Applied Data Science Coding | Forecasting in Python | Holt Winters model | Air Quality Dataset

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Applied Data Science Coding | Forecasting in Python | Holt Winters model | Air Quality Dataset

 

Applied Data Science Coding is the process of using programming languages and tools to analyze and extract insights from data. In this example, we will focus on forecasting, which is the process of making predictions about future events or values based on historical data. We will use Python, one of the most popular programming languages for data science, and the Holt-Winters model, a specific type of time series forecasting model.

The Holt-Winters model is a type of exponential smoothing model that is used to forecast time-series data. It takes into account both the trend and seasonality of the data. The model consists of three components: a level component, which represents the average value of the series, a trend component, which represents the long-term direction of the series, and a seasonal component, which represents the repeating patterns in the series.

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 the Holt-Winters model to predict the temperature based on the historical data.

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

In summary, Applied Data Science Coding for Forecasting in Python using Holt Winters model and Air Quality dataset is a powerful combination of a programming language, model and dataset that can be used to predict future values of temperature. Holt-Winters model is a time series forecasting model that takes into account both the trend and seasonality of the data and it is used to predict temperature based on historical data.

 

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: Forecasting in Python | Holt Winters model | Air Quality Dataset.

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