# Applied Data Science Coding | Forecasting in R | Polynomial 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.

A polynomial model is a type of mathematical equation that uses polynomials (a polynomial is a mathematical expression that involves a sum of powers in one or more variables) to fit a curve to a set of data points. Polynomial models can be used to forecast data that follow a non-linear pattern. They are useful when we suspect that a linear model is not able to capture the underlying relationship between the variables in the data.

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 polynomial 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 model. Next, we would need to use R libraries and tools such as `polynom` to train the models using historical air pollution data and use them to make predictions about future air pollution levels.

Overall, polynomial 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 | Polynomial model | Air Quality Dataset.

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