Machine Learning Lab

This repository contains the machine learning lab files for various experiments covered in the course. Each lab covers a specific machine learning algorithm or technique, implemented using Python and various ML libraries.

Labs Overview

Lab 1: Cost Function

• Objective: Implementation of the cost function for a simple linear regression model.
• Description: This lab introduces the concept of the cost function, including Mean Squared Error (MSE), and demonstrates how it is used to evaluate the performance of a machine learning model.

Lab 2: Linear Regression

• Objective: Implementation of linear regression from scratch.
• Description: This lab covers the fundamentals of linear regression, including hypothesis functions, gradient descent, and the computation of model parameters.

Lab 3: Logistic Regression & Multiclass Logistic Regression

• Objective: Implement logistic regression for binary classification and extend it to multiclass classification using one-vs-all.
• Description:
  • Part 1: Binary logistic regression implementation.
  • Part 2: Multiclass logistic regression using the one-vs-all approach for multiple classes.

Lab 4: Support Vector Machine (SVM)

• Objective: Implementation of SVM for both regression and classification tasks.
• Description:
  • Part 1: Support Vector Regression (SVR) implementation.
  • Part 2: Support Vector Classification (SVC) implementation using a linear and non-linear kernel.

Lab 5: Decision Tree

• Objective: Implementation of Decision Tree for classification tasks.
• Description: This lab demonstrates the usage of decision trees, including the creation of a model, tree pruning, and evaluation using different datasets.

Prerequisites

• Python 3.x
• Libraries:
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib (for visualization)

Install the required libraries using:

pip install numpy pandas scikit-learn matplotlib# MachineLearning