University of the Cumberlands Credit Card Fraud Detection Discussion Introduction Abstract Explain the Data set Visualize the Data Visualize and analyz

University of the Cumberlands Credit Card Fraud Detection Discussion Introduction

Abstract

Explain the Data set

Visualize the Data

Visualize and analyze the results

What algorithms you are going to use in this research

Explain the code using R Studio

Recommendations

Further Analysis

References

Paper should be of 12 pages

15 PPT slides explaining about the project

APA format with Scholarly references and citations

Context

Introduction

Abstract

Explain the Data set

Visualize the Data

Visualize and analyze the results

What algorithms you are going to use in this research

Explain the code using R Studio

Recommendations

Further Analysis

References

Paper should be of 12 pages

15 PPT slides explaining about the project

APA format with Scholarly references and citations

Context

–Marks secured by the students

Content

–This data set consists of the marks secured by the students in various subjects.

Acknowledgements

–http://roycekimmons.com/tools/generated_data/exams

Inspiration

–Understand the influence of the parents background, test preparation etc on students performance

–Use the student data of test results, create a fictitous variable of pass or fail.

–Predict whether a student passes or fails using these any classification method

Examples:

Check under kernels tab

https://www.kaggle.com/spscientist/students-perfor…

https://www.kaggle.com/anaking/classification-mode…

Using the link above, follow the same steps:

Reading the dataset

Summarizing

Visualizing the dataset , please use visualization 1 ,2,3 or 4 as an example.

You can just do 1 visualization and explain why you want to choose only that visualization based on factors such as ethinicity, parental education, test prep course etc.

Then summarize about the visualization you did

Plot graphs for each visualization

You may choose bar/box/histogram or scatter which is easily understood by the user

Next step is build a model using the visualization

You can use either decision tree/random forest, linear regression or k means algorithm which ever algorithm will give the best accuracy for the model

Explain why you would choose that particular algorithm and not others

Explain confusion matrix for each of those

Final aim is that this dataset should be easily understood by anyone who sees the plots and good for business

Make sure you go through the link below and refer to kernels section for examples and build visualization and model on your own

Please perform each and every step including reading the dataset

https://www.kaggle.com/mlg-ulb/creditcardfraud/

Using the link above, follow the same steps:

Reading the dataset

Summarizing

Visualizing the dataset , please use visualization 1 ,2,3 or 4 as an example.

Then summarize about the visualization you did

Plot graphs for each visualization

You may choose bar/box/histogram or scatter which is easily understood by the user

Next step is build a model using the visualization

You can use either decision tree/random forest, linear regression or k means algorithm which ever algorithm will give the best accuracy for the model

Explain why you would choose that particular algorithm and not others

Explain confusion matrix for each of those

Final aim is that this dataset should be easily understood by anyone who sees the plots and good for business

Make sure you go through the link below and refer to kernels section for examples and build visualization and model on your own

Please perform each and every step including reading the dataset