49 lines
1.9 KiB
Markdown
49 lines
1.9 KiB
Markdown
# Smart Infrastructure
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<!--toc:start-->
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- [Smart Infrastructure](#smart-infrastructure)
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- [General Methodology](#general-methodology)
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<!--toc:end-->
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## General Methodology
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1. Business understanding: What is the problem to solve
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- What is holding Londoners back from cycling?
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1. Analytics approach: How can I use data to answer Q1?
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- Using data, only 2% of trips are done cycling, why? Try to learn causes by
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the data
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- Types of analytics:
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- Descriptive: what happened
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- diagnostic: Why
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- Predictive: What will happen
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- Prescritive: How to make it happen
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1. Data requirements: What Existing Do I need to analyze the problem
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- Cyclists’ casualties data
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- City data
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- Cycle thefts
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1. Data collection: collect new data
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- Try to collect data using sensors
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1. Data understanding: Verify if the data collected can solve the problem
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- Using tools like _uni-variate_, _pairwise correlation_, and histogram
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1. Data preparation (loop back to data collection): If the data is usable, or if
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preparation must be done,
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- Possible problems:
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- Structural error
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- Merging of data
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- Outlier analysis
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- Redundancy
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- Data collected contains observations (values), and attributes / features
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(keys), can be:
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- Continuous or Discrete
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- Numeric or nominal (labels like "London" or "Beijing")
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1. Modeling: Visualizing the data to answer questions
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- Using ML: split dataset to train, validate and test them
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- Train: to fit the model
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- Validate: provide unbiased evaluation while training (tuning
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hyper-parameters)
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- Test: provide evaluation on final model fit
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1. Evaluation: Does the model answer the question or is change needed
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1. Deployment: Using the model in practice
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1. Feedback (loop back to modeling): Use feedback and new data, to possibly
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re-train or fine-tune the model, and answer the initial question.
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