From 90bbaf4d7aa8dc31484cb81665eaa2e411b31f2c Mon Sep 17 00:00:00 2001 From: ShayanHodai Date: Fri, 31 May 2024 23:04:02 -0400 Subject: [PATCH] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4dadeec..5665fc3 100644 --- a/README.md +++ b/README.md @@ -9,11 +9,11 @@ The dataset: ![Example Image](images/dataset.png) The dataset is highly imbalanced as, less than 1% of total transactions are fraud -![Example Image](images/imbalanced dataset.png) +![Example Image](images/imbalanced\dataset.png) features histograms: as seen, most features are centred around 0 -![Example Image](images/features histogram.png) +![Example Image](images/features\histogram.png) Time and Amount features need scaling Time feature is scaled by StandardScaler -> range between 0 to 1 @@ -26,13 +26,13 @@ correlation of fraud/normal transactions with non-redundant features Machine learning classification models evaluation metrics: As the cost of False Positive and False Negative in this problem varies, Precision and Recall and, eventually, f1-score are the evaluation metrics of the model performance Logistic regression: -![Example Image](images/logistic regression.png) +![Example Image](images/logistic\regression.png) KNN: ![Example Image](images/KNN.png) SVM: ![Example Image](images/SVM.png) Decision tree classifier, which is highly overfitting: -![Example Image](images/Decision Tree.png) +![Example Image](images/Decision\Tree.png) ROC carve: ![Example Image](images/ROC.png) @@ -41,4 +41,4 @@ Fine-tuning the best performing model, which is logistic regression: ![Example Image](images/fine-tuning.png) Evaluation on the test set: -![Example Image](images/evaluation on test.png) +![Example Image](images/evaluation\on\test.png)