Detecting-Credit-Card-Fraud

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Detecting-Credit-Card-Fraud

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Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.

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# Detecting Credit Card Fraud 
This is a data science project with machine learning in R.

## Aim of Project
The aim of this R project is to build a classifier that can detect credit card fraudulent transactions. I used a variety of machine learning algorithms like Decision Trees, Logistic Regression, Artificial Neural Networks and finally, Gradient Boosting Classifier that will be able to discern fraudulent from non-fraudulent ones.

### Dataset used 
For carrying out the credit card fraud detection, I used the [Card Transactions dataset](https://drive.google.com/file/d/1CTAlmlREFRaEN3NoHHitewpqAtWS5cVQ/view) that contains a mix of fraud as well as non-fraudulent transactions.

### Data Exploration
First I imported the datasets that contain transactions made by credit cards. I then explored the data that is contained in the ```creditcard_data``` dataframe. After displaying the ```creditcard_data``` using the ```head()``` function as well as the ```tail()``` function, I proceeded to explore the other components of this dataframe.

### Data Manipulation
In this section of the project, I scaled the data using the ```scale()``` function. I applied this to the amount component of our ```creditcard_data``` amount. With the help of scaling, the data is structured according to a specified range. Therefore, there are no extreme values in the dataset that might interfere with the functioning of the model.

### Data Modelling
After standardizing the entire dataset, I split the dataset into training set as well as test set with a split ratio of ```0.80```. This means that 80% of the data will be attributed to the ```train_data``` whereas 20% will be attributed to the ```test_data```. I then found the dimensions using the ```dim()``` function.

### Fitting Logistic Regression Model
In this section of the project, I fit the first model. I began with logistic regression. I used it for modeling the outcome probability of fraud/not fraud. I proceeded to implement this model on the test data. Once I summarised the model, I visualized it through plots. 
In order to assess the performance of the model, I portrayed the Receiver Optimistic Characteristics or ```ROC curve```. For this, I first imported the ROC package and then plotted the ROC curve to analyze its performance.

### Fitting a Decision Tree Model
Next, I implemented a decision tree algorithm to plot the outcomes of a decision through which I could conclude as to what class the object belongs to. I then implemented the decision tree model and plotted it using the ```rpart.plot()``` function. I specifically used the recursive parting to plot the decision tree.

### Artificial Neural Network
Artificial Neural Networks are a type of machine learning algorithm that are modeled after the human nervous system. The ANN models are able to learn the patterns using the historical data and are able to perform classification on the input data. I imported the ```neuralnet``` package that allowed me to implement the ANNs. Then I proceeded to plot it using the ```plot()``` function. Now, in the case of Artificial Neural Networks, there is a range of values that is between 1 and 0. I set a threshold of 0.5, that is, values above 0.5 will correspond to 1 and the rest will be 0.

### Gradient Boosting (GBM)
Gradient Boosting is a popular machine learning algorithm that is used to perform classification and regression tasks. This model comprises of several underlying ensemble models like weak decision trees. These decision trees combine together to form a strong model of gradient boosting. I implemented gradient descent algorithm in the model.

### AUC-ROC Curve
In the last section of the project, I calculated and plotted an ROC curve measuring the sensitivity and specificity of the model. The ```print``` command plots the curve and calculates the area under the curve. The area of a ROC curve can be a test of the sensivity and accuracy of a model.

## Conclusion
Concluding our R Data Science project, I learnt how to develop a credit card fraud detection model using machine learning. I used a variety of ML algorithms to implement this model and also plotted the respective performance curves for the models. I also learnt how data can be analyzed and visualized to discern fraudulent transactions from other types of data.

Cr:DataFlair

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