hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

As an early diagnosis step machine learning classifiaction algorithms could be used in finding if the patient is prone to parkinsons disease.

In this comprehensive machine learning project, I executed the entire machine learning life cycle. Designed a streamlined and visually appealing interface using Streamlit. Ensuring a user-friendly experience for individuals to input their relevant information effortlessly. Handed off well-documented and easily modifiable code.

Machine Learning Algorithms

Stata package to implement cross-validation methods for statistical models

Using Machine Learning to predict Rossmann stores sales

Data collected from the patients of Sylhet Diabetes Hospital, Bangladesh.

This is a blog of how machine learning algorithms are used to detect if a person is prone to heart disease or not.

This repository consists the Jupyter Notebook files containing code of Artificial Neural Network with different tuning parameters for a similar scenario.

This is a Premiere Project done by Team Gitlab in Hamoye Data Science Program Dec'22. Out of 5 models used on the data, Random Forest Classifier was used to further improve the prediction of characters death. With parameter tuning and few cross validation, we were able to reduce the base error by 5.42% and increase accuracy by 2,42%.

In this project, we will predict the price for AMES House and learn Machine Learning Algorithms, different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

Different Techniques to Handle Imbalanced Data Set

This is a supervised machine learning project using telecom customer data to predict customers that would churn based on customer Age Group, Relationship Status, Subscribed Services, Charges, and Financial Responsibilities, etc.

Práctica de clasificación con Machine Learning en el dataset del Titanic, abordando exploración de datos, preprocesamiento, selección de métricas y modelos, con el objetivo de analizar detalladamente los resultados obtenidos.

Group Project for Primo Academy

An automated cross-validation framework for machine learning models, offering streamlined and efficient model evaluation.

Implemented SVC on the Olivetti dataset to predict if a person is wearing glasses or not by using cross-validation techniques in depth.

Stroke: Statistical analysis of risk factors and creation of predictive models using machine learning