I want a python code: miniproject

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: The objective of this miniproject is to study bias detection and mitigation. Please use your

favorite dataset from the UCI Machine Learning Datasets or Kaggle Datasets

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links given in

previous miniproject descriptions or can be easily found by Googling

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$$You need to build two

machine learning models

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using decision forests and neural networks respectively

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$$based on this

dataset to predict the appropriate output variable.

You need to choose a suitable protected variable

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such as race, gender, etc

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$$Any appropriate

feature may be used as a protected variable. The test set needs to be split into different subsets by

the values of the protected feature, and the classification prediction accuracies on these subsets need

to be calculated.

Generate bar figures that show the accuracies of the models on these groups with different values

of the protected variable with each of the two ML methods. If there is clear difference in prediction

accuracy for these groups, we perceive that a bias exists in this ML classification model in terms of

prediction accuracy or effectiveness. This may be used as a bias measurement criterion. Then try to

mitigate this potential bias by sampling differentially from the training sets according to the values

of the protected variable. For example, if a particular protected group shows lower accuracy, you

may sample this protected group more

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at the expense of other protected groups

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$$in the training set

to try to improve the accuracy over this protected group.

Please submit a

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$$or

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$$page report with the following components:

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i

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$$Problem Statement and description

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ii

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$$Brief descriptions of the methods used

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iii

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$$The results on the entire test set with each of the two ML methods

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iv

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$$The results on the test set partitioned by the protected variable as described above with each

of the two ML methods

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v

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$$Results on the test set with your proposed bias mitigation strategy to show if it reduces the

bias as measured by your chosen criteri