Hyperparameter Tuning with Sklearn GridSearchCV and RandomizedSearchCV

About Our Dataset

The data set contains details of bank customer churn. Customer churn refers to when a customer ceases his or her relationship with a company. The goal is to create a machine learning model to predict whether a customer will leave the bank services or not. The dataset consists of 1000 rows and 14 columns in total





  • It is as good as the set of hyperparameters provided by you as input. It does not magically search for all possible hyperparameters unless you give them as part of the input.
  • To go through all combinations of hyperparameters  GrisSearchCV can take too much computing resources.


  • Veer Kumar

    I am passionate about Analytics and I am looking for opportunities to hone my current skills to gain prominence in the field of Data Science.

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