ML-LAB-VI-SEM
Exercise 07
Implementation of Support Vector Machines (SVM) using sklearn
Aim
To implement Support Vector Machine (SVM) algorithm using Python and sklearn for classification tasks.
Procedure/Program
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
# sample dataset (Student Scores and Pass/Fail Status)
data = {
"Hours_Studied": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
"Previous_Scores": [50, 55, 60, 62, 65, 70, 75, 80, 85, 90],
"Pass": [0, 0, 0, 0, 1, 1, 1, 1, 1, 1] # 1 = Pass, 0 = Fail
}
# DataFrame
df = pd.DataFrame(data)
# splitting independent (X) and dependent (y) variables
X = df[["Hours_Studied", "Previous_Scores"]]
y = df["Pass"]
# training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# creating and training the SVM model with a linear kernel
model = SVC(kernel='linear', random_state=42)
model.fit(X_train, y_train)
# predicting pass/fail status
y_pred = model.predict(X_test)
# model evaluation
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
# results
print("Accuracy:", accuracy)
print("\nConfusion Matrix:\n", conf_matrix)
print("\nClassification Report:\n", class_report)
# plotting Decision Boundaries (Visualization)
x_min, x_max = X["Hours_Studied"].min() - 1, X["Hours_Studied"].max() + 1
y_min, y_max = X["Previous_Scores"].min() - 1, X["Previous_Scores"].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.3)
plt.scatter(X_train["Hours_Studied"], X_train["Previous_Scores"], c=y_train, label="Training Data")
plt.scatter(X_test["Hours_Studied"], X_test["Previous_Scores"], c=y_pred, marker="*", s=100, label="Predicted Test Data")
plt.xlabel("Hours Studied")
plt.ylabel("Previous Scores")
plt.title("SVM Classification: Pass/Fail Prediction")
plt.legend()
plt.show()
Output/Explanation
-
Output:
Accuracy: 1.0 Confusion Matrix: [[1 0] [0 1]] Classification Report: precision recall f1-score support 0 1.00 1.00 1.00 1 1 1.00 1.00 1.00 1 accuracy 1.00 2 macro avg 1.00 1.00 1.00 2 weighted avg 1.00 1.00 1.00 2The program trains a Support Vector Machine (SVM) classification model and predicts whether a student passes or fails based on Hours Studied and Previous Scores. Expected output includes:
- Accuracy Score (percentage of correctly predicted values)
- Confusion Matrix (shows true vs predicted values)
- Classification Report (precision, recall, F1-score)
- Decision boundary plot visualizing predictions.
-
Explanation:
- The dataset contains Hours Studied and Previous Scores as input (
X) and Pass/Fail status as output (y). - The data is split into training and testing sets.
- A Support Vector Machine (SVM) model with a linear kernel is trained using
SVC(). - The model predicts pass/fail status and evaluates performance using accuracy, confusion matrix, and classification report.
- A decision boundary plot helps visualize how the SVM algorithm classifies students based on features.
- The dataset contains Hours Studied and Previous Scores as input (