Difference between revisions of "Group14 proposal"

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== Data Description ==
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In this dataset, each row represents a customer, each column contains customer’s attributes described on the column Metadata. The raw data contains 7043 rows (customers) and 21 columns (features). The “Churn” column is our target, which represents whether customers who left within the last month.
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{| class="wikitable" style="width: 100%; height: 14em;"
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|-
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! Data Fields !! Description !! Example !! Datatype
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| Customer ID  || Customer ID  || 7590-VHVEG  || Numeric
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| gender  || Whether the customer is a male or a female  || 1 || Binary
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| SeniorCitizen  || Whether the customer is a senior citizen or not (1, 0)  || 0 || Binary
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| Dependents || Whether the customer has dependents or not (Yes, No) || No|| Binary
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== <big>Methodology and Approach</big> ==
 
== <big>Methodology and Approach</big> ==

Revision as of 03:16, 2 March 2020

Home - PicSource: https://medium.com/@timenalls/how-to-predict-customer-churn-with-pyspark-fb0d30f55253


Motivation and Objectives


Critique of Existing Visualization


Data Source


Data Description

In this dataset, each row represents a customer, each column contains customer’s attributes described on the column Metadata. The raw data contains 7043 rows (customers) and 21 columns (features). The “Churn” column is our target, which represents whether customers who left within the last month.

Methodology and Approach


Proposed R Packages


Team Members


References


Data Fields Description Example Datatype
Customer ID Customer ID 7590-VHVEG Numeric
gender Whether the customer is a male or a female 1 Binary
SeniorCitizen Whether the customer is a senior citizen or not (1, 0) 0 Binary
Dependents Whether the customer has dependents or not (Yes, No) No Binary