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Data Cleanse & Imputation

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Analytics

Actable AI provides state-of-the-art level Causal AI and ML-based analytics. It’s not only a tool that helps to get prediction value but also provides you with the rationale behind each prediction. Results generated by the model are available to download or save as a new table on Actable AI to be used for further analysis.

We cover the following seven analytics.

  • Data Cleanse & Imputation: Detect missing or invalid values and suggest fixing values to complete the data.
  • Causal Inference: Goes beyond simple associations between variables to estimate the causal relationships to answer whether a change in a treatment variable affects an outcome.
  • Segmentation: A machine learning technique that segments data points into segments where data points in each segment are similar to each other. This does not require class labels as in classification.
  • Classification: A machine learning technique that classifies data points into different classes (or categories). This requires training data with class labels for each data point. Example use cases include predicting existing customers to be a churn/no churn or predicting a tumour is benign or malicious.
  • Regression: A machine learning technique that predicts continuous values. This requires training data with ground truth values for each data point. An example use case is the prediction of rental prices of apartments.
  • Time-series Forecasting: Cutting-edge algorithm to predict future values of variables based on historical data. Examples include predicting the future price of a stock, the future price of a bond, the future price of a commodity, etc.
  • Association Rules: A machine learning technique that predicts which variables are associated with each other. This is a powerful tool to understand the relationships between variables. Example use cases is in basket analysis to understand which items are most likely to be bought together.