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I built several predictive models to predict consumer spending, and conducted GridSearch to find the best hyperparameter for every models. Models I used including: Ensemble Stacking/ Deep Neural Network/ Support Vector Regressor/ ElasticNet/ Regression Tree/ KNN Regressor

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evelyncy96/Consumer-Spending-Prediction

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Consumer-Spending-Prediction

  • Goal: Predict the amount of purchase for consumers
  • Data source: please refer to the dataset in the repository
  • Model: Ensemble Model(Stacking)/ Deep Neural Network/ Support Vector Regressor/ ElasticNet/ Regression Tree/ KNN Regressor

In this project, I first created a function to compiled the normalization and gridsearch into pipeline. The return of the function including the best gridsearch score, best parameter of the model, and the MSE of test set of each model. The result shows that Deep Neural Network is the best model with the lowest MSE score.

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I built several predictive models to predict consumer spending, and conducted GridSearch to find the best hyperparameter for every models. Models I used including: Ensemble Stacking/ Deep Neural Network/ Support Vector Regressor/ ElasticNet/ Regression Tree/ KNN Regressor

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