Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

Model that uses 10 different algorithms to predict the revenue of a movie before it's release

multi-variate deep time series forecasting ensemble models

Evaluation and Implementation of various Machine Learning models for creating a "Banking/Financial Transaction Fraud Prevention System"

Project work related to various hackathons

Comparison of ensemble learning methods on diabetes disease classification with various datasets

An NHL expected goals (xG) model built with light gradient boosting.

Coding challenge for a job interview examining the predictors of vehicle accident severity using GB Road Safety Data

How to do a simple end-to-end machine learning classification project using the telco churn dataset

A model build on RAVDESS dataset, for speech emotion recognition. 85.59% validation accuracy

A binary classification task performed with machine learning in Python. The dataset's target distribution is heavily imbalanced. The model performance was evaluated with F1 scores.

This repository contains the project where the goal is to develop a machine learning model that can accurately predict car prices based on various features. We explored multiple models including K-Nearest Neighbor, Decision Tree, Catboost Classifier, and Light Gradient Boosting Classifier.