jeh0753/tractor_case

Predicted tractor sales prices at auctions using multiple datasets. Applied linear regression, imputing missing data with k-Nearest Neighbors (kNN), and evaluated results using root mean squared logarithmic error (RMSLE score).

Regression Case Study

The goal is to predict the sale price of a particular piece of heavy equipment at auction based on it's usage, equipment type, and configuration. The data is sourced from auction result postings and includes information on usage and equipment configurations.

Evaluation

We evaluated our model based on Root Mean Squared Log Error. Which is computed as follows:

where p_i are the predicted values and a_i are the target values.

Note that this loss function is sensitive to the ratio of predicted values to the actual values, a prediction of 200 for an actual value of 100 contributes approximately the same amount to the loss as a prediction of 2000 for an actual value of 1000.

This loss function is implemented in score_model.py.

Data

The data for this case study are in ./data.