Cryptocurrency Price Change Prediction

Use Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes. This project offers an exciting opportunity to explore the world of cryptocurrencies and develop a predictive model that can help cryptocurrency enthusiasts and traders make informed decisions.

Overview

Cryptocurrency markets are known for their volatility, and traders often seek to understand short-term (24-hour) and longer-term (7-day) price trends. In this challenge, your primary goals are as follows:

• Data Collection: Gather relevant data on cryptocurrency prices, including historical price changes for both 24-hour and 7-day periods.

• Data Preprocessing: Clean and preprocess the data, handling missing values, outliers, and other data quality issues.

• Unsupervised Learning: Apply unsupervised learning techniques to discover patterns and relationships within the cryptocurrency data.

• Classification Model: Build a predictive classification model to determine whether cryptocurrencies are affected by 24-hour or 7-day price changes.

• Model Evaluation: Assess the performance of your model using appropriate evaluation metrics, such as accuracy, precision, recall, and F1-score.

Dataset

You will have access to a dataset containing historical cryptocurrency price data, including features related to different cryptocurrencies, their prices, and price changes over 24-hour and 7-day periods. This dataset will serve as the foundation for your predictive modeling efforts.

Getting Started

1. Rename the Crypto_Clustering_starter_code.ipynb file as Crypto_Clustering.ipynb.

2. Load the crypto_market_data.csv into a DataFrame.

3. Get the summary statistics and plot the data to see what the data looks like before proceeding.

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.

  • The first five rows of the scaled DataFrame should appear as follows:

    

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.

  • The first five rows of the PCA DataFrame should appear as follows:

    

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.
• 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?

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.
• Answer the following question:
  • What is the impact of using fewer features to cluster the data using K-Means?

References

Data for this dataset was generated by edX Boot Camps LLC, and is intended for educational purposes only.