credit card fraud detection 3

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Introduction

Abstract

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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

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