Machine learning model for Credit Card fraud detection

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.

Spam detection in SMS messages with BERT model and Machine Learning algorithms

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.

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.

Prediction of basic soil nutrients (phosphorus, potassium, boron, calcium, magnesium and manganese) using reflectance from Hyperspectral Satellite Images (HSI).

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

Electricity Fraud Detection in Smart Grids

A compilation of codes for SMA, DC, ADS

An implementation of SMOTE

A model that recommends University based on details of an applicant.

No description provided.

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。

RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem

Gear detection using OpenCv and Machine Learning

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

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 develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

This project predicts traffic accident severity using the 2023 STATS19 dataset. It employs machine learning and LIME to not only forecast outcomes like 'Fatal' or 'Slight' but also to provide clear, understandable explanations for its predictions, creating a powerful tool for enhancing road safety.

This is a program to predict the possible risk of default on credit card use.

Bank bankruptcy predictions on FDIC bank failure data using tensorflow, keras, sklearn, ensemble, and imblearn libraries.

Course Project for CS273A: Machine Learning at UCI

Using Random undersampling, Tomeklinks, Random oversampling, SMOTE, SMOTE+Tomeklinks and ADASYN from inbuilt imbalanced learn library and found how many records added or discarded.

The main objective is to build a predictive model that predicts whether a new client will subscribe to a term deposit or not, based on data from previous marketing campaigns.

A minority oversampling method for imbalance data set

A fake news detection app leveraging Ensemble ML models and NLP context-analysis to evaluate the credibility of news articles by cross-referencing claims with reliable databases.

Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.

A machine learning project for predicting diabetes using data preprocessing, exploratory data analysis, and classification models.