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Skills - Logistic regression , Support Vector Machine, K Nearest Neighbours,Decision Tree , Random Forest , XGBoost, F1 Score, ROC-AUC Curve, Data Visualisation , Exploratory Data Analysis , Data Science application in Finance , Machine Learning
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Tools - Google Colab , Jupyter Notebooks , Python , Numpy , Pandas , Matplotlib , Seaborn , Sklearn
The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
I have use IQR method and different machine learning algorithms supervised in order to extract better conclusions.
The data was taken from Kaggle site : https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
The code of the project : https://github.com/NidhiSingh1111/Credit-Card-Fraud-Detection/blob/main/Credit-Card-Fraud-Detection.ipynb