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Search results for jupyter notebook transaction
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23 search results found
Credit Card Fraud Detection Using Autoencoders In Keras
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289
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
Fraud Detection Using Machine Learning
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98
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Fraud Detection Using Deep Learning
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95
Kaggle Ieee
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32
Thesis Bitcoin Clustering
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31
The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idiom
Kaggle Santander Customer Transaction Prediction 5th Place Partial Solution
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29
Kaggle Competition notebooks
Fraud Detection In Online Transactions
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23
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
Fxcm_currency
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18
Using Machine Learning for live currency trading
Customer_lifetime_value_analysis
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17
Customer life time analysis (CLV analysis). We are using Gamma-Gamma model to estimate average transaction value for each customer.
Bank Note Authentication
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16
💸 Authenticate Bank Notes on the basis of Genuity and Forged using Sklearn and deployed on Heroku and FastAPI Server 💳 💲
Credit Card Fraud Detection
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15
A project is built to predict that user's current transaction record is normal or abnormal based on previous transaction records.
Av Amex19
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14
Analytics Vidhya's ML competittion ~~ AMEX - 19
Vpin
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13
Order flow toxicity; Volume-Synchronized Probability of Informed Trading
Anti Money Laundering Using Keras
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12
Utilize the deep learning library Keras to classify transactions as fraudulent(1) or non-fraudulent(0).
Stock_price_movement_prediction_rnn_cnn_ffnn
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12
The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using transaction data from the Limit Order Book (LOB).
Real Estate
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11
Machine learning Regression problem with easy understandable solutions
Mjolnir
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9
A tool for deploying and benchmarking Ethereum Clients
Credit Card Fraud Detection
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9
It is Based on Anamoly Detection and by Using Deep Learning Model SOM which is an Unsupervised Learning Method to find patterns followed by the fraudsters.
Archival_network
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9
Investigating the frequency of alternative blocks, reorganizations, potential double-spend attacks, selfish mining, and more.
Trustkeeper
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8
A fraud prevention system for Peer-to-Peer transaction networks (Jahir M Gutierrez)
Iecse Ml Winter 2020
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8
The 2020 winter Machine Learning project of IECSE
Mustaching
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8
A small Python package inspired by Mr. Money Mustache for summarizing and plotting personal finance data
Credit Card Fraud Prediction
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7
Using simple logistic regression to predict credit card fraud transactions
Dbii Workshops
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7
Databases II - Workshops
Transaction Data 2019
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6
Transaction Data of MinsaPay 2019
Credit Card Fraud Detection
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6
A project on machine learning techniques dealing with imbalanced classification (Python)
Trgan
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6
TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation
Frauddetection Enhanced Using Graph Algorithms
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6
Real_estate_tycoon_project
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6
Predict Housing Prices using Machine Learning.
Blockchain Transaction Classification
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6
Bitcoin blockchain transaction classification project
Blockchain Anomaly Detection
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5
Tbrain_credit_card
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5
Solution of TBrain's Credit Card Fraudulent Transaction Detection
Strike
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5
:squirrel: (💳) Strike is a Credit Card Fraud Detection Project that we will be developing for demonstration purposes showing unsupervised machine learning using anomaly detection from sklearn package.
Case Study Fraud Detection
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5
Fraud Case Study Challenge: Given historical data, create a web based front-end with machine learning back-end to enable quick triage of potential new fraud. Classify transactions as high, medium or low risk.
Kaggle Kkbox Churn Prediction Top5 Percentage
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5
Kaggle kkbox-chrun-prediction challenge Top5% (32/575)
Ircnn Keras
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5
Inception Recurrent Convolutional Neural Network
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