There are 0 repository under lgbmclassifier topic.
Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.
Contains our Approach for the competition organized at Udyam'21
Example notebooks to produce the models used in the SexEst web application.
A machine learning based forecasting system for taxi demand prediction
Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
This project is about to detecting the text generated by different LLM given prompt. The instance is labeled by Human and Machine, and this project utilised both traditional machine learning method and deep learning method to classify the instance.
This approach has the potential to create accurate, generalizable and adaptable machine learning methods that effectively and sustainably address agricultural tasks such as yield prediction and early disease identification.
Machine Learning model for heart failure prediction using LGBM Classifier.
Learning to Rank - Cross Sell
Early prediction of Mortality Risk among Covid -19 Patients in early stages when patients gets admitted into the hospital.
Using LGBMClassifier to solve To-Be Challenge, which is a machine learning challenge on CodaLab Platform that aims to adress the problems of medical imbalanced data classification.
Spectral type classification using LGBM and deployed using FastAPI, Pydantic, and Docker
End to end Heart Diseases Prediction Model with webapp using Flask
Interconnect : Clients Churn Prediction using ML
An innovative system for filtering and categorizing movie reviews
Participated in Analytics Vidya Hackathon ( JOB-A-THON | May 2021 ). This Repository contains all code, reports and approach.
how to predict score credit to home credit indonesia with machine learning modeling, find more to Home Credit Indonesia
Predicting Next Booking Destinations for Airbnb Users. Feel free to access the Streamlit App in the link below.
Rank 4/125 MachineHack
The task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help us make these predictions, we are given a set of personal records recovered from the ship's damaged computer system.
The classification problem of student dropout data of an institute
This repository contain my final projekt on the Data science Skillbox school on the topic: "Development of a machine learning algorithm to predict the behavior of customers of the "SberAvtopodpiska"
Music Genre Recommender website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). An accuracy of 90% was achieved on the test set by tuning the hyperparameters of the model with Optuna.
Déploiement d'une API Flask du modèle de classification déployée sur Heroku (OpenClassrooms | Data Scientist | Projet 7)
Various classification algorithms are implemented to predict whether a person is prone to or is suffering from heart disease.
Loan Eligibility - Classification (Python)
Credit card fraud detection using various sampling methods and machine learning algorithms.
MlFlow Project creating pipelines and using Grid-Search Cross Validation to find optimal parameters for Old School Runescape Machine Learning datasets.
The goal of this Project is to predict whether a mushroom is edible or poisonous based on its physical characteristics.
Summary of Assignment Two from the Second semester of the MSc in Data Analytics program. This repository contains the CA2 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.
This repository features my Kaggle projects, highlighting advanced techniques in data preprocessing, feature engineering, model selection, and optimization across various competitions.
This project focuses on predicting heart disease using a comprehensive dataset containing patient information. The goal is to build machine learning models that can predict the presence of heart disease based on various health parameters.
Building and leveraging a LGBM model to predict whether a mushroom is edible or poisonous
Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.