Cryptocurrency-Clusters

Background

I am on the Advisory Services Team of a financial consultancy. One of MY clients, a prominent investment bank, is interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. They’ve asked me to create a report that includes what cryptocurrencies are on the trading market and determine whether they can be grouped to create a classification system for this new investment.
I have been handed raw data, so I will first need to process it to fit the machine learning models. Since there is no known classification system, I will need to use unsupervised learning. I will use several clustering algorithms to explore whether the cryptocurrencies can be grouped together with other similar cryptocurrencies. I will use data visualization to share my findings with the investment bank.

Read crypto_data.csv into Pandas. The dataset was obtained from CryptoCompare.
Discarded all cryptocurrencies that are not being traded. In other words, filtered for currencies that are currently being traded. Once I did this, dropped the IsTrading column from the dataframe.
Removed all rows that have at least one null value.
Filtered for cryptocurrencies that have been mined. That is, the total coins mined should be greater than zero.
In order for my dataset to be comprehensible to a machine learning algorithm, its data should be numeric. Since the coin names do not contribute to the analysis of the data, deleted the CoinName from the original dataframe.
My next step in data preparation is to convert the remaining features with text values, Algorithm and ProofType, into numerical data. To accomplish this task, used Pandas to create dummy variables. Examined the number of rows and columns of my dataset now.
Standardized my dataset so that columns that contain larger values do not unduly influence the outcome.

Created dummy variables above dramatically increased the number of features in my dataset. Performed dimensionality reduction with PCA.
Rather than specify the number of principal components when you instantiate the PCA model, it is possible to state the desired explained variance. For example, say that a dataset has 100 features. Thus used, PCA(n_components=0.99) to created a model that will preserve approximately 99% of the explained variance, whether that means reducing the dataset to 80 principal components or 3. For this project, preserved 90% of the explained variance in dimensionality reduction.
Next, I further reduced the dataset dimensions with t-SNE and visually inspect the results. In order to accomplish this task, ran t-SNE on the principal components: the output of the PCA transformation. Then created a scatter plot of the t-SNE output. Observed whether there are distinct clusters or not.

Created an elbow plot to identify the best number of clusters. Used a for-loop to determine the inertia for each k between 1 through 10. Determined, where the elbow of the plot is, and at which value of k it appears.