https://github.com/machinelearningzuu/credit-card-fraud-detection
Identify fraudulent credit card transactions
https://github.com/machinelearningzuu/credit-card-fraud-detection
Last synced: 12 months ago
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Identify fraudulent credit card transactions
- Host: GitHub
- URL: https://github.com/machinelearningzuu/credit-card-fraud-detection
- Owner: machinelearningzuu
- Created: 2020-10-10T14:26:15.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-12-25T02:53:55.000Z (over 5 years ago)
- Last Synced: 2025-02-04T17:17:47.596Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 9.54 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Credit-Card-Fraud-Detection
The objective of the task is to Identify fraudulent credit card transactions.It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.
# Dataset
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, owners cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
# Methodology
- Data Preprocessing and Tacking the unbalnce
- Using Deep Learning classfication identify fraudulent credit card transactions.
- Inference the model using tensorflow lite
# Techniques
- Supervised Deep Learning
- Artificial Neural Networks
- Tensorflow Lite Inference
# Tools
* TensorFlow - Deep Learning Model
* pandas - Data Extraction and Preprocessing
* numpy - numerical computations
* scikit learn - Advanced preprocessing and Machine Learning Models
### Installation
Install the dependencies and conda environment
```sh
$ conda create -n envname python=python_version
$ activate envname
$ conda install -c anaconda tensorflow-gpu
$ conda install -c anaconda pandas
$ conda install -c anaconda matplotlib
$ conda install -c anaconda scikit-learn
```