deep learning model trained on the Insurance Premium Prediction dataset.

• Rows: 1048576
• Columns: 21
• Variables:
  • id: Unique identifier
  • Age: Age
  • Gender: Gender
  • Annual Income: Annual income
  • Marital Status: Marital status
  • Number of Dependents: Number of dependents
  • Education Level: Education level
  • Occupation: Occupation
  • Health Score: Health score
  • Location: Location
  • Policy Type: Policy type
  • Previous Claims: Previous claims
  • Vehicle Age: Vehicle age
  • Credit Score: Credit score
  • Insurance Duration: Insurance duration
  • Policy Start Date: Policy start date
  • Customer Feedback: Customer feedback
  • Smoking Status: Smoking status
  • Exercise Frequency: Exercise frequency
  • Property Type: Property type
  • Premium Amount: Premium amount (target variable)

Data Source: Kaggle Playground Series S4E12

3. Your Task

Your task is to build a regression model to predict insurance premiums.

1. Data Exploration and Preprocessing:
   • Load the dataset using Pandas.
   • Explore the data to understand the distribution of features.
   • Handle missing values, if any.
   • Convert categorical variables to numerical format using techniques like one-hot encoding.
   • Scale numerical features to ensure fair treatment in the model.
2. Feature Engineering (Optional):
   • Create new features that may improve model performance.
3. Model Building:
   • Split the data into training and validation sets.
   • Choose a suitable regression algorithm (e.g., Linear Regression, Random Forest, Gradient Boosting).
   • Train the model on the training data.
4. Model Evaluation:
   • Evaluate the model's performance on the validation data.
   • Use metrics such as RMSE, MAE, and R-squared.
   • Visualize the results using scatter plots.
5. Prediction and Submission:
   • Make predictions on the test data.

Python Libraries: Pandas, NumPy, scikit-learn, Matplotlib/Seaborn.