Unsupervised learning using K Means and Principal Component Analysis (PCA)

This project uses unsupervised learning techniques including k-means clustering and PCA to predict changes cryptocurrencies.

Prepare the Data

-Use the StandardScaler() module from scikit-learn to normalize the data from the CSV file.
-Create a DataFrame with the scaled data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.

Data discovery

-Find the Best Value for k Using the Original Scaled DataFrame.
-Use the elbow method to find the best value for k using the following steps:
-Create a list with the number of k values from 1 to 11.
-Create an empty list to store the inertia values.
-Create a for loop to compute the inertia with each possible value of k.
-Create a dictionary with the data to plot the elbow curve.
-Plot a line chart with all the inertia values computed with the different values of k to visually identify the optimal value for k.
-Answer the following question in your notebook: What is the best value for k?

Cluster Cryptocurrencies with K-means Using the Original Scaled Data

-Use the following steps to cluster the cryptocurrencies for the best value for k on the original scaled data:
-Initialize the K-means model with the best value for k.
-Fit the K-means model using the original scaled DataFrame.
-Predict the clusters to group the cryptocurrencies using the original scaled DataFrame.
-Create a copy of the original data and add a new column with the predicted clusters.
-Create a scatter plot using hvPlot as follows:
-Set the x-axis as "price_change_percentage_24h" and the y-axis as "price_change_percentage_7d".
-Color the graph points with the labels found using K-means.
-Add the "coin_id" column in the hover_cols parameter to identify the cryptocurrency represented by each data point.

Optimize Clusters with Principal Component Analysis

-Using the original scaled DataFrame, perform a PCA and reduce the features to three principal components.
-Retrieve the explained variance to determine how much information can be attributed to each principal component and then answer the following question in your notebook:
-What is the total explained variance of the three principal components?
-Create a new DataFrame with the PCA data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.

Find the Best Value for k Using the PCA Data

Use the elbow method on the PCA data to find the best value for k using the following steps:
-Create a list with the number of k-values from 1 to 11.
-Create an empty list to store the inertia values.
-Create a for loop to compute the inertia with each possible value of k.
-Create a dictionary with the data to plot the Elbow curve.
-Plot a line chart with all the inertia values computed with the different values of k to visually identify the optimal value for k.

Cluster Cryptocurrencies with K-means Using the PCA Data

Use the following steps to cluster the cryptocurrencies for the best value for k on the PCA data:
-Initialize the K-means model with the best value for k.
-Fit the K-means model using the PCA data.
-Predict the clusters to group the cryptocurrencies using the PCA data.
-Create a copy of the DataFrame with the PCA data and add a new column to store the predicted clusters.
-Create a scatter plot using hvPlot as follows:
-Set the x-axis as "PC1" and the y-axis as "PC2".
-Color the graph points with the labels found using K-means.
-Add the "coin_id" column in the hover_cols parameter to identify the cryptocurrency represented by each data point.