Unsupervised Learning: Clustering Techniques for Data Analysis

This project delves into clustering, a core unsupervised learning technique, to group data points based on inherent similarities. Various clustering algorithms are applied to datasets to identify patterns and meaningful structures without labeled data.

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Project Overview

1. Clustering Techniques

   • K-Means Clustering: Partitions data into a predefined number of clusters using centroid minimization.
   • Hierarchical Clustering: Forms a dendrogram to group similar points hierarchically.
   • DBSCAN (Density-Based Spatial Clustering): Groups points based on density and separates noise.

2. Data Preprocessing

   • Handled missing values and normalized data to improve clustering accuracy.

3. Evaluation Metrics

   • Silhouette scores to assess cluster quality.
   • Visualization techniques (e.g., scatter plots) to understand cluster distribution.

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File Contents

• UnsupervisedLearning.py
  • The Jupyter Notebook containing:
    • Data preprocessing steps.
    • Implementation of clustering algorithms.
    • Evaluation and visualizations.

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Key Learnings

• K-Means: Effective for well-separated, spherical clusters but requires specifying the number of clusters.
• Hierarchical Clustering: Useful for visualizing relationships between clusters but computationally expensive for large datasets.
• DBSCAN: Handles noisy data well and discovers clusters of arbitrary shapes.

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Dependencies

Install required libraries with:

pip install numpy pandas matplotlib scikit-learn scipy