Given a dataset containing numerical values and corresponding binary labels (0/1), train a logistic regression classifier using scikit-learn and make predictions on the unseen test dataset. Problem: Build a Logistic Regression model that can predict the target variable. For each record in the test set (test.csv), predict the value of the 'label' variable. Submit a CSV file with a header row and one row per test entry. The file submissions.csv should have exactly 1 column: label. Evaluation Metric: The metric used for evaluating the performance of the model is Accuracy and Macro- F1 Score. The model will be tested on a dataset that is different from the training dataset to test its robustness