Update README.md

README.md

@@ -2,13 +2,13 @@
 The repository contains code to analyze credit card transactions and predict whether transactions are fraudulent using machine learning algorithms. Machine learning workflow has been followed to train and fine-tune classification models. The steps are data collection and exploration, data processing, feature correlations, automating processing by pipelines, building models, evaluating performance by cross-validation, and fine-tuning the best-performing model based on precision, recall, and F1 score metrics.
 The link to the kaggle dataset is: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
 
-To work with the code, clone the repository: 
+# To work with the code, clone the repository: 
 git clone https://github.com/ShayanHodai/fraud-detection.git
 
-The dataset:
+# The dataset:
 ![Example Image](images/dataset.png)
 
-The dataset is highly imbalanced as, less than 1% of total transactions are fraud
+# The dataset is highly imbalanced as, less than 1% of total transactions are fraud
 ![Example Image](images/imbalanced%20dataset.png)
 
 Features histograms: as seen, most features are centred around 0
@@ -22,7 +22,7 @@ And Amount feature is scaled by RobustScaler -> deals better with outliers
 correlation of fraud/normal transactions with non-redundant features
 ![Example Image](images/corr2.png)
 
-Machine learning classification models evaluation metrics:
+# Machine learning models:
 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%20regression.png)
@@ -33,11 +33,11 @@ SVM:
 Decision tree classifier, which is highly overfitting:
 ![Example Image](images/Decision%20Tree.png)
 
-ROC carve:
+# ROC carve:
 ![Example Image](images/ROC.png)
 
-Fine-tuning the best performing model, which is logistic regression:
+# Fine-tuning the best performing model, which is logistic regression:
 ![Example Image](images/fine-tuning.png)
 
-Evaluation on the test set:
+# Evaluation on the test set:
 ![Example Image](images/evaluation%20on%20test.png)