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This team project consists of Python code and a recommendation deck on flight price prediction analysis using machine learning (linear regression and random forest) and deep learning (neural network) models. The model performance and model optimisation are then compared.

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fairuznajla/Flight-Price-Prediction

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Recommendation Deck for Flight Price Prediction using Regression Algorithms

This team project is the implementation of an Independent Study program at Celerates x MSIB Kampus Merdeka Batch 6 that focuses on Data Science and Data Analytics.

We use regression algorithms by generating machine learning models, which are Linear Regression and Random Forest, also deep learning models, which is Neural Networks. After generating the model, model evaluation is performed to compare model performance. The best model, Random Forest, has the lowest probability of prediction error. The model is then optimised using hyper-parameter tuning techniques, such as Randomised Search and Grid Search, to see the precision of the model.

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This team project consists of Python code and a recommendation deck on flight price prediction analysis using machine learning (linear regression and random forest) and deep learning (neural network) models. The model performance and model optimisation are then compared.

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