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20 AI Projects for Students with Source Code (2026)

Last updated: April 2026 · Reading time: 22 minutes · By Codersarts

20 AI Projects for Students with Source Code (2026)

There's a specific moment every engineering student recognises. You open a blank file, name it something hopeful like ai_project.py, and then sit there for forty minutes wondering what to actually build. Your syllabus covered neural networks in one lecture and convolutional networks in another. You've done the labs. You can recite what backpropagation is. And yet, when it comes to picking a project, you freeze.

That's the gap this post is built for.


Below are twenty AI project ideas spanning every major branch of the field — classical machine learning, natural language processing, computer vision, reinforcement learning, and the newer generative AI / LLM space that's become impossible to ignore in 2026. Each project is pitched at a real student workload, with a working source code option, and organised so you can pick based on where you are in your degree and how much time you have.


This is a longer list than our final-year-specific post. That one targets the capstone moment. This one is meant for students across all years — second-years looking for something to build over a weekend, third-years padding their GitHub before internships, final-years weighing up major project options, MCA students comparing against BTech students comparing against MTech students. Whatever your stage, there's something here sized right for you.



Quick navigation: If you already know what you're looking for, jump to beginner projects, intermediate projects, or advanced projects. If you want help picking, read the selection guide near the end.
Need the code, dataset, and setup guide? Every project below is available from Codersarts with full source code, commented walkthroughs, and setup support. Request Any Project →


How we organised these 20

The projects are grouped by difficulty, not topic. We tried category-based grouping in an earlier version of this post and it was worse — students kept skipping to "advanced" prematurely because a topic interested them, then getting stuck. Difficulty-first is the healthier order.


For each project you'll see:

  • What you'll build — plain-English description

  • Tech stack — what you'll actually use

  • Dataset — where you'll get the data (all public unless noted)

  • Time to complete — realistic hours, including debugging

  • Concept you'll learn — the thing that makes this project worth your time beyond just "it works"

  • Best for — the type of student this fits





BEGINNER PROJECTS (1–7)

These assume you know Python basics and have written maybe a handful of scripts. They don't assume you've trained a model before. Each one can be done in a weekend.


1. Iris Flower Classification

The rite-of-passage project. Given 150 rows of flower measurements, classify each flower into one of three species. It's a tiny dataset on purpose — you're here to learn the workflow, not fight the data.


  • What you'll build: A three-class classifier using logistic regression and a decision tree, with a comparison of their accuracy

  • Tech stack: Python, scikit-learn, pandas

  • Dataset: Iris (built into scikit-learn, no download needed)

  • Time: 1 hour

  • Concept you'll learn: The universal scikit-learn pattern — fit, predict, score. Every classical ML project you ever do will follow this shape.

  • Best for: First-ever ML project, or anyone who's done tutorials but never finished one




2. Spam SMS Classifier

Your first text-based ML project. The system learns what spam looks like from thousands of labelled SMS messages, then correctly flags new ones. This is where you discover that text has to be converted into numbers before a model can do anything with it.


  • What you'll build: A Naive Bayes classifier over TF-IDF vectors, plus a small Flask API where you paste a message and see the prediction

  • Tech stack: Python, scikit-learn, NLTK, Flask

  • Dataset: SMS Spam Collection (public, ~5,500 messages)

  • Time: 3–4 hours

  • Concept you'll learn: Text vectorisation (bag-of-words, TF-IDF) and why it's the bridge between language and maths

  • Best for: Students interested in NLP but not yet ready for neural networks




3. House Price Prediction

Regression instead of classification. Predict a continuous number (price) from a set of features (area, bedrooms, location, age). The dataset is messy enough to teach you something about data cleaning without being so messy that you quit.


  • What you'll build: A linear regression model, then an ensemble (Random Forest or XGBoost), plus a Jupyter notebook that walks through feature engineering step by step

  • Tech stack: Python, scikit-learn, XGBoost, pandas, matplotlib

  • Dataset: Boston Housing or California Housing (public)

  • Time: 4–6 hours

  • Concept you'll learn: Feature engineering, why MAE and RMSE differ, when ensembles beat linear models

  • Best for: Students who want to show they understand more than "accuracy" as a metric





4. MNIST Handwritten Digit Recognition

Your first neural network. A feedforward network that reads 28×28 pixel images of handwritten digits and predicts which number (0–9) they are. Boring dataset, transformative first experience.


  • What you'll build: A simple multilayer perceptron in Keras, then upgrade to a CNN and watch accuracy climb from ~97% to ~99%

  • Tech stack: TensorFlow/Keras, matplotlib

  • Dataset: MNIST (70,000 images, built into Keras)

  • Time: 3 hours (training is fast)

  • Concept you'll learn: What a neural network actually is in code, and why CNNs beat plain networks on image tasks

  • Best for: Anyone who wants to put "deep learning" on their CV and mean it




5. Titanic Survival Prediction

The second rite-of-passage. Kaggle's gateway dataset. Predict who survived the Titanic based on passenger details — class, age, sex, family size, fare. It's genuinely small, genuinely messy, and genuinely teaches you that real data is nothing like textbook data.


  • What you'll build: A full EDA (exploratory data analysis) notebook with visualisations, feature engineering, and a classifier that scores on Kaggle's leaderboard

  • Tech stack: pandas, seaborn, scikit-learn, XGBoost

  • Dataset: Titanic (free on Kaggle)

  • Time: 5–7 hours

  • Concept you'll learn: Exploratory data analysis — the single most underrated skill in practical ML

  • Best for: Students preparing for data-science-leaning interviews




6. Simple Rule-Based Chatbot

Not every chatbot needs an LLM. This one uses intent classification and pattern matching to run a basic customer-service-style conversation. You'll be surprised how far you can get with 200 lines of Python and some thoughtful rule-writing.


  • What you'll build: A chatbot that greets, answers FAQs, and escalates unknown queries — wrapped in a Streamlit interface

  • Tech stack: Python, NLTK, Streamlit

  • Dataset: Small FAQ corpus you build yourself (part of the exercise)

  • Time: 4–5 hours

  • Concept you'll learn: Intent recognition fundamentals, the distinction between retrieval and generation

  • Best for: Students who want a working chatbot without GPU or API keys




7. Movie Rating Sentiment Analysis

Given a movie review, predict whether it's positive or negative. You'll train on 50,000 labelled IMDB reviews and end up with a model that genuinely works on reviews you type in yourself. Satisfying.


  • What you'll build: A logistic regression baseline using TF-IDF, then a small LSTM to compare, plus a Gradio demo

  • Tech stack: scikit-learn, TensorFlow/Keras, Gradio

  • Dataset: IMDB Movie Reviews (50,000, public)

  • Time: 5–6 hours

  • Concept you'll learn: Baseline models matter — a simple TF-IDF + logistic regression often gets within 5% of a neural network, which is a lesson about engineering taste

  • Best for: Anyone interested in NLP






INTERMEDIATE PROJECTS (8–14)

These assume you've completed at least a couple of beginner projects and you're comfortable with Jupyter notebooks, basic plotting, and the train-test workflow. Expect each to take a full week of evening sessions.



8. Credit Card Fraud Detection

A classic industry problem disguised as a class-imbalance puzzle. Less than 0.2% of transactions in the dataset are fraudulent, which means a model that predicts "not fraud" for everything gets 99.8% accuracy — and is completely useless. Learning to handle this is where real ML begins.


  • What you'll build: An XGBoost classifier, SMOTE-based oversampling, threshold tuning, and a precision-recall curve

  • Tech stack: Python, scikit-learn, XGBoost, imbalanced-learn, matplotlib

  • Dataset: Kaggle Credit Card Fraud (284,807 transactions)

  • Time: 6–8 hours

  • Concept you'll learn: Why accuracy is the wrong metric most of the time, and how to pick the right one for the business problem

  • Best for: Students preparing for industry interviews — this scenario comes up constantly




9. Movie Recommendation Engine

Build a recommendation system that suggests movies based on user history. You'll implement two approaches — content-based (using movie metadata) and collaborative filtering (using user-rating patterns) — and compare them.


  • What you'll build: Two recommenders side by side, evaluated using RMSE and a top-N hit rate, plus a simple front-end where a user rates a few movies and gets suggestions

  • Tech stack: Python, scikit-learn, Surprise library, Streamlit

  • Dataset: MovieLens 100K (public)

  • Time: 8–10 hours

  • Concept you'll learn: Cosine similarity, matrix factorisation, the cold-start problem

  • Best for: Students who want a genuinely conversational portfolio project — everyone understands Netflix




10. Fake News Detection

Given a news headline or article, classify it as real or fake. Not an easy problem once you try it on real-world data, which is the lesson.


  • What you'll build: A logistic regression baseline, an LSTM, and a fine-tuned DistilBERT — three models compared on the same dataset

  • Tech stack: Python, scikit-learn, PyTorch, Hugging Face Transformers

  • Dataset: LIAR or FakeNewsNet (public)

  • Time: 10–12 hours

  • Concept you'll learn: The ladder of NLP approaches — classical → LSTM → transformer — and the cost/benefit tradeoffs at each step

  • Best for: Students interested in media, politics, or the intersection of ML with social issues




11. Plant Disease Detection from Leaf Images

Transfer learning in action. Take a pre-trained CNN (MobileNetV2 or EfficientNet), swap out its final layer, and fine-tune it on images of diseased and healthy leaves. You'll hit 95%+ accuracy with a surprisingly small amount of code.


  • What you'll build: A CNN classifier for 38 plant disease classes, plus a mobile-friendly web app where a user uploads a leaf photo and gets a diagnosis

  • Tech stack: TensorFlow/Keras, Flask or FastAPI

  • Dataset: PlantVillage (54,000 images, public)

  • Time: 8–10 hours

  • Concept you'll learn: Transfer learning (training a model "from scratch" is almost never the right call in 2026), plus image data augmentation

  • Best for: Students who want to demonstrate CV skills with a socially meaningful application




12. Stock Market Prediction with LSTM

The project students want to build and often build wrong. The right version isn't "predict tomorrow's price profitably" — that's not happening in an undergrad project and pretending it does will get you questioned hard in viva. The right version is "build a time-series LSTM and understand what it can and can't do."


  • What you'll build: An LSTM that predicts next-day closing price, compared against a naïve baseline ("tomorrow's price = today's price"), with an honest analysis of why your model barely beats it

  • Tech stack: TensorFlow/Keras, yfinance, pandas

  • Dataset: Yahoo Finance (free API)

  • Time: 8–10 hours

  • Concept you'll learn: Sequence modelling, sliding windows, and the critical concept of look-ahead bias (which you will almost certainly introduce your first time)

  • Best for: Students interested in finance who want to see why "AI for trading" is harder than YouTube makes it look




13. Text Summarizer (Extractive + Abstractive)

Given a long article, output a short summary. You'll build two versions: extractive (picks important sentences from the original) and abstractive (generates new sentences using a pre-trained model).


  • What you'll build: An extractive summarizer using TextRank, and an abstractive summarizer using a pre-trained BART model from Hugging Face, with a side-by-side comparison interface

  • Tech stack: Python, spaCy, Hugging Face Transformers, Gradio

  • Dataset: CNN/DailyMail news corpus (public via Hugging Face)

  • Time: 7–9 hours

  • Concept you'll learn: The fundamental tradeoff between extractive (safe, boring) and abstractive (interesting, can hallucinate)

  • Best for: Students preparing for content/media-related roles




14. Real-Time Face Detection and Emotion Recognition

Point your webcam at yourself. The system draws a box around your face and labels your current emotion — happy, sad, angry, neutral, surprised. It feels like magic the first time you run it.


  • What you'll build: A two-stage pipeline — face detection with a pre-trained model (Haar cascade or MTCNN), then emotion classification with a small CNN — running live on your webcam feed

  • Tech stack: Python, OpenCV, TensorFlow/Keras

  • Dataset: FER-2013 for emotion training (public, Kaggle)

  • Time: 8–10 hours

  • Concept you'll learn: Pipeline architecture — most real CV systems are chains of models, not single models

  • Best for: Students whose portfolio needs a "watch this live" moment






ADVANCED PROJECTS (15–20)


These assume you're past intermediate and you want to build something that genuinely reflects 2026 state-of-the-art. They'll take two to four weeks of focused work, and they're the projects that make recruiters stop scrolling.


15. Object Detection with YOLOv8

The leap from "what's in this image" to "what's in this image, where is it, and what's its bounding box." Modern object detection runs in real time on consumer hardware.


  • What you'll build: Real-time object detection on webcam video using a pre-trained YOLOv8, then fine-tune the model on a custom dataset you label yourself (10-20 images is enough for a demo)

  • Tech stack: Ultralytics YOLOv8, OpenCV, Roboflow (for labelling)

  • Dataset: COCO pre-trained, plus your own custom labels

  • Time: 12–15 hours

  • Concept you'll learn: Bounding-box detection, mean Average Precision (mAP), transfer learning for detection

  • Best for: Students building CV portfolios or working on autonomous-systems projects




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


The most in-demand project of 2026. Build a chatbot that answers questions about a custom knowledge base — your college handbook, a set of textbooks, your own notes, whatever — in English plus one regional language, with citations back to source documents.


  • What you'll build: A full RAG pipeline — document ingestion, chunking, embedding, vector storage, retrieval, LLM generation, plus an evaluation harness that scores retrieval quality

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

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

  • Time: 20–25 hours

  • Concept you'll learn: The full RAG architecture, embedding models, chunking strategy, retrieval evaluation (hit rate, MRR)

  • Best for: Students targeting AI/ML roles at any company — RAG is the single most commercially-applied AI pattern right now




17. AI Code Review Assistant

A developer-productivity tool. Paste in a code snippet or point it at a GitHub pull request, and the assistant flags bugs, suggests improvements, and explains what the code does.


  • What you'll build: A web service that takes a code diff, sends it to an LLM with a carefully engineered prompt, and returns structured feedback (issues, severity, suggestions), plus a UI that renders the feedback inline

  • Tech stack: Python, LangChain or direct OpenAI/Claude API, tree-sitter for code parsing, FastAPI, a simple React or Streamlit UI

  • Dataset: Test on open-source repos (SWE-bench subset is ideal)

  • Time: 18–22 hours

  • Concept you'll learn: Prompt engineering for technical tasks, context-window management for long files, evaluation of generative outputs

  • Best for: CS/SE students — recruiters actively understand and value this




18. Autonomous Car Lane Detection and Steering

Classical and deep CV combined. The system processes a dashcam video frame by frame, detects lane lines using Hough transforms, then uses a small CNN to predict steering angle — a la the original NVIDIA end-to-end paper.


  • What you'll build: A lane-detection pipeline (colour thresholding, Canny edge detection, Hough transforms) and a steering-angle prediction CNN trained on the Udacity self-driving dataset, with a demo playing over dashcam video

  • Tech stack: OpenCV, TensorFlow/Keras, moviepy

  • Dataset: Udacity Self-Driving Car dataset (public)

  • Time: 18–20 hours

  • Concept you'll learn: End-to-end deep learning vs modular pipelines — a debate that's still live in the self-driving industry

  • Best for: Students interested in autonomous systems, robotics, or computer vision research




19. Generative AI Art with Stable Diffusion Fine-Tuning

Take a pre-trained diffusion model and fine-tune it on a small custom image set (say, 10–20 images of a specific style or subject). Afterwards, generate new images in that style from text prompts. This is the technique behind every "AI avatar" app in the last three years.


  • What you'll build: A DreamBooth or LoRA fine-tune of Stable Diffusion on a custom concept, plus a Gradio demo that takes a text prompt and outputs an image in your fine-tuned style

  • Tech stack: Hugging Face Diffusers, PyTorch, accelerate, Gradio

  • Dataset: Your own curated image set (10–20 images)

  • Time: 20–25 hours

  • Concept you'll learn: Diffusion models fundamentals, LoRA (low-rank adaptation), prompt engineering for image generation

  • Best for: Creative students, or anyone building a portfolio that actually looks different

  • Hardware note: You'll want a GPU or Colab Pro for this one




20. Multimodal AI Content Moderator

The 2026 gold standard. 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 matters: text-only moderators miss image memes, image-only moderators miss captioned hate.


  • What you'll build: A CLIP-based joint text-image embedding model feeding into a classifier head, plus an evaluation harness, plus a moderator dashboard for manual review of borderline cases

  • Tech stack: CLIP (via Hugging Face), PyTorch, FastAPI, a React or Streamlit dashboard

  • Dataset: Facebook's Hateful Memes dataset (public, requires signup)

  • Time: 25–30 hours

  • Concept you'll learn: Multimodal embeddings, joint representation learning, evaluation of classifiers on socially-sensitive data

  • Best for: Students aiming for applied ML or research roles at large companies — multimodal is where the field is going






Quick comparison: all 20 projects at a glance


#

Project

Category

Difficulty

Time

2026-Relevance

1

Iris Classification

Classical ML

Beginner

1 hr

⭐⭐

2

Spam SMS Classifier

NLP

Beginner

3–4 hrs

⭐⭐

3

House Price Prediction

Regression

Beginner

4–6 hrs

⭐⭐⭐

4

MNIST Digit Recognition

Deep Learning

Beginner

3 hrs

⭐⭐

5

Titanic Survival

Classical ML

Beginner

5–7 hrs

⭐⭐

6

Rule-Based Chatbot

NLP

Beginner

4–5 hrs

⭐⭐

7

Sentiment Analysis

NLP

Beginner

5–6 hrs

⭐⭐⭐

8

Fraud Detection

Classical ML

Intermediate

6–8 hrs

⭐⭐⭐⭐

9

Movie Recommender

Recommender

Intermediate

8–10 hrs

⭐⭐⭐

10

Fake News Detection

NLP

Intermediate

10–12 hrs

⭐⭐⭐⭐

11

Plant Disease Detection

CV

Intermediate

8–10 hrs

⭐⭐⭐

12

Stock Prediction LSTM

Time Series

Intermediate

8–10 hrs

⭐⭐⭐

13

Text Summarizer

NLP

Intermediate

7–9 hrs

⭐⭐⭐⭐

14

Emotion Recognition

CV

Intermediate

8–10 hrs

⭐⭐⭐

15

YOLO Object Detection

CV

Advanced

12–15 hrs

⭐⭐⭐⭐

16

RAG Chatbot

GenAI

Advanced

20–25 hrs

⭐⭐⭐⭐⭐

17

Code Review Assistant

GenAI

Advanced

18–22 hrs

⭐⭐⭐⭐⭐

18

Lane Detection

CV

Advanced

18–20 hrs

⭐⭐⭐⭐

19

Stable Diffusion Fine-Tune

GenAI

Advanced

20–25 hrs

⭐⭐⭐⭐⭐

20

Multimodal Moderator

GenAI

Advanced

25–30 hrs

⭐⭐⭐⭐⭐


2026-Relevance = how much of a "current" feel this project has to a recruiter or examiner in 2026. Five stars means the project uses techniques that are front-of-mind today. Two stars means it's a solid learning experience but won't itself impress anyone — you'll need to extend it.





Which project should you actually pick?

Twenty is a lot. Here's a faster way to choose.


If you're a 2nd-year student with one weekend: #1, #4, or #6. They're quick, satisfying, and give you a starting point.


If you're a 3rd-year student building a GitHub portfolio: pick three — one classical (#3 or #8), one CV (#11 or #14), one NLP or GenAI (#10 or #16). The category mix matters more than any single project.


If you're a final-year BTech / MTech student looking for a major project: #10, #15, #16, #17, #19, or #20. For final-year specifically, we've written a separate, deeper post covering 15 projects structured around the viva and report. Read that one too.


If you're an MCA student: #8, #9, #10, #16 play well with MCA syllabi (slightly more application-focused than research-focused).


If you're preparing for ML/AI interviews: #8 (imbalanced classes), #9 (recommenders), #16 (RAG), #17 (LLMs). These four come up in interviews constantly.


If you're a Master's student doing a research-ish project: #18, #19, or #20 — each has enough depth to extend into a publishable thesis.


If you just want to build something fun this weekend: #15 (YOLO). It runs live on your webcam within an hour of setup.




How to set yourself apart from every other student with the same project


The blunt truth: thousands of students will build "MNIST digit recognition" or "Titanic survival prediction" this year. Running the code isn't what gets you noticed. Here's what does, from what we've seen actually matter:


Explain the tradeoffs. If you can say "I chose XGBoost over logistic regression because the feature interactions are highly non-linear, but I gave up interpretability, so I used SHAP values to explain individual predictions" — you're already in the top 10% of student reports. Generic "I used ML to solve the problem" is in the bottom 50%.


Build one deliberate extension. Took a standard project and added something your own — a new dataset, a new UI, a deployment, an ablation study, a bias analysis. Extensions are what separate students who "completed the project" from students who "understood the project."


Write honest limitations. Say what doesn't work, and why. "My face recognition model performs 8% worse on darker skin tones, which reflects a known bias in the training dataset." That single sentence in your report is worth more than three more decimal places of accuracy.



Demo it live. A project that runs on demand during an interview or viva is worth ten projects that are "just a GitHub link." Always test your demo on a different machine than the one you built it on.


Write about it. A 500-word blog post explaining your project — what you built, what broke, what you learned — is the single highest-ROI thing you can do with a project once it's done. Not just for your career, but because the act of writing about it locks in the learning.




FAQ


Can I build these projects without a GPU? For projects 1–10, yes, comfortably. For 11–15, a GPU helps but Google Colab's free tier handles most of them. For 16–20, you'll want either Colab Pro (~₹1,000/month) or access to your college's GPU lab. Projects that call LLM APIs (16, 17) don't need a GPU at all.


What Python version should I use? Python 3.10 or 3.11 for everything on this list. 3.12 is fine for most but causes compatibility issues with a few libraries. 3.13 is too new — wait another six months.


What if I don't finish a project and get stuck? The code we deliver includes a troubleshooting guide per project covering the ~10 most common setup and runtime issues. If you're still stuck, email support is included for 30 days after purchase.


Are these projects "plagiarism-free" for university submission? The code is original and not public on GitHub. However, no one can guarantee Turnitin-style similarity — especially for well-known problem statements (everyone's Titanic project will have some overlap with every other). What actually protects you is understanding what you submit. The mentor-call option on our paid bundles is specifically for this.


Which project has the highest "career ROI"? Honestly: #16 (RAG Chatbot). Every company with internal documentation is trying to build one. If you can walk an interviewer through the architecture, discuss your chunking strategy, and explain how you evaluated retrieval quality, you're immediately interview-ready for most AI/ML roles at companies that build real products.


What's the difference between the free code and the paid bundle? Free code = the code runs, with a basic README. Paid bundle = code + dataset setup + architecture diagrams + a 60–80 page project report (for advanced projects) + PPT + mentor call. Different price points for different needs. If you're just learning, free is fine. If you're submitting for marks or interviews, the bundle pays for itself.


Can I pay monthly or is it a one-time fee? Codersarts projects are one-time purchases. We don't currently do subscriptions (and probably won't — subscriptions for one-off projects doesn't make sense).


I don't see a project on X topic. Can you build custom? Yes. Send us your topic or problem statement, and we'll scope a custom build. Starts at ₹9,999 depending on complexity.


How fast is delivery? Beginner and intermediate projects: within 24 hours of payment. Advanced projects: within 48 hours. Custom projects: 5–10 days with milestone check-ins.




What you get when you request a project






Every project on this list ships with:

  • Full source code — tested, with a real README (not "pip install -r requirements.txt and pray")

  • Dataset — or the exact public link + download instructions

  • Walkthrough notes — annotated sections explaining the tricky parts of the code

  • Setup support — if it doesn't run on your machine, we'll help make it run

  • Colab notebook (where applicable) — so you can run it GPU-free


Upgrade options for students submitting projects for marks:

  • Project report (60–80 pages) — abstract, lit survey, methodology, results, limitations, references

  • Presentation slides — 15–20 slides, viva-ready

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

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


Pricing

  • Basic project (code + README + dataset): ₹799

  • Project with report + PPT: ₹2,999

  • Full final-year bundle (code + report + PPT + synopsis + viva prep + mentor call): ₹6,999

  • Custom project: from ₹9,999


Delivery: 24–48 hours for most, 5–10 days for custom builds.



Get started in 3 ways

If you know which project you want: Message us with the project number. Example: "Project #16 — RAG Chatbot." We'll confirm price and send a payment link.


If you're between two or three options: Book a free 15-minute call. We'll ask about your year, your university's requirements, your interests, and recommend the fit.


If you want something custom: Send us your topic and deadline. We'll scope and quote within 24 hours.




Codersarts has delivered AI projects to students at 200+ universities across India, the US, the UK, and Australia since 2017. Every project ships tested, documented, and with a human on the other end when something breaks.


Related reads:

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

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

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

  • 12 Free AI Projects with Source Code You Can Run Today


Tags: artificial intelligence projects for students with source code, ai projects for cse students, ai projects for engineering students, ai projects for btech students, ai project ideas 2026, ai projects with source code github, ai projects for college students

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