Gopinath Asokan (gopiashokan)

gopiashokan

Geek Repo

Company:GUVI Geek Network

Location:Chennai, Tamil Nadu, India

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Gopinath Asokan's repositories

AI-Powered-Resume-Analyzer-and-LinkedIn-Scraper-with-Selenium

Developed an AI application using LLM to analyze user resumes and provided the summarization, strengths, weaknesses, suggestions, suitable job titles, and also scraping job details from LinkedIn using Selenium. This application reduces time by 30% and helps candidates tailor their resumes effectively.

Language:Jupyter NotebookLicense:MITStargazers:80Issues:2Issues:3

Youtube-Data-Harvesting-and-Warehousing

This repository hosts a project that enables efficient YouTube data extraction, storage, and analysis. It leverages SQL, MongoDB, and Streamlit to develop a user-friendly application for collecting and visualizing data from YouTube channels.

Language:PythonLicense:MITStargazers:6Issues:1Issues:0

Airbnb-Analysis-with-Tableau

Built an interactive Tableau dashboard to analyze the Airbnb data extracted from MongoDB Atlas. Developed a Streamlit application for trend analysis, pattern recognition, and data insights using EDA. Explored variations in price, location, property type, and seasons through dynamic plots and charts.

Language:Jupyter NotebookLicense:MITStargazers:2Issues:1Issues:0

Rental-Property-Price-Prediction-using-Machine-Learning

Empower your real estate decisions with our data-driven model, delivering precise rental predictions for landlords and comprehensive insights for tenants in a dynamic market landscape.

Language:Jupyter NotebookLicense:MITStargazers:2Issues:1Issues:0

Voice-AI-Automatic-Speech-Recognition

Developed a Marathi speech-to-text application using the Hugging Face whisper ASR models. Trained the model with a custom audio dataset and fine-tuned it for optimized performance. Deployed the model on the Hugging Face Model Hub, achieving a WER of 0.74 for the base model.

Language:Jupyter NotebookLicense:MITStargazers:2Issues:1Issues:0

Bird-Sound-Classification-using-Deep-Learning

Engineered a robust deep learning model using Convolutional Neural Networks and TensorFlow to classify 114 bird species based on audio recordings. Model achieved an impressive accuracy of 93.4%, providing valuable insights for conservationists and ecologists in the wildlife & ecological research sectors.

Language:Jupyter NotebookStargazers:1Issues:1Issues:0

Educational-Management-System

An IIT Internship project Build a comprehensive management application to automate the administrative and academic processes in educational institutions. Key features included user authentication, handwriting verification, examination management, and performance tracking. The system facilitating seamless communication with stakeholders.

Language:PythonLicense:MITStargazers:1Issues:2Issues:0

Financial-Document-Classification-using-Deep-Learning

Engineered an advanced deep learning model to automate the classification of financial documents, including Balance Sheets, Cash Flow and Income Statements using Bidirectional LSTM and TensorFlow. The model achieved an impressive accuracy of 96.2%, enhancing efficiency and reducing errors in document management for the finance and banking sectors.

Language:HTMLLicense:MITStargazers:1Issues:0Issues:0

IMDB-Movie-Analysis-with-PowerBI

Developed an interactive Power BI dashboard to analyze the factors influencing IMDB movie success. Statistical analysis of genres, language, duration, director, and budget, revealing impact on IMDB scores. Provided valuable insights to producers, directors, and investors for decision-making in the film industry.

Language:Jupyter NotebookLicense:MITStargazers:1Issues:1Issues:0

Industrial-Copper-Modeling-using-Machine-Learning

We harness the power of machine learning and data analysis to real challenges in the copper industry. Our documentation covers data preprocessing, feature engineering, classification, regression, and model selection. Discover how we've optimized predictive capabilities for manufacturing solutions.

Language:Jupyter NotebookLicense:MITStargazers:1Issues:2Issues:0

Library-Management-Data-Structure

This algorithm is designed to assist in the management of books in a library. It provides functionality to track books, lend them to users, and manage the book database.

Language:PythonLicense:MITStargazers:1Issues:0Issues:0

Phonepe-Pulse-Data-Visualization-and-Exploration

Developed a Streamlit application for analyzing transactions and user data from the Pulse dataset. Explored data insights on states, years, quarters, districts, transaction types, and brands through EDA. Visualized trends and patterns using plots and charts to optimize decision-making in the Fintech industry.

Language:PythonLicense:MITStargazers:1Issues:3Issues:0

Retail-Sales-Analysis-and-Forecast-using-Machine-Learning

Build a machine learning model to predict weekly sales with 97.4% accuracy. Integrated Exploratory Data Analysis tools to analyze trends, patterns, and actionable insights. The solution enables detailed sales comparisons, evaluates feature impacts and ranges, and identifies top performers, greatly enhancing decision-making in the retail industries.

Language:Jupyter NotebookLicense:MITStargazers:1Issues:0Issues:0

Analysis-on-Amazon-Prime-Videos-Using-Tableau

Explore Amazon Prime Video with Tableau! Gain valuable insights into content library, user engagement & preferences. Interactive dashboards and captivating visualizations reveal the magic of your favorite entertainment. Uncover data-driven stories now!

License:MITStargazers:0Issues:0Issues:0

BizCardX-Extracting-Business-Card-Data-with-OCR

Effortless Business Card Data Management Revolutionize business card data handling with BizCardX. Extract, store, and manage contact details seamlessly using OCR and PostgreSQL integration. Experience the future of efficient information management.

Language:PythonLicense:MITStargazers:0Issues:1Issues:0
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Potato-Disease-Classification-using-Deep-Learning

Developed a deep learning model using TensorFlow and Convolutional Neural Networks to classify disease images of potato plants, including early blight, late blight, and overall plant health in agriculture. Model achieved an impressive accuracy of 97.8%, empowering farmers with precise treatment applications to enhance crop yield and quality.

Language:Jupyter NotebookLicense:MITStargazers:0Issues:0Issues:0

Diabetes-Prediction-using-Machine-Learning

Experience predictive healthcare with our Streamlit app. Utilizing Random Forest, our tool analyzes medical data to assess diabetes risk swiftly. Ideal for healthcare professionals and researchers, this user-friendly app simplifies risk evaluation. Join us in the fight against diabetes.

Language:PythonLicense:MITStargazers:0Issues:1Issues:0