There is a Python 3.7 pickle file containing one Python object. The object will contain training data and labels for generating a chemometric classification model. In addition to the pickle file, there is a labels.txt file which will contain the mapping of the categorical Y variables to their ‘real world’ names. Submission: A Python 3.7 module file challenge.py containing a class that will be called in the following way, and will predict the class of each spectra from challenge import SpecPredict

model = SpecPredict(‘path/to/your/model’)

model.predict(testing_data)

testing_data will be a [sample x feature] numpy array and will return a [sample x class] numpy array.

In addition to the challenge.py module, The followings are included ● The script you used to train the model. ● A model file. ● An explanation of how to set up an environment to run the code(s). ● Describing the plot(s) to try to understand the spectra from the data ● A walkthrough of your model building process - please briefly describe different parts of the process such as feature selection, choosing a model architecture, etc. ● An explanation for speed optimizations and why it might be important. ● An explanation of how to estimate the model's performance on unseen data. ● An explanation of what performance metric(s) to use to evaluate the quality of classification of your solution. ● other utility codes