Stellar Classification Project

CU Boulder [CSCA 5622] Introduction to Machine Learning : Supervised Learning - Final Project

Project Overview

This repository contains the final project for the CSCA 5652 : Introduction to Machine Learning: Supervised Learning course at CU Boulder.

The primary goal of this project is to apply supervised machine learning techniques to a classification problem. Specifically, the project focuses on Stellar Classification, utilizing a dataset of astronomical observations to classify celestial objects (like stars, galaxies, and quasars).

The project includes:

1. Exploratory Data Analysis (EDA) to understand the dataset's characteristics.
2. Model Analysis and evaluation of various supervised learning algorithms.

Project Structure

The core of the project is contained within a single Jupyter Notebook.

• Stellar_Classification_Project.ipynb: The main notebook containing all the data loading, EDA, feature engineering, model training, and performance analysis.
• (You may need to add a folder here for data/ if the dataset is included in the repository.)

Getting Started

Prerequisites

This project is built using Python. You will need a working Python environment (3.x) and the following libraries.

• Python 3.x
• Jupyter Notebook or Jupyter Lab
• Standard ML Stack: pandas, numpy, scikit-learn, matplotlib, seaborn

Installation

1. Clone the repository:

   git clone https://github.com/tejasphatak/CSCA-5622-Supervised-Learning-Final-Project.git
   cd CSCA-5622-Supervised-Learning-Final-Project

2. Install dependencies: It is highly recommended to use a virtual environment.

   pip install -r requirements.txt

Usage

To replicate the analysis and view the results:

1. Start the Jupyter Notebook server in the project directory:

   jupyter notebook

2. Open the file Stellar_Classification_Project.ipynb.
3. Run the cells sequentially to execute the EDA, train the classification models, and generate the final results and visualizations.

Technologies Used

• Language: Python
• Environment: Jupyter Notebook
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn