Wine-Price-Prediction

This is an class kaggle competition on predicting the price of wine.

Summary

• Predicting the price of wine based on a collection of reviews and other product features.

• We use the Random Forest Regressor and the XGBoost Algorithm to predict the prices of wine.

• Feature Engineering : We perform the following feature engineering ;

  • Label encoding
  • One Hot Encoding
  • TF-IDF

• 10 Fold cross validation applied in both models.

• Random Forest performs better than XGBoost.

• Combining the models gives even better results.

• We used two models after cross validation.

• Their predictions were Averaged and RMSE of 20.4 was achieved.