https://github.com/rakibhhridoy/handlingimbalanceddataset-business
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 but by others illegally. Some huge transactions can also done by suspicious figure, it need to catch em.
https://github.com/rakibhhridoy/handlingimbalanceddataset-business
auc business-intelligence fraud-detection imbalanced-data imbalanced-learning machine-learning oversampling precision recall smote transcations
Last synced: 2 months ago
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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 but by others illegally. Some huge transactions can also done by suspicious figure, it need to catch em.
- Host: GitHub
- URL: https://github.com/rakibhhridoy/handlingimbalanceddataset-business
- Owner: rakibhhridoy
- Created: 2020-08-13T09:40:55.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-14T09:05:27.000Z (over 4 years ago)
- Last Synced: 2024-12-25T20:25:05.628Z (4 months ago)
- Topics: auc, business-intelligence, fraud-detection, imbalanced-data, imbalanced-learning, machine-learning, oversampling, precision, recall, smote, transcations
- Language: Jupyter Notebook
- Homepage: https://rakibhhridoy.github.io
- Size: 975 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# *Handling Imbalanced Dataset In Business*
*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.
This dataset is a bespoke dataset which contains transactions made by credit cards. This dataset contains transactions, where we have 180 frauds out of 4700 transactions. The dataset is highly imbalanced, the positive class (frauds) account for 3.83% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. The input features are transformed to maintain the confidentiality of the original features and more background information about the data. Features PC1, PC2, … PC5 are the principal
components obtained with PCA, the only feature which have not been transformed with PCA is 'ID' and 'Class'. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.*
### Handling Overview
*For this dataset let's consider fraudulent transactions (which are denoted as 1 in the dataset) is positive class and the non fraudulent transactions (which are denoted as 0 in the dataset) is negative class.*
* ```TP``` transactions which are actually fraudulent and the model also able correctly identify them as fraudulent transactions
* ```FP``` transactions which are actually non fraudulent transactions but the model is predicting them as fraudulent transactions
* ```TN``` transactions which are actually non fraudulent transactions and model is also predicting them as non fraudulent transactions
* ```FN``` transactions which are actually fraudulent but the model is predicting them as non fraudulent transactions.*
### Before Imbalancing solved model accuracy
```
precision recall f1-score support
0 0.97 0.99 0.98 1360
1 0.15 0.04 0.06 50
accuracy 0.96 1410
macro avg 0.56 0.52 0.52 1410
weighted avg 0.94 0.96 0.95 1410
```
### After Solving Imbalanced Issue
##### Applying oversampling method ```SMOTE```
```
precision recall f1-score support
0 0.98 0.95 0.96 1360
1 0.23 0.42 0.30 50
accuracy 0.93 1410
macro avg 0.60 0.68 0.63 1410
weighted avg 0.95 0.93 0.94 1410
```
Applying Hyperparameter Tuning
```
precision recall f1-score support
0 1.00 0.82 0.90 1360
1 0.17 1.00 0.29 50
accuracy 0.83 1410
macro avg 0.59 0.91 0.60 1410
weighted avg 0.97 0.83 0.88 1410
```
#### Our Target was correctly identified transactions as normal or TPR. After Tuning our TPR/Recall became 1.00.. So our model is now well generalized.
### Get touch with me
Connect- [linkedin](https://linkedin.com/in/rakibhhridoy)
Website- [RakibHHridoy](https://rakibhhridoy.github.io)