Mood Classification

Fill the form and predict the amount your customer will purchase on Black Friday.

We can improve this model by changing the model architecture, and adding more training data. At this level I wanted to showcase my work towards implementation and deployment of Machine Learning model.

Development summary

High-level architecture of the application

Model summary

In this dataset we have a set of Categorical Independent Variables and Continuous Dependent Variable. Based on the analysis I prefered to try out following algorithms.

1. Decision Tree
2. Random Forest
3. Support Vector Regression (SVR)
4. Neural Networks also a prefered option.

I used GridSearchCV to search for optimal hyperparameters.

Best params:  {'max_depth': 10, 'min_samples_leaf': 10, 'min_samples_split': 50}
Best score:  0.6528669709738966
Best estimator:  
DecisionTreeRegressor(max_depth=10, min_samples_leaf=10, min_samples_split=50)