CGS616 Course Project Repository

Welcome to the CGS616 Course Project Repository!

This repository contains a collection of four distinct projects developed as part of the coursework. Each project is implemented using Jupyter Notebooks, providing interactive Python environments ideal for data analysis, visualization, and experimentation.

Table of Contents

1. Project 1: Data Analysis
2. Project 2: Machine Learning
3. Project 3: Data Visualization
4. Project 4: Web Scraping

Project 1: Data Analysis

• Description: This project focuses on performing exploratory data analysis on a dataset using Pandas and NumPy.
• Key Features:
  • Data cleaning and preparation
  • Summary statistics
  • Data distributions

Project 2: Machine Learning

• Description: This project involves building machine learning models using Scikit-Learn.
• Key Features:
  • Model training and evaluation
  • Hyperparameter tuning
  • Predictions based on input data

Project 3: Data Visualization

• Description: This project showcases data visualization techniques using Matplotlib and Seaborn.
• Key Features:
  • Creating various types of plots (scatter, line, histogram, etc.)
  • Customizing visual aesthetics for better insights

Project 4: Web Scraping

• Description: This project demonstrates how to scrape data from websites using Beautiful Soup and Requests.
• Key Features:
  • Extracting data from HTML content
  • Storing data in a usable format (CSV, JSON)

Getting Started

To run the Jupyter Notebooks:

1. Ensure you have Python and Jupyter Notebook installed.
2. Clone this repository:

   git clone https://github.com/kritnandan/CGS616-Project-

3. Navigate to the project directory:

   cd CGS616-Project-

4. Launch Jupyter Notebook:

   jupyter notebook

Contributing

Contributions are welcome! Please submit a pull request with your proposed changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.