IS407 Introduction to Data Science | UIUC伊利诺伊大学 | Assignment1-5, project代写 | python jupyter notebook代写

IS 407: Assignment 5
Due by Sunday, December 5, 2021 11:59PM
Complete the homework assignment in Python.
Submit both your .ipynb syntax file and a HTML of your homework assignment.
Note: Make sure to check for missing values. You may use any variables of your
choosing, but make sure to explain why you chose these variables.

1. Read in bank-additional-full.csv using pandas. The data contains bank marketing data,
   where rows represent banking clients and the columns represent different bank campaigning
   information.
   A) Create a logistic regression for explaining the odds ratios of term deposit.

• Provide the odds ratios, odd ratio 95% confidence intervals, and p-values.
• Explain what variables are contributing to the outcome and what
  variables are preventing or less likely for the outcome to occur.
  B) Create a logistic regression to predict the outcome of term deposit.
• Provide the classification report. Report the performance metrics and
  explain what the metrics means in predicting the outcome.
• Provide the confusion matrix. Use it to calculate the specificity and the
  negative predictive value. Explain what these metrics mean in predicting
  the outcome.
  C) Create a Decision Tree to predict the outcome of term deposit.
• Provide the Decision Tree flowchart. What do you learn from the
  association rules?
• Provide the classification report. Report the performance metrics and
  explain what the metrics means in predicting the outcome.
• Provide the confusion matrix. Use it to calculate the specificity and the
  negative predictive value. Explain what these metrics mean in predicting
  the outcome.
  D) Create use Feature selection to choose the top 30% variables of importance for the
  decision tree predicting term deposit.
• Rerun the model and provide the classification report. Report the
  performance metrics and explain what the metrics means in predicting
  the outcome.
  E) Compare your different models from parts B through D. How do these models
  perform? Would you feel confident using them for a real-world research question? If
  so, which model would you use and why?
  Data Dictionary
  Bank client data:
• Age (numeric)
• Job : type of job (categorical: ‘admin.’, ‘blue-collar’, ‘entrepreneur’, ‘housemaid’,
  ‘management’, ‘retired’, ‘self-employed’, ‘services’, ‘student’, ‘technician’, ‘unemployed’,
  ‘unknown’)
• Marital : marital status (categorical: ‘divorced’, ‘married’, ‘single’, ‘unknown’ ; note: ‘divorced’
  means divorced or widowed)
• Education (categorical: ‘basic.4y’, ‘basic.6y’, ‘basic.9y’, ‘high.school’, ‘illiterate’,
  ‘professional.course’, ‘university.degree’, ‘unknown’)
• Default: has credit in default? (categorical: ‘no’, ‘yes’, ‘unknown’)
• Housing: has housing loan? (categorical: ‘no’, ‘yes’, ‘unknown’)
• Loan: has personal loan? (categorical: ‘no’, ‘yes’, ‘unknown’)
  Related with the last contact of the current campaign:
• Contact: contact communication type (categorical:
  ‘cellular’,’telephone’)
• Month: last contact month of year (categorical: ‘jan’, ‘feb’, ‘mar’,
  …, ‘nov’, ‘dec’)
• Dayofweek: last contact day of the week (categorical:
  ‘mon’,’tue’,’wed’,’thu’,’fri’)
• Duration: last contact duration, in seconds (numeric). Important
  note: this attribute highly affects the output target (e.g., if
  duration=0 then y=’no’). Yet, the duration is not known before a call
  is performed. Also, after the end of the call y is obviously known.
  Thus, this input should only be included for benchmark purposes and
  should be discarded if the intention is to have a realistic
  predictive model.
  Other attributes:
• Campaign: number of contacts performed during this campaign and for
  this client (numeric, includes last contact)
• Pdays: number of days that passed by after the client was last
  contacted from a previous campaign (numeric; 999 means client was not
  previously contacted)
• Previous: number of contacts performed before this campaign and for
  this client (numeric)
• Poutcome: outcome of the previous marketing campaign (categorical:
  ‘failure’,’nonexistent’,’success’)
  Social and economic context attributes
• Emp.var.rate: employment variation rate – quarterly indicator
  (numeric)
• Cons.price.idx: consumer price index – monthly indicator (numeric)
• Cons.conf.idx: consumer confidence index – monthly indicator
  (numeric)
• Euribor3m: euribor 3 month rate – daily indicator (numeric)
• Nr.employed: number of employees – quarterly indicator (numeric)
  Output variable (desired target):
• y – has the client subscribed a term deposit? (binary: ‘yes’, ‘no’)