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.

My daily ML practices

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.

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.

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

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%.

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

Group Project for Primo Academy

Recommendation Systems

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

This is a School assignment, on churn prediction using scikit learn and traditional ML models

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

Different Techniques to Handle Imbalanced Data Set

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.

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.

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

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

A machine learning model (Classification) to predict customer churn for banks. It helps identify at-risk customers, enabling proactive retention efforts to reduce revenue loss and improve customer satisfaction.

Linear Regression Project to predict the total goals to be scored in a match

Predicting Math Scores for Brazilian high School National Exams

Use of Supervised and Unsupervised Learning Techniques to determine critical components for development of countries

Implémentation des algorithmes simples de Data Science

This is a supervised machine learning project using loan customer data to predict customer risk flag based on their Income, Age, Marital Status, Profession, Financial Responsibilities, etc

Code templates for different ML algorithms

Predicts the F1 driver given in an image of their face.