Applied Forecasting in Python | Air Quality Dataset | ARIMA Model | Temperature Prediction

Python is a powerful programming language that is widely used for data analysis and scientific computing. It has a large ecosystem of libraries and packages that provide a wide range of forecasting algorithms and tools.

In this example, we will be using the Air Quality dataset, which contains information about the temperature, humidity, and other factors that affect the air quality. We will use a specific type of forecasting model called an ARIMA (Auto-Regressive Integrated Moving Average) model to predict the temperature.

An ARIMA model is a type of time series forecasting model that combines the strengths of three other models: the Auto-Regressive (AR), Integrated (I) and Moving Average (MA) model. The AR model looks at the past values of the series to predict future values, the I model accounts for trend and seasonality in the data and the MA model looks at the error between the predicted values and the actual values.

First, we will preprocess the Air Quality dataset and split it into training and testing sets. Next, we will use the ARIMA model to train on the training data. We will then use the model to predict the temperature for the testing data.

In summary, Applied Forecasting in Python using Air Quality dataset, ARIMA model and Temperature prediction is a powerful combination of a programming language, dataset and techniques that can be used to predict future values of temperature. ARIMA model is a time series forecasting model that combines the strengths of three other models, the Auto-Regressive (AR), Integrated (I) and Moving Average (MA) model, and it is used to predict the temperature based on past values, trend, seasonality and error.

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: ARIMA Model.

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

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