https://github.com/adithivs/prodigyy_ds_03

https://github.com/adithivs/prodigyy_ds_03

data data-visualization datapreprocessing decision-tree-classifier

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/adithivs/prodigyy_ds_03
• Owner: AdithiVS
• License: bsd-2-clause
• Created: 2024-06-17T07:43:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-18T07:37:39.000Z (over 1 year ago)
• Last Synced: 2025-03-15T04:42:22.060Z (11 months ago)
• Topics: data, data-visualization, datapreprocessing, decision-tree-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 2.47 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# PRODIGYY_DS_03

## TASK 3

Build a decision tree classifier to predict whether a customer will purchase a product or service based on their demographic and behavioral data. Use a dataset such as the Bank Marketing dataset from the UCI Machine Learning Repository 

## About the Dataset

This data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe to a term deposit.

  

  

    

      Variable Name

      Role

      Type

      Description

    

    

      age

      Feature

      Integer

      Client's age in years

    

    

      job

      Feature

      Categorical

      Occupation (type of job)

    

    

      marital

      Feature

      Categorical

      Marital Status (married, single, divorced, unknown)

    

    

      education

      Feature

      Categorical

      Education Level (basic.4y, basic.6y, basic.9y, high.school, illiterate, professional.course, university.degree, unknown)

    

    

      default

      Feature

      Binary

      Has credit in default?

    

    

      balance

      Feature

      Integer

      Average yearly balance (euros)

    

    

      housing

      Feature

      Binary

      Has housing loan?

    

    

      loan

      Feature

      Binary

      Has personal loan?

    

    

      contact

      Feature

      Categorical

      Contact communication type (cellular, telephone)

    

    

      day_of_week

      Feature

      Date

      Last contact day of the week

    

    

      month

      Feature

      Date

      Last contact month of year (jan, feb, mar, ..., nov, dec)

    

    

      duration

      Feature

      Integer

      Last contact duration in seconds (important for benchmark purposes only)

    

    

      campaign

      Feature

      Integer

      Number of contacts performed during this campaign (includes last contact)

    

    

      pdays

      Feature

      Integer

      Number of days since last contact from previous campaign (-1 means not previously contacted)

    

    

      previous

      Feature

      Integer

      Number of contacts performed before this campaign

    

    

      poutcome

      Feature

      Categorical

      Outcome of the previous marketing campaign (failure, nonexistent, success)

    

    

      y

      Target

      Binary

      Subscribed to term deposit (yes/no)