Title: Code Refactoring and Enhancements in "grademaster" Script
Introduction: The "grademaster" script is a program designed to manage course grades, make grade projections, and provide various analysis tools for grade data. In this write-up, we will focus on the code refactoring and enhancements made to the script.
Code Refactoring:
Updated Libraries: The script now imports the necessary libraries, such as numpy, pandas, and matplotlib, to utilize their functionalities effectively. The older generation implemented Dict of Dict
Improved Data Structure: The data structure of the input file has been revamped to use a Pandas DataFrame. This allows for more efficient data handling, manipulation, and analysis.
Replaced optparse with argparser: The script transitioned from using optparse to argparser, a more modern and feature-rich library for parsing command-line arguments. This change provides better flexibility and readability in handling command-line options.
Enhancements:
Function for Percent to Letter Grade Conversion: The script introduces a function, percent2lettergrade(), which converts a percentage grade to its corresponding letter grade based on a predefined conversion scheme. This function enhances the script's functionality by facilitating grade conversions.
Statistical Calculations: The script includes functions for calculating statistics such as median, distribution statistics, and histogram data. These calculations provide valuable insights into the grade distribution and help analyze the data effectively.
Code Reusability: Several functions have been implemented to handle specific tasks, promoting code reusability and modularization. Functions like print_df(), median_func(), distribution_stat(), and histogram() serve distinct purposes and can be reused in other parts of the script or in different projects.
Improved Data Cleaning: The script includes data cleaning operations to remove empty entries and ensure the validity of the data structure. This enhances data integrity and avoids errors during analysis.
Streamlined Workflow: The script now follows a more structured workflow, with clear sections for data acquisition, cleaning, key identification, and validity checks. This improves code readability and makes it easier to understand the logic of the script.
The Feature Implemented:
The ML model is trained using a labelled dataset where the inputs are the features (e.g., student demographics, past performance) and the target variable is the grade. The dataset is split into training and validation sets to assess the model's performance. Evaluation metrics such as mean squared error or accuracy are used to measure the model's predictive performance.
To ensure optimal model performance, hyperparameter tuning is performed. Hyperparameters control the learning process of the model, and tuning them involves selecting the best combination that yields the highest performance on the validation set. Techniques like grid search or random search can be used to explore different hyperparameter values.
Once the model is trained and evaluated, it is saved for future use. This allows for quick predictions without having to retrain the model every time. The "grademaster" script includes a function called predict_grades() that takes new input data and uses the trained model to generate grade predictions.
Additionally, the script provides interpretation and visualization features. These features help in understanding the model's behavior and the factors influencing the predicted grades. Visualizations such as scatter plots, feature importance rankings, or partial dependence plots can be used to gain insights into the model's decision-making process.
Conclusion: The refactoring and enhancements in the "grademaster" script have significantly improved its functionality, readability, and flexibility. The transition to a Pandas DataFrame for data handling, the inclusion of statistical calculations, and the use of argparser for command-line options are notable improvements. These updates make the script more efficient, user-friendly, and adaptable to various grading scenarios. the "grademaster" script combines ML techniques, feature engineering, model training and evaluation, hyperparameter tuning, and interpretation/visualization features to enable accurate grade prediction based on input data.