Make a logistic regression model using the csv file contained inside bank-additional.zip, attached next to this assignment. Please train your model to predict whether a client has subscribed for a term deposit or not – variable 21 in the csv file.
Zip file has information about the data, and additional information can be obtained through following link:
https://www2.1010data.com/documentationcenter/prod/Tutorials/MachineLearningExamples/BankMarketingDataSet.html
Creating logistic model itself is very easy as Python has a builtin function for it. But you would need to perform few data cleansing and exploratory steps (similar to the code I showed during last cladd, for example:
1: create heatmap to determine null values.
2: Identify which column(s) can be cleansed or imputation technique can be applied (add the reasoning in a separate document) and perform the cleansing/imputation steps
3: Identify which column(s) can be dropped (add the reasoning with the document) and drop the columns
4: Identify which columns require replacement from categorical string values to numeric values and perform the required steps.
Deliver:
Your Python code
Document describing the reasoning behind steps 2-4 (identified above)
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