Built to order

Community Skill-Swap Platform for Peer Learning

This project builds a community skill-swap platform where members list what they can teach and what they want to learn, and the platform matches complementary pairs — guitar lessons for Python tutoring, Marathi conversation for baking. Profiles carry verified swap history and ratings, requests turn into scheduled sessions with fair-swap rules, and a review system keeps quality honest. Suitable for B.E./B.Tech final-year projects in Computer Science, IT and Web Development.

Web Development PillowPython 3 · DjangoHTML · CSS · JavaScriptDockerCelery · RedisPostgreSQL · PostGISDjango ChannelsSoftware
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The problem

Everyone knows something someone nearby wants to learn, and wants to learn something a neighbor could teach — but the exchange never happens because there is no trusted way to find each other and agree on fair terms. Tutoring marketplaces solve this with money; a skill-swap solves it with reciprocity, which needs different mechanics: matching complementary teach/learn pairs, session scheduling both sides honor, and reputation that reflects teaching quality rather than payment. This project builds that platform: member profiles with teach/learn skill tags, search and category filters, swap requests that become scheduled session plans, in-thread messaging, post-session ratings, and fair-swap rules (alternating teach/learn halves, show-up tracking) that keep exchanges balanced.

SYSTEM FLOW

5 components 8 connections

Live flow
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userMember A

Teaches one skill and learns another.

Follow the working sequence

How it works

  1. A member creates a profile with teach skills, learn interests, location and availability.
  2. Searching a skill returns complementary members — people who teach what you want and want what you teach — ranked by a transparent score.
  3. The requester proposes a swap (skill pair, number of sessions); the other member accepts, declines or counters.
  4. Acceptance opens a swap thread with messaging and a session planner; slots are proposed, confirmed and reminded.
  5. After each session both sides mark attendance and leave a rating with a short review.
  6. Completed swaps, ratings and show-up rates accumulate on profiles, feeding future match rankings.
  7. Community rules enforce fair exchange: alternating halves per session and no-show penalties.

Tech stack:

  • Python 3 with Django (profiles, swaps, reviews)
  • PostgreSQL with PostGIS (location-aware search)
  • HTML, CSS and JavaScript (browse, profile, thread UI)
  • Django Channels (in-thread messaging)
  • Celery with Redis (reminders and digest emails)
  • Pillow (profile media handling)
  • Docker (one-command deployment)
Parameter Value
Match ranking Rating 40%, distance 25%, responsiveness 20%, completion 15% (design weights)
Search radius Configurable 5–50 km, default 10 km
Session length 45–90 minutes, configurable per swap
Ratings 1–5 stars with text review per session
No-show policy 3 no-shows pause account for 30 days (configurable)
Messaging In-thread, per swap
Deployment Single Docker container

Project features

  • [Teach/learn profiles] Members list skills they can teach and want to learn, with levels, past swap counts and response-time stats.
  • [Complementary matching] Search surfaces members whose "teaches" overlaps your "wants" and vice versa, ranked by rating, distance and responsiveness.
  • [Swap requests with session plans] Requests propose a skill pair and a session count; acceptance opens a thread with a shared plan.
  • [Session scheduling] Per-session slots with venue, confirmation states and reminders keep both sides coordinated.
  • [Ratings and show-up rate] Post-session reviews and attendance tracking build a reputation that reflects reliability, not just friendliness.
  • [Fair-swap rules] Sessions alternate teach/learn halves by rule, and repeated no-shows pause accounts — documented in the community guidelines.
  • [Category browsing] Music, languages, tech, cooking, fitness and art categories with skill-level filters.

What is included

  • Platform source code (profiles, matching, swaps, reviews, messaging)
  • Match-ranking logic with documented weights
  • Database schema with PostGIS location search
  • Seed dataset of members and swaps for the viva demo
  • Community guidelines and fair-swap rules document
  • Project report PDF (background, matching design, trust mechanics, methodology)
  • PPT presentation for final review
  • Viva Q&A preparation document (matching algorithms, reputation systems, location search)

Limitations & prerequisites

  • Match quality depends on member density — the demo seeds a realistic dataset, but a fresh city starts sparse (cold-start is discussed honestly in the report).
  • Identity verification is profile-level (phone OTP); background checks are out of scope.
  • The platform facilitates meetings; it cannot guarantee session quality or safety — meeting guidelines are documented.
  • Payments are out of scope by design; this is a reciprocity platform, not a tutoring marketplace.
  • Ranking weights are design choices documented for the viva, not tuned on production data.

Frequently Asked Questions

How is this different from a tutoring marketplace?

No money changes hands. Matching is complementary — each side teaches and learns — and reputation tracks teaching quality and reliability rather than transactions.

How does matching work?

Search finds members whose teach-skills overlap your learn-interests and vice versa, then ranks them by a documented blend of rating, distance, responsiveness and completion history.

What keeps swaps fair?

Sessions alternate teach/learn halves by rule, attendance is tracked per session, and repeated no-shows pause the account. The rules are visible to both sides before accepting.

What if nobody near me teaches what I want?

The radius filter widens the search and an "online session" option matches beyond geography; the report discusses the cold-start problem openly.

Can I see someone's track record before requesting?

Yes — profiles show completed swaps, per-session reviews, show-up rate and average response time.

Is this project suitable for a final-year project?

Yes — for Computer Science, IT and Web Development programs. Matching logic, reputation design and location search give it real technical depth. Suitable for B.E./B.Tech final-year projects in Computer Science, IT and Web Development.

Components & software requirements
  • Python 3 with Django (profiles, swaps, reviews)
  • PostgreSQL with PostGIS (location-aware search)
  • HTML, CSS and JavaScript (browse, profile, thread UI)
  • Django Channels (in-thread messaging)
  • Celery with Redis (reminders and digest emails)
  • Pillow (profile media handling)
  • Docker (one-command deployment)
Delivery information

Built-to-order project. Delivery timeline is shared after order confirmation based on current queue.

Support terms

Complete documentation, setup guide, and viva preparation included. Support for setup and explanation provided.

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