Insurance Cost Prediction Using Linear Regression

This project demonstrates how to predict insurance costs using a linear regression model. By leveraging Python and its powerful data science libraries, we import and analyze the data, preprocess it, and then build and evaluate a predictive model.

Key Features

• Data Collection & Analysis: Load and explore the insurance dataset.
• Data Visualization: Use plots to understand data distribution and relationships.
• Preprocessing: Encode categorical variables and split data into features and target variables.
• Model Training: Train a linear regression model to predict insurance costs.
• Model Evaluation: Evaluate the model's performance using metrics like R-squared.

Components

• Python libraries: NumPy, pandas, matplotlib, seaborn, scikit-learn.
• Jupyter Notebook for interactive data analysis and model building.

How It Works

1. Import Dependencies: Import necessary libraries for data manipulation, visualization, and modeling.
2. Data Collection & Analysis: Load the insurance dataset and explore its structure, including checking for missing values and basic statistical measures.
3. Data Visualization: Plot various features like age, gender, BMI, number of children, smoker status, and region to understand their distributions and relationships.
4. Data Preprocessing: Encode categorical variables (e.g., sex, smoker, region) into numerical values. Split the data into features (X) and target variable (Y).
5. Train-Test Split: Divide the dataset into training and testing sets.
6. Model Training: Train a linear regression model on the training data.
7. Model Evaluation: Evaluate the model's performance on both training and testing data using the R-squared metric.
8. Predictive System: Build a system to predict insurance costs for new data inputs.

Applications

• Predict insurance costs based on individual attributes.
• Understand factors that influence insurance costs.