Add Predictive Analytics on Your Dataset in 3 Steps

Machine Learning Data Engineer

Knowi Predictive Analytics brings machine learning capabilities to every Data Engineer.

Integrate machine learning into your analytics workflows and drive data directed actions.

The platform provides two options:

  • Out-of-the-box predictive analytics capabilities that test a dataset against a variety of forecasting models to determine the model best suited to the data,  with the least Sum of Absolute Errors (SAE).  
  • Built-in Predictive and Machine Learning algorithms that can be plugged into data workflows.

This post focuses on the first option above that takes a hands-on look at how it works.  We’ll take monthly stock prices for Amazon to determine predicted values over a three month period starting in July in a few simple steps. No signup is required to follow along.  

Models used include:

  • Simple Exponential, Double Exponential, Triple Exponential Smoothing Models
  • Moving Averages and Weighted Moving Averages
  • Naive Forecasting Model
  • Regression and Polynomial Regression Model
  • Multiple Linear Regression Model

The monthly stock prices looks like this:

Date, Price

  • 06/01/16, 719.14
  • 05/01/16, 683.85
  • 04/01/16, 598.00
  • 03/01/16, 579.00
  • 02/01/16, 574.81
  • 01/05/16, 633.79
  • 12/01/15, 679.36
  • 11/2/15, 628.35
  • 10/1/15, 625.90
  • 9/1/15, 511.89
  • 8/3/15, 512.89
  • 7/1/15, 536.15
  • 6/1/15, 434.09
  • 5/1/15, 429.23
  • 4/1/15, 421.78
  • 3/2/15, 372.10
  • 2/2/15, 380.16
  • 1/2/15, 354.53

Steps:

1. Copy and paste the above dataset into https://www.knowi.com/csv-json-files-analytics.

2. Click on Show me. The data will be parsed and visualized immediately. 

3. To perform predictions:

   i. Click on Analyze from the menu of the time series chart. This opens up an Analysis mode.

  ii. Drag date into the Grouping field. 

  iii. Click on ‘Add a derived Field’ option. Enter a name (“Predictions”, for example) and in the operation, enter predict(price,date,07/01/2016,1m,3). This will choose the best model based on historical accuracy of the model to determine the projected prices over a three month period, on a monthly basis. 

That’s it! In a few simple steps, you can apply predictive analytics on any of your own datasets. Enjoy!

To get started with Knowi Predictive Analytics head to https://knowi.com and try it out free.

Additional Machine Learning Resources:

Predictive Analysis docs: https://docs.knowi.com/hc/en-us/articles/115006493407-Predictive-Analytics-Forecasting

Advanced Machine Learning (AI) Capabilities: https://knowi.com/adaptive-intelligence-machine-learning

All Documentation: https://docs.knowi.com

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