WEBVTT

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Music

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Document the project process, including data analysis methods and conclusions drawn.

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Understanding the problem.

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So, we started with a big challenge. Urban traffic jams.

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Everyone's stuck in traffic, and we want to figure out how to make it better.

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Gathering traffic. Data. First, we collected data. Like, how many cars are on the road?

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What's the condition of the roads? Basically, everything related to traffic.

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Looking at the data. Next, we took a good look at the data.

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It's like opening a treasure chest. So many numbers and so much information.

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We decided to focus on two things. How heavy the traffic is. We call it traffic intensity.

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And the condition of the roads.

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Making sense of the numbers. Now, numbers can be overwhelming, right?

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So, we used a trick called K means clustering. Think of it as grouping similar things.

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We found three groups, like saying, these roads have similar traffic and conditions.

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Drawing on a map. To make it more fun, we drew a map.

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Each group got its own color on the map. It's like saying, here are the roads that behave similarly.

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Checking the map. Now, looking at the map, we could see patterns.

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Some areas always had heavy traffic. Some were kind of okay. It's like solving a puzzle.

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Understanding why some places are more jam-packed than others.

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Figuring out solutions. Finally, armed with our map and insights, we brainstormed ideas.

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Maybe adjusting traffic lights or suggesting different routes could help ease the congestion.

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Rapping it up. So, that's our journey. From a big traffic problem to understanding it with numbers,

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making a cool map, and brainstorming ways to make it better.

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It's similar to the work of a traffic investigator, where we use data as our primary tool

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to solve complex problems and uncover hidden insights.