his application demonstrates how to connect Python to the Moodle API, work with the data, and apply Machine Learning to generate predictive insights.
This app demonstrates how to connect Python with the Moodle API using the following libraries:
- requests: A simple and popular library for making HTTP requests. It is used to interact with the Moodle API, send requests, and receive data from the server.
- urllib3: A powerful HTTP library used alongside requests to manage HTTP connections securely. It handles things like connection pooling and SSL certificates for safe communication with external servers.
- pandas: A data manipulation library that simplifies the handling of structured data. It is used to organize, clean, and transform the data retrieved from Moodle into a tabular format (DataFrame).
- numpy: A library for numerical computing that enables efficient handling of large arrays and matrices. It is essential for mathematical operations on data, particularly when performing data analysis and statistical operations.
- matplotlib: A widely-used library for creating static, animated, and interactive visualizations. It is used here to generate visual representations of data such as graphs and charts.
- seaborn: Built on top of matplotlib, seaborn provides a high-level interface for creating more complex and aesthetically pleasing statistical graphics, such as heatmaps or regression plots.
- scikit-learn: A machine learning library in Python, which is used for data modeling and prediction.
- LinearRegression: A linear model used for predicting a continuous variable based on one or more features.
- train_test_split: Splits the dataset into training and test sets to evaluate machine learning models.
- mean_squared_error: A metric used to assess the performance of regression models by measuring the average squared difference between predicted and actual values.
- r2_score: A metric used to evaluate how well the regression model fits the data.
These tools are integrated to retrieve and analyze data from Moodle using its API, visualize the data, and apply machine learning models for predictions and insights.