https://github.com/vatshayan/final-year-credit-card-fraud-detection-project
Credit Card Fraud Detection Project with Code and Documents
https://github.com/vatshayan/final-year-credit-card-fraud-detection-project
btech-project btech-projects btechfinalyear btechprojects college-project credit-card credit-card-fraud credit-card-fraud-detection credit-card-project final-exam final-project final-year-project finalproject finalyearproject machine-learning-project major-project minor-project semester-project semester-projects university-project
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Credit Card Fraud Detection Project with Code and Documents
- Host: GitHub
- URL: https://github.com/vatshayan/final-year-credit-card-fraud-detection-project
- Owner: Vatshayan
- Created: 2022-03-10T12:01:57.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-05-14T08:41:52.000Z (over 2 years ago)
- Last Synced: 2025-03-26T03:33:22.679Z (6 months ago)
- Topics: btech-project, btech-projects, btechfinalyear, btechprojects, college-project, credit-card, credit-card-fraud, credit-card-fraud-detection, credit-card-project, final-exam, final-project, final-year-project, finalproject, finalyearproject, machine-learning-project, major-project, minor-project, semester-project, semester-projects, university-project
- Language: Jupyter Notebook
- Homepage: https://www.finalproject.in/
- Size: 447 KB
- Stars: 35
- Watchers: 2
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Final-Year-Credit-Card-Fraud-Detection-Project
Credit Card Fraud Detection Project with Research paper, Code and Documents
### Youtube Implementation Video : https://youtu.be/CiEnP4xE0dY
### Dataset Information
The dataset contains transactions made by credit cards in September 2013 by European cardholders. 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, we 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-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.
Coorelation Matrix
Two main Machine Learning Algorihtm are used:
1. Logistic Regresion
2. Random forest
### Need Code, Documents & Explanation video ?
## How to Reach me :
### Mail : vatshayan007@gmail.com
### WhatsApp: **+91 9310631437** (Helping 24*7) **[CHAT](https://wa.me/message/CHWN2AHCPMAZK1)**
### Website : https://www.finalproject.in/
### 1000 Computer Science Projects : https://www.computer-science-project.in/
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## Youtube Implementation Video : https://youtu.be/CiEnP4xE0dY