The following project consist of an analysis of crypto investments, where I combined Python with Unsupervised learning. Through K-Means and PCA, I examined the best values for K using PCA.

In order to run the code make sure you are in the dev environment. To create a dev enviroment that runs python 3.7, go to your terminal and: conda create -n dev python=3.7 anaconda Once the enviroment is created just go to your terminal and type conda activate dev and to deactivate enviroment, conda deactivate dev. If you have any running the code, please go to the requirements.txt file and make sure to install the require packages.

I imported a crypto market data to jupyter notebook, where I could check the price percentage change in the last 24 hours, 7 days, 14 days, 30 days, 60 days, 200 days and 365 days. The following image repressents the change of each coin in the dataset.

Following the analysis of the dataset, I prepared the data using StandardScaler module to normalize the CSV file.

Once the data was standarlized, I then found the best value for k. In order to find the value, I coded the elbow algorithm method and found that the best value for k is 4. And the following is the result of that;

Once the K value was found, i fited the K-Means using the original data and predict the cluster to group of the cryptocurrencies. I then created a scatter plot to analyzie the price percentage change in the last 24 hours to the price percentage change in the last 7 days.

Created a PCA model and set it to 3 as the number of components. With the PCA set, i then tried to evaluate the best value for K using the PCA data. The result was that the best value for k was still 4. As you can see on the following graph.

The difference from the best value for k on the original data and the PCA data. The difference was on the k value of the inertia. On the original data, the inertia has a k value = 79.022, while the PCA data has a k value with an inertia = 49.665.

I once again plotted a scatter so that I could compare the PC1 x PC2. And the following shows a very cluster of points towards the mid left of the graph.

I went further and plotted how the 3 PCAs compared to each other, finding interesing results as you can see.

After analyzing the results, we can see quiet a bit of difference when analyzing the PCA results. Using fewer features we can see see how these clusters tend to focus more on the left/mid part of the graphic while before clustering they are more to the concentrated to the right.