This project addresses the challenges of AI-generated content, such as misinformation and bias, by developing a machine-learning algorithm that distinguishes between AI-generated and human-generated texts. This solution enhances content authenticity and mitigates associated risks.

Ensemble based approach compared to traditional machine learning models

Time Series Ensemble Forecasting

Identify which customer is willing to possess the insurance policy, so we campaign efficiently.

A Time-Series Machine Learning project aimed at accurate predicting curreny pair exchange rates using ARIMA, SARIMA and LSTM.

Spotify Music Classifier with Machine Learning Using Spotify API

This project Integrated machine learning models including Support Vector Machine (SVM), Random Forest, k-Nearest Neighbors, and Neural Networks into a stacked ensemble for predicting potential COVID-19 infections based on the collected data, facilitating proactive healthcare interventions and management.

A Novel Approach for Alzheimer's Classification Utilizing Ensemble Learning on Pre-trained Neural Networks Fine-tuned on Pre-processed and Augmented Alzheimer's Dataset

A Novel Stacked Ensemble Model of Random Forest, Gradient Boosting & Bagging Regressor with RNN as the meta-learner layer for Crop Yield Prediction

This project is dedicated to accurately classify Alzheimer's disease into Demented, Non-demented and Converted Category.

2024 상반기 LG Aimers 오프라인 해커톤 대회(최우수상)

learning python day 14

🏆데이콘 AI해커톤 대회 우수상 솔루션🏆

This streamlit app predicts the churn rate using Gradient Boosting models (XGBoost, Catboost, LightGBM) on IBM Customer Churn Dataset

A collection of fundamental Machine Learning Algorithms Implemented from scratch along-with their applications for various ML tasks like clustering, thresholding, data analysis, prediction, regression and image classification.

It was studied factors of subscription for a term deposit after the direct marketing campaign.

To predict patients with breast cancer using classification models such as random forest, support vector machine, and stacking ensemble method

Implementation of two major ensemble learning methodologies, Bagging and Stacking, over the tasks of classification and regression. Also, compared the results of Random Forests with multiple Boosting Techniques.

Visa approval process by leveraging machine learning on OFLC's extensive dataset, aiming to recommend suitable candidate profiles for certification or denial based on crucial drivers.