Amit Kedia
amitkedia007
A data enthusiast at heart, I am a Data Scientist with a knack for uncovering insights from a sea of data. My joy lies in working in Python
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The aim of this dissertation is to assess the effectiveness of LLMs such as FinBERT and GPT-2 in detecting fraudulent activities in financial reports and statements. This repo provides the code for implementing LLMs, traditional machine learning and deep learning models on the labelled dataset
This project is a social media chat analyzer built with Python and Streamlit. The application provides various analyses on a chat log, including top statistics, activity timelines, activity maps, word cloud, most common words, emoji analysis, and sentiment analysis. The analysis can be done for a specific user or for the overall chat.
This project is an end-to-end machine learning solution for predicting blueberry yield based on various environmental and biological factors. Using Python and Flask for the back-end and Bootstrap for the front-end, it incorporates data ingestion, transformation, model training, and prediction stages. The prediction model is powered by CatBoost Algo
This repository contains the Tableau Dashboard of Walmart sales data. It also contains the detailed analysis and usage of the dashboard.
This repo explains the implementation of Map-Reduce Algorithm on the AirBnb data to understand the consumer satisfaction region and country wise. This is the effective use of parallel distributed computing to resolve the big data problems
Repositories
28This project is a social media chat analyzer built with Python and Streamlit. The application provides various analyses on a chat log, including top statistics, activity timelines, activity maps, word cloud, most common words, emoji analysis, and sentiment analysis. The analysis can be done for a specific user or for the overall chat.
No description provided.
The aim of this dissertation is to assess the effectiveness of LLMs such as FinBERT and GPT-2 in detecting fraudulent activities in financial reports and statements. This repo provides the code for implementing LLMs, traditional machine learning and deep learning models on the labelled dataset
No description provided.
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This project provides the code for 'Audio-Visual Saliency Prediction with Multisensory Perception and Integration', Image and Vision Computing, 2024.
This project is an end-to-end machine learning solution for predicting blueberry yield based on various environmental and biological factors. Using Python and Flask for the back-end and Bootstrap for the front-end, it incorporates data ingestion, transformation, model training, and prediction stages. The prediction model is powered by CatBoost Algo
APT Malware Dataset Containing over 3,500 State-Sponsored Malware Samples
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Code for Benchmarking two ML Approaches performing Authorship Attribution
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This repository contains the Tableau Dashboard of Walmart sales data. It also contains the detailed analysis and usage of the dashboard.
This project is a comprehensive data analysis endeavor aimed at uncovering the key factors influencing student dropout and completion rates in higher education. Using a blend of Python and R, the project delves into the complexities of educational data, offering insights into student success and retention.
This repo explains the implementation of Map-Reduce Algorithm on the AirBnb data to understand the consumer satisfaction region and country wise. This is the effective use of parallel distributed computing to resolve the big data problems
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This project focuses on predicting the popularity of online news articles based on a variety of features such as the article's title length, the number of images, the number of videos, and more. The dataset used in this project is derived from the UCI Machine Learning Repository's Online News Popularity dataset.
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This project presents a Tableau dashboard built on Australian road crash data. From research question formulation to final implementation, it provides insights into improving road safety. The journey from prototype to the final dashboard, and the learning experience, is shared.
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This repository contains the materials and codes for the course project. The main goal of this project is to clean, analyze, and model a dataset of housing prices. The analysis is done in R and the codes are presented in an R Notebook..
This repository contains a comprehensive analysis of a dataset about cat breeds. The project was part of the CS5802 module, a course under the Computer Science department of the College of Engineering, Design & Physical Sciences (CEDPS), Brunel University London.
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All the Data Analysis related projects are uploaded here.
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A robot powered training repository :robot: