My learning outcomes and followup of a well instructed Coursera guided project by Ari Anastassiou.

We were provided with taxi rank location data of North American Region and had to solve a problem of defining the key clusters of these taxis where service stations for all taxis operating in that area can be built.

Task 1: Exploratory Data Analysis

Task 2: Visualizing Geographical Data

Task 3: Clustering Strength / Performance Metric

Task 4: K-Means Clustering

Task 5: DBSCAN

Task 6: HDBSCAN

Task 7: Addressing Outliers

• Visulaization
• Machine Learning
• Clustering
• Data Analysis
• Map BuildingVisualizing

Understanding the problem and data provided through basic data analysis and visualizations.

• Checking for duplicate and empty data cells
• Removing the redundant data
• Finally plotting the cleared data

Trying various interactive means to further improve my learnings about the data.

• Plotting the data on the world map with the co-ordinates provided

Evaluating the strength of a clustering algorithm.

• Calculating the silhouette score
• Plotting the graph for various blobs

Gaining the theoretical knowledge about k-means clustering algorithm and implementing it for our data.

• Visualizing the K-means on sample data
• Calculating the best silhouette score for our data
• Plotting the data on the basis of the algorithm

Gaining theoretical and practical knowledge of Density-Based Spatial Clustering of Applications with Noise(DBSCAN).

• Calculating the best silhouette score for our data
• Plotting the data on the map for density based approach

Gaining theoretical and practical knowledge of Hierarchical DBSCAN or HDBSCAN to alleviate constraints of classical DBSCAN.

• Calculating the best silhouette score for our data
• Plotting the data on the map for density based approach

Addressing outliers classified by various density-based models

• Using K-neighbour classifier and calculating its silhouette score
• Comparing Hybrid and K-Means Approaches

After completing this project I am able to do basic data manipulations required for any data processing field throughly and through various visual means. Further I got a more deep insight on how various clustering algorithms differ from each other and how I can evaluate their strength on basis of various data. Lastly this project provided a good insight to how some real world problems can be solved using these means.