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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.
Customer Lifetime Value Prediction using ML
This project builds a data-driven pipeline for customer lifetime value prediction 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.
Fake Job Posting Detection using ML
This project builds a data-driven pipeline for fake job posting 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.
Employee Salary Prediction using ML Regression
This project builds a data-driven pipeline for employee salary prediction regression 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.
Used Car Price Prediction System
This project builds a data-driven pipeline for used car price prediction system 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.
Employee Performance Evaluation System using ML
This project builds a data-driven pipeline for employee performance evaluation 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.
AI-Powered Smart Attendance via Voice Recognition
This project designs and implements attendance via voice recognition by applying recommendation algorithms to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
Customer Segmentation using K-Means Clustering
This project builds a data-driven pipeline for customer segmentation using Gradient Boosting (LightGBM/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.
AI-Based Toxic Comment Detection for Online Forums
This project designs and implements toxic comment detection for online forums by applying hybrid rule-based and ML reasoning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Based Voice Assistant for Smart Home Control
This project designs and implements voice assistant for home control by applying transformer-based NLP to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Based Deepfake Detection System
This project designs and implements deepfake detection system by applying graph-based similarity reasoning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Powered Insurance Claim Fraud Detection System
This project designs and implements insurance claim fraud detection system by applying graph-based similarity reasoning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.
AI-Based Loan Eligibility Prediction System
This project designs and implements loan eligibility prediction system by applying ensemble machine learning to a real-world decision-support problem. The system ingests relevant input data, processes it through a trained model, and exposes predictions/insights through a simple web dashboard for end users.