Only a small portion of the dataset’s variables can be used to build a machine learning model; the others are either repetitive or irrelevant. In order to remove the unnecessary features from the data, it is crucial to discover and choose the most appropriate features from the data, which is accomplished with the aid of variable selection in machine learning.
Airfares are determined by a variety of factors, and as a result, they fluctuate significantly, with each airline using its own pricing algorithm. A travel business wishes to find new solutions to ascertain the most critical elements influencing airfares and forecast airfares for several popular itineraries.
Travel agencies build predictive models for forecasting fares, though how does one choose the factors with most explaining power.
Learn how different variable selection techniques can be used by travel agencies for maximizing sales.
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It is always important to keep the data that adds value to the analysis & remove the insignificant ones.
Variable selection is intended to select the best subset of predictors.
The aim is to construct a model that predicts well or explains the relationships in the data.
The data is summarized, and various visualization techniques were applied.
A variable is correlated with another variable(s), such Multicollinear variables are removed using the Variance Inflation Factor (VIF).
By selecting the variables which tends to have an influence on the Airfare, we fit 3 different regression models on the data.
Measuring the predictor relation, and response predictor relation is important to recognize the relationship exist.Multicollinear variables can be identified and removed from model building.
Selecting highly significant variables can improve the overall predictive capacity of machine learning models.
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Difficulty: Intermediate
Duration: Approximately 2 hours
