Cryptocurrency_Clusters

Background

Created a report that includes what cryptocurrencies are on the trading market. Determined whether they can be grouped to create a classification system for this new investment.

Processed data to fit t-SNE & k-Means.

Steps

Data Preparation

• Read crypto_data.csv into Pandas. The dataset was obtained from CryptoCompare.

• Discard all cryptocurrencies that are not being traded.

• Remove all rows that have at least one null value.

• Filter for cryptocurrencies that have been mined.

• Covert dataset to numeric be comprehensible to a machine learning algorithm.

• Use Pandas to create dummy variables for Algorithm and ProofType, into numerical data.

• Standardize the dataset so that columns that contain larger values do not unduly influence the outcome.

Dimensionality Reduction

• Performed dimensionality reduction with PCA. Preserved 90% of the explained variance in dimensionality reduction.

• Further reduced the dataset dimensions with t-SNE and visually inspect the results.

Cluster Analysis with k-Means

• Create an elbow plot to identify the best number of clusters.
• Determine the inertia for each k between 1 through 10 to identify the number of the clusters

Recommendation

• Based on the visualization and analysis above, this cryptocurrencies dataset can be clustered into 4 distinct groups. I would recommand my clients at the investment bank that the cryptocurrencies can be grouped and created a classification system in order to develop a new investment portfolio for their customers.