EduRegion Explorer is an innovative tool designed for in-depth analysis and visualization of educational data. It emphasizes university enrollments and regional educational insights, particularly in Australia. The application is a blend of machine learning, advanced data visualization, natural language processing, and real-time data processing via Snowflake, making it a comprehensive tool for educational data analysis.
Interactive Chatbot Leverages OpenAI's GPT-3.5 model to interpret user queries and generate informative responses. Capable of creating complex SQL queries from natural language inputs to interact with Snowflake databases. Provides data tables and insightful analytics in response to diverse user queries.
Direct data extraction and manipulation from Snowflake databases. Functions for data cleaning, exploratory data analysis (EDA), and interactive filtering. Linear regression model for enrollment prediction and trend analysis. Visualizations such as bar charts, pie charts, and state-wise enrollment distributions for intuitive data understanding.
Clean and accessible sidebar dedicated to machine learning functionalities. Attractive and informative background designs for both the main application and the sidebar. User guide and documentation along with a GitHub repository link.
A feature for users to rate their experience, aiding in continuous enhancement of the application.
Snowflake Integration Utilizes Snowflake, a cloud-based data platform, for robust and scalable data storage and retrieval. Ensures up-to-date and reliable educational data is available for analysis. The integration allows for querying large datasets efficiently, providing real-time analytics.
Streamlit: For crafting the interactive web application interface. Pandas & Matplotlib/Seaborn: For data manipulation and rich visualizations. Sklearn: For building and evaluating the machine learning model. OpenAI: For incorporating the GPT-3.5 model in the chatbot. Snowflake Database:- For connection and query purposes.
Comprehensive EDA presenting statistical summaries and trends. Visualization tools to articulate data distributions, gender-based enrollment trends, and state-wise enrollment figures.
Predictive analytics using a Linear Regression model to forecast enrollment figures. Evaluation of the model's performance using metrics like MSE and R² Score.
Starting Steps Dataset Selection: Choose from "Regional University Enrollment Data" or "Australian Educational Institutions Insights". Interactive Chat: Engage with the chatbot for data insights, powered by natural language processing. Machine Learning Features Load Data: Retrieves and displays data directly from the Snowflake database. Clean Data: Standardizes and refines the dataset for analysis. Perform EDA: Delivers statistical analysis and data breakdowns. Interactive Data Filter: Customizes data views based on user-defined criteria. Create Visualizations: Produces graphical representations of data for better comprehension. Run Enrollment Prediction Model: Utilizes a regression model for forecasting enrollment figures.
Users can rate their application experience. The User Guide offers comprehensive instructions and assistance.
Integration of advanced NLP models for improved query understanding and processing. Inclusion of global educational datasets for a broader analysis scope. Enhancement of the prediction models with additional features and sophisticated algorithms.
EduRegion Explorer stands out as a pioneering application in educational data science. It demonstrates the power of integrating modern technologies like Snowflake, AI, and machine learning to transform educational data analysis, offering stakeholders an unparalleled tool for insights and decision-making.