Credit Risk Analysis with Machine Learning
-
Updated
May 2, 2023 - Jupyter Notebook
#
lightbgm
Here are 12 public repositories matching this topic...
4 Boosting Algorithms You Should Know – GBM, XGBoost, LightGBM & CatBoost
data-science random-forest decision-trees boosting-algorithms gradient-boosting xgboost-algorithm lightbgm catboost-algorithm
-
Updated
Aug 27, 2022 - Jupyter Notebook
This project detects AI-generated text using an ensemble of classifiers: Multinomial Naive Bayes, Logistic Regression, LightGBM, and CatBoost. It includes robust data preprocessing, model development, and evaluation, ensuring accurate identification of AI-generated content from a diverse text dataset.
numpy tokenizer naive-bayes scikit-learn transformers pandas logistic-regression catboost sgd-classifier tfidf-vectorizer lightbgm
-
Updated
Feb 3, 2024 - Jupyter Notebook
Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio
graphviz supervised-learning logistic-regression decision-tree-classifier gradient-boosting-classifier random-forest-classifier ensemble-models bagging-trees lightbgm xgboost-classifier randomizedsearchcv adaboostclassifier
-
Updated
Aug 2, 2020 - Jupyter Notebook
Repository contains Vehicle Loan Default Prediction of L&T which involved EDA, Statistical Analysis and Model Building
python numpy scikit-learn eda pandas statistical-analysis logistic-regression random-forest-classifier lightbgm
-
Updated
Nov 1, 2020 - Jupyter Notebook
All Possible Machine Learning algorithms implementation in jupyter notebook with csv file.
random-forest linear-regression machine-learning-algorithms jupyter-notebook naive-bayes-classifier logistic-regression handwritten-digit-recognition svm-classifier gradient-boosting id3-algorithm mini-project xgboost-algorithm knn-classifier lightbgm svm-mode cat-boost find-s-algorithm
-
Updated
Oct 11, 2021 - Jupyter Notebook
predicting a continuous target (regression) based on number of anonymous features columns given in the data.
-
Updated
Feb 24, 2021 - Jupyter Notebook
Analytics Vidhya AmExpert ML Competition
python machine-learning cross-validation classification boosting analytics-vidhya-competition lightbgm
-
Updated
Oct 5, 2019 - Jupyter Notebook
data-science machine-learning jupyter-notebook tabular-data hyperparameter-optimization classification datavisualization anaconda3 gradient-boosted-trees lightbgm
-
Updated
Nov 21, 2019 - Jupyter Notebook
Deploying Flight Price Prediction via Microsoft Azure
azure hyperparameters xgboost-model random-forest-regression extra-tree-regressor catboost-model lightbgm randomizedsearchcv flight-price-prediction
-
Updated
Sep 16, 2022 - HTML
Data Storm 1.0 - Business Problem - Team Fixzels
python data-science machine-learning neural-network jupyter random-forest numpy machine-learning-algorithms jupyter-notebook credit-card kaggle kaggle-competition colab business-solutions svm-classifier xgboost-algorithm knn-classifier lightbgm
-
Updated
Feb 27, 2020 - Jupyter Notebook
Improve this page
Add a description, image, and links to the lightbgm topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the lightbgm topic, visit your repo's landing page and select "manage topics."