Regression Analysis of Steel Products Offer Prices.

INTRODUCTION

• We have 181630 rows of data of various steel items having following features: 'Id', 'item_date', 'quantity tons', 'customer', 'country', 'status', 'item type', 'application', 'thickness', 'width', 'material_ref’, 'product_ref', 'delivery date', 'selling_price'.
• The project performs a regression analysis with selling_price feature to be predicted.
• We divide data into 90-10% train-test split respectively. Analysis and model is done on the train set and tested on unseen test data.

SUMMARY

• Selected Model: XGBoost
• Hyperparameter search method: Bayesian Search CV
• Best r2 score:
  • Test r2 score: 0.90
  • Train r2 score: 0.92
• Features used:
  • ‘quantity tons’
  • ‘application’
  • ‘thickness’
  • ‘width’
  • ‘product_ref’
  • ‘delivery date’
  • ‘status’ .
• Categorical features shortlisting criteria: ANOVA test F statistical scores. (for details, refer EDA notebook)
• High value of ANOVA F statistics shows that, the mean and spread of data among different categories are easily separable so such features gives better r2 score and explaining variance for model becomes easy.
• Numerical features were power transformed and standardized to get near gaussian like distribution.
• Categorical features were one-hot encoded.
• Total input features: 98
• Important Features:
  • ‘status’
  • ‘delivery date’

Model	Train r2 score	Val r2 score	Test r2 score	Hyperparameter search method
Linear Regresion	0.83	0.83	0.82	Grid Search
Decision Tree	0.86	0.80	not performed	Grid Search
Random Forest	0.70	0.69	not performed	Bayesian Search
XGBoost	0.92	0.89	0.90	Bayesian Search
Neural Network	0.90	0.89	0.89	None