Roadmap

Implemented Features

###Imputation - Fill in missing values

• Deletion: Remove records with missing values
• Dummy substitution: Replace missing values with a dummy value: e.g, unknown for categorical or 0 for numerical values.
• Mean substitution: If the missing data is numerical, replace the missing values with the mean.
• Frequent substitution: If the missing data is categorical, replace the missing values with the most frequent item
• Regression substitution: Use a regression method to replace missing values with regressed values.
• Classification based substitution for categorical variables

####Type Analyzer

• Automatically infer data types from datasets
  • Integer
  • Decimal
    • Fraction
    • Percentage
  • Location
    • Lat/Long based geo coordinates
    • Address
    • Zip code
    • Country
    • City
  • Currency
  • Date
  • Timestamp
  • Email
  • URL
  • Phone number
    • Landline
    • Mobile
  • SSN
  • Name

###Data Smoothing

• Moving average
• Weighted moving average

###Data Normalization Data normalization re-scales numerical values to a specified range. Popular data normalization methods include:

• Min-Max Normalization: Linearly transform the data to a range, say between 0 and 1, where the min value is scaled to 0 and max value to 1.
• Z-score Normalization: Scale data based on mean and standard deviation: divide the difference between the data and the mean by the standard deviation.
• Decimal scaling: Scale the data by moving the decimal point of the attribute value.

###Data Transformation

• Merge columns
• Split columns
• De-duplication of rows