Authors

Amey Thakur	Mega Satish

Overview

Unsupervised Machine Learning - Task 2 is a core Data Science exploration conducted under the Graduate Rotational Internship Program (GRIP) at The Sparks Foundation. The project focuses on the Iris Dataset to predict the optimum number of clusters and represent them visually.

By leveraging the K-Means Clustering algorithm, the system iteratively partitions data points into $K$ distinct non-overlapping subgroups (clusters) where each data point belongs to the cluster with the nearest mean.

Computational Objectives

The analysis is governed by strict exploratory principles ensuring cluster validity:

• Elbow Method: Determining the optimal value of $K$ by plotting the Within-Cluster Sum of Squares (WCSS).
• Cluster Centroids: Computing the central vector for each species group.
• Dimensionality Visualization: Plotting the classified clusters in a 2D feature space.

Features

Tech Stack

• Runtime: Python 3.x
• Data Manipulation: Pandas, NumPy
• Visualization: Matplotlib
• Machine Learning: Scikit-Learn (sklearn)
• Environment: Jupyter Notebook / Google Colab

Project Structure

TSF-UNSUPERVISED-MACHINE-LEARNING/
│
├── docs/                                            # Technical Documentation
│   └── SPECIFICATION.md                             # Architecture & Design Specification
│
├── Mega/                                            # Archival Attribution Assets
│   ├── Filly.jpg                                    # Companion (Filly)
│   ├── Mega.png                                     # Author Profile Image (Mega Satish)
│   └── ...                                          # Additional Attribution Files
│
├── Source Code/                                     # Core Implementation
│   └── TSF_INTERNSHIP_TASK_2_UNSUPERVISED_LEARNING.ipynb  # Jupyter Notebook (Analysis Kernel)
│   └── Iris.csv                                     # Empirical Data Source
│
├── screenshots/                                     # Result Visualization (Empty for setup)
│
├── .gitattributes                                   # Git configuration
├── .gitignore                                       # Repository Filters
├── CITATION.cff                                     # Scholarly Citation Metadata
├── codemeta.json                                    # Machine-Readable Project Metadata
├── LICENSE                                          # MIT License Terms
├── README.md                                        # Project Documentation
└── SECURITY.md                                      # Security Policy

Results

1. Optimal K Selection: The Elbow Method
Determining the ideal number of clusters by minimizing WCSS.

From the above plot, a clear elbow is seen to be formed at 3. Thus, the optimum number of clusters is 3.

2. Model Inference: Cluster Visualization


Scatter plot exhibiting the separation of Iris species into 3 distinct clusters: Setosa (Blue), Versicolor (Green), and Virginica (Yellow), with Centroids marked in Red.

Quick Start

1. Prerequisites

• Python 3.7+: Required for runtime execution. Download Python
• Jupyter Environment: For interactive code execution (JupyterLab or Notebook).

2. Installation

Establish the local environment by cloning the repository and installing the computational stack:

# Clone the repository
git clone https://github.com/Amey-Thakur/TSF-UNSUPERVISED-MACHINE-LEARNING.git
cd TSF-UNSUPERVISED-MACHINE-LEARNING

# Install clustering dependencies
pip install pandas numpy matplotlib seaborn scikit-learn

3. Execution

Launch the analysis kernel to reproduce the findings:

jupyter notebook "Source Code/TSF_INTERNSHIP_TASK_2_UNSUPERVISED_LEARNING.ipynb"

Usage Guidelines

This repository is openly shared to support learning and knowledge exchange across the academic community.

For Students
Use this project as reference material for understanding unsupervised learning pipelines, K-Means clustering, and optimum cluster prediction. The source code is available for study to facilitate self-paced learning and exploration of Hyperparameter tuning (Elbow Method).

For Educators
This project may serve as a practical lab example or supplementary teaching resource for Data Science and Applied Statistics courses. Attribution is appreciated when utilizing content.

For Researchers
The documentation and architectural approach may provide insights into academic project structuring, predictive inference, and industrial internship artifacts.

License

This academic submission, developed for the Graduate Rotational Internship Program (GRIP) at The Sparks Foundation, is made available under the MIT License. See the LICENSE file for complete terms.

Copyright © 2021 Amey Thakur & Mega Satish

About This Repository

Created & Maintained by: Amey Thakur & Mega Satish

Role: Data Science & Business Analytics Interns
Program: Graduate Rotational Internship Program (GRIP)
Organization: The Sparks Foundation

This project features Unsupervised Machine Learning - Task 2, a clustering analytics study conducted as part of the GRIP Internship. It explores the application of K-Means to solve grouping problems.

Connect: GitHub  ·  LinkedIn  ·  ORCID

Acknowledgments

Grateful acknowledgment to Mega Satish for her exceptional collaboration and scholarly partnership during the execution of this data science internship task. Her analytical precision, deep understanding of clustering algorithms, and constant support were instrumental in refining the models used in this study. Working alongside her was a transformative experience; her thoughtful approach to problem-solving and steady encouragement turned complex analytical challenges into meaningful learning moments. This work reflects the growth and insights gained from our side-by-side academic journey. Thank you, Mega, for everything you shared and taught along the way.

Special thanks to the mentors at The Sparks Foundation for providing this platform for rapid skill development and industrial exposure.