User-adjustable dataset clustering Python algorithm

I propose the development of a user-adjustable Python algorithm for dataset clustering, aimed at handling data from CSV files. This project seeks to create a versatile and interactive tool that enables me to cluster specific data efficiently, customize clustering parameters, and visualize results. Project Objectives: Methodology Selection: Please evaluate clustering methodologies, such as K-Means, DBSCAN, and hierarchical clustering, to determine the most suitable approach for the given dataset. Python Script Development: design and implement a Python script that reads data from CSV files. It should provide a clear and user-friendly interface for parameter input and result visualization. Parameter Customization: user should have the option to specify clustering parameters, such as the number of clusters, similarity metrics, and feature selection, tailored to their dataset and objectives. Interactive Visualization: present the results with visualization tools, including scatter plots and dendrograms, to display clustering results. Users should be able to explore multiple clustering examples and choose the one that best fits their needs. Refinement Options: The algorithm will offer user the ability to refine clustering results through methods such as outlier removal, feature scaling, and cluster merging, allowing for the fine-tuning of clusters. Enhanced Data Understanding: Visualizations should facilitate the interpretation of clusters and the validation of clustering results. Adaptability: This tool should accommodate a wide range of datasets of the same type but wide range of dataset sizes and support multiple clustering scenarios. The dataset characteristics: All datasets are available as csv files, with specific columns describing numerical data for clustering (position of points in 6D space). It should be done in 3 days max.