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Loan Approval Prediction using Ensemble ML
This project builds a data-driven pipeline for loan approval 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.
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.
Neural Style Transfer Application for Image Art
This project develops neural style transfer application for image art using a CNN-LSTM hybrid architecture 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.
Student Dropout Prediction System using ML
This project builds a data-driven pipeline for student dropout 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.
Water Potability Classification System using ML
This project builds a data-driven pipeline for water potability classification system 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.
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.
Handwritten Digit & Character Recognition using CNN
This project develops handwritten digit & character recognition using a Transformer-based model (BERT/ViT) 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.
Customer Segmentation for Targeted Marketing using ML
This project builds a data-driven pipeline for customer segmentation for targeted marketing 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.
Video Action Recognition using 3D-CNN
This project develops video action recognition 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.
Traffic Accident Severity Predictor using ML
This project builds a data-driven pipeline for traffic accident severity predictor 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.
Deep Learning-Based License Plate Recognition System
This project develops license plate recognition system using a 3D-CNN for spatio-temporal data 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.
Deep Learning-Based Medical Tumor Detection from Scans
This project develops medical tumor detection from scans using an LSTM/GRU sequence model 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.