clangstonhinton / crypto-clustering-challenge

Employ unsupervised machine learning to predict outcomes with K-Means and PCA

Crypto-Clustering-Challenge

Deploy unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes leveraging, Python, K-Means and Principal Component Analysis.

Data Description

• The dataset contained pricing data for 41 cryptocurrencies such as: bitcoin, ethereum, tether and ripple among others.
• 7 columns contain pricing changes for 24-hour, 7-day, 14-day, 30-day, 60-day, 200-day and 1 year periods for each cryptocurrency.

Approach

• K-means:
  • The data were normalized using the StandardScaler module from scikit-learn
  • Used the elbow method to find the best value for k for the scaled dataset
  • Used K-means to cluster the cryptocurrencies in the scaled dataset
• PCA (Principal Component Analysis)
  • Using the original scaled data, PCA was performed to optimize clusters by reducing the features to three principal components
  • The explained variance was determined for the three principal components
  • The elbow method was used on the PCA data to find the best value for k
  • K-means was used to cluster the cryptocurrencies in the PCA data
• Comparison of K-Means Original Features to the PCA Features
  • Plots of the elbow graph and clustered scatter plots were compared between the two approaches

Findings

1. The optimal value for k against the original scaled data was determined to be 4 clusters using the elbow-method.

2. The features were reduced to 3 in the PCA approach and explained 89% of the variance in the dataset.

3. The PCA approach indicated that the best value for k continued to be 4 based on the elbow-method. However, the inertia values were smaller with PCA than KMeans approach.

4. When comparing scatter plots of the clusters from the two approaches, the PCA approach with 3 features produced more tightly grouped clusters than the original KMeans approach.

5. The two outliers (celsius-degree-token and ethelend) are the same in both the KMeans Original Scatter and the PCA Scatter plots. It may be beneficial to re-run the models after removing the two outliers from the dataset.