In this challenge, I used my knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.
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Rename the Crypto_Clustering_starter_code.ipynb file as Crypto_Clustering.ipynb.
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Load the crypto_market_data.csv into a DataFrame.
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Get the summary statistics and plot the data to see what the data looks like before proceeding.
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Use the StandardScaler() module from scikit-learn to normalize the data from the CSV file.
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Create a DataFrame with the scaled data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.
- The first five rows of the scaled DataFrame should appear.
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?
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.
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Using the original scaled DataFrame, perform a PCA and reduce the features to three principal components.
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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?
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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.
- The first five rows of the PCA DataFrame should appear as follows:
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.
- Answer the following question in your notebook:
- What is the best value for k when using the PCA data?
- Does it differ from the best k value found using the original 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 "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.
- Answer the following question:
- What is the impact of using fewer features to cluster the data using K-Means?
Debugged successfully with the help of colleagues and stackoverflow. This data was generated for educational use and does not reflect real values in investing.