digamjain / Clustering-Geolocation-Data-Intelligently

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

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Clustering-Geolocation-Data-Intelligently

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.

Project Outline

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

Skills Developed

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

Task 1: Exploratory Data Analysis

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

Screenshot from 2021-01-27 15-53-29

Task 2: Visualizing Geographical 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

Screenshot from 2021-01-27 16-14-01

Task 3: Clustering Strength / Performance Metric

Evaluating the strength of a clustering algorithm.

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

image

Task 4: K-Means Clustering

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

image

Task 5: DBSCAN

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

image

Task 6: HDBSCAN

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

image

Task 7: Addressing Outliers

Addressing outliers classified by various density-based models

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

image

Outcome

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.

About

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


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