Predict Air Quality with Machine Learning: A Coding Tutorial

The video tutorial covers the impact of industrial activities on air quality, highlighting pollutants like ozone and carbon monoxide. It explains how poor air quality affects health and introduces the Air Quality Index (AQI). The tutorial then shifts to predicting AQI using machine learning, specifically LSTM models. It provides a step-by-step guide on downloading data from the EPA, preparing it in Google Sheets, and using Python in Google Colab to train and evaluate models for AQI prediction.

What are some common causes of poor air quality mentioned in the video?

• Industrial activities and transportation

• Excessive rainfall

• High humidity levels

• Increased solar activity

Which pollutant is known to trigger asthma and other respiratory issues?

• Carbon monoxide

• Ozone

• Sulfur dioxide

• Nitrogen dioxide

What does the Air Quality Index (AQI) categorize?

• Air quality levels

• Types of industrial waste

• Noise pollution levels

• Levels of water pollution

Which machine learning model is highlighted for predicting time-based sequences?

• Decision Trees

• Support Vector Machines

• LSTM models

• K-Nearest Neighbors

What is the first step in setting up the project notebook?

• Purchasing a new computer

• Installing a new operating system

• Downloading the project notebook

• Creating a new email account

Where can you download air quality data for the project?

• From the EPA website

• From a local library

• From a weather app

• From a social media platform

What is the purpose of normalizing data in the preprocessing step?

• To make data more readable

• To ensure data consistency

• To reduce data complexity

• To increase data size

What is the default window size for the initial LSTM model prediction?

• 30 days

• 3 days

• 14 days

• 7 days

What percentage of the dataset is used for training the model?

• 40%

• 70%

• 50%

• 60%

What is the final step in the coding tutorial?

• Documenting the model

• Running the model

• Exporting the model

• Evaluating the model