GitHub topics: isolation-forest-algorithm

Repositories

MarkNgugi/AnomalyFinder-AI

AnomalyFinder-AI is an AI tool for detecting and analyzing anomalies in log data from various systems and applications. It identifies irregular patterns, provides descriptions of anomalies, and suggests solutions to prevent issues.

Language: Python - Size: 144 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 1 - Forks: 0

msimms/LibIsolationForest

C++, rust, julia, python2, and python3 implementations of the Isolation Forest anomaly detection algorithm.

Language: Python - Size: 124 KB - Last synced at: 7 days ago - Pushed at: over 3 years ago - Stars: 35 - Forks: 11

Sanyamjin/BRIDGE_ANAMOLY_DETECTION

Developed a Machine Learning Model for SpectoV for an internship second screening round. Generated a Dataset with temperature, strain , vibration as features and class anamoly.

Language: Jupyter Notebook - Size: 38.1 KB - Last synced at: 3 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

Meepo69/Breast-Cancer-Detection

Language: Jupyter Notebook - Size: 618 KB - Last synced at: about 1 month ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

shivam1808/Credit-Card-Fraud-Detection

Credit Card Fraud Detection using Isolation Forest Algorithm and Local Outlier Factor(LOF) Algorithm.

Language: Jupyter Notebook - Size: 812 KB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 10 - Forks: 5

rajdip-i/CONSUMER-ENERGY-MANAGEMENT

Utilizing LSTM Neural Networks to forecast energy cosumption trends with time series analysis. Employing Collaborative Filtering with Matrix Factorization and SVD, the system suggests personalized actions based on user behavior, fostering energy conservation.Leveraging Isolation Forest to detect anomalies in consumption patterns.

Language: Jupyter Notebook - Size: 1.86 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

kimfoo/Anomaly-detection-of-sensor-data

This project focuses on building a anomaly detection model to detect wafer runs that are anomalous. The dataset does not contain labelled data (anomalous/non-anomalous), therefore an unsupervised learning method is utilised. Python and the Sci-kitLearn machine learning libraries are the primary tools used in this project.

Language: Jupyter Notebook - Size: 4.43 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

abhik12295/credit_card_fraud_detection

Used Linear Regression Model and Isolation Forest Model to detect the fraud and anomaly detection

Language: Jupyter Notebook - Size: 1.05 MB - Last synced at: 10 months ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0

SrinithiSaiprasath/Data_Extraction_and_Analysis 📦

The workflow includes data exploration, dimension reduction, and visualization, with the integration of machine learning concepts for advanced analysis. The GitHub repository provides comprehensive documentation and instructions for replicating the analysis and findings.

Language: Python - Size: 864 KB - Last synced at: 10 months ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

aakinlalu/Volume-Based-Anomaly-Detection-Notification-using-IsolationForest-and-MLFlow

Use Isolation Forest and MLflow to prototype anomaly detection that could send email notification if there is any slight anomaly or empty.

Language: Python - Size: 161 KB - Last synced at: about 1 year ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

haniye6776/outlier-detection

Language: Jupyter Notebook - Size: 4.29 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Saurabh620/Mental_Health_Analysis_Machine-_Learning

Analyzing a dataset to understand mental health factors, this project employs Python tools for preprocessing, exploration, segmentation, trend analysis, and modeling.

Language: Jupyter Notebook - Size: 204 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

hrootscraft/sensor-data-analysis

Analyze motion sensor data to find patterns in a person's behavior

Language: Jupyter Notebook - Size: 1.77 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

SINGHxTUSHAR/Assignment-79

Language: Jupyter Notebook - Size: 12.7 KB - Last synced at: 3 months ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

absaw/Surface-Water-Quality-Data-Anomaly-Detection

Surface water quality data analysis and prediction of Potomac River, West Virginia, USA. Using time series forecasting, and anomaly detection : ARIMA, SARIMA, Isolation Forest, OCSVM and Gaussian Distribution

Language: Jupyter Notebook - Size: 14.4 MB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 7 - Forks: 7

kgkeklikci/ENS492-Graduation-Project-Implementation

Language: Jupyter Notebook - Size: 32.5 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

priyanka641329/Credit-Card-Fraudlent

Fraud Detection model based on anonymized credit card transactions

Language: Python - Size: 5.86 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Lakshmi786/Practical-Transactional-Anomaly-Detector

Language: Jupyter Notebook - Size: 1.57 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

ajitsingh98/Credit-Card-Fraud-Detection-ML-Model

Language: HTML - Size: 289 KB - Last synced at: almost 2 years ago - Pushed at: over 5 years ago - Stars: 0 - Forks: 1

aakinlalu/TimeSeries-Anomaly-App

Simple machine learning framework for Timeseries application to identify anomaly in dataset using Machine learning and Deep neural network

Language: Python - Size: 258 KB - Last synced at: about 1 year ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 1

PARAVPREET17/Credit-Card-Fraud-Detection

Fraud Detection model based on anonymized credit card transactions based on Isolation Forest Algorithm and Local Outlier Factor

Language: Jupyter Notebook - Size: 691 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

Pradnya1208/Credit-card-fraud-detection-using-Isolation-Forest-and-LOF

This project aims to detect credit card fraud using Anamoly detection techniques such as Isolation Forest and Local Outlier Factor algorithms.

Language: Jupyter Notebook - Size: 1.06 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

PriyabrataThatoi/Anomaly-Detection---IF-LOF

Anomaly detection using unsupervised method is a challenging one. Isolated Random Forest and Local Outlier Factor are the most promising one. They detect outlier with highest recall possible.

Language: Jupyter Notebook - Size: 87.9 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 0