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NSE Stock Sector Clustering using Unsupervised Learning

Target year: TE Sem 5-6 (Mini-Project-IIA/IIB) AICTE: 3 credits · ~75 hrs Bloom: Analyze MU CBCS: DS601/DS701 Mini-Project 2A/2B

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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Milestones

Available mentors

Enrolled students

Core skills

About this project

Use 5-year price data from 200+ NSE-listed stocks. Compute return-correlation matrix. Apply K-means + hierarchical clustering + DBSCAN to discover sectoral groupings. Compare automatic clustering vs official NSE sector classifications. Find anomalous stocks (outliers).

Course Learning Outcomes (CLOs):
CLO1: Apply unsupervised learning to a real, noisy financial dataset.
CLO2: Compare clustering algorithms quantitatively.
CLO3: Communicate findings via visualizations (dendrograms, t-SNE plots).
CLO4: Use cluster-validity indices appropriately.
CLO5: Reason about clustering vs ground-truth labels.

Industry/societal relevance: Indian quant-fintech (WorldQuant India, Tower Research, Alphaster) hire data scientists with portfolio-clustering skills; placement-relevant.

Milestones

1. Data Acquisition

15 marks 5d

200 stocks × 5 yrs daily close → Pandas DataFrame. Compute daily log returns. Clean splits/bonus.

2. EDA + Correlation Matrix

15 marks 10d

Plot return distributions, correlation heatmap. Identify the most + least correlated pairs.

3. K-Means + Hierarchical

25 marks 14d

Run K-means (sweep k=2-15). Dendrogram. Compare cluster assignments visually.

4. DBSCAN + Outlier Detection

20 marks 14d

Tune ε + minPts. Identify outlier stocks (anomalous in correlation space). Investigate why.

5. Comparison Report

25 marks 18d

Adjusted Rand Index vs NSE-published sectors. t-SNE visualization. 8-page report.

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Upcoming sessions

Session	Window	Enrolled
NSE Stock Sector Clustering using Unsupervised Learning	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Python (Pandas + scikit-learn)Unsupervised Learning (K-meanshierarchicalDBSCAN)PCA + t-SNEFinancial returns analysisCluster validation (silhouetteARI)

Tools used

Python 3.11Pandasscikit-learnyfinance / nsepymatplotlib + seabornPlotlyJupyter NotebookGitHub

Prerequisites

Python intermediate; basic statistics (correlationvariance); intro to ML; finance basics

Available mentors

Priyang Kumar

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