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@akash-das's Projects

abstractive-text-summarization icon abstractive-text-summarization

Abstractive text summarization models having encoder decoder architecture built using just LSTMs, Bidirectional LSTMs and Hybrid architecture and trained on TPU. Also pre-trained word embedding is used to speed up the process.

apple icon apple

Apple's landing page built with React, GSAP, and Three.js, and monitoring with Sentry.

articles icon articles

Projects for articles published on Medium.

axios-101 icon axios-101

Important features of axios package used in Next JS using TypeScript.

basic-calculator-gui icon basic-calculator-gui

A Simple Calculator GUI that can perform addition, subtraction, multiplication and division.

bloare icon bloare

πŸ“ Blog app build using πŸ”₯ Django Framework. Key Features of this app are πŸ”. Custom Authentication, 😎 User Profile using Signals, πŸ“„ Posts, 🍻. Comments & Replies, πŸ‘ Likes & πŸ‘Ž Dislikes

camps-for-champs icon camps-for-champs

A camping service where you find place to relax, connect with nature, and yourself.

camps-for-champs-old icon camps-for-champs-old

Imaginary πŸ¦„ startup that provides camping services and sells camping accessories

classify-customer-churn icon classify-customer-churn

Classification model to classify whether a customer is going to churn or not. Using the dataset EDA is done.

classify-news-as-real-or-fake icon classify-news-as-real-or-fake

πŸ“° News: πŸ‘ Real or πŸ‘Š Fake ❓ ... using πŸ€– machine learning to build a system that identifies unreliable πŸ—ž news articles.

craft icon craft

A place where professionals share their thoughts and ideas regarding the craft they're passionate about.

craft9999 icon craft9999

Place where professional share their thoughts and ideas regarding the craft they're passionate about

curtains icon curtains

Video streaming πŸŽ₯ front-end build using the server components of Next.js 13 `app` & Tailwind CSS.

customer-segmentation icon customer-segmentation

Perform EDA on a dataset of customers and then segment the customers using KMeans as our clustering algorithm.

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