hasansust32

Drug_classification

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%. 

me

P2P_chat_application_android

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.

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.

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. 

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.

Word_Frequency_Based_Bangla_Fake_News_Detection

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.

hasansust32/P2P_chat_application_android

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.

hasansust32/Drug_classification

hasansust32/hasansust32

hasansust32/me

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. 

hasansust32/Word_Frequency_Based_Bangla_Fake_News_Detection

hasansust32/Bangla_Feke_News_Detection

hasansust32/Bio-informatics_Assignment

hasansust32/Blockchain_efiling_system

hasansust32/CodeForces

Python solutions to CodeForces problems

hasansust32/ExamPractice

hasansust32/Hackerrank_Python_Domain_Solutions

Solutions of challenges of Hackerrank Python domain

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%. 

hasansust32/nlp_bangla

হাতেকলমে ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং (এনএলপি) - শুরুর ধারণা

hasansust32/Python_Backend_Development

hasansust32/Satellite_Image_Classification_By_CNN

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.

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.

hasansust32/SUST-LMS

hasansust32/TensorFlow2

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