15 AI Projects with Source Code for Final Year Students (2026)

Last updated: April 2026 · Reading time: 18 minutes · By Codersarts

Final-year project season is a peculiar kind of stressful. You've spent three or four years learning things in pieces — a bit of Python here, a machine learning module there, a data structures lab somewhere in the middle — and now you're being asked to stitch it all together into one capstone that convinces an examiner you actually get this stuff.

And in 2026, the bar has quietly moved.

A face recognition project that used to earn easy marks in 2021 now looks dated. Examiners have seen ChatGPT. They've seen students ship entire RAG applications in a weekend. They're not impressed by a scikit-learn model trained on the Iris dataset anymore — and if your internal guide reads LinkedIn, they already know what a transformer is.

This guide is for the student who wants to clear that higher bar without burning three months figuring out what to build. We've put together 15 AI project ideas that actually work for final-year submissions in 2026 — each one with a real problem statement, a clear tech stack, what an evaluator will probe you on, and an estimate of how long it takes to get running end-to-end. Every project on this list is available from Codersarts with full source code, a complete project report (60–80 pages), presentation slides, synopsis, and viva preparation notes.

Let's get into it.

How to use this list

Before you scroll through, a quick note on how we've ordered things.

The 15 projects are grouped by what they actually demonstrate to an examiner, not by tech stack. Some show off classical machine learning. Some are computer vision. A handful are GenAI / LLM-based — and frankly, in 2026, having at least one modern LLM component in your project is becoming the unofficial standard. Your internal examiner wants to see that you've kept up.

For each project you'll see:

• Problem statement — the real-world thing it solves

• Tech stack — what you'll actually write in

• Difficulty — beginner, intermediate, or advanced (final-year appropriate means intermediate or above in our experience)

• Dataset — what you'll train or evaluate on, and whether it's publicly available

• What examiners ask — the specific viva questions that come up again and again

• Build time — realistic end-to-end, including debugging

And at the bottom, a selection guide to help you pick the one that actually fits your situation.

The 15 projects

1. Fake News Detection with BERT and Explainable AI

Misinformation is the kind of topic that makes an examiner sit up, because it's topical, it's serious, and it's harder than it looks. The interesting version of this project isn't "classify news as real or fake" — anyone can fine-tune a classifier. The interesting version is: why did the model decide it was fake? That's where Explainable AI comes in.

• Tech stack: Python, Hugging Face Transformers, BERT, LIME / SHAP, Streamlit for the demo

• Dataset: LIAR or FakeNewsNet (both publicly available)

• Difficulty: Advanced

• Build time: 3–4 weeks

• What examiners ask: Why BERT over LSTM? How do you handle class imbalance? What's the difference between LIME and SHAP? Can your model explain a specific misclassification?

The Codersarts version ships with a fine-tuned BERT checkpoint, a Streamlit interface that highlights the words that drove the decision, and a report section dedicated to limitations (bias in training data, domain drift) — which is where most final-year reports fall flat. 

2. Real-Time Face Mask and PPE Detection

A computer vision staple that still works because it's visual, it runs live in the demo room, and it gives you something to actually show during viva. The trick is not to stop at "detect mask yes/no." Build it to detect multiple PPE items — mask, gloves, helmet — and flag compliance in real time. That's the version that earns marks.

• Tech stack: Python, YOLOv8, OpenCV, PyTorch

• Dataset: Roboflow PPE dataset (free tier)

• Difficulty: Intermediate

• Build time: 2–3 weeks

• What examiners ask: Why YOLO over R-CNN? What's your mAP score? How does it handle low-light conditions? Can it detect if a mask is worn incorrectly?

Setup tip: most students get stuck on GPU inference speed. The delivered bundle includes a quantized ONNX version that hits 25+ FPS on a laptop CPU, which means you can demo without needing a CUDA machine. 

3. AI-Powered Resume Screener using LLMs

This one is genuinely useful for examiners who live in the real world — it's the kind of application HR teams are actually deploying right now. The project parses a batch of resumes (PDF or DOCX), extracts structured data, and ranks candidates against a job description using LLM-based semantic matching instead of keyword matching.

• Tech stack: Python, LangChain, OpenAI or Claude API (or Ollama for offline), spaCy, FAISS vector store, Streamlit

• Dataset: Build your own using publicly available resume corpora from Kaggle

• Difficulty: Intermediate-to-Advanced

• Build time: 3–4 weeks

• What examiners ask: How do you handle hallucinations? What's your chunking strategy? How would this scale to 10,000 resumes? What about bias?

Examiner reality check: they will ask about fairness and bias. Don't skip that chapter in the report. The Codersarts bundle includes a full bias-audit section that takes this head-on. 

4. Crop Disease Detection from Leaf Images

Agriculture projects land well with examiners because they have clear social impact and the datasets are high-quality. The build is a CNN trained to classify plant diseases from leaf photographs, deployed as a mobile-friendly web app so farmers could theoretically use it from a phone.

• Tech stack: TensorFlow / Keras, transfer learning on MobileNetV2 or EfficientNet, Flask or FastAPI, simple mobile-friendly UI

• Dataset: PlantVillage (publicly available, 38 classes, ~54,000 images)

• Difficulty: Intermediate

• Build time: 2–3 weeks

• What examiners ask: Why MobileNet instead of ResNet? How did you handle the class imbalance in PlantVillage? What's your approach to real-world images (lighting, angles) that differ from the clean dataset?

The honest weakness of this project is generalization — models trained on PlantVillage struggle on real field images. Acknowledge this in your report; examiners respect students who know their limitations better than students who pretend they don't have any. 

5. Multi-Lingual Chatbot with RAG (Retrieval-Augmented Generation)

The most-requested project in our 2026 inventory, and for good reason. A RAG chatbot ticks every 2026 examiner box: LLMs, vector databases, embeddings, evaluation metrics. Build one that can answer questions about a custom knowledge base (your college handbook, a set of textbooks, whatever) in English plus one Indian language.

• Tech stack: Python, LangChain or LlamaIndex, Ollama (for local LLM) or OpenAI/Claude API, ChromaDB or FAISS, Gradio

• Dataset: Your own documents + a test Q&A set you create

• Difficulty: Advanced

• Build time: 4–5 weeks

• What examiners ask: What's your chunking strategy? Which embedding model did you choose and why? How do you evaluate retrieval quality (hit rate, MRR)? How do you prevent the model from answering out-of-context questions?

This is the project that impresses. It's also the one students underestimate — evaluation is where most builds fall apart. The Codersarts version includes a proper eval pipeline using RAGAS, which almost no undergraduate reports cover. 

6. Stock Market Prediction with LSTM and Sentiment Analysis

A hybrid project that combines time-series forecasting (LSTM on historical price data) with NLP (sentiment analysis on financial news headlines) to produce a next-day directional prediction. The hybrid angle is what makes this pass for final-year — pure LSTM stock prediction is 2019 territory.

• Tech stack: Python, TensorFlow/Keras, yfinance for data, VADER or FinBERT for sentiment, Streamlit dashboard

• Dataset: Yahoo Finance (free API) + a scraped or Kaggle-sourced financial news dataset

• Difficulty: Intermediate

• Build time: 3 weeks

• What examiners ask: Why LSTM and not Transformer? How do you prevent look-ahead bias? What's your trading strategy evaluation — accuracy, Sharpe ratio, or something else? (Hint: accuracy alone is a trap.)

Be honest in your report that predicting stock prices profitably is genuinely hard and your model's real-world usefulness is limited. That intellectual honesty lands far better than overclaiming. 

7. Medical Report Summarizer using LLMs

Healthcare AI projects are well-received because the domain is important and the technical challenge is real: medical text is dense, full of abbreviations, and wrong summaries have consequences. Build an LLM pipeline that takes a long patient report and outputs a structured summary (diagnosis, key findings, recommendations) suitable for a busy doctor to skim.

• Tech stack: Python, LangChain, a medical-domain LLM (BioBERT or a fine-tuned Llama), Gradio interface

• Dataset: MIMIC-III (requires approval) or MT-Samples (public, no approval needed — use this for your final-year project)

• Difficulty: Advanced

• Build time: 4 weeks

• What examiners ask: What's your hallucination mitigation strategy? How do you evaluate a summary's correctness — ROUGE, BERTScore, or human eval? What are the ethical considerations?

Ethics is a full chapter in the report for this one, not an afterthought. Medical AI without a serious ethics section gets marked down. 

8. Traffic Sign Recognition for Autonomous Driving

CNN-based traffic sign classification is a classic, but the final-year version should add real-time video inference and a discussion of adversarial robustness (what happens when someone puts a sticker on a stop sign — because that's an actual published attack, and examiners love when you reference real research).

• Tech stack: PyTorch, custom CNN or pre-trained ResNet, OpenCV for video pipeline

• Dataset: GTSRB (German Traffic Sign Recognition Benchmark — public, ~50,000 images, 43 classes)

• Difficulty: Intermediate

• Build time: 2–3 weeks

• What examiners ask: How does your model handle occluded or dirty signs? Have you tested adversarial examples? What's the latency per frame?

This project pairs nicely with a lit-review chapter citing the Eykholt et al. "Robust Physical-World Attacks" paper, which the Codersarts bundle already includes in the report. 

9. Credit Card Fraud Detection with Anomaly Detection

Don't underestimate a well-executed classical ML project. Fraud detection is still an industry-relevant problem and a good venue to demonstrate that you understand the harder parts of applied ML: extreme class imbalance, the precision-recall trade-off, and evaluation on skewed data where accuracy is meaningless.

• Tech stack: Python, scikit-learn, XGBoost, SMOTE for resampling, Isolation Forest and Autoencoders for the anomaly-detection angle

• Dataset: Kaggle Credit Card Fraud Detection (284,807 transactions, 0.17% fraud)

• Difficulty: Intermediate

• Build time: 2 weeks

• What examiners ask: Why not just use accuracy? How did you tune the decision threshold? What's the business cost of a false negative vs a false positive? Why isolation forest and not one-class SVM?

The build is shorter than most on this list, which is fine — a focused, well-analyzed 2-week project beats a sprawling 6-week mess. 

10. AI Interview Coach with Speech Analysis

A multi-modal project that records a candidate answering interview questions and analyzes three things: the content of the answer (via LLM), the delivery (pace, filler words, sentiment via speech), and facial confidence cues (via basic emotion recognition). Output is a coaching report.

• Tech stack: Python, Whisper for transcription, an LLM for content eval, librosa for audio features, OpenCV + a pre-trained emotion model, Streamlit

• Dataset: Record your own test clips — that becomes part of your demo

• Difficulty: Advanced

• Build time: 4–5 weeks

• What examiners ask: How do you validate that your "confidence score" actually correlates with interview success? What's the privacy story? How would you handle accents in Whisper?

  
  

Ambitious but doable, and it demos beautifully because examiners can try it live. 

11. Code Review Assistant using LLMs

A developer-productivity project: paste in a code snippet or point it at a pull-request diff, and the assistant flags bugs, suggests improvements, and explains what the code does. This is the project a CS examiner will play with themselves during viva, so make sure it actually works on messy real-world code, not just toy examples.

• Tech stack: Python, LangChain or a direct OpenAI/Claude API call, tree-sitter for parsing code structure, a simple web UI

• Dataset: Test on open-source repos with known bugs (the SWE-bench subset is perfect)

• Difficulty: Intermediate

• Build time: 3 weeks

• What examiners ask: How do you handle long files that exceed context window? What's your prompt engineering strategy? How do you measure whether the suggestions are actually useful?

Niche but strong — it's the kind of project that can land you a conversation during placements because it's something a recruiter understands.

12. Mental Health Support Chatbot with Emotion Detection

Sensitive topic, high impact. A conversational agent that listens to a user's message, detects the emotion (sad, anxious, angry, content), and responds with a supportive, non-clinical reply plus resources. 

Important: the report should be very explicit that this is a supportive tool, not a therapist or crisis resource, and should include a referral pathway to real help.

• Tech stack: Python, a transformer-based emotion classifier (GoEmotions dataset is great), an LLM for response generation with carefully engineered prompts, Streamlit

• Dataset: GoEmotions (58k Reddit comments labeled with 27 emotions)

• Difficulty: Intermediate-to-Advanced

• Build time: 3–4 weeks

• What examiners ask: How do you handle someone expressing a crisis? (You must have a hardcoded safety path.) What's your evaluation methodology? What are the ethical limits of this system?

This is one where the ethics section of your report matters more than the accuracy numbers. Take it seriously. 

13. Handwritten Prescription Reader

A tough, real-world CV-plus-NLP challenge: handwritten medical prescriptions are notoriously hard to read. The system does OCR on the prescription image, cross-checks the extracted drug names against a medical database, flags ambiguities, and outputs a clean structured version.

• Tech stack: Python, a fine-tuned TrOCR or PaddleOCR for handwriting, a drug-name database (public), fuzzy string matching for the cross-check

• Dataset: IAM Handwriting + a custom set of prescription-style images (build it yourself — it'll be 50–100 examples, and this is your contribution)

• Difficulty: Advanced

• Build time: 4–5 weeks

• What examiners ask: What's your error rate on your custom dataset? How do you handle drugs not in your database? What's your false-positive rate on drug-name matching?

Building your own small dataset is the differentiator here — most undergraduates use only public data, and saying "I collected and annotated 100 samples myself" genuinely moves the needle in viva. 

14. Smart Attendance System using Face Recognition

A classic, but do it correctly: real-time face detection from a classroom camera feed, recognition against a registered student database, automatic attendance marking with timestamp, and — this is where most students stop short — an anti-spoofing layer that rejects photos and video replays.

• Tech stack: Python, dlib or face_recognition library, OpenCV, a lightweight liveness-detection model, SQLite or Firebase for the backend, a simple web dashboard for the teacher

• Dataset: Your own registered faces (build it as part of the demo)

• Difficulty: Intermediate

• Build time: 3 weeks

• What examiners ask: How does your system handle twins? What happens if lighting changes drastically? How does your liveness detection work — and how would you fool it?

  
  

The anti-spoofing layer is what separates a 60%-mark project from an 85%-mark one. Don't skip it. 

15. AI Content Moderator for Social Media (Multimodal)

Build a system that takes a social media post (text + image together) and classifies whether it contains harmful content — hate speech, graphic violence, misinformation. The multimodal angle is critical: text-only moderators miss image-based memes, and image-only moderators miss captioned hate.

• Tech stack: Python, CLIP for joint text-image embeddings, a classifier head trained on labeled data, FastAPI for the service, a moderator dashboard

• Dataset: Hateful Memes (Facebook AI's multimodal hate-speech dataset)

• Difficulty: Advanced

• Build time: 4–5 weeks

• What examiners ask: Why multimodal? How do you handle sarcasm? What's the false-positive cost to legitimate users? How do you handle languages other than English?

Multimodal projects are becoming the gold standard in 2026 and will remain so. This project alone can headline your placement interviews. 

Quick comparison: all 15 projects at a glance

How to choose the right project for you

Fifteen options is a lot. Here's a simple decision path we've watched hundreds of students use to pick the one that fits:

If you have less than 3 weeks: go with project #9 (Fraud Detection) or #8 (Traffic Sign Recognition). Both are well-scoped, well-documented, and forgiving if things go wrong.

If you want to impress examiners with modern AI: #5 (RAG Chatbot), #3 (Resume Screener), or #15 (Content Moderator). These are the projects that signal you understand 2026 AI, not 2020 AI.

If your placements are coming up and you want conversation-starter projects: #11 (Code Review Assistant), #5 (RAG Chatbot), or #10 (Interview Coach). These make recruiters stop scrolling your resume.

If you care about a specific domain:

• Healthcare: #7 (Medical Summarizer) or #13 (Prescription Reader)

• Finance: #6 (Stock Prediction) or #9 (Fraud Detection)

• Agriculture: #4 (Crop Disease)

• Social good: #12 (Mental Health) or #1 (Fake News)

If you want the highest-scoring project regardless of difficulty: #5, #10, or #15. These are advanced, they demo well, and they give examiners plenty to test you on (which, counterintuitively, is what you want — a project with lots of interesting questions is a project that scores high).

What an examiner actually looks for (and what most students miss)

We've reviewed a lot of final-year reports over the years. Here's what separates the projects that score 85%+ from the ones stuck at 65%:

Strong problem framing. Weak projects open with "AI is an important field and has many applications." Strong projects open with a specific problem ("Indian farmers lose ~₹90,000 crore annually to crop diseases that could be detected earlier with low-cost smartphone diagnosis") and then show how the project addresses it.

Acknowledging limitations. It sounds backwards, but projects that openly list what their model can't do score higher than projects that claim to be universally applicable. Examiners are trained to find gaps. If you name them first, you look sophisticated, not vulnerable.

Evaluation that goes beyond accuracy. Precision, recall, F1 at minimum. For imbalanced problems, precision-recall curves. For generative projects, human evaluation on a test sample. "My model is 94% accurate" as the only metric is a red flag.

A clear, working demo. A project that doesn't run live during viva has a very hard ceiling on its score. Test your setup on a friend's machine before demo day. Always.

References to real research. Three or four citations of published papers — in your actual methodology, not just the lit review — signal that you read around your topic. Examiners notice.

An honest discussion of ethics and bias. Especially for projects involving faces, health, mental health, or content moderation. Skipping this section is the fastest way to lose marks in 2026.

What's included when you get a project from Codersarts

Every project on this list is delivered as a complete final-year bundle:

• Source code — fully commented, with a README that actually explains setup (not the "pip install -r requirements.txt and pray" version)

• Project report (60–80 pages) — structured to match typical university formats: abstract, literature survey, methodology, implementation, results, limitations, conclusion, references. Plagiarism-checked before delivery.

• Presentation (15–20 slides) — clean, presentable, viva-ready

• Synopsis — the 3–5 page version your guide will want first

• Dataset or dataset link — with setup instructions

• Viva preparation document — the 20–30 questions examiners actually ask for your specific project, with suggested answers

• 1-hour mentor call — walk through the code, prep for viva, discuss extensions

Delivery: 48 hours for most projects. Rush delivery (24 hours) available as an add-on.

Pricing: ₹6,999 for the standard Final-Year Bundle. Rush add-on ₹2,000.

Frequently asked questions

Can my university detect that the code isn't originally mine? This is the most asked question and it deserves an honest answer. The code we deliver is original and isn't public on GitHub, but no one can promise that a sufficiently determined examiner won't question a piece of code you can't explain. This is why the mentor call matters — you need to understand every meaningful section of your own project. We walk you through it exactly for this reason.

Will my project pass plagiarism checks like Turnitin? The report is written for you and checked against Turnitin-equivalent tools before delivery. Target similarity is under 10%. If it comes back higher, we revise it.

Can you customize a project to my specific topic? Yes. If you like one of these 15 but need it adapted — different dataset, different domain, different language — that's a customization. Starts at ₹9,999 depending on scope.

What if my guide asks me to change something during the build? Included in the bundle — we revise once within the delivery window at no extra cost. Major pivots may need a re-scope.

Do these projects run on my laptop? All 15 projects are tested to run on a laptop with 8 GB RAM and no dedicated GPU. GenAI projects that call an LLM API work fine. For projects that train from scratch (not most of these — we use transfer learning where possible), we provide Colab notebooks as an alternative.

Which programming language? All 15 are in Python, because Python is what examiners expect for AI/ML final-year projects in 2026. If you need a different stack (Javascript, Java, C++), ask us — some projects can be ported.

What about viva? I'm not confident answering technical questions. The mentor call is specifically for this. We'll ask you the questions your examiner is most likely to ask, listen to your answers, and coach you on gaps. Most students feel meaningfully more prepared after one 60-minute session.

Do I get ongoing support after delivery? Thirty days of email support included — setup issues, clarification questions, minor fixes. After that, further support is billed separately.

Can I see a sample report before I buy? Yes — reach out and we'll share redacted samples from previous deliveries (student names removed).

What happens if my examiner rejects the topic? We'll work with you to pivot to a closely related topic at no extra cost, as long as you reach out before we've delivered the full bundle.

Ready to pick your project?

If you've scrolled this far, you're probably serious. Here's how to take the next step:

Option 1 — You know which project you want. Message us with the project number (e.g., "Project #5 — RAG Chatbot") and your delivery deadline. We'll confirm timeline and send a payment link. Code + report in your inbox within 48 hours.

Option 2 — You're between two or three options. Book a free 15-minute consultation. We'll ask about your university's specific requirements, your interests, and your strengths, and recommend the one that fits you best.

Option 3 — You want something custom. Tell us your topic or problem statement. We'll scope a custom build and quote within 24 hours.

WhatsApp us → | Email: contact@codersarts.com | Request a Quote →

Codersarts has delivered final-year AI projects to students at over 200 universities across India, the US, the UK, and Australia since 2017. Every project ships with source code, full documentation, and mentor support. No templates, no shortcuts, no shared GitHub links — every build is your own.

Related reads:

• Top 10 Python AI Projects with Source Code — Beginner to Advanced

• 20 Artificial Intelligence Projects for Students with Source Code

• 7 Generative AI Projects with Source Code (LangChain, RAG, LLMs)

• How to Prepare for Your Final-Year Project Viva: 30 Questions Examiners Actually Ask

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