Bike Rental Prediction

This workflow reads in a dataset.It then Predicts the number of bikes to be rented in any given hour.

Workflow

Below is the workflow. It does the following:

• Reads data from a sample dataset.
• Extracts hour from time using datatype timestamp.
• Calculates Count to datatype double.
• Assembles features for modelling.
• Calculates vectorindexer.
• Splits it.
• GBTRegression.
• Prediction.
• RegressionEvaluator.
• Correlation with columns.
• Summary analysis.
• Calculate count for rental per hour.
• Analyse using Graph.

Reading from Dataset

It reads sample Dataset file.

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Extract hour from time using datatype timestamp

It Extracts hour from time using datatype timestamp using DateTimeFieldExtract Node.

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Calculate Count to datatype double

It Calculates cast the Count field to datatype double using CastColumnType Node.

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Assemble features for modelling

It Assembles features columns into a feature vector using VectorAssembler Node.

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Calculate vectorindexer

It identifies categorical features and index them using vectorindexer Node.

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Split it

It will split our dataset into seperate training and test sets using split Node.

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GBTRegression

It validates held out test sets in order to know about high confidence using GBTRegression Node.

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Prediction

It will make prediction on future data using Prediction Node.

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RegressionEvaluator

It validates held out test sets in order to know about high confidence using RegressionEvaluator Node.

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Correlation with columns

It will analyse correlation between various columns using Correlation Node.

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Summary analysis

It visualizes our data to get sense of whether the features are meaningful using Summary Node.

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Calculate count for rental per hour

It calculates count for rental per hour using query with SQL Node.

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Analyse using Graph

It will analyse graph with bike rental counts and hours of the day using GraphValue Node.

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