Add Project — Regime Detection using K-Means

[keshripritesh]

🧠 Issue: Add Project — Regime Detection using K-Means

Description:
This issue proposes adding a new project titled "Regime Detection using K-Means".
The project focuses on identifying different market regimes (phases) in financial data using K-Means clustering. The model helps detect transitions between bull, bear, and sideways market conditions before applying trading strategies.

Key Tasks:

• Implement K-Means clustering for financial time series data
• Use the Elbow method to determine the optimal number of regimes
• Visualize clusters and market phase transitions
• Analyze results and interpret detected regimes

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Tech Stack:

• Python
• pandas, numpy, matplotlib, seaborn
• scikit-learn

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Expected Outcome:
A clear visualization and understanding of different market regimes based on clustering analysis.