Applied Data Science Coding | Forecasting in R | Linear and Non-linear model | Air Quality Dataset

Applied Data Science Coding | Forecasting in R | Linear and Non-linear model | Air Quality Dataset

Data science is a field that uses various techniques to extract insights and knowledge from data. One important aspect of data science is forecasting, which involves using historical data to predict future events. R is a popular programming language for data science and statistics, and there are many libraries and tools available for forecasting in R.

In forecasting, we often use different models to make predictions. A linear model is a mathematical equation that represents a straight line and is used for forecasting when the relationship between the variables is linear. On the other hand, a non-linear model is a mathematical equation that represents a curve and is used for forecasting when the relationship between the variables is not linear.

The Air Quality dataset is a dataset that contains information on air pollution levels in a certain area. This dataset can be used to predict future air pollution levels in the area, which is important for public health and environmental protection.

In order to use linear and non-linear models to forecast air pollution levels, we first need to clean and prepare the dataset. This may involve removing missing or incomplete data, and transforming the data into a format that can be used by the models. Next, we would need to use R libraries and tools, such as forecast and nlme to train the models using historical air pollution data, and use them to make predictions about future air pollution levels.

Overall, linear and non-linear models are powerful tools for forecasting time series data, and can be used in a variety of applications, such as air quality forecasting. By using data science techniques and tools like R, we can gain valuable insights and make predictions about the future, which can help us make more informed decisions.


In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Applied Data Science Coding | Forecasting in R | Linear and Non-linear model | Air Quality Dataset.

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