Machine learning model for Credit Card fraud detection
-
Updated
Jan 10, 2021 - Jupyter Notebook
Machine learning model for Credit Card fraud detection
Spam detection in SMS messages with BERT model and Machine Learning algorithms
The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.
Predict the enzyme class of a given FASTA sequence using deep learning methods including CNNs, LSTM, BiLSTM, GRU, and attention models along with a host of other ML methods.
Electricity Fraud Detection in Smart Grids
System to tell apart the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card.
An implementation of SMOTE
Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods
A compilation of codes for SMA, DC, ADS
Credit Card fraud detection
使用比赛方提供的脱敏数据,进行客户信贷流失预测。
Repository for "Data Mining - Advanced Topics and Applications" projects exam.
Obstructive Sleep Apnea classification with help of numerical data set which having the physical body characteristics with the help of machine learing
This project provides a comprehensive analysis of the Eurovision Song Contest, with insights derived from both traditional statistical methods and machine learning techniques.
Gear detection using OpenCv and Machine Learning
A model that recommends University based on details of an applicant.
Prediction of basic soil nutrients (phosphorus, potassium, boron, calcium, magnesium and manganese) using reflectance from Hyperspectral Satellite Images (HSI).
RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem
Course Project for CS273A: Machine Learning at UCI
Add a description, image, and links to the smote-sampling topic page so that developers can more easily learn about it.
To associate your repository with the smote-sampling topic, visit your repo's landing page and select "manage topics."