Update README.md

README.md

@@ -12,7 +12,7 @@ The dataset is highly imbalanced as, less than 1% of total transactions are frau
 ![Example Image](images/imbalanced%20dataset.png)
 
 features histograms: as seen, most features are centred around 0
-[View the PDF](images/features\histogram.pdf)
+[View the PDF](images/features%20histogram.pdf)
 
 Time and Amount features need scaling
 Time feature is scaled by StandardScaler -> range between 0 to 1
@@ -25,13 +25,13 @@ 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%20regression.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%20Tree.png)
 
 ROC carve:
 ![Example Image](images/ROC.pdf)
@@ -40,4 +40,4 @@ 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%20on%20test.png)