We humans have been using glass since ancient times for a variety of applications from building construction to making decorative objects. With technology, glass and its applications have evolved, and today, we have different varieties of glass used for very different purposes from a computer monitor to a bulletproof car window depending on the grade of the glass produced. And not all grades or varieties are manufactured the same way. In this data science challenge, you as a data scientist must use the given data to predict the grade of the glass produced based on the given factors.
Given are 15 distinguishing factors that can provide insight into what grade of the glass is being produced. Your objective as a data scientist is to build a machine learning model that can predict the grade of glass based on the given factors.
The unzipped folder will have the following files.
- Train.csv – 1358 observations.
- Test.csv – 583 observations.
- Sample Submission – Sample format for the submission.
Target Variable: class
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Below are the file formats for the provided data
The submission must contain the probabilities of the target classes 1 and 2.
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