This is my version of creating a flight delay prediction using the following algorithms
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Catboost
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GaussianNB
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Random Forest Classifier
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K Neighbors Classifier
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XGB Classifier
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Logistic Regression
This notebook consist of Data exploration of this dataset, Prediction Modelling and in the end i tried my hand on using hyperparameter to improve the accuracy of K Neighbors Classifier.
A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data. As a data scientist(or machine learning practitioner, whatever the name it is), model hyperparameters are unknown to us. We can only find the optimum values based on the default values given, rules of thumb, or trial and error by searching around the discrete space of hyperparameters. To give you a clearer picture, some of the hyperparameters are the learning rate for training a neural network, C and sigma values for Support Vector Machine (SVM), or the k value in k-nearest neighbours (KNN).
Hyperparameters are crucial as they control the overall behaviour of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that minimizes a predefined loss function to give better results.