Object Detection and Segmentation using YOLO and Faster-RCNN

This project offers a sophisticated solution in object detection and segmentation, employing the advanced technologies of YOLO and Faster R-CNN. Tailored to various applications such as surveillance, autonomous vehicles, or medical imaging, our approach combines YOLO's real-time detection capabilities with the precision of Faster R-CNN for detailed segmentation. Clients will benefit from a well-defined implementation strategy, a comprehensive analysis of deployment costs, and a detailed comparison of the models, highlighting their respective advantages and limitations. Our commitment is to provide a seamless, adaptable, and result-oriented solution, ensuring client satisfaction through the latest advancements in detection and segmentation technology.

Book Recommendation Pipeline Using Big Data Technologies

This project delivers a sophisticated book recommendation system using big data technologies, structured into three key phases. Initially, it involves setting up a PostgreSQL database and importing data into HDFS using Sqoop. The data is then prepared and stored in Hive tables using the compressed AVRO format for efficient analytics. The core of the project lies in its data analysis, where Exploratory Data Analysis (EDA) is performed using HiveQL on the Tez engine, followed by Predictive Data Analysis (PDA) using SparkML, employing Alternating Least Squares and Decision Trees algorithms. This comprehensive approach ensures a seamless, data-driven recommendation experience, making it ideal for clients looking to offer personalized book suggestions based on robust big data insights

IELTS Chatbot

The IELTS Chatbot is an innovative tool designed to assist individuals preparing for the IELTS (International English Language Testing System) writing task. This AI-powered chatbot aims to enhance writing skills, provide real-time feedback on practice essays, and offer estimated scores based on IELTS criteria. The project included the following key components: 1. Gathering essays and corresponding feedback to build a comprehensive dataset . 2. Dataset Preparation. 3. Fine-tuning of the GPT-3 model. 4. Fine-tuning of open-source Language Model Models (LLMs), such as LAMA. 5. Utilization of a customized variant of the GPT-4 model. 6. A comprehensive analysis involving cost breakdown and comparative evaluation of the aforementioned methodologies and approaches. 7. Chatbot deployment.