REFER TO THE FOLLOW LINK TO PROVIDE ANSWERS: Individual Project | Kaggle PLEASE ANSWER THE FOLLOWING: 1. Describe the dataset in your own words (1/2 page) 2. State at least three questions you will be...

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REFER TO THE FOLLOW LINK TO PROVIDE ANSWERS: Individual Project | Kaggle PLEASE ANSWER THE FOLLOWING: 1. Describe the dataset in your own words (1/2 page) 2. State at least three questions you will be asking of the dataset through the analysis (1/2 page) For each question: a. Define the outcome variable for your analysis and show how it is linked with the question and problem you identified. b. Define at least two models with at least two different variables that help predict the outcome variable. c. Describe these models in words and as formulas. d. Report on the analyses you conducted in Python using these models by describing the analyses and their results. Include your Python code. e. Provide at least one table and at least one plot for each model. Include your Python code. 3. Provide an executive summary of your findings (1 page) https://www.kaggle.com/stevegallegos/individual-project/notebook https://www.kaggle.com/stevegallegos/individual-project/data
Answered 6 days AfterFeb 23, 2022

Answer To: REFER TO THE FOLLOW LINK TO PROVIDE ANSWERS: Individual Project | Kaggle PLEASE ANSWER THE...

Sathishkumar answered on Mar 02 2022
116 Votes
Dataset
This dataset is regarding to direct marketing of the banking sector. which gives the detail
s about customer. This dataset describes the phone call between customer and marketing team about whether deposit is required or not. 
* bank-additional-full.csv is the name of the dataset.
* The dataset is changed as a bank.csv after delimited
Goal:
Our main goal is to classify the customer deposit about “yes” or “no”.
Attribute Information- Input Variables:
1. Bank Client Data
2. Other Attributes
3. Social and Economic Context Attributes     
Numeric data:
· age,
· duration of the call
· campaign
· pdays
· previous
· emp.var.rate
· cons.price.idx
· cons.conf.idx
· euribor3m
· nr.employed
Categorical data:
· Job
· Maritial
· Education
· default(credit in default)
· housing,
· loan
· contact
· month
· day_of_the_week
· poutcome
Target variable:
· deposit column...
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