Bank Note Analysis-Using Logistic Regression, RandomForest, KNN, SVM, Multilayer Perceptron

In this project, we will classify a banknote as fake or genuine based on the given dataset from the UCI machine learning repository which consists of about 1372 rows with 5 columns.

We will be using different algorithms such as Logistic Regression, Support Vector Machine, RandomForestClassifier, KNeighborsClassifier, Multilayer Perceptron

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Data Set Information:

Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tools were used to extract features from images.

Attribute Information:

1. variance of Wavelet Transformed image (continuous)
2. skewness of Wavelet Transformed image (continuous)
3. curtosis of Wavelet Transformed image (continuous)
4. entropy of image (continuous)
5. class (integer)

Download Dataset here

Before we start, please note that this tutorial is part of the The Full Stack Data Scientist Bootcamp, which is a practical hands-on data science tutorial for anyone to learn data science right from the basics to advance by building projects and great Data Science Portfolio. Feel free to check it out.

In order for you to make the best out of this tutorial, I have put this tutorial in the form of a video for better understanding

Watch and work along

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INTRODUCTION

PART 1

PART 2

PART 3

PART 4

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