data-mining-analytics

KO | EN

gitlite — search

#python #llm #go #golang #linux #docker #macos #windows #awesome #cli #vue #awesome-list #ios #openai

data-mining-analytics

(★ 10)

Data mining and dimensionality reduction techniques using PCA, Correspondence Analysis, and Multivariate Data Analysis on the Iris dataset.

data-mining-analytics 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

Data Mining | Dimensionality Reduction | Statistical Learning

# Data Mining and Analytics

This project explores how raw data becomes insight using the mathematical foundations of data mining and statistical learning.  
Instead of relying on black-box machine learning libraries, this repository focuses on understanding, visualizing, and interpreting data using linear algebra and geometry.

Using the Iris dataset, this project implements and analyzes:
- Principal Component Analysis (PCA)  
- Correspondence Analysis (CA)  
- Multiple Correspondence Analysis (MCA)  
- Distance-based data geometry

These are the same techniques used in feature extraction, clustering, and exploratory data analysis.

---

## Why this project matters

Most machine-learning projects show what a model predicts.  
This project shows why the data behaves the way it does.

It answers questions like:
- Which variables matter most?  
- How do classes separate geometrically?  
- What hidden structure exists in the data?

This depth is what distinguishes a data scientist from someone who only runs libraries.

---

## Dataset

The project uses the Iris dataset containing:
- 150 samples  
- 4 numerical features  
- 3 flower species

It is one of the most widely used benchmark datasets in machine learning and statistics.

---

## Methods implemented

| Technique | Purpose |
|----------|--------|
| PCA | Reduces dimensionality while preserving variance |
| CA | Analyzes relationships between categorical variables |
| MCA | Extends CA to multiple categorical variables |
| Distance metrics | Measures similarity using Euclidean, Minkowski, and chi-square distances |

These form the mathematical core of modern data mining pipelines.

---

## Repository structure

report/ -> Full project report with theory and experiments  
results/ -> PCA, CA, and MCA visualizations  
docs/ -> Methodology and explanations

---

## Results

The results folder contains:
- PCA projections of the Iris dataset  
- Correspondence Analysis biplots  
- Multiple Correspondence Analysis factor maps

These visuals show how meaningful structure emerges from raw data.

---

## Author

Sai Teja Bandaru  
Data Scientist and Machine Learning Engineer

---

This project demonstrates an understanding of the mathematics behind machine learning, not just how to use libraries.  
That skill is essential for data science, machine learning engineering, and research roles.

data-mining-analytics

data-mining-analytics 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

data-mining-analytics

data-mining-analytics 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README