Data Science- Credit Card Fraud Detection Model Building

Data Science: Credit Card Fraud Detection – Model Building, available at $39.99, has an average rating of 4.5, with 32 lectures, based on 21 reviews, and has 151 subscribers.

You will learn about 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 This course is ideal for individuals who are Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation. or Students and professionals who wants to learn RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN It is particularly useful for Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation. or Students and professionals who wants to learn RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN.

Enroll now: Data Science: Credit Card Fraud Detection – Model Building

Summary

Title: Data Science: Credit Card Fraud Detection – Model Building

Price: $39.99

Average Rating: 4.5

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: ?999

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation.

Students and professionals who wants to learn RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN

Target Audiences

Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation.

Students and professionals who wants to learn RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN

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.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1  :  Installing Packages.

Task 2  :  Importing Libraries.

Task 3  :  Loading the data from source.

Task 4  :  Understanding the data

Task 5  :  Checking the class distribution of the target variable

Task 6  :  Finding correlation and plotting Heat Map

Task 7  :  Performing Feature engineering.

Task 8  :  Train Test Split

Task 9 :   Plotting the distribution of a variable

Task 10 :  About Confusion Matrix, Classification Report, AUC-ROC

Task 11 :  Created a common function to plot confusion matrix

Task 12 :  About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models

Task 13 :  Created a common function to fit and predict on a Logistic Regression model

Task 14 :  Created a common function to fit and predict on a KNN model

Task 15 :  Created a common function to fit and predict on a Tree models

Task 16 :  Created a common function to fit and predict on a Random Forest model

Task 17 :  Created a common function to fit and predict on a XGBoost model

Task 18 :  Created a common function to fit and predict on a SVM model

Task 19 :  About RepeatedKFold and StratifiedKFold.

Task 20 :  Performing cross validation with RepeatedKFold and Model Evaluation

Task 21 :  Performing cross validation with StratifiedKFold and Model Evaluation

Task 22 :  Proceeding with the model which shows the best result till now

Task 23 :  About Random Oversampler, SMOTE, ADASYN.

Task 24 :  Performing oversampling with Random Oversampler with StratifiedKFold cross

           validation and Model Evaluation.

Task 25 :  Performing oversampling with SMOTE and Model Evaluation.

Task 26 :  Performing oversampling with ADASYN and Model Evaluation.

Task 27 :  Hyperparameter Tuning.

Task 28 :  Extracting most important features

Task 29 :  Final Inference.

Data Analysis, Model Building is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of Machine learning in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. All the datasets used in the course are in the resources section.

3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!

Happy Learning !!

[Please note that this course and its related contents are for educational purpose only]

[Music : bensound]

Course Curriculum

Chapter 1: Introduction and Getting Started

Lecture 1: Project Overview

Lecture 2: High Level Overview of the steps to be performed

Lecture 3: Installing Packages

Chapter 2: Data Understanding & Exploration

Lecture 1: Importing Libraries

Lecture 2: Loading the data from source

Lecture 3: Understanding the data

Chapter 3: Data Analysis & Feature Engineering

Lecture 1: Checking the class distribution of the target variable

Lecture 2: Finding correlation and plotting Heat Map

Lecture 3: Performing Feature engineering

Chapter 4: Data Preparation

Lecture 1: Train Test Split

Lecture 2: Plotting the distribution of a variable

Chapter 5: Model Building – Creating Common Functions

Lecture 1: About Confusion Matrix, Classification Report, AUC-ROC

Lecture 2: Created a common function to plot confusion matrix

Lecture 3: About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM models

Lecture 4: Created a common function to fit and predict on a Logistic Regression model

Lecture 5: Created a common function to fit and predict on a KNN model

Lecture 6: Created a common function to fit and predict on a Tree models

Lecture 7: Created a common function to fit and predict on a Random Forest model

Lecture 8: Created a common function to fit and predict on a XGBoost model

Lecture 9: Created a common function to fit and predict on a SVM model

Chapter 6: Model Building and Evaluation

Lecture 1: About RepeatedKFold and StratifiedKFold

Lecture 2: Performing cross validation with RepeatedKFold and Model Evaluation

Lecture 3: Performing cross validation with StratifiedKFold and Model Evaluation

Lecture 4: Proceeding with the model which shows the best result till now

Lecture 5: About Random Oversampler, SMOTE, ADASYN

Lecture 6: Performing Oversampling with Random Oversampler with StratifiedKFold

Lecture 7: Performing oversampling with SMOTE and Model Evaluation

Lecture 8: Performing oversampling with ADASYN and Model Evaluation

Lecture 9: Hyperparameter Tuning

Lecture 10: Extracting most important features

Lecture 11: Final Inference

Chapter 7: Project Files and Code

Lecture 1: Full Project Code

Instructors

AutomationGig .
ELEARNING HUB

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 4 votes

5 stars: 11 votes

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