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Data & Analytics

University Admission Merit History Explorer for Applicants

A final year project that builds a searchable history of university closing merits and cut-off scores, extracted from the PDF merit lists universities publish each year. Applicants and counsellors can compare programmes across years and see realistic chances, instead of guessing from last year's single list.

Topics
Difficulty

Beginner

Achievable by one student in a semester with familiar tools.
Effort

1 semester · 1 student, or 2 semesters · 2 students

Deliverables

6 to ship

3 optional extras

Suggested stack

Pythonpdfplumber or CamelotpandasPostgreSQLNext.jsChart.js
A suggestion, not a requirement. Swap anything for what you already know.

What you should ship

  • Extraction scripts for merit or cut-off lists from at least four universities over at least three years, with a raw archive of every source file and its download date.
  • A cleaned, normalised table (university, campus, programme, category or quota, round, year, closing merit) with programme names mapped to a standard list.
  • A data quality report per source: rows extracted, rows rejected, and a manual check of 100 random rows against the PDFs.
  • A public search page to filter by programme and university and see closing merit by year and round as a chart.
  • A 'where do I stand' page where an applicant enters their aggregate and sees programmes where they were above, near or below past closing merits, with a clear note that past lists do not guarantee admission.
  • A short user test with at least eight applicants or counsellors, measuring task time against searching the PDFs directly.

If you have time left

  • A monitor that checks university admission pages daily during the admission season and alerts the admin when a new list is published.
  • Aggregate calculator per university formula (weights for matric, intermediate and entry test).
  • Round-to-round movement charts showing how much merits usually drop between the first and final list.

The problem applicants face with merit lists

Every admission season, applicants and their families try to answer one question: with my marks, where do I have a realistic chance? Universities publish merit lists or cut-offs, but usually as PDFs or images on a news page, one list per round, per campus, per category. Last year's lists often disappear when the website is updated. Families end up relying on rumours in WhatsApp groups, or applying everywhere and paying many application fees.

The data is public; it is just not in a usable shape. Turning it into one tidy, comparable table is a real data engineering job, and a good first one.

What already exists and the gap

  • JoSAA in India publishes opening and closing ranks officially, and sites such as Shiksha and Collegedunia turn them into searchable cut-off pages.
  • UCAS in the UK publishes entry requirements per course, which are stated targets rather than historical outcomes.
  • Individual university portals publish their own lists, in their own formats, for the current year.
  • Education news sites repost lists as images or PDFs, with no history or comparison.

The gap: for many countries and universities there is no multi-year, cross-university history in one place. The Indian cut-off sites show the idea works and people use it; your project applies the pattern to universities where the data still sits in PDFs.

What you build

Two roles: visitor (search and compare) and admin (adds sources, reviews extraction problems).

Flows:

  1. Admin registers a source (university, year, round, URL) and the raw file is archived.
  2. An extractor turns the PDF table into rows. Each university needs a small parser config: column positions, header text, how categories are written.
  3. Programme names are normalised ("BS Computer Science", "BSCS", "B.S. (CS)") to a standard list using a mapping table the admin can edit.
  4. Visitors search by programme, see merit trends by year and round, and compare universities.
  5. An applicant enters their aggregate and sees a banded list: comfortably above, near, below past closing merits.

The technically interesting part

Reliable extraction and normalisation from messy PDFs. Tables break across pages, headers repeat, merged cells shift columns, and some lists are scanned images. Show your supervisor a pipeline that is honest about quality: every extracted row keeps a reference to its file and page, rows that fail validation (merit out of range, missing programme) go to a reject table, and a per-source quality report is generated on every run.

The second part is schema design for comparison. Universities use different scales (percentages, aggregates out of 100, ranks). Store the original value and a normalised one, and never compare across scales silently.

How to evaluate it

  • Extraction accuracy: manually check 100 random rows per source against the PDF; report field-level accuracy.
  • Coverage: number of university-programme-year combinations captured versus what was published.
  • Pipeline: time to add a new source using the parser config, measured on two new universities.
  • Usability: eight or more applicants or counsellors find the closing merit for three programmes; compare time and errors against searching the PDFs.

Suggested plan

  1. Weeks 1 to 2: choose universities, collect and archive every available list.
  2. Weeks 3 to 6: extractors and parser configs, reject table, quality report.
  3. Weeks 7 to 9: normalisation, programme mapping, database schema.
  4. Weeks 10 to 12: search, trend charts and the "where do I stand" page.
  5. Weeks 13 to 15: user test, accuracy check, write-up.

Path to a product

Applicants search heavily during a few months each year, so the site can grow through search traffic and sharing in student groups. Revenue can come from colleges and academies sponsoring clearly labelled placements, or from a paid counsellor dashboard that tracks many students. Universities are a later customer: a clean history of their own merits is useful to them too.

Scope warning

Do not promise admission predictions or percentages of chance; show history and bands only. Do not build a full admission application portal. Do not collect applicants' personal data beyond what the calculator needs, and do not store it.

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The main decision is how many universities to cover so the extraction quality stays high. If you want a second opinion on scope, stack or how to evaluate it before you pitch it to your supervisor, a mentor can help you shape it. You still build it; the guidance is about making the plan realistic and the results defensible. New here? create a free account first.

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