Group14 proposal

Proposal

Poster

Application

Research Paper

Motivation and Objectives

Nowadays, all industries in the world are facing fierce competition. With the development of telecom technology and social media, the Telco companies play more and more important role in the society. There are growing number of wireless carriers in the world. The U.S has four main wireless carriers and lots of little wireless carriers. It is no surprise that the companies in this industry face very fierce competition. Since this condition, the most significant problem for these organizations are customer remaining. As we know, companies from these industries often have customer service department. Their target is that winning back clients who is churn. Because it is generally acknowledged that recovering long-term customers can be worth much more to a company than acquiring new customers.

In order to understand more directly the main factors that affect customer churn and better maintain the relationship with customers, relevant models will be built so as to select and visualize important variables. Lastly, we will present the comparison among models towards Recall, Accuracy, Precision and F1 score and evaluate the performances of different models.

Critique of Existing Visualization

This radar chart has all the variables from the dataset to be presented in a single graph. We can acknowledge that by different series (“0” as “No”, “1” as “Yes”), each variable their number of customers. However, even though this visualization has revealed plenty of valuable information, such as unbalanced data in several variables with a fair enough graph, some questions can be spotted from the graph. For instance, too many variables have presented on the chart at the same time. The best way to solve this problem is to use feature engineering, then to draw a new graph to visualize better the influence of different factors to churn or not churn.

Data Description

The data set is from IBM Community. There are 1471 entity instances in total with 30 attributes. Some of the information from the data set are recorded as numbers, we need to identify them as categorical variables.

PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best'

Data Fields	Description	Datatype
Age	The age of employee	Numerical
Attrition	Turnover status of employee, stored as "Yes" or "No"	Binary
BusinessTravel	Whether the employee travel frequently or not	Categorical
DailyRate	The daily rate of employee	Numerical
Department	Which department does this employee belongs to	Categorical
DistanceFromHome	Distance from home to office	Numerical
Education	Education level of employee, 1 as 'Below College', 2 as 'College', 3 as 'Bachelor', 4 as 'Master', 5 as 'Doctor' || Categorical
EducationField	Field of Education of employee	Categorical
EnvironmentSatisfication	The satisfication level of one employee in working environment, 1 as 'Low', 2 as 'Medium', 3 as 'High', 4 as 'Very High'	Categorical
Gender	Gender of employee	Binary
JobInvolvement	Job involvement of employee, 1 as 'Low', 2 as 'Medium', 3 as 'High', 4 as 'Very High'	Categorical
JobLevel	Job level of employee	Numerical
JobRole	Job role of employee	Categorical
JobSatisfaction	Job satisfaction of employee, 1 as 'Low', 2 as 'Medium', 3 as 'High', 4 as 'Very High'	Categorical
MaritalStatus	Marital status of employee	Categorical
MonthlyIncome	Monthly income of employee	Categorical
NumCompaniesWorked	The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))	Categorical
OverTime	The amount charged to the customer monthly	Numerical
PercentSalaryHike	The total amount charged to the customer	Numerical
Churn	Whether the customer churned or not (Yes or No)	Binary
Churn	Whether the customer churned or not (Yes or No)	Binary
Churn	Whether the customer churned or not (Yes or No)	Binary
Churn	Whether the customer churned or not (Yes or No)	Binary
Churn	Whether the customer churned or not (Yes or No)	Binary
Churn	Whether the customer churned or not (Yes or No)	Binary

Methodology and Approach

In Feature engineering, we will generate variables from the previous ones and compose multiple features together, after which we will separate churn and not churn customer and separate categorical and numerical columns. The main point of this is selecting effective variables which would result in customer attrition. Apart from that, we may transform multi value variables to the dummy variable in the last step of this stage, we can obtain the variable summary like below:

Secondly, a correlation matrix of this model will be visualized to present the relationship among different variables and primarily understand the influence of each variables.

Models

We intend to build four different models and visualize them and their performances: (1) In Logistic regression model performance, confusion matrix and receiver operating characteristic will be visualized to evaluate the performance of the Logistic regression model. Besides, we can get the variable importance while comparing all variables by bar chart. Furthermore, obtaining the appropriate threshold for logistic regression will also be visualized to understand what is beneath the model. (2) Synthetic minority oversampling Technique will be applied to build the advanced logistic regression, and we will try to form user portrait using the most important features which are selected from the model. (3) The decision tree will be generated refer to the results of the feature score and compare the GINI coefficient to measure the degree of inequality of the distribution. And we intend to use high-score categorical features to make a ternary plot to visualize the distribution of two groups. (4) In order to control over-fitting and improve the predictive accuracy, we will also build and visualize Random Forest Classifier and compare different trees. Lastly, we will visualize the comparison among four models towards Recall, Accuracy, Precision and F1 score and compare the performances of different models.

Proposed R Packages

Package Name	Description
shiny	Make the interactive web applications for data visualization
reshape	Give new shapes to an array without changing its data
plotly	Create interactive bar graphs and scatter plots
tidyverse	A set of packages to plot out various visualizations and EDA
readr	To read rectangular data
recharts	Create interactive radar chart
DT	Create data table
ggraph	An extension of ggplot2 to build plots layer by layer
corrgram	Create correlation matrix
ggthemes	To apply themes to Shiny applications
ggcorrplot	Visualize correlation matrix using ggplot2
plotrix	Create plot with two ordinates

Team Members

• Qiu Yang
• Shi Yawen
• Zhu Keyu