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Working IoT, AI/ML and embedded builds with abstracts, component lists, documentation, and developer support — ready for your final-year submission.
GAN-Based Image Super Resolution System
This project develops gan-based image super resolution system using a ResNet/EfficientNet backbone trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Real-Time Object Detection using YOLO for Surveillance
This project develops real-time object detection using a Convolutional Neural Network (CNN) trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Image Captioning using CNN-LSTM Architecture
This project develops image captioning using a ResNet/EfficientNet backbone trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Employee Attendance Anomaly Detection using ML
This project builds a data-driven pipeline for employee attendance anomaly detection using Random Forest and XGBoost trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
House Rent Prediction System using ML
This project builds a data-driven pipeline for house rent prediction system using Decision Trees and Random Forest trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Product Recommendation Engine for E-Commerce using ML
This project builds a data-driven pipeline for product recommendation engine for e-commerce using regression models (Linear, Ridge, Lasso) trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Exam Result Prediction & Risk Flagging System using ML
This project builds a data-driven pipeline for exam result prediction & risk flagging system using K-Means and hierarchical clustering trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Customer Complaint Category Classifier using ML
This project builds a data-driven pipeline for customer complaint category classifier using Logistic Regression and SVM trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Energy Consumption Forecasting using ML
This project builds a data-driven pipeline for energy consumption forecasting using Decision Trees and Random Forest trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Credit Score Classification using ML
This project builds a data-driven pipeline for credit score classification using regression models (Linear, Ridge, Lasso) trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Employee Resignation Risk Predictor using ML
This project builds a data-driven pipeline for employee resignation risk predictor using ensemble stacking of multiple classifiers trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Cab Fare Prediction System using ML Regression
This project builds a data-driven pipeline for cab fare prediction system regression using Random Forest and XGBoost trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.