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Credit Card Fraud Detection as a Classification Problem

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.
4.74.7

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What will you learn

  • Handle imbalance data
  • Creation classifier
  • Compare accuracy
  • Use deep learning to classify
  • Implementation using either R or Python

What will you get

  • Access to recording of the complete credit card fraud detection project
  • Access to all material related to the data science project like - credit card fraud detection project documentation, credit card transaction dataset, solution files etc.

Prerequisites

  • R 3.3.1 or latest R-Studio
  • Python 2.7

Project Description

The Credit Card Fraud detection Dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset present 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.

The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Loading the dataset
04:39
  Understanding the Dataset - Overview
06:51
  Solution Overview
06:01
  Exploratory Data Analysis (EDA) - Overview
01:07
  Understanding the Dataset - Time Relation
02:06
  Perform EDA
10:19
  EDA - Computation of Principal Components
04:43
  EDA - Feature & Density Plot
02:46
  Compare supervised classification algorithms
13:09
  EDA - Troubleshooting
11:00
  EDA - Feature Plot Troubleshooting
06:53
  Handle Imbalanced Data
26:17
  Feature Selection
13:18
  Data Splitting for Training & Testing
08:59
  Set-up Cross Validation
12:28
  Model 1 - Linear Discriminant Analysis
12:31
  Model 2 - Logisitic Regression Model
08:58
  Accuracy, Kappa, Std Deviation
23:13
  Model 3 - GLM Model
09:38
  Model 4 - SVM
04:24
  Model 5 - KNN
00:59
  Model 6 - Naive Bayes
02:11
  Model 7 - CART Model (Classification and Regression Tree)
01:28
  Model 8 - C5.0 & Bagging CART Models
04:24
  Model 9 - Random Forest, Gradient Boosting, Neural Network Models
09:01
  Compare Models
06:27
  Binary Classification using Keras as Deep Learning
18:04
  CNN Explanation
34:29