Awesome

Explorative Data Analysis

Get data into a usable format!
Find out if the following predictive modeling phase will be successful!

• Combine everything into a single big table
  • Convert files to .csv
  • Merge files
  • Fix encoding issues
  • Clean column names (english, no whitespace, no special chars)
  • Are there duplicate columns?
  • Fix datatypes (datetime, int, float, string)
• Look at the raw data
  • Sort data
  • Filter data by various criteria
• Investigation
  • Non-sensical observations/artifacts?
  • Coding of categorical features?
  • Missing values?
  • Outliers?
  • Constant values (=Zero Importance)?
  • Low importance features?
  • Collinear, correlated or otherwise dependent features?
  • Highly skewed features?
  • Irrelevant features?
• Univariate Analysis
  • Look at mean, median, min, max, std, iqr, quantiles (1%, 5%, 25%, 50%, 75%, 95%, 99%)
  • Draw boxplots, histograms
• Multivariate Analysis
  • Draw scatter plots
  • Create correlation matrix
• Time Series? -> Plot variables over time
• Fixing issues
  • Impute missing values (mode, median, mean)
  • Remove variables that have too many missings
  • Remove observations that have too many missings
  • Select appropriate time slice
• Preparation
  • Clip values that are too small/too large
  • Scale to [0,1] or normalize (mean=0, std=1) or Robust / Quantile Scaling
  • One-hot encoding, Label Encoding (0,1,2,3)
  • Create log-transformed versions for highly skewed variables
  • Create binned versions for variables
  • Combine categories for highly skewed categorical variables
  • Create sum/difference/product/quotient of variables
  • Create polynomial features

Deep Learning Tasks

• Storing training data (browsable training data catalog)
• Storing ground-truth annotations (when, by whom, which annotation guidelines)
• Pre-processing steps
• Training: Architecture + Loss function + Learning algorithm
• Storing models and possibly ancestors (in-case not training from scratch)
• Storing training hyperparameters, training environment (installed packages, CUDA drivers, VM machine images)
• Storing loss function history and final performance metrics on test set.
• Tools: Weights and Biases, Neptune, Iterative