Machine learning

by | Mar 14, 2023 | Human Resource Management

Develop logistic regression, decision tree and neural network models that will identify
whether stores will perform well or poorly. You can use Orange, Python, R, or any data mining
package of your choice. The data for the assignment is in a file storedata.csv, which you can
download from the same place you found this document. The data dictionary is given at the
end of this document. You must follow the correct methodology to use the data to build and
test your models.

Sample solution

Dante Alighieri played a critical role in the literature world through his poem Divine Comedy that was written in the 14th century. The poem contains Inferno, Purgatorio, and Paradiso. The Inferno is a description of the nine circles of torment that are found on the earth. It depicts the realms of the people that have gone against the spiritual values and who, instead, have chosen bestial appetite, violence, or fraud and malice. The nine circles of hell are limbo, lust, gluttony, greed and wrath. Others are heresy, violence, fraud, and treachery. The purpose of this paper is to examine the Dante’s Inferno in the perspective of its portrayal of God’s image and the justification of hell. 

In this epic poem, God is portrayed as a super being guilty of multiple weaknesses including being egotistic, unjust, and hypocritical. Dante, in this poem, depicts God as being more human than divine by challenging God’s omnipotence. Additionally, the manner in which Dante describes Hell is in full contradiction to the morals of God as written in the Bible. When god arranges Hell to flatter Himself, He commits egotism, a sin that is common among human beings (Cheney, 2016). The weakness is depicted in Limbo and on the Gate of Hell where, for instance, God sends those who do not worship Him to Hell. This implies that failure to worship Him is a sin.


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God is also depicted as lacking justice in His actions thus removing the godly image. The injustice is portrayed by the manner in which the sodomites and opportunists are treated. The opportunists are subjected to banner chasing in their lives after death followed by being stung by insects and maggots. They are known to having done neither good nor bad during their lifetimes and, therefore, justice could have demanded that they be granted a neutral punishment having lived a neutral life. The sodomites are also punished unfairly by God when Brunetto Lattini is condemned to hell despite being a good leader (Babor, T. F., McGovern, T., & Robaina, K. (2017). While he commited sodomy, God chooses to ignore all the other good deeds that Brunetto did.

Finally, God is also portrayed as being hypocritical in His actions, a sin that further diminishes His godliness and makes Him more human. A case in point is when God condemns the sin of egotism and goes ahead to commit it repeatedly. Proverbs 29:23 states that “arrogance will bring your downfall, but if you are humble, you will be respected.” When Slattery condemns Dante’s human state as being weak, doubtful, and limited, he is proving God’s hypocrisy because He is also human (Verdicchio, 2015). The actions of God in Hell as portrayed by Dante are inconsistent with the Biblical literature. Both Dante and God are prone to making mistakes, something common among human beings thus making God more human.

To wrap it up, Dante portrays God is more human since He commits the same sins that humans commit: egotism, hypocrisy, and injustice. Hell is justified as being a destination for victims of the mistakes committed by God. The Hell is presented as being a totally different place as compared to what is written about it in the Bible. As a result, reading through the text gives an image of God who is prone to the very mistakes common to humans thus ripping Him off His lofty status of divine and, instead, making Him a mere human. Whether or not Dante did it intentionally is subject to debate but one thing is clear in the poem: the misconstrued notion of God is revealed to future generations.

 

References

Babor, T. F., McGovern, T., & Robaina, K. (2017). Dante’s inferno: Seven deadly sins in scientific publishing and how to avoid them. Addiction Science: A Guide for the Perplexed, 267.

Cheney, L. D. G. (2016). Illustrations for Dante’s Inferno: A Comparative Study of Sandro Botticelli, Giovanni Stradano, and Federico Zuccaro. Cultural and Religious Studies, 4(8), 487.

Verdicchio, M. (2015). Irony and Desire in Dante’s” Inferno” 27. Italica, 285-297.

Sample Solution

For this assignment, I will be using Python to develop logistic regression, decision tree and neural network models that will identify whether stores will perform well or poorly. The dataset used for the project can be found in the storedata.csv file which contains a data dictionary at the end of the document. To begin we must first import all necessary libraries including pandas, numpy, matplotlib and sklearn.

Sample Solution

For this assignment, I will be using Python to develop logistic regression, decision tree and neural network models that will identify whether stores will perform well or poorly. The dataset used for the project can be found in the storedata.csv file which contains a data dictionary at the end of the document. To begin we must first import all necessary libraries including pandas, numpy, matplotlib and sklearn.

The next step is to load our csv file into a pandas dataframe using read_csv and assign it to an object called store_df. We can then use the describe() method to check our database’s contents by executing print(store_df). From here we need to separate our independent variables (X) from dependent variable (Y), which in this case is “Performed Well” column as Y and rest of them as X.

After splitting out target from input datasets, we need preprocess or normalize/standardize values if needed by using StandardScaler(). We can now initiate Logistic Regression classifier model with LogisticRegression(), Decision Tree Classifier model with DecisionTreeClassifier()and Neural Network Classifier Model with MLPClassifier().

Once models are initiated,we need fit each one of them separately on train set and predict for test set for accuracy evaluation by applying accuracy score() method after calling prediction on each model respectively i.e., lrPrediction = lrModel.predict(XTest), dtPrediction=dtmodel.predict(XTest) ,mlpPrediction=mlpmodel.predict(XTest) .

We also have option to compare different models performance based on their respective accuracy scores with statement like
if lrScore > dtscore & mlpscore:
print(“Logistic Regrssion has Higher Accuracy”)

Finally we can generate visualization plots for further analysis of individual models results using library matplotlib such as confusion matrix plot etc.. With these steps completed successfully we should now be able to identify whether stores perform well or not accurately by building successful logistic regression, decision tree and neural network models.

What other methods could you use besides logistic regression, decision trees and neural networks?

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