Project 1

Here’s a comprehensive summary of the steps taken to analyze the insurance dataset:

Project Summary: Insurance Charges Prediction

In this project, we conducted a thorough analysis and modeling of an insurance dataset to predict insurance charges based on several factors. The main steps taken were:

1. Data Exploration and Preprocessing:

• Data Loading: We loaded the insurance dataset (`insurance.csv`) and examined its structure. The dataset contains columns such as age, sex, bmi, children, smoker, region, and charges.
• Supervised Learning Identification: We identified this as a supervised learning problem with charges as the target variable.
• Data Cleaning: We addressed missing values, removed duplicate rows, handled outliers using the Interquartile Range (IQR) method, and converted categorical variables to numerical values using encoding techniques.

2. Outlier Removal:

• Function Creation: We created a function to remove outliers for all numeric columns using the IQR method to ensure the dataset was free from anomalies that could skew the results.

3. Statistical Independence Checks:

• Chi-Squared Test: We conducted a chi-squared test to check if `sex` and `smoker` are statistically independent.
• Variance Inflation Factor (VIF): We calculated the VIF to check for multicollinearity among the independent variables. This included converting categorical variables into dummy variables for accurate calculation.

4. Target Encoding for Categorical Variables:

• We applied target encoding to the categorical variables (`sex`, `smoker`, and `region`) to capture the relationship between these features and the target variable `charges`.

5. Dependency Analysis:

• Correlation Analysis: We used correlation matrices and heatmaps to visualize and understand the relationships between the independent variables and the target variable.

6. Modeling:

• Linear Regression: We built a linear regression model to predict charges based on the independent variables.
• Model Evaluation: We evaluated the model using metrics such as R-squared and RMSE to determine its accuracy.

7. Prediction and Interpretation:

• New Data Prediction: We used the trained model to predict insurance charges for a new data point (Age = 29, Sex = Female, BMI = 28, Children = 1, Smoker = Yes, Region = Southeast).
• Percentage Error Calculation: We calculated the Mean Absolute Percentage Error (MAPE) to understand the prediction error.
• Confidence Interval: We computed the 95% confidence interval for the average insurance charges to provide an estimate range for the mean charge.

Conclusion

Through these steps, we successfully cleaned the data, analyzed dependencies, built a predictive model, and evaluated its performance. The project provides insights into the factors influencing insurance charges and demonstrates a robust approach to predictive modeling in insurance analytics.