Supervised machine learning classification: Customer Churn prediction (not guided )

First, separate the target and the features into two variables.

Store the features in X and the target y.

Then, use train_test_split to split the data into training and testing sets. Split the dataset in 80% training, 30% testing, and random_state=0.

Store the values in the variables in X_train,X_test,y_train, y_test,random_state .

Train an XGBoost Classification model using the training data, and store the model in model.

Calculate the accuracy of both the training and testing sets, and the macro-average precision, recall and F1-score of the testing set and run the code in a Jupyter Notebook.

Store the results in the variables test_precision, test_recall and test_f1score.

Note: The expected evaluation metrics for a simple problem varies depending on the specifics of the problem and data. For this particular problem, you are required to achieve a precision of over 70%.

Now, let's submit your predictions for the test dataset to get a score from the platform.

The RandomizedSearchCV() function takes in the following arguments:

• estimator: The estimator being fit, here it's XGBoost.
• param_distributions: Unlike params - this is the distribution of possible hyperparameters to use.
• cv: Number of cross-validation iterations
• n_iter: Number of hyperparameter combinations to choose from verbose: Prints more output

For this assessment, load the train_tuning.csv dataset. You do not need to preprocess this dataset. After loading the dataset, follow the instructions and complete the exercise:

1. Create a parameter grid called rs_param_grid that contains:

   • 'max_depth': list((range(3,12)))
   • 'alpha': [0,0.001, 0.01,0.1,1]
   • 'subsample': [0.5,0.75,1]
   • 'learning_rate': np.linspace(0.01,0.5, 10)
   • 'n_estimators': [10, 25, 40]

2. Create a RandomizedSearchCV object called xgb_rs, passing in the parameter grid to param_distributions. Also, specify verbose=2, cv=3, and n_iter=5.

3. Your objective is to maximize F1-score.

4. Fit the RandomizedSearchCV object to X and y.

What are the best parameters?