Step 1: Assign training data and labels.

Make ‘Click on Ad’ as labels; use the remaining variables as data.

Step 2: Train Test Split.

Split the training data into training and test sets with train_test_split().

Set parameter test_size = 0.3, random_state to the last two digits of your student ID. (e.g., suppose your student id is S00123410, then, set random_state = 10)

Please attach your name and student id in a separate markdown cell as a proof.

Step 3: Training and Fitting the model. Predictions from the trained model. Set up the classification model, SVM, with Scikit-learn. Train the model with training data. Make predictions on the test set.

Step 4: Model Evaluation

Evaluate your prediction with confusion_matrix() and classification_report()

Step 5: Analysis Report.

Create a new markdown cell and provide analysis of your evaluation results: what’s the FP, TP, FN, TN of your prediction? How about the precision, recall, and f1-score of your model?