120+ ML Project Ideas · Updated 2026
Machine Learning Project Ideas for Final Year 2026
Browse 120+ curated machine learning project ideas across computer vision, NLP, healthcare, and predictive analytics. Every project comes with Python source code, real datasets, a trained model, and full documentation — delivered in 12 hours from Inno Projects, Coimbatore.
Machine learning remains the most in-demand domain for final year engineering projects in 2026. At Inno Projects, Coimbatore, we have curated 120+ ML project ideas spanning computer vision (YOLO, ResNet, OpenCV), natural language processing (BERT, GPT, LSTM), healthcare and medical imaging (disease prediction, MRI analysis), and predictive analytics (time-series forecasting, classification, regression). Every idea on this list can be turned into a fully working, submission-ready project within 12 hours — complete with Python code, real dataset, trained model file, IEEE-format report, and PPT. Whether you are a BE CSE, MCA, or MSc CS student, we have the right ML project for your skill level and college requirements.
Computer Vision Projects
Real-Time Face Mask Detection using YOLOv8
Tech: Python, YOLOv8, OpenCV, PyTorch
Detects whether individuals in a live video stream are wearing face masks using YOLOv8 object detection. Achieves 97% mAP on a custom-annotated dataset of 5,000+ images.
Get This Project →Pothole Detection in Road Images using Faster R-CNN
Tech: Python, PyTorch, Faster R-CNN, torchvision
Identifies and localises road potholes from dashcam images using Faster R-CNN, enabling automated infrastructure quality reporting for smart city applications.
Get This Project →Plant Leaf Disease Classification using ResNet-50
Tech: Python, TensorFlow, ResNet-50, Keras
Classifies 38 plant diseases from leaf photographs with 96% accuracy using ResNet-50 transfer learning on the PlantVillage dataset of 54,000 images.
Get This Project →Drowsy Driver Detection using CNN and Facial Landmarks
Tech: Python, OpenCV, dlib, Keras
Monitors driver eye aspect ratio in real time using facial landmark detection and a CNN classifier to trigger alerts when drowsiness is detected.
Get This Project →Signature Verification using Siamese Neural Network
Tech: Python, PyTorch, Siamese Network
Verifies handwritten signatures by learning a similarity metric between signature pairs using a Siamese network, achieving 94% verification accuracy on the CEDAR dataset.
Get This Project →Crowd Density Estimation using CSRNet
Tech: Python, PyTorch, CSRNet, OpenCV
Estimates crowd count and density maps from surveillance images using CSRNet (Congested Scene Recognition Network), applicable to smart city crowd management.
Get This Project →Natural Language Processing Projects
Fake News Detection using BERT Fine-Tuning
Tech: Python, HuggingFace Transformers, BERT, PyTorch
Fine-tunes a pre-trained BERT model on the LIAR dataset to classify news articles as real or fake, achieving 91% accuracy with attention-based explainability.
Get This Project →Multilingual Sentiment Analysis using XLM-RoBERTa
Tech: Python, HuggingFace, XLM-RoBERTa, Flask
Performs sentiment classification across English, Tamil, and Hindi product reviews using XLM-RoBERTa, enabling cross-lingual opinion mining for e-commerce platforms.
Get This Project →Resume Screening and Job Matching using NLP
Tech: Python, spaCy, TF-IDF, cosine similarity
Automatically ranks candidate resumes against a job description using TF-IDF vectorisation and cosine similarity, with skill extraction via spaCy NER.
Get This Project →Medical Question Answering using BioBERT
Tech: Python, BioBERT, HuggingFace, Streamlit
Answers clinical questions from PubMed abstracts using BioBERT fine-tuned on the BioASQ dataset, with a Streamlit interface for healthcare professional use.
Get This Project →Automated Text Summarisation using T5 Transformer
Tech: Python, HuggingFace T5, PyTorch, ROUGE
Generates concise abstractive summaries of long news articles and research papers using the T5 (Text-to-Text Transfer Transformer) model, evaluated with ROUGE scores.
Get This Project →Hate Speech Detection on Social Media using RoBERTa
Tech: Python, RoBERTa, Transformers, scikit-learn
Detects and categorises hate speech, offensive language, and neutral content in tweets using a fine-tuned RoBERTa model with class-weighted training.
Get This Project →Healthcare & Medical ML Projects
Diabetic Retinopathy Grading using EfficientNet-B4
Tech: Python, TensorFlow, EfficientNet, OpenCV
Grades diabetic retinopathy severity (0–4) from fundus photographs using EfficientNet-B4 with Grad-CAM visualisation to highlight affected retinal regions.
Get This Project →Breast Cancer Classification using Support Vector Machine
Tech: Python, scikit-learn, SVM, SHAP
Classifies breast tumours as malignant or benign using SVM on the Wisconsin Breast Cancer dataset, with SHAP values explaining the most influential features.
Get This Project →COVID-19 Detection from Chest X-Rays using VGG-16
Tech: Python, Keras, VGG-16, Grad-CAM
Distinguishes COVID-19, pneumonia, and normal chest X-rays using VGG-16 transfer learning with Grad-CAM heatmaps for clinical interpretation.
Get This Project →Alzheimer's Disease Detection from MRI using 3D CNN
Tech: Python, TensorFlow, 3D CNN, ADNI dataset
Classifies Alzheimer's disease stages (CN, MCI, AD) from volumetric MRI scans using a 3D convolutional neural network trained on the ADNI dataset.
Get This Project →Heart Disease Risk Prediction using Ensemble Methods
Tech: Python, scikit-learn, XGBoost, SHAP
Predicts 10-year cardiovascular disease risk by ensembling Random Forest, XGBoost, and Logistic Regression on the Framingham Heart Study dataset.
Get This Project →Mental Health Disorder Prediction from Social Media Text
Tech: Python, BERT, Reddit dataset, scikit-learn
Identifies signs of depression, anxiety, and PTSD from Reddit posts using fine-tuned BERT, contributing to early mental health intervention systems.
Get This Project →Predictive Analytics Projects
Stock Price Forecasting using LSTM with Attention
Tech: Python, TensorFlow, LSTM, yfinance
Forecasts next-day closing prices of NSE stocks using a stacked LSTM network with self-attention mechanism, trained on 5 years of historical OHLCV data.
Get This Project →E-Commerce Sales Forecasting using Prophet and XGBoost
Tech: Python, Facebook Prophet, XGBoost, Pandas
Predicts weekly sales demand for product categories using Facebook Prophet for trend decomposition and XGBoost for residual modelling, reducing forecast error by 23%.
Get This Project →Employee Attrition Prediction using Random Forest
Tech: Python, scikit-learn, Random Forest, SHAP
Identifies employees at risk of leaving using Random Forest on IBM HR Analytics data, with SHAP explanations to surface actionable retention strategies.
Get This Project →Energy Consumption Prediction for Smart Buildings
Tech: Python, TensorFlow, GRU, time-series
Predicts hourly energy consumption of commercial buildings using GRU (Gated Recurrent Unit) networks on multi-variate sensor and weather data.
Get This Project →Customer Lifetime Value Prediction using Gradient Boosting
Tech: Python, LightGBM, scikit-learn, Pandas
Estimates future revenue per customer using LightGBM on RFM (Recency, Frequency, Monetary) features and purchase behaviour, enabling targeted marketing.
Get This Project →Loan Default Prediction using Logistic Regression and XGBoost
Tech: Python, scikit-learn, XGBoost, imbalanced-learn
Predicts loan default probability using Logistic Regression and XGBoost on credit bureau data, handling class imbalance with SMOTE to achieve 88% recall on defaulters.
Get This Project →Why Choose Inno Projects?
Frequently Asked Questions
- What are the best machine learning project ideas for final year 2026?
- Top ML ideas for 2026 include: Fake News Detection using BERT, Plant Disease Classification using ResNet-50, Diabetic Retinopathy Grading using EfficientNet, Stock Price Forecasting using LSTM with Attention, and Real-Time Face Mask Detection using YOLOv8. At Inno Projects Coimbatore, we have 120+ curated ML ideas across computer vision, NLP, healthcare, and predictive analytics — all submission-ready.
- Which ML framework should I use for my final year project?
- For deep learning (CNN, LSTM, Transformers), TensorFlow/Keras or PyTorch are the industry standard. For classical ML (SVM, Random Forest, XGBoost), scikit-learn is ideal. For NLP, HuggingFace Transformers gives access to BERT, RoBERTa, and T5. We can build your project in any of these frameworks based on your college's preference.
- Can I get an ML project with an IEEE base paper?
- Yes. We have 80+ IEEE base papers mapped to ML project titles from journals like IEEE Transactions on Neural Networks, IEEE Access, and Pattern Recognition Letters (2024–2026). We include the base paper, implement the methodology, and prepare a full project report in IEEE format.
- How long does it take to get a machine learning final year project from Inno Projects?
- Most machine learning projects are delivered within 12 hours of order confirmation. Deep learning projects with large datasets (MRI, 3D CNN) may take 18–24 hours. We also offer same-day express delivery. WhatsApp us at +91 9600309140 to confirm availability.
- Do your ML projects work on my laptop, or do I need a GPU server?
- All our ML projects are optimised to run on standard student laptops (8GB RAM, no dedicated GPU required for most projects). For deep learning models, we pre-train and save the model file (.h5 / .pt) so you run inference locally without retraining. We also provide Google Colab notebooks for GPU-intensive training.
Get Your ML Final Year Project Today
WhatsApp us your preferred ML topic and we will deliver a complete, submission-ready project within 12 hours — source code, dataset, model, report, and PPT included.
Inno Projects — 28, Baba St, Janaki Nagar, Venkitapuram, Coimbatore 641025Phone: +91 96003 09140