Data Science: Credit Card Fraud Detection - Model Building
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 32 lectures (1h 40m) | Size: 658.4 MB
A practical hands on Data Science Project on Credit Card Fraud Detection using different sampling and Model Building
What you'll learn:
Data Analysis and Understanding
Data Preprocessing Techniques
Model Building using Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models
RepeatedKFold and StratifiedKFold
Random Oversampler, SMOTE, ADASYN
Classification Metrics
Model Evaluation
Requirements
Knowledge of Python
Description
In this course I will cover, how to develop a Credit Card Fraud Detection model to categorize a transaction as Fraud or Legitimate with very high accuracy using different Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model.
This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation. We will explore RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.