hasansust32/Drug_classification

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hasansust32/Drug_classification

hasansust32

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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.

Jupyter Notebook

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

SUST_LMS

P2P_chat_application_android

me

Bio-informatics_Assignment

Jupyter Notebook

pricefxRestApi

This is a project where i fetch data from Pricefx Rest API and manipulate the data using this API. I have to read the data using API and partially update the data and upsert it in the primary folder.

Word_Frequency_Based_Bangla_Fake_News_Detection

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.

Jupyter Notebook

100_Days_of_code

This is a 100-day coding challenge. This is a completely free Python programming Bootcamp. For this tutorial, I followed a comprehensive lesson from Udemy. This course includes Python from basic to advanced, Flask Web Development Toolkit, Data Science Basics, Web Development Fundamentals, and so many other things. There are 100 Python projects in 100 days of work and I want to finish all of these projects.