Air Quality Index Prediction in Machine Learning using Python - Videos | Geeksfo...

Air Quality Index Prediction in Machine Learning using Python

In this video, we will build a machine learning model that can be trained to predict Air Quality Forecasting/Prediction system. The machine is all about data and we will be feeding the data into the machine learning model, then this model tries to learn from this data, and on the basis of this data, it can make future predictions.

What are the modules used in creating Air quality forecasting model? 1) NumPy: This module has a high-performance multidimensional array object which can be used to perform statistical computing in Python. 2) Pandas: It is used for data manipulation and analysis in Python. 3) Prophet: Prophet is a forecasting procedure implemented in R and Python

What's the Work Flow of this model - Air Quality Forecasting/Prediction? 1) Data Collection: It is the process of gathering suitable data to identify the problem and prepare the best model of it.

2) Understanding the data: It is the basics analysis of the data, which can perform a basic understanding of data - Reshaping, how many rows and columns are in the dataset, how we handle the missing value in the dataset, etc

3) Data processing: Once we have raw data we cannot feed this data into the machine learning model, we have to process the data to pass the data into machine learning model.

FP Prophet Model: FP Prophet model is developed by Facebook. One of the most important forecasting models is FP Prophet model. It carries out the forecasting-related things for time series data. Since it has the capability of handling stationarity within the data it is considered a powerful library.

To perform this forecasting we are using Google Colb(https://www.geeksforgeeks.org/how-to-use-google-colab/) however you can use any Notebook.

Related Article: https://www.geeksforgeeks.org/python-numpy/ https://www.geeksforgeeks.org/introduction-to-pandas-in-python/