This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

Simulations for the paper "Inter node Hellinger Distance based Decision Tree by Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib"

Implement Decision Tree classifier to classify the data.

Predicting which customers are at high risk of leaving your company or canceling a subscription to a service, based on their behavior with your product.

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

A final year project for the University of Exeter, using machine learning to study patterns in millions of chess games (~350 GB). Ranked 1st in the cohort for undergraduate projects (85%).

ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.

Develop a web application using Streamlit and create a free service in Render and integrate the work done to be able to deploy the web application online.

Coursework on Introduction to Machine Learning - CS M146

Data Science Project - Full Depth analysis AND Prediction Using Decision Tree and Random Forest

Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.

A library to train, evaluate and make inference using random forests.

Predicting Loan Defaults using 15+ Features and over 250,000 records

Predicting the likelihood of candidates joining the company or not

Machine learning model used to predict the species of the iris flower

Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.

XGBoost (Extreme Gradient Boosting) is a highly efficient and accurate machine learning algorithm based on gradient boosting, excelling in structured data tasks. It includes features like regularization, handling missing values, and parallel processing. Widely used in competitions and industry, it supports multiple programming languages.

Classifying which released prisoners in Iowa will return to a life of crime using Next-Gen Gradient Boosted Trees

ML Project implementing decision trees, boosting and svm classification from scratch.