Tasks:

$1.$ Data Preprocessing and Cleaning: Address any missing or inconsistent data entries as needed. You are free to apply any data transformations to prepare the dataset for predictive modeling.

$2.$ Feature Engineering: Develop and select features that you believe will effectively predict grades. Use exploratory data analysis to guide your feature selection and engineering.

$3.$ Model Selection and Development: Choose and develop any predictive models you consider appropriate for this task. This could include, but is not limited to$,$ traditional statistical models, machine learning algorithms, or ensemble methods.

$4.$ Model Optimization: Tune your selected models to maximize the F$1$ score. Experiment with different algorithms, parameter settings, and feature combinations to find the best solution.

$5.$ Model Evaluation: Evaluate the performance of your models based on their F$1$ scores. The model with the highest F$1$ score will be considered the most successful.