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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.
Bank Marketing Campaign Success Prediction
This project builds a data-driven pipeline for bank marketing campaign success prediction 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.
Stock Price Trend Prediction using ML Regression
This project builds a data-driven pipeline for stock price trend prediction 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.
Heart Disease Risk Prediction using ML
This project builds a data-driven pipeline for heart disease risk prediction 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.
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.
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.
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.
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.
Student Performance Prediction using ML Algorithms
This project builds a data-driven pipeline for student performance prediction algorithms 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.
Crop Yield Prediction using ML Regression
This project builds a data-driven pipeline for crop yield prediction 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.
Telecom Customer Churn Prediction System
This project builds a data-driven pipeline for telecom customer churn prediction system 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.
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.