Bank-Customer-Churn-Prediction-using-R-programming

#a. Gender versus Exited.

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#b. Geography versus Active Members Gender wise

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#c. Age versus Exited (churn numbers)

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#d. Balance versus Exited

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#e. Age versus Credit score

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1.average credit score of females and males in France. gender gender_avg male 649 female 650

2.average credit score of people in the age brackets 20-30,31-40,41-50. age group age_avg 1 651 2 651 3 649 3.correlation between credit score and estimated salary. Credit Score Estimated Salary Credit Score 1.000000000 -0.001384293 Estimated Salary -0.001384293 1.000000000

4.statistical model to explain and establish a mathematical relationship between credit score (dependent) and gender, age, estimate salary.

MULTI LINEAR REGRESSION

Residuals:

Min       1Q   Median       3Q      Max 

-300.630 -66.880 1.262 66.930 201.174

Coefficients: Estimate Std. Error t value Pr(>|t|)

(Intercept) 6.525e+02 4.254e+00 153.382 <2e-16 ***

Age -3.739e-02 9.221e-02 -0.405 0.685

GenderMale -5.785e-01 1.942e+00 -0.298 0.766

EstimatedSalary -2.416e-06 1.681e-05 -0.144 0.886

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 96.67 on 9996 degrees of freedom

Multiple R-squared: 2.659e-05, Adjusted R-squared: -0.0002735

F-statistic: 0.0886 on 3 and 9996 DF, p-value: 0.9663