Department of Computer Engineering Course: Laboratory Practice-III

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Assignment No:2
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Title of the Assignment: Classify the email using the binary classification method. Email
Spam detection has two states:
a) Normal State – Not Spam,
b) Abnormal State – Spam.
Use K-Nearest Neighbors and Support Vector Machine for classification. Analyze
their performance.

Dataset Description: The csv file contains 5172 rows, each row for each email. There are
3002 columns. The first column indicates Email name. The name has been set with numbers
and not recipients' name to protect privacy. The last column has the labels for prediction : 1
for spam, 0 for not spam. The remaining 3000 columns are the 3000 most common words in
all the emails, after excluding the non-alphabetical characters/words. For each row, the
count of each word(column) in that email(row) is stored in the respective cells. Thus,
information regarding all 5172 emails are stored in a compact dataframe rather than as
separate text files.

Link: https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv

Objective of the Assignment:

Students should be able to classify email using the binary Classification and implement
email spam detection technique by using K-Nearest Neighbors and Support Vector Machine
algorithm.

Prerequisite:
1. Basic knowledge of Python
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Department of Computer Engineering Course: Laboratory Practice-III

2. Concept of K-Nearest Neighbors and Support Vector Machine for classification.

Contents of the Theory:

1. Data Preprocessing
2. Binary Classification
3. K-Nearest Neighbours
4. Support Vector Machine
5. Train, Test and Split Procedure

Data Preprocessing:
Data preprocessing is a process of preparing the raw data and making it suitable for a machine
learning model. It is the first and crucial step while creating a machine learning model.
When creating a machine learning project, it is not always a case that we come across the clean and
formatted data. And while doing any operation with data, it is mandatory to clean it and put in a
formatted way. So for this, we use data preprocessing task.

Why do we need Data Preprocessing?

A real-world data generally contains noises, missing values, and maybe in an unusable format
which cannot be directly used for machine learning models. Data preprocessing is required tasks
for cleaning the data and making it suitable for a machine learning model which also increases the
accuracy and efficiency of a machine learning model.
It involves below steps:

● Getting the dataset

● Importing libraries
● Importing datasets
● Finding Missing Data
● Encoding Categorical Data
● Splitting dataset into training and test set
● Feature scaling

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Binary Classification

Binary classification refers to those classification tasks that have two class labels.

Examples include:

● Email spam detection (spam or not).

● Churn prediction (churn or not).
● Conversion prediction (buy or not).

Typically, binary classification tasks involve one class that is the normal state and another
class that is the abnormal state.

For example “not spam” is the normal state and “spam” is the abnormal state. Another
example is “cancer not detected” is the normal state of a task that involves a medical test
and “cancer detected” is the abnormal state.

The class for the normal state is assigned the class label 0 and the class with the abnormal
state is assigned the class label 1.

It is common to model a binary classification task with a model that predicts a Bernoulli
probability distxribution for each example.

The Bernoulli distribution is a discrete probability distribution that covers a case where an
event will have a binary outcome as either a 0 or 1. For classification, this means that the
model predicts a probability of an example belonging to class 1, or the abnormal state.

Popular algorithms that can be used for binary classification include:

● Logistic Regression
● k-Nearest Neighbors
● Decision Trees
● Support Vector Machine
● Naive Bayes

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Some algorithms are specifically designed for binary classification and do not natively
support more than two classes; examples include Logistic Regression and Support Vector
Machines.

Support Vector
Machine:

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which
is used for Classification as well as Regression problems. However, primarily, it is used for
Classification problems in Machine Learning.

The goal of the SVM algorithm is to create the best line or decision boundary that can segregate
n-dimensional space into classes so that we can easily put the new data point in the correct category
in the future. This best decision boundary is called a hyperplane.

SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme
cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
Consider the below diagram in which there are two different categories that are classified using a
decision boundary or hyperplane:

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Department of Computer Engineering Course: Laboratory Practice-III

Example: SVM can be understood with the example that we have used in the KNN classifier.
Suppose we see a strange cat that also has some features of dogs, so if we want a model
that can accurately identify whether it is a cat or dog, so such a model can be created by
using the SVM algorithm. We will first train our model with lots of images of cats and dogs
so that it can learn about different features of cats and dogs, and then we test it with this
strange creature. So as support vector creates a decision boundary between these two data
(cat and dog) and choose extreme cases (support vectors), it will see the extreme case of
cat and dog. On the basis of the support vectors, it will classify it as a cat. Consider the
below diagram:

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Department of Computer Engineering Course: Laboratory Practice-III

SVM algorithm can be used for Face detection, image classification, text
categorization,
etc.

Types of SVM

SVM can be of two types:

● Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can
be classified into two classes by using a single straight line, then such data is termed as
linearly separable data, and classifier is used called as Linear SVM classifier.

● Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means
if a dataset cannot be classified by using a straight line, then such data is termed as non-
linear data and classifier used is called as Non-linear SVM classifier.

Train, Test, Split Procedure:

Train test split is a model validation procedure that allows you to simulate how
a model would perform on new/unseen data. Here is how the procedure works:

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Department of Computer Engineering Course: Laboratory Practice-III
1. ARRANGE THE DATA

Make sure your data is arranged into a format acceptable for train test split. In
scikit-learn, this consists of separating your full data set into “Features” and
“Target.”

2. SPLIT THE DATA

Split the data set into two pieces — a training set and a testing set. This
consists of random sampling without replacement about 75 percent of the rows
(you can vary this) and putting them into your training set. The remaining 25
percent is put into your test set. Note that the colors in “Features” and “Target”
indicate where their data will go (“X_train,” “X_test,” “y_train,” “y_test”) for a
particular train test split.

3. TRAIN THE MODEL

Train the model on the training set. This is “X_train” and “y_train” in the image.

4. TEST THE MODEL

Test the model on the testing set (“X_test” and “y_test” in the image) and
evaluate the performance.

Conclusion:

In this way we have explored Concept of Email Spam detection by using

binary classification.

Assignment Questions:

1. What is Binary Classification?

2. Explain Support Vector Machine?
3. Explain K-Nearest Neighbour algorithm for Machine Learning?

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