EX_Support_Vector_Machine-classification.py

#!/usr/bin/env python
# coding: utf-8

# # Support Vector Machine classification

# #### Support Vector Machine (SVM) classification is a powerful supervised learning algorithm used primarily for binary classification tasks. Here’s a concise overview of its key concepts, workings, and applications.

# ##### SVM is designed to find the best decision boundary (hyperplane) that separates data points of different classes in a high-dimensional space. 

# ##### Key Concepts : 
#   
# #####  Hyperplane: A hyperplane is a flat affine subspace that divides the data into two classes. In a two-dimensional space, it is simply a line; in three dimensions, it is a plane.  
# ##### Support Vectors: These are the data points that are closest to the hyperplane and influence its position and orientation. The SVM algorithm focuses on these points for constructing the decision boundar .
# #####  Margin: The margin is the distance between the hyperplane and the nearest support vectors from either class. SVM aims to maximize this margin, which helps improve classification accurac.

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# ###### How SVM Works
# ######  Training Phase: During training, SVM identifies the optimal hyperplane that maximizes the margin between classes using support vectors.
# ######    Prediction Phase: For new data points, SVM determines which side of the hyperplane they fall on to classify them into one of the categories.

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')


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dataset = pd.read_csv('Social_Network_Ads.csv')


dataset


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# plot the distribution

plt.scatter(dataset['Age'],dataset['EstimatedSalary'],c=dataset['Purchased'],cmap='rainbow')
plt.xlabel('Age')
plt.ylabel('Est. Salary')
plt.show()


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# transform the dataset in Rows and Cloumn

X = dataset.iloc[:,[2,3]].values

X


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Y = dataset.iloc[:,-1].values
Y


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# import the sklearn libraries

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0)


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# import the SVC packages

from sklearn.svm import SVC


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# prepare the model

model = SVC(kernel='rbf')

#### kernel='rbf': This parameter specifies the type of kernel function to be used in the SVC model. 
#  The RBF kernel is a popular choice for handling non-linear classification problems.
# It maps input features into a higher-dimensional space where a linear separation is possible.


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model.fit(X_train,y_train)


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# model prediction

y_pred = model.predict(X_test)

y_pred


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# Analyse the prediction with actual data

from sklearn.metrics import confusion_matrix,classification_report


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print(confusion_matrix(y_test,y_pred))


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# analyse the overall model score 

print(classification_report(y_test,y_pred))


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