Anamoly detection in credit card transactions using different Machine learning algorithms in python Used supervised learning algolithms -Logistic regression,Decision tree, Random forest, Bagging, Xgbost,KNN SVM classifiers and Isolation forest. Unsupervised learning algorithms- local outlier Factor
1.Loading packages The packages are Pandas to load data and to do data analysis, Numpy to work with arrays, scikit-learn is used for building the model and evaluating it,seaborn and matplotlib for data visualisation, pydotplus to visualize the decision tree and finally xgboost model
2.Loading data - The dataset used in the project is the Kaggle Credit Card Fraud Detection dataset https://www.kaggle.com/mlg-ulb/creditcardfraud
The dataset consists of 284807 transcation data with features- 'Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount','Class'
3.Data analysis-Exploring the dataset to gain an understanding of the type, quantity, and distribution of data in our dataset. Data analysis is done to check any outliers ,missing values and categorical variables in the dataset. Data visualization is done using seaborn and matplotlib pacakges
4.Creating training and testing data - Using scikit-learn train-test split function data set is divided 80% as train and 20% as test.
5.Creating model- Using scikit-learn , 9 different classification models are built. Logistic regression,Decision tree, random forest, bagging, xgbost,KNN , SVM classifiers,Isolation forest and local outlier Factor
6.Model evaluation- We evaluated our built models using the evaluation metrics provided by the scikit-learn package. The evaluation metrics we are going to use are the accuracy score metric, f1 score metric, and finally the confusion matrix.
Found and accuracy 99.9% for all the classification models