Feature engineering and using different metrics to evalute the performance of Linear regression on original data - Githubissues

MoreeZ / sweng-2022

Regression/Classification/Deep learning based models. We take any regression or classification use case and do our predictions. This is easier said than done. We need to follow all the assumptions of the algorithm that we use. We need to use various algorithms, stacking models. tons of parameter tunings and model use various model evaluation tests. I personally would go for regression model. Note: In all the models, based on the use case chosen a dashboard needs to be created.

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Feature engineering and using different metrics to evalute the performance of Linear regression on original data #21

Open mingweiiiiiiiiii opened 2 years ago

mingweiiiiiiiiii commented 2 years ago

NormalityOfY Normality of dependent variable after feature engineering

mingweiiiiiiiiii commented 2 years ago

After feature engineering,there are only 20 features .

mingweiiiiiiiiii commented 2 years ago

MSE,R-Squared, AIC of LR model for data: MSE

Rsqured

AIC

mingweiiiiiiiiii commented 2 years ago

@1978abhay

mingweiiiiiiiiii commented 2 years ago

Do we need to use Principal decomposition anaysis to reduce the varible?

1978abhay commented 2 years ago

Do we need to use Principal decomposition anaysis to reduce the varible?

Up to you @8n76nn98 .. I am a client here.