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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.
Email Spam Classification using Naive Bayes
This project builds a data-driven pipeline for email spam classification 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.
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.
Diabetes Prediction System using ML Classifiers
This project builds a data-driven pipeline for diabetes prediction system classifiers 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.
Retail Sales Forecasting using Time Series ML
This project builds a data-driven pipeline for retail sales forecasting 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.
Wine Quality Prediction using ML Classification
This project builds a data-driven pipeline for wine quality prediction classification 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.
AI-Based Question Answering System for College FAQs
This project designs and implements question answering system for college faqs by applying classical ML classifiers 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 Personality Prediction from Social Media Activity
This project designs and implements personality prediction from social media activity by applying speech and audio processing models 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 Fitness Form Correction using Pose Estimation
This project designs and implements fitness form correction by applying classical ML classifiers 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.
Employee Attrition Prediction using ML Classification
This project builds a data-driven pipeline for employee attrition prediction classification 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.
AI-Based Multilingual Voice-to-Text Transcription System
This project designs and implements multilingual voice-to-text transcription system 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.