In depth analysis and modeling of personal typing test data from monkeytype with an interactive streamlit jbreffle.github.io/monkeytype-app.

• Set up your python environment based on ./requirements.txt
• Replace the data in ./data/raw/ with your own data downloaded from your monkeytype account using the "Export CSV" button
• Run the notebook as desired
• Note: monkeytype only retains the 1000 most recent trials, so to store all of your results you must periodically download your data
• Note: The "Export CSV" button on the monkeytype.com/account page will export only the data you are currently viewing, so make sure "all" is selected under the "filters" option in order to export all of your data

• 1_explore: What does the data look like when we apply basic visualizations and analysis?
• 2a_z_scoring: Can we compare trials with different difficulties by z-scoring by trial-type?
• 2b_learning_curve: Can we fit a learning curve to the data?
• 3a_sim_simple: What is the intrinsic correlation between accuracy and performance if we model typing as consisting of random errors with random error-correction times?
• 3b_sim_poisson: Does modeling typing as a Poisson process produce the same results?
• 4_nn_predict: Can we train a neural network to predict WPM performance from trial-type labels and number of trials completed?
• 5_nn_hyperopt: Using hyperopt to optimize the hyperparameters of the neural network
• state-space: Can we fit a state-space model to the data that takes into account trial-type difficulty to predict performance across time?
• stationary-performance: What would we expect the data to look like if there were no long-run improvement in performance?
• z_dynamodb_example.ipynb: Testing out reading/writing to a DynamoDB table
• z_s3_example.ipynb: Testing out reading/writing to an S3 bucket

• Calculate wpm v acc correlation for each trial type
• Run parameter grids in the simulation analyses
• Try simpler models of skill analysis: XGBregressor, fit a combined Learning Curve model
• Finalize some sort of "skill" metric that accounts for wpm difference by trial type
• Finish state_space analysis
• Finish stationary_performance analysis
• Correlate performance with health data from watch: sleep score, resting heart rate, stress levels, air quality, etc.
• Try out TimeGPT https://arxiv.org/pdf/2310.03589.pdf or other transformer for skill analysis