Developed a machine learning model to predict Energy Efficiency for an Indusrial company, achieving an accuracy of 92%. The project involved data cleaning, feature engineering, model training, and deployment using Flask and Docker.
Built an End-to-End Students Math Score Prediction model. Utilized matrix factorization techniques to improve user experience by suggesting personalized Movies.
Built a collaborative filtering recommendation system for a streaming service. Utilized matrix factorization techniques to improve user experience by suggesting personalized Movies.
Developed a machine learning model to detect fake news using various machine learning algorithm, achieving an accuracy of more than 92%. The system is trained on a dataset of news articles that have been preprocessed and count vectorized.
an English-to-Urdu translation application built using a Sequence-to-Sequence (Seq2Seq) model with Gated Recurrent Units (GRU) and Bahdanau Attention mechanism.
This project aims to develop a Next Word Prediction Model using stacked Bidirectiona LSTMs and GRU techniques to enhance text input efficiency and user experience.
This project implements a Region-based Convolutional Neural Network (RCNN) model for detecting wheat crops in images. The model is designed to assist in agricultural monitoring, enabling farmers and researchers to assess crop health and density efficiently.
This project implements a web application for classifying chicken diseases using deep learning image recognition. The application leverages Keras' pre-trained VGG-16 model built upon TensorFlow to achieve high accuracy in disease detection.
This project demonstrates how to perform grammar correction on sentences using a pre-trained transformer model and a spell checker.
1- Correcting grammatical errors using a pre-trained transformer
2- Correcting spelling errors using a spell checker.
Efficient implementation of a complete Long-Term Memory (LLM) project using Pinecone's managed vector database. The project demonstrates how to leverage Pinecone's ease of use, scalability, and low latency to create a high-performance AI application.
Developing an end to end LLm application using Google Gemini Pro where we will create Text To SQL LLM App and later retrieving query from sql database
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