1. Agglomerative_Custering.ipynb:

   • This Jupyter Notebook likely contains code and explanations related to agglomerative clustering, a hierarchical clustering algorithm where data points are grouped together based on their similarity.

2. Cluster_Visualization.ipynb:

   • This notebook may involve the visualization of clustering results. Visualizations could include plots, graphs, or other representations of clustered data.

3. Cluster_assessment_metrics.ipynb:

   • This file likely focuses on assessing the performance of clustering algorithms using metrics such as silhouette score, Davies-Bouldin index, or other clustering evaluation measures.

4. Data_Visualization.ipynb:

   • This notebook may involve general data visualization techniques using libraries like Matplotlib or Seaborn. It might include various types of plots and graphs to understand and analyze the data.

5. Divisive_Hierarchial_Clustering.ipynb:

   • This Jupyter Notebook likely contains code and explanations related to divisive hierarchical clustering, another hierarchical clustering algorithm that starts with all data points in a single cluster and then recursively splits them.

6. Haar_wavelet.ipynb:

   • This file may involve Haar wavelet transforms, a mathematical technique used in signal processing and image compression. It might include code for applying Haar wavelet transformations and examples of its applications.

7. Linear_Regression.ipynb:

   • This notebook probably contains code and explanations related to linear regression, a statistical method for modeling the relationship between a dependent variable and one or more independent variables.

8. ScalableClustering.ipynb:

   • This notebook may involve scalable clustering techniques, likely addressing methods suitable for handling large datasets. This could include algorithms designed for efficiency and scalability.

9. data_cleaning_2.ipynb:

   • This file likely focuses on data cleaning techniques, including code for handling missing values, outliers, or other data preprocessing steps.

10. dataprocessing.ipynb:

• This notebook probably includes code for general data processing tasks. It might cover tasks like data cleaning, feature engineering, or other data manipulation steps.