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This project is implemented using ML to predict the demand of the given dataset concerned with reviews of customers of a food company. The data was analysed using python and certain trends, findings were discovered in the process. Three ML models were trained namely Random Forest regressor, XGB Regressor, Extra Trees Regressor. Extra Trees Regre…

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ashwin-srdy/Restaurant_Cuisine_Demand_Predictor

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This project is implemented using ML to predict the demand of the given dataset concerned with reviews of customers of a food company. The data was analysed using python and certain trends, findings were discovered in the process. Three ML models were trained namelyRandom Forest regressor, XGB Regressor, Extra Trees Regressor. Extra Trees Regressor shows highest r2_score with Random Forest regressor following it.

DEPLOYMENT LINKS:

  1. https://cruisnien.herokuapp.com/
  2. https://crusine-dkr-app.herokuapp.com/

ALGORITHMS r2_scores:

  • Random Forest regressor :- 0.9286105766896842
  • XGB Regressor :- 0.7636484968153312
  • Extra Trees Regressor :- 0.9385324707057678

Future Scope:

Time Series predictions can be done on the data for more accurate understanding and better accuracy.

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This project is implemented using ML to predict the demand of the given dataset concerned with reviews of customers of a food company. The data was analysed using python and certain trends, findings were discovered in the process. Three ML models were trained namely Random Forest regressor, XGB Regressor, Extra Trees Regressor. Extra Trees Regre…

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