a business problem/ objective corresponding to the dataset

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

We have to create use cases for any business problem (1 for classification and 1 for regression) and submit 2 word documents [1 for regression and 1 for classification] . . The solution and insights for each business case will be submitted in a separate word document which will have R code and results for each algorithm along with the interpretation of results for each algorithm. 

 

Both the word documents should consist of 

1. a business problem/ objective corresponding to the dataset

2. understanding the dataset with visualization

3. feature processing (Zero variance, missing values, data type converter, outliers etc.) 

4. followed by algorithms. The results, interpretation, and insights should be explained for each algorithm.

For Regression document: Linear, k-NN, decision tree, bagging, random forest and gradient boosting

For Classification document: Binomial/Multinomial, Naive Bayes, k-NN, decision tree, bagging, random forest, and gradient boosting

Lastly, the table will be displayed to compare the accuracy of all the algorithms.

 

Attachments:

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