jcmeunier77 / ml_cluster_challenge

Project sample of using cluster analysis and visualization

ML_Cluster_challenge

1. Purpose and project objective

Purpose

• Find chipotle epicentres to live your ideal chipotle lifestyle by clustering the chipotle dataset.

Objectives

• Consolidate the knowledge in Python, specifically in : NumPy, Pandas, Sklearn, Matplotlib,...
• visualization: to be able to use geopandas, matplotlib (and seaborn) to visualize clustered data onto a map
• Consolidate knowledge of data science and machine learning algorithm for developping an accurate clustering model
• data analysis: to be able to determine appropriate clustering methods and variables to cluster
• data analysis: to evaluate the chosen clustering method in comparison to other methods

Features

Must-have

• A visualisation of the USA with chipotle locations
• Visualization of the different clusters
• Intrinsic analysis comparison of the clusters of at least 2 methods with varying arguments (using euclidian distance as criteria)
• A chosen centroid to live. Make your argument of why the chosen centroid is superior to others.

Nice-to-Have

• Colour coded cluster visualisation
• Clear graph legends

Context of the project

• All the work achieved was done during the BeCode's AI/data science bootcamp 2020-2021

2. The project

Notebooks

• Data cleaning and preparation
• cluster analyses

Ouput samples

• Elbow method for choosing the right number of clusters

• Dendogram

• Facilities on map (using geopandas)

• Clusters on map (using sklearn.mean_shift for cluster and folium for map visualization)