To build a classification methodology to determine whether a person defaults the credit card payment for the next month.

Deployment of classification algorithms for customer classification

Descriptive Statistical Analysis of the NBA Defensive Player of the Year race since 2013-14

The main part of this project is dedicated to conducting a binary classification task on the Amazon Kindle Store reviews data to predict whether a new review is helpful or not.

It is a web application which you aggregate Wildfire data, and make prediction of causes by the help of ML

Using machine learning (XG boost) to increase the annualized return of a personal loan portfolio.

Comparision of classifiers titanic dataset

Our project employs machine learning to pinpoint phishing URLs with 97.4% accuracy, leveraging HTTPS and website traffic as critical indicators. Insights into features like AnchorURL enhance cybersecurity strategies, showcasing the power of AI in combating online threats.

TitanicClassification.py file contains project based on binary classification. The dataset comprises of data related to passengers and binary value of whether they survived or not.

This project aims to predict credit risk for individuals applying for loans, classifying whether they will default based on features such as age, income, employment length, loan amount, interest rate, percentage of income, credit length, home ownership, and loan intent.

Model using XGBoost_Classifier

Salary Prediction API using Flask predicts salaries for freshers joining organizations based on factors like past experience, company switches, courses completed, and academic marks. This Flask-based API allows users to input their details and receive a salary prediction. With no user interface, it's designed for integration into other applications

This codes are from a research project of mine that I conducted under the supervision of department of CSE , BRAC University

AI-Models for Smart Irrigation System

Malicious URL detector built with deep exploration on feature engineering.

This repo illustrate the example of credit score prediction using different algorithm model from sklearn for classifying whether the person have good or bad credit score.

I constructed a knowledge graph of stakeholders of Bavarian state ministries and used network analysis to calculate statistics. Furthermore time-series feature forecasting and topological link prediction was employed to analyze the evolution of the network.