{"id":63385,"date":"2019-07-17T11:16:10","date_gmt":"2019-07-17T05:46:10","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=63385"},"modified":"2024-08-02T15:49:23","modified_gmt":"2024-08-02T10:19:23","slug":"data-science-machine-learning-project-credit-card-fraud-detection","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-science-machine-learning-project-credit-card-fraud-detection\/","title":{"rendered":"Data Science Project &#8211; Detect Credit Card Fraud with Machine Learning in R"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' 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11:27:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-12 12:45:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-16 00:39:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-20 09:15:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-23 14:52:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-26 22:29:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-30 18:56:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-04 02:56:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-07 08:25:03&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-07 08:25:03&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>This is the 3rd part of the\u00a0<em><strong>R project series designed by DataFlair<\/strong><\/em>. Earlier we talked about <em><strong><a href=\"https:\/\/data-flair.training\/blogs\/r-data-science-project-uber-data-analysis\/\">Uber Data Analysis Project<\/a><\/strong><\/em> and today we will discuss the Credit Card Fraud Detection Project using Machine Learning and R concepts. In this R Project, we will learn how to perform detection of credit cards. We will go through various algorithms like Decision Trees, Logistic Regression, Artificial Neural Networks and finally, Gradient Boosting Classifier. To detect credit card fraud, we will make use of the Card Transactions dataset that contains a mix of fraud as well as non-fraudulent transactions.<\/p>\n<h2>Machine Learning Project &#8211; How to Detect Credit Card Fraud<\/h2>\n<p>The aim of this R project is to build a classifier that can detect credit card fraudulent transactions. We will use a variety of <a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-algorithm\/\"><em><strong>machine learning algorithms<\/strong><\/em><\/a> that will be able to discern fraudulent from non-fraudulent one. By the end of this machine learning project, you will learn how to implement machine learning algorithms to perform classification.<\/p>\n<p><strong><em>The dataset used in this project is available here<\/em> &#8211; <em><a href=\"https:\/\/drive.google.com\/file\/d\/1CTAlmlREFRaEN3NoHHitewpqAtWS5cVQ\/view\">Fraud Detection Dataset<\/a><\/em><\/strong><\/p>\n<h3>1. Importing the Datasets<\/h3>\n<p>We are importing the datasets that contain transactions made by credit cards-<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(ranger)\r\nlibrary(caret)\r\nlibrary(data.table)\r\ncreditcard_data &lt;- read.csv(\"\/home\/dataflair\/data\/Credit Card\/creditcard.csv\")<\/pre>\n<p><strong>Input Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63494\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library.png\" alt=\"Importing Libraries\" width=\"908\" height=\"176\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library.png 908w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library-150x29.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library-300x58.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library-768x149.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Library-520x101.png 520w\" sizes=\"auto, (max-width: 908px) 100vw, 908px\" \/><\/a><\/p>\n<p><em><strong>Before moving on, you must revise the <a href=\"https:\/\/data-flair.training\/blogs\/r-data-frame-introduction-operations\/\">concepts of R Dataframes<\/a><\/strong><\/em><\/p>\n<h3>2. Data Exploration<\/h3>\n<p>In this section of the fraud detection ML project, we will explore the data that is contained in the creditcard_data dataframe. We will proceed by displaying the creditcard_data using the head() function as well as the tail() function. We will then proceed to explore the other components of this dataframe &#8211;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">dim(creditcard_data)\r\nhead(creditcard_data,6)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-Exploration-in-data-science-project.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63615\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-Exploration-in-data-science-project.png\" alt=\"data science project in R\" width=\"679\" height=\"550\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-Exploration-in-data-science-project.png 679w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-Exploration-in-data-science-project-150x122.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-Exploration-in-data-science-project-300x243.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-Exploration-in-data-science-project-520x421.png 520w\" sizes=\"auto, (max-width: 679px) 100vw, 679px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">tail(creditcard_data,6)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63495 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-1.png\" alt=\"credit card fraud detection\" width=\"617\" height=\"443\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-1.png 617w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-1-150x108.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-1-300x215.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-1-520x373.png 520w\" sizes=\"auto, (max-width: 617px) 100vw, 617px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">table(creditcard_data$Class)\r\nsummary(creditcard_data$Amount)\r\nnames(creditcard_data)\r\nvar(creditcard_data$Amount)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Exploration-2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63496 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Exploration-2.png\" alt=\"credit card fraud detection\" width=\"575\" height=\"517\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Exploration-2.png 575w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Exploration-2-150x135.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Exploration-2-300x270.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Exploration-2-520x468.png 520w\" sizes=\"auto, (max-width: 575px) 100vw, 575px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">sd(creditcard_data$Amount)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-3.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63497\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-3.png\" alt=\"data exploration\" width=\"344\" height=\"88\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-3.png 344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-3-150x38.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/data-exploration-3-300x77.png 300w\" sizes=\"auto, (max-width: 344px) 100vw, 344px\" \/><\/a><\/p>\n<p><em><strong><a href=\"https:\/\/data-flair.training\/blogs\/r-tutorials-home\/\">Learn everything about R<\/a> for FREE and master the technology<\/strong><\/em><\/p>\n<h3>3. Data Manipulation<\/h3>\n<p>In this section of the R data science project, we will scale our data using the scale() function. We will apply this to the amount component of our creditcard_data amount. Scaling is also known as feature standardization. With the help of scaling, the data is structured according to a specified range. Therefore, there are no extreme values in our dataset that might interfere with the functioning of our model. We will carry this out as follows:<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">head(creditcard_data)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63499 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-1.png\" alt=\"credit card fraud detection\" width=\"739\" height=\"580\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-1.png 739w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-1-150x118.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-1-300x235.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-1-520x408.png 520w\" sizes=\"auto, (max-width: 739px) 100vw, 739px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">creditcard_data$Amount=scale(creditcard_data$Amount)\r\nNewData=creditcard_data[,-c(1)]\r\nhead(NewData)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63500 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-2.png\" alt=\"credit card fraud detection\" width=\"747\" height=\"618\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-2.png 747w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-2-150x124.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-2-300x248.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Manipulation-2-520x430.png 520w\" sizes=\"auto, (max-width: 747px) 100vw, 747px\" \/><\/a><\/p>\n<h3>4. Data Modeling<\/h3>\n<p>After we have standardized our entire dataset, we will split our dataset into training set as well as test set with a split ratio of 0.80. This means that 80% of our data will be attributed to the train_data whereas 20% will be attributed to the test data. We will then find the dimensions using the dim() function &#8211;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(caTools)\r\nset.seed(123)\r\ndata_sample = sample.split(NewData$Class,SplitRatio=0.80)\r\ntrain_data = subset(NewData,data_sample==TRUE)\r\ntest_data = subset(NewData,data_sample==FALSE)\r\ndim(train_data)\r\ndim(test_data)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63501 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1.png\" alt=\"credit card fraud detection\" width=\"815\" height=\"578\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1.png 815w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1-150x106.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1-300x213.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1-768x545.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-1-520x369.png 520w\" sizes=\"auto, (max-width: 815px) 100vw, 815px\" \/><\/a><\/p>\n<h3>5. Fitting Logistic Regression Model<\/h3>\n<p>In this section of credit card fraud detection project, we will fit our first model. We will begin with logistic regression. A logistic regression is used for modeling the outcome probability of a class such as pass\/fail, positive\/negative and in our case &#8211; fraud\/not fraud. We proceed to implement this model on our test data as follows &#8211;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">Logistic_Model=glm(Class~.,test_data,family=binomial())\r\nsummary(Logistic_Model)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63503 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model.png\" alt=\"credit card fraud detection\" width=\"815\" height=\"299\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model.png 815w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model-150x55.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model-300x110.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model-768x282.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Logistic-Model-520x191.png 520w\" sizes=\"auto, (max-width: 815px) 100vw, 815px\" \/><\/a><\/p>\n<p>After we have summarised our model, we will visual it through the following plots &#8211;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">plot(Logistic_Model)<\/pre>\n<p><strong>Input Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63506\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-2.png\" alt=\"Data Modeling \" width=\"328\" height=\"35\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-2.png 328w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-2-150x16.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-2-300x32.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Data-Modeling-2-320x35.png 320w\" sizes=\"auto, (max-width: 328px) 100vw, 328px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-66031\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1.png\" alt=\"logistic regression model output\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-model-output-1-1-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Next Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-66033\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3.png\" alt=\"logistic output 3\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-3-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-66034\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3.png\" alt=\"logistic regression output\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-regression-output-3-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-66035\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4.png\" alt=\"logistic output\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-output-4-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p>In order to assess the performance of our model, we will delineate the ROC curve. ROC is also known as Receiver Optimistic Characteristics. For this, we will first import the ROC package and then plot our <a href=\"https:\/\/en.wikipedia.org\/wiki\/Receiver_operating_characteristic\">ROC<\/a> curve to analyze its performance.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(pROC)\r\nlr.predict &lt;- predict(Logistic_Model,train_data, probability = TRUE)\r\nauc.gbm = roc(test_data$Class, lr.predict, plot = TRUE, col = \"blue\")<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-ROC.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-66028\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-ROC.png\" alt=\"credit card fraud detection project\" width=\"694\" height=\"375\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-ROC.png 694w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-ROC-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-ROC-300x162.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic-ROC-520x281.png 520w\" sizes=\"auto, (max-width: 694px) 100vw, 694px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63531\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC.png\" alt=\"fraud detection - R project\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/logistic_ROC-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<h3>6. Fitting a Decision Tree Model<\/h3>\n<p>In this section, we will implement a decision tree algorithm. <em><strong><a href=\"https:\/\/data-flair.training\/blogs\/r-decision-trees\/\">Decision Trees<\/a><\/strong><\/em> to plot the outcomes of a decision. These outcomes are basically a consequence through which we can conclude as to what class the object belongs to. We will now implement our decision tree model and will plot it using the rpart.plot() function. We will specifically use the recursive parting to plot the decision tree.<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(rpart)\r\nlibrary(rpart.plot)\r\ndecisionTree_model &lt;- rpart(Class ~ . , creditcard_data, method = 'class')\r\npredicted_val &lt;- predict(decisionTree_model, creditcard_data, type = 'class')\r\nprobability &lt;- predict(decisionTree_model, creditcard_data, type = 'prob')\r\nrpart.plot(decisionTree_model)<\/pre>\n<p><strong>Input Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/DecisionTree-ML-Code.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63597\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/DecisionTree-ML-Code.png\" alt=\"ML project - decision tree\" width=\"641\" height=\"142\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/DecisionTree-ML-Code.png 641w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/DecisionTree-ML-Code-150x33.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/DecisionTree-ML-Code-300x66.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/DecisionTree-ML-Code-520x115.png 520w\" sizes=\"auto, (max-width: 641px) 100vw, 641px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63599\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot.png\" alt=\"machine learning project - decision tree\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Decision-Tree-machine-learning-Plot-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<h3>7. Artificial Neural Network<\/h3>\n<p><em><strong><a href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network\/\">Artificial Neural Networks<\/a><\/strong><\/em> 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.\u00a0<span style=\"font-weight: 400\">We import the neuralnet package that would allow us to implement our ANNs. Then we 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. We set a threshold as 0.5, that is, values above 0.5 will correspond to 1 and the rest will be 0. We implement this as follows &#8211;\u00a0<\/span><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(neuralnet)\r\nANN_model =neuralnet (Class~.,train_data,linear.output=FALSE)\r\nplot(ANN_model)\r\n\r\npredANN=compute(ANN_model,test_data)\r\nresultANN=predANN$net.result\r\nresultANN=ifelse(resultANN&gt;0.5,1,0)<\/pre>\n<p><strong>Input Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Networks.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63516\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Networks.png\" alt=\"Neural Networks\" width=\"500\" height=\"146\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Networks.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Networks-150x44.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Networks-300x88.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Network.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-63537 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Network.png\" alt=\"Artificial Neural network in R\" width=\"700\" height=\"432\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Network.png 700w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Network-150x93.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Network-300x185.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Neural-Network-520x321.png 520w\" sizes=\"auto, (max-width: 700px) 100vw, 700px\" \/><\/a><\/p>\n<h3>8. Gradient Boosting (GBM)<\/h3>\n<p><em><strong><a href=\"https:\/\/data-flair.training\/blogs\/gradient-boosting-algorithm\/\">Gradient Boosting<\/a><\/strong><\/em> 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. We will implement gradient descent algorithm in our model as follows &#8211;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">library(gbm, quietly=TRUE)\r\n\r\n# Get the time to train the GBM model\r\nsystem.time(\r\n       model_gbm &lt;- gbm(Class ~ .\r\n               , distribution = \"bernoulli\"\r\n               , data = rbind(train_data, test_data)\r\n               , n.trees = 500\r\n               , interaction.depth = 3\r\n               , n.minobsinnode = 100\r\n               , shrinkage = 0.01\r\n               , bag.fraction = 0.5\r\n               , train.fraction = nrow(train_data) \/ (nrow(train_data) + nrow(test_data))\r\n)\r\n)\r\n# Determine best iteration based on test data\r\ngbm.iter = gbm.perf(model_gbm, method = \"test\")\r\n\r\n<\/pre>\n<p><strong>Input Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Gradient-boosting-in-R-project.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63607\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Gradient-boosting-in-R-project.png\" alt=\"gradient boosting algorithm in R\" width=\"690\" height=\"490\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Gradient-boosting-in-R-project.png 690w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Gradient-boosting-in-R-project-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Gradient-boosting-in-R-project-300x213.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/Gradient-boosting-in-R-project-520x369.png 520w\" sizes=\"auto, (max-width: 690px) 100vw, 690px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">model.influence = relative.influence(model_gbm, n.trees = gbm.iter, sort. = TRUE)\r\n#Plot the gbm model\r\n\r\nplot(model_gbm)<\/pre>\n<p><strong>Input Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gradient-boosting-plotting-in-R.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63614\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gradient-boosting-plotting-in-R.png\" alt=\"R project - gradient boosting\" width=\"692\" height=\"82\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gradient-boosting-plotting-in-R.png 692w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gradient-boosting-plotting-in-R-150x18.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gradient-boosting-plotting-in-R-300x36.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gradient-boosting-plotting-in-R-520x62.png 520w\" sizes=\"auto, (max-width: 692px) 100vw, 692px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63533\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2.png\" alt=\"Gradient Boosting\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot2-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63534\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot.png\" alt=\"gradient boosting\" width=\"1344\" height=\"960\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot.png 1344w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot-150x107.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot-300x214.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot-768x549.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot-1024x731.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-plot-520x371.png 520w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><\/a><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\"># Plot and calculate AUC on test data\r\ngbm_test = predict(model_gbm, newdata = test_data, n.trees = gbm.iter)\r\ngbm_auc = roc(test_data$Class, gbm_test, plot = TRUE, col = \"red\")<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-input.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63535\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-input.png\" alt=\"gbm input\" width=\"580\" height=\"165\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-input.png 580w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-input-150x43.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-input-300x85.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-input-520x148.png 520w\" sizes=\"auto, (max-width: 580px) 100vw, 580px\" \/><\/a><\/p>\n<p><strong>Code:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">print(gbm_auc)<\/pre>\n<p><strong>Output Screenshot:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-63536\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc.png\" alt=\"Credit card fraud detection\" width=\"773\" height=\"174\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc.png 773w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc-150x34.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc-300x68.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc-768x173.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/07\/gbm-auc-520x117.png 520w\" sizes=\"auto, (max-width: 773px) 100vw, 773px\" \/><\/a><\/p>\n<h2>Summary<\/h2>\n<p>Concluding our R Data Science project, we learnt how to develop our credit card fraud detection model using machine learning. We used a variety of ML algorithms to implement this model and also plotted the respective performance curves for the models. We learnt how data can be analyzed and visualized to discern fraudulent transactions from other types of data.<\/p>\n<p>Machine learning is an expanding field and there are always different models when it comes to fraud detection, which showcases how the methods implemented in this project include multiple models. This means that after comparing the logistic regression, decision trees, artificial neural networks, and gradient boosting we will be able to see which of the four models is more appropriate for our dataset type as well as the problem domain.<\/p>\n<p>The models should be further developed and improved consistently as the nature of the financial transactions changes with sophisticated frauds. Using a machine learning approach we are able to come up with more enhanced fraud detection systems that have the ability to not only identify existing fraud especially the frequent ones but to also identify new ones.<\/p>\n<p>So, now you are ready to detect the fraud. Machine Learning and R are the important technologies of this decade and will last forever. What are you waiting for? Start learning the machine learning concepts for FREE with the help of DataFlair&#8217;s<a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorials-home\/\"><em><strong> Machine Learning Tutorial Series<\/strong><\/em><\/a>.<\/p>\n<p>Hope you enjoyed the above R project. Share your experience and queries through comments.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This is the 3rd part of the\u00a0R project series designed by DataFlair. Earlier we talked about Uber Data Analysis Project and today we will discuss the Credit Card Fraud Detection Project using Machine Learning&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":63786,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51],"tags":[20621,20622,20584,20614,20623,20541],"class_list":["post-63385","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-credit-card","tag-credit-card-fraud-detection","tag-data-science-project","tag-machine-learning-projects","tag-ml-project","tag-r-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Science Project - Detect Credit Card Fraud with Machine Learning in R - DataFlair<\/title>\n<meta name=\"description\" content=\"Now you can detect credit card fraud using machine learning algorithm and R concepts. 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