Wajahat Ahmad (wajahati)

wajahati

Geek Repo

Company:@Alachisoft

Location:Islamabad, Pakistan

Home Page:http://wajahati.me

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Wajahat Ahmad's repositories

ZAROORAT-ReactNative-Firebase-App

ZAROORAT is a react native app where users can buy and sell stuff.

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AiDoku-Ai-Sudoku-Solver

This is a AI sudoku solver in python. It uses a backtracking algorithm to solve the sudoku puzzle. For GUI PyGame is used.

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Category_PriceSuggestion-ML-API

This repo contain the API for Category Prediction and Price Suggestion which predicts using title and description

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CategoryPrediction-ML-FastAPI

This ML model is trained on BestBuy dataset and predicts 10 categories of product on the basis of title and description.

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Course-Test

This repo contain the solution of the tasks required by UCLovain along with some test cases

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Coursera-WebDev-A4

Module 4 Assignment

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Guidance-of-Quran-Android-App

CSC-291 Software Engineering Concepts

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Images-Similarity-Checker-SIFTAlgo-FASTAPI

Check and return similar images when array of URLs given

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Roman-Urdu-Profanity-Filter-ML-FastAPI

This repo is connected with heroku and host API for Roman-Urdu profanity filter used in the project ZAROORAT

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ZAROORAT-React-Native-Firebase

Marketplace where any one can buy, sell and rent any product. This application contain in-app messenger for buyer and seller to communicate easily.

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github-testing-repo

Testing here.

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Group_Chat_Application

This application uses socket programming to create group chats for different clients on same server.

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Image-Similarity-Optimized-SIFTAlgo-API

Optimized Image Similarity using Garbage Collector as it helps with slug size problem in heroku.

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IMDb-Clone

This is a desktop application.

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M4

Module 4 Assignment

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Product-Category-Prediction

Machine learning pipeline to predict relevant categories based on a product name and product description, using NLP and Deep learning concepts.

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Tourism-Managment-System

This application was semester project for Object Oriented Programming.

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WLASL

WACV 2020 "Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison"

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