This task involves developing an AI-powered analytics engine that enables the Academic Bank of Credits (ABC) platform to generate intelligent insights, trend forecasts, and anomaly detection based on academic data uploaded by institutions.

The ABC platform currently receives large volumes of academic records in structured formats (e.g., marksheets, credit logs, transcripts). This task aims to analyze the data over time and across institutions to provide insights such as course popularity, enrollment trends, common delays, credit accumulation patterns, and outlier detection.

The engine should leverage AI/ML models to perform pattern recognition, generate real-time visual dashboards, and produce downloadable reports for nodal officers and academic bodies. It should support institution-level and national-level metrics and make predictions such as likely delays, probable verification issues, or gaps in data.

• Design and build a reporting and analytics engine using AI/ML techniques to:

  • Analyze academic data across institutions (e.g., semester-wise grades, course registrations, credit accumulation).

  • Forecast trends such as:

    • Enrollment and graduation patterns

    • Data upload delays

    • Course popularity and dropout indicators

  • Detect discrepancies, outliers, or unusual activity (e.g., identical grades across all students).

  • Auto-generate institution-level summaries and periodic reports (weekly/monthly/quarterly).

  • Create dashboards with filters by Academic Year, Course Type, Program ID, Region, etc.

  • Visualize KPIs like average CGPA, pass/fail ratios, and document upload rates.

• Implement model training using sample institutional data.

• Ensure data privacy and anonymization where required.

• Provide the option to export reports as PDF/CSV.

• A functional predictive analytics tool with:

  • AI-generated forecasts and summaries based on institutional data.

  • Dashboards displaying academic insights across multiple parameters.

  • Automated report generation at defined intervals (monthly, term-wise, etc.).

  • Prediction of potential delays or missing data based on historical upload patterns.

  • Anomaly detection to flag outliers (e.g., identical scores, backdated entries).

  • Exportable reports and visualizations in user-friendly formats.

  • Sample datasets, pre-trained models, and a user guide are included.

• Clone the Repository: Begin by cloning the project's official Git repository using the provided URL.

• Create a Branch: Use Git to create a new branch with a clear and relevant name for your task (e.g., ai-template-suggestion-tool).

• Implement the Solution: Complete the development work as per the task requirements. Ensure proper code documentation, commit messages, and testing.

• Push Your Changes: Push the code changes to your branch in the repository.

• Generate a Pull Request (PR): Submit your completed task by creating a pull request from your branch to the main/master branch of the repository.

• Include Documentation: Make sure your PR includes:

  • A detailed README (if applicable)

  • Usage instructions

  • Sample input/output or test data

  • Dependencies and setup steps

• Notify the Mentor/Reviewer: Once the PR is created, tag the assigned mentor or reviewer for review and approval.

https://openforge.gov.in/plugins/git/abc-collabwithgov/abc-predictive-analytics

Organization Type: Government Publisher Name : Academic Bank of Credits (Digital India Corporation)