New England College Santander Bank Case Study Report

User Generated

unevxnagu

Computer Science

New England College

Description

Question: Santander Bank Case Study

Write a formal report on your findings from the last several weeks for the classification of the Santander Bank Case Study.

The main objective is to write a fully executed R-Markdown program performing classification using the best models found for logistic regression, SVM, Random Forest and XGBoost algorithms, and comparing the values of their cost functions and accuracy scores.

Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes.

Please note that all code assignments must be submitted as a screenshot with a slice of your desktop showing the timestamp.

Put the screenshots in a word document, make sure to comment the code (explain what it does) and interpret the graph if applicable(explain what its depicting)

Course :Machine learning

Reference: 1. https://www.kaggle.com/c/santander-customer-satisf..

2. https://www.kaggle.com/c/santander-customer-satisf...

3. https://drive.google.com/file/d/1dh0qqXsyYlPaQ-FZI... (I have uploaded in the drive)

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Explanation & Answer

View attached explanation and answer. Let me know if you have any questions.

#Analysis of Santander Bank Case Study #The main objective is to this assignment is to
write a fully executed R-Markdown program performing classification using the best
models found for logistic regression, SVM, Random Forest and XGBoost algorithms, and
comparing the values of their cost functions and accuracy scores. #the following packages
are first installed in to R part 1**
library(gridExtra)
library(grid)
library(ggplot2)
library(lattice)
library(usdm)
## Loading required package: sp
## Loading required package: raster
library(caret)
library(rpart)
library(DataCombine)
##
## Attaching package: 'DataCombine'
## The following object is masked from 'package:raster':
##
##
shift
library(pROC)
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
##
cov, smooth, var
library(ROSE)
## Loaded ROSE 0.0-4
library(e1071)
##
## Attaching package: 'e1071'
## The following object is masked from 'package:raster':
##
##
interpolate
library(xgboost)

#setwd("/Users/Admin/Desktop/New folder (6)"
#Reading test and train data frame
train =read.csv('train.csv')
test =read.csv('test.csv')
dim(train)
## [1] 200000

202

dim(test)
## [1] 200000

201

summary(train)
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##

ID_code
Length:200000
Class :character
Mode :character

##
##

3rd Qu.: 4.935
Max.
: 27.907

var_2
Min.
: 2.117
1st Qu.: 8.722
Median :10.580
Mean
:10.715
3rd Qu.:12.517
Max.
:19.353
var_6

target
Min.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean
:0.1005
3rd Qu.:0.0000
Max.
:1.0000
var_3
Min.
:-0.0402
1st Qu.: 5.2541
Median : 6.8250
Mean
: 6.7965
3rd Qu.: 8.3241
Max.
:13.1883
var_7

var_0
Min.
: 0.4084
1st Qu.: 8.4538
Median ...


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