Homework 5

For any exercise where you’re writing code, insert a code chunk and make sure to label the chunk. Use a short and informative label. For any exercise where you’re creating a plot, make sure to label all axes, legends, etc. and give it an informative title. For any exercise where you’re including a description and/or interpretation, use full sentences. Make a commit at least after finishing each exercise, or better yet, more frequently. Push your work regularly to GitHub, and make sure all checks pass.

For this Homework, find the types of clustering, their pros and cons, and implementation here.

Clustering Techniques on Energy Data

Welcome to this analytical exploration using unsupervised machine learning, specifically clustering algorithms, on a dataset related to energy production and consumption. This assignment, leveraging the #TidyTuesday owid-energy.csv dataset, aims to uncover hidden structures within the energy sector data.


Implement various clustering methods to reveal patterns in energy production, consumption, and their environmental impacts. Optimize clustering parameters and analyze the clusters to extract actionable insights.


Part 1: Data Preparation

Task 1: Data Preprocessing

  • Import and initially explore the dataset.
  • Clean the data by handling missing values and duplicates.
  • Standardize data types for analysis.
  • Conduct a preliminary analysis to understand the features related to energy metrics.

Task 2: Exploratory Data Analysis (EDA)

  • Visualize potential clusters within the energy data.
  • Select features for clustering based on energy consumption types, production sources, and environmental impacts.


  • Use visualization libraries to explore distributions and relationships. Consider creating scatter plots or pair plots to identify potential clusters.
  • Perform correlation analysis to aid in feature selection, focusing on variables that represent different aspects of energy data.

Part 2: Clustering Methods Implementation and Analysis

Task 1: K-Means Clustering

  • Use the Elbow Method and Silhouette Score to determine the optimal number of clusters.
  • Implement the K-Means algorithm and visualize the resulting clusters.
  • Interpret the clusters focusing on energy production and consumption patterns.

Task 2: Hierarchical Clustering

  • Perform hierarchical clustering and create a dendrogram to visualize the cluster hierarchy.
  • Compare the clusters obtained with K-Means and interpret their relevance to energy data.

Task 3: DBSCAN

  • Implement the DBSCAN algorithm to identify dense clusters within the energy data.
  • Analyze the sensitivity of DBSCAN parameters and their impact on cluster formation.
  • Interpret the clusters, especially focusing on outliers and anomalies in energy patterns.

Part 3: Cluster Characterization and Comparative Analysis

Task 1: Cluster Interpretation and Analysis

  • Characterize each cluster based on key features such as types of energy sources, consumption per capita, and carbon intensity.
  • Detect outliers and anomalies within the energy data, discussing their potential implications.

Deliverables: - A comprehensive Jupyter Notebook containing all code, visualizations, and analyses. - A detailed report summarizing the exploratory data analysis, clustering techniques used, and insights derived from the clusters.

Submission Guidelines: - Submit your Jupyter Notebook and report via GitHub, ensuring that your repository is well-organized and your commit messages are informative.

Grading Rubric: Your submission will be evaluated based on the completeness and correctness of the analysis, the quality of visualizations, the thoroughness of the documentation, and adherence to submission guidelines.

Best of luck! May your analysis shed light on significant patterns within the energy sector.