Project
The project was to explore various machine learning techniques to estimate manufacturing cost of aircraft parts
Project Details
A data set containing 1000+ parts along with its machining requirement, machine time, labor cost and the material cost was provided. The challenge was to build a machine learning model to predict each cost (labour, machining and material cost). The major part of the time was utilized for preprocessing the data to train the model. I chose two models to preprocess and build a regression model for prediction. The pre-processing involved feature reduction and feature selection to reduce 28+ variables for prediction.The regression model built using filter and wrapper methodd. The R^2 could explain about 95 % of the variation.
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