Machine Learning Lab - CS4172

Welcome to the Machine Learning Lab repository for CS4172. This repository is organized for the Machine Learning Lab course, including assignments, datasets, and lecture notes that provide an in-depth understanding of the fundamental concepts and practical applications in machine learning. Each assignment is designed to give hands-on experience with key machine learning algorithms and their implementation.

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📂 Repository Structure

• Assignment01: Hands-on with Linear Regression and exploratory data analysis (EDA).
• Assignment02: Implementation of Logistic Regression for binary classification tasks.
• Assignment03: Decision Trees and Random Forests for supervised learning tasks.
• Assignment04: SVMs (Support Vector Machines) and kernel functions for complex data.
• Assignment05: Implementation of Clustering algorithms using K-Means for unsupervised learning tasks.
• Lecture Notes: Detailed notes on machine learning concepts, algorithms, and techniques.

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⚙️ Setup & Installation Instructions

To get started with the Machine Learning Lab repository, follow these steps:

1. Clone the repository:

git clone https://github.com/RP2025/Machine-Learning-Lab-CS4172.git

2. Navigate to the project directory:

cd Machine-Learning-Lab-CS4172

3. Install the required libraries:

Ensure you have Python 3.8 or above installed. Then, use the requirements.txt to install the necessary Python packages:

pip install -r requirements.txt

4. Libraries & Tools:

The assignments rely on the following libraries:

• numpy
• pandas
• matplotlib
• scikit-learn
• seaborn

These libraries can be easily installed using the pip install -r requirements.txt command.

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🧪 Assignments Overview

Each assignment is designed to give you a hands-on experience of implementing various machine learning algorithms:

1. Assignment01: Linear Regression and basic exploratory data analysis (EDA). The goal is to explore the relationship between variables and implement linear regression models.
2. Assignment02: Logistic Regression for classification problems. This assignment focuses on binary classification and model evaluation using logistic regression.
3. Assignment03: Decision Trees and Random Forests. Here, we implement and explore decision trees and ensemble methods such as random forests for improved performance.
4. Assignment04: Support Vector Machines (SVMs) and exploring kernel functions. Learn how SVMs can be used for complex data classification.
5. Assignment05: Clustering using the K-Means algorithm. This assignment covers unsupervised learning where you will apply clustering techniques to group data based on similarities.

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📘 Resources

• Lecture Notes: The Lecture Notes folder contains class notes, lectures, and additional materials covering key machine learning concepts and algorithms.
• Datasets: The Datasets folder contains real-world datasets used for building machine learning models and performing analysis in the assignments.

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🤝 Contributing

Contributions are welcome! If you'd like to contribute to the repository, feel free to fork it, create a branch, and submit a pull request with your changes. Contributions could include adding new assignments, improving code quality, or updating documentation.

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📄 License

This repository is licensed under the MIT License. For more details, see the LICENSE file in the repository.

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📫 Contact

For any queries, feedback, or collaboration opportunities, connect with me:

• GitHub: RP2025
• LinkedIn: Raksha Pahariya