mdjahidhasan009

mdjahidhasan009's Stars

• coollabsio/coolify

  An open-source & self-hostable Heroku / Netlify / Vercel alternative.

  Language:PHP35.7k1202.1k2k

• yangshun/top-javascript-interview-questions

  200 important JavaScript interview questions for busy Front End Engineers

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• learning-zone/javascript-basics

  JavaScript Basics

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• learning-zone/react-basics

  React Basics ( v18.x )

  1.5k184424

• TamimEhsan/interview-questions-bangladesh

  A crowd sourced collection of interview questions asked in Bangladeshi Tech companies

  40381106

• codinginflow/next-auth-v5

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• wpcodevo/nextauth-nextjs14-prisma

  In this tutorial, you will learn how to set up NextAuth v5 in Next.js 14. With the release of NextAuth v5, a couple of breaking changes have been introduced, and getting NextAuth up and running in a Next.js 14 project requires a bit of setup.

  Language:TypeScript491122

• BlastWind/Konva-Diagram-Maker

  Feature Extracted from my website

  Language:JavaScript314217

• hasansust32/Phishing_Detection_Using_Machine_Learning

  This is a completely machine-learning based task. We used a dataset from kaggle with 1154 website details with 32 features. More significantly, we experimented with a considerable number of machine learningmethods on actual phishing datasets and against various criteria. We identify phishing websites using six distinct machine learningclassification methods. This research obtained a maximumachievable accuracy rate of 97.17 percent for the Random Forestrule and 94.75 percent for the Gradient Boost Classifier. The Provisioningaccuracy is 94.69 percent with the Decision Tree classifier, 92.76 percent with Logistic Regression, 60.45 percent with KNN, and 56.04 percent with SVM.

  Language:Jupyter Notebook5100

• hasansust32/P2P_chat_application_android

  Language:Java3100

• hasansust32/Prostate_Cancer_Predictio

  His study addresses these concerns by predicting prostate cancer using six (6) machine learningtechniques: Random Forest, SVM, KNN, Logistic Regression, Neutral Network, and the Ensemble model. We gathered data from 100 patients who were placed in ten different circumstances. The data was categorised as malignant or non-cancerous. Among the six machine learning techniques, logistic regression, neuralnetworks, and ensemble learning have the potential to reach an accuracy of 95.00 percent. Ensemble learning can detect 96.55%of true positive prostate cancer in our model. KNN has a 90%accuracy rate, whereas SVM and Random Forest have an 85%accuracy rate.

  Language:Jupyter Notebook3100

• hasansust32/Drug_classification

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• hasansust32/hasansust32

  2100

• hasansust32/me

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• hasansust32/SigmaHacks_2.0

  This is an international hackathon that I participated in for a contest. In this hackathon project, I created a website based on three features: an eCommerce website, a charity with donations and an information blog update for Covid 19 on a single platform. This is a PHP-based website with WordPress CMS. 

  Language:PHP2100

• hasansust32/Word_Frequency_Based_Bangla_Fake_News_Detection

  2200

• hasansust32/Bangla_Feke_News_Detection

  Language:Jupyter Notebook110

• hasansust32/Bio-informatics_Assignment

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• hasansust32/Blockchain_efiling_system

  Language:Python110

• hasansust32/CodeForces

  Python solutions to CodeForces problems

  Language:Python100

• hasansust32/ExamPractice

  110

• hasansust32/Hackerrank_Python_Domain_Solutions

  Solutions of challenges of Hackerrank Python domain

  Language:Python100

• hasansust32/Heart_Attack_Predic_Using_Machine_Learning_Algorithm

  This is my machine learning course work. I have collected this dataset from kaggle. There are 303 patient records with 14 features. I applied Exploratory Data Analysis methods and nine different machine learning models to predict the heart attack disease with this accuracy: XGBoost: 95.08% AdaBoost: 93.44% MLPClassifier: 93.44% Random Forest: 91.8% Gradient Boosting: 91.8% Logistic Regression: 90.16% SVM: 90.16% KNN: 88.52% Decision Tree: 81.97%. 

  Language:Jupyter Notebook1100

• hasansust32/nlp_bangla

  হাতেকলমে ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং (এনএলপি) - শুরুর ধারণা

  Language:Jupyter Notebook100

• hasansust32/Python_Backend_Development

  110

• hasansust32/Satellite_Image_Classification_By_CNN

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• hasansust32/SkyForce

  This is our Java project and we created a game, basically a 2D game for this project. This game shows a standard level bucket and a ball. We protect the ball from falling into the bucket. This is an amazing game and we created it with the Java Swing framework.

  Language:Java1100

• hasansust32/StudentsMarks

  StudentMarks is a Java-based small project which can correctly detect student marks with some given value. It is mainly based on JavaFX for the GUI, and I use basic Java programming for this project. Core Java programming knowledge is needed for this project.

  Language:Java110

• hasansust32/SUST-LMS

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• hasansust32/TensorFlow2

  হাতেকলমে পাইথন ডিপ লার্নিং (TensorFlow 2.x) বইয়ের ব্যবহৃত নোটবুক, লিংক: http://bit.ly/bn_dl

  Language:Jupyter Notebook100