https://github.com/patilni3/project_machine_learning_1
Credit Card Fraud Detection using Random Forest Algorithm
https://github.com/patilni3/project_machine_learning_1
Last synced: 5 months ago
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Credit Card Fraud Detection using Random Forest Algorithm
- Host: GitHub
- URL: https://github.com/patilni3/project_machine_learning_1
- Owner: PatilNi3
- Created: 2022-11-02T19:05:45.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-07T11:07:09.000Z (over 2 years ago)
- Last Synced: 2024-07-21T10:53:29.499Z (10 months ago)
- Language: Jupyter Notebook
- Size: 58.6 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine_Learning_Project
Credit Card Fraud Detection using Random Forest Algorithm
## About Project
The challenge is to recognize fraudulent credit card transaction so that the customers of credit card companies are not charged for items that they did not purchase.
## Main challenges involved in credit card fraud detection are:
1. Enormous Data is processed every day and the model build must be fast enough to respond to the scam in time.
2. Imbalanced Data i.e most of the transactions (99.8%) are not fraudulent which makes it really hard for detecting the fraudulent ones.
3. Data availability as the data is mostly private.
4. Misclassified Data can be another major issue, as not every fraudulent transaction is caught and reported.
5. Adaptive techniques used against the model by the scammers.
## Project Detail
### Libraries Used
• Pandas
• Matplotlib
• Seaborn
• Sklearn
### Algorithm Used:
• Random Forest Algorithm
### Dataset:
• creditcard.csv (https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)
### Insights:
- Only 0.17% fraudulent transaction out all the transactions.
- The data is highly Unbalanced.
### Accuracy:
• The accuracy score is: 100%
• The precision score is: 0: 100% and 1: 92%
• The recall score is: 0: 100% and 1: 80%
• The f1-score is: 0: 100% and 1: 85%
## Conclusion:
• Comparison with the other algorithms without dealing with the imbalancing of the data. The Random Forest Model gives the better result.
# Thank You.☻