Parameters of extreme gradient boosting model are fine tuned to achieve better accuracy

Data analysis, visualization, classification

My approach to the hackathons-( Public Leaderboard : 115 Private Leaderboard:110 )

A Machine Learning project

Flight ticket prices can be something hard to guess, today we might see a price, check out the price of the same flight tomorrow, it will be a different story. This is the reason why flight prices are quiet unpredictable. Data consisting of several details and prices of flight tickets for various airlines between the months of March and June of …

Data Science and Machine Learning using Python and R

This is a prediction of the duration of taxi trips in new york. The main work is based on the dataset adding a weather datasaet and many other features (rush hours, weekend days, speed, etc...). You can check my medium publication where I explain all the work done. MEDIUM: https://medium.com/cuny-csi-mth513/new-york-city-taxi-trip-duration-predi…

Forecasting diabates using machine learning algorithms: End-to-end solution.

Churn Modelling using XGBoost

AI

This project focuses on predicting house prices in Miami using regression techniques. By exploring and analyzing the data, performing feature selection and scaling, trying out different models, and tuning hyperparameters, we aim to develop an accurate model for predicting house prices.

Machine learning project for cardiovascular risk prediction. The goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD)

Diabetes Health Indicator Dataset analysis with R

In this project, the system in focus is the Air Pressure system (APS) which generates pressurized air that are utilized in various functions in a truck, such as braking and gear changes. The datasets positive class corresponds to component failures for a specific component of the APS system.

Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.

The dataset contains measurements of columns such as: "wind direction", "wind speed", "humidity", "temperature" etc. for the past 4 months and you have to predict the level of: "radiation".

Ariba Code-A-Thon 2018

homesite kaggle