Price forecasting model in Excel
Budget: $250 – $750 AUD
We operate an insurance brokerage.
Our challenge is to create a simple insurance price forecasting tool based on historical pricing data.
Specifically:
1. We have an excel sheet with 14,073 rows and 7 columns.
2. Columns are; insurance price, Vehicle Make, Vehicle Model, Vehicle Year, Driver Gender, Driver Age, Driver Location (name of an Australian State).
3. The rows are insurance prices.
We want to create a forecasting model using multivariate regression, or another approach, to be able to forecast what the dependant variable (insurance price) might be based on the various independent variables, such as vehicle make, model, year and various driver characteristics.
The reason why we need help is that the data has a large number of categorical variables. There are 43 different types of vehicle makes and 434 different vehicle models. As a result, we don't know how to program the dummy variables. Using 0/1 dummy variables would likely fail.
Data:
- We are using a small dataset in an excel sheet.
Data Preprocessing:
- No cleaning or preprocessing of the data is required.
Intended Usage:
- The price forecasting model is intended for us being able to offer instant quotes to customers.
When customers apply for insurance through our brokerage, we want to be able to instantly quantify how much the policy might cost, before we proceed to obtaining a legally binding quotation from the insurance company.
Skills and Experience:
- Strong proficiency in Excel, regression and data analysis.
- Experience in developing forecasting models.
Our challenge is to create a simple insurance price forecasting tool based on historical pricing data.
Specifically:
1. We have an excel sheet with 14,073 rows and 7 columns.
2. Columns are; insurance price, Vehicle Make, Vehicle Model, Vehicle Year, Driver Gender, Driver Age, Driver Location (name of an Australian State).
3. The rows are insurance prices.
We want to create a forecasting model using multivariate regression, or another approach, to be able to forecast what the dependant variable (insurance price) might be based on the various independent variables, such as vehicle make, model, year and various driver characteristics.
The reason why we need help is that the data has a large number of categorical variables. There are 43 different types of vehicle makes and 434 different vehicle models. As a result, we don't know how to program the dummy variables. Using 0/1 dummy variables would likely fail.
Data:
- We are using a small dataset in an excel sheet.
Data Preprocessing:
- No cleaning or preprocessing of the data is required.
Intended Usage:
- The price forecasting model is intended for us being able to offer instant quotes to customers.
When customers apply for insurance through our brokerage, we want to be able to instantly quantify how much the policy might cost, before we proceed to obtaining a legally binding quotation from the insurance company.
Skills and Experience:
- Strong proficiency in Excel, regression and data analysis.
- Experience in developing forecasting models.