Jhedie/Credit-Card-Fraud-Detection

Credit-Card-Fraud-Detection With Machine Learning in Python with Google Colab 14/08/2021

0. Link to Notebook: https://colab.research.google.com/drive/1NpuGXwu9Kq4juCpEn0v0hdJ4x73vmFp9?usp=sharing

1. Requirements:

   Dataset containing transactions between people

2. Aims:

   • Exploring supervised machine learning algorithms
   • Learning how to build classication models
   • Exploring and analysing large datasets
   • Improving python programming

3. Method:

   • Building a classification Model which can distinguish fraud transactions.
     • Classification is the process of predicting a category of given data points.
     • For example, spam detection in email service providers.
     • Credit approval
     • Medical diagnoses
   • It involves utilizing a mapping function (f) from input variables (x) to generate discrete output variables (y) for example yes or no.
   • Classification belongs to supervised learning where answers are provided with the input data.

4. Steps:

   • Importing python packages
   • Importing data
   • Processing data
   • Exploratory Analysis
   • Feature selection and Data split
   • Learning and building classification models
     • Decision Tree
     • K - Nearest Neighbor
     • Logistic Regression
     • SVM
     • Random Forest Tree
     • XGBoost
   • Evaluation

5. Tools:

   • Google Colab
   • Python

6. Sources:

   • https://medium.com/codex/credit-card-fraud-detection-with-machine-learning-in-python-ac7281991d87 -Project Guide

   • https://towardsdatascience.com/machine-learning-classifiers-a5cc4e1b0623 -machine leaning classifiers

   • https://www.kaggle.com/mlg-ulb/creditcardfraud - The Dataset used

   • https://www.kdnuggets.com/2020/04/data-transformation-standardization-normalization.html - Learning about data standardization/normalisation under feature scaling.

   • https://www.bitdegree.org/learn/train-test-split - learning more about train_test_split function

   • https://youtu.be/P2NqrFp8usY - understanding training and testing

   • https://www.youtube.com/watch?v=MufPx3L7nXM&t=954s - Intro to Machine Learning with Python 3: Train Test and Base Modelling

   • https://youtu.be/NU-nSh6xE3o - Decision Tree Regression

   • https://towardsdatascience.com/decision-tree-classifier-explained-in-real-life-picking-a-vacation-destination-6226b2b60575 - Decision Tree Classifier

     • https://youtu.be/u4IxOk2ijSs - Gini impurity index

   • https://youtu.be/UR2ag4lbBtc - K Nearest Neighbour explained

   • https://youtu.be/yIYKR4sgzI8 - Logistical regression

   • https://youtu.be/jbluHIgBmBo - Logistical regression (more related to project)

   • https://youtu.be/Lpr__X8zuE8 - A friendly introduction into Support Vector Machines

   • https://youtu.be/Y6RRHw9uN9o - SVMs Fun machine learning

Version Control (MS WORD DOCUMENT):

Date	Change/Addition
14/08/2021	Reading around projects and drawing outlines
16/08/2021	Adding notes and sources
18/08/2021	Adding notes and sources
19/09/2021	Google Colab link added