diff --git a/4lab.R b/4lab.R
new file mode 100644
index 0000000000000000000000000000000000000000..1acb2a90f3342bad6533eb5a947f83ac88af978a
--- /dev/null
+++ b/4lab.R
@@ -0,0 +1,198 @@
+# ************************************************
+# This work is licensed under a Creative Commons
+# Attribution-NonCommercial 4.0 International License.
+# ************************************************
+#
+#
+# Department of Computer Science
+# University of Surrey
+# GUILDFORD
+# Surrey GU2 7XH
+#
+# 24th October 2019
+#
+# UPDATE
+# 1.00 1/2/2017 Initial Version
+# 1.01 19/2/2018 Updated function calls
+# 1.02 6/3/2018 Corrected bug removed "inputs_inputs"
+# 1.03 24/2/2019 Lab 4 2019 updates
+# 1.04 1/3/2019 Added "PerformanceAnalytics" as required library
+# 1.05 22/10/2019 Updated for PBA (Basic solution)
+# 1.08 29/05/2020 main() initialise allResults
+# 1.09 2/6/2020 Use lab4DataPrepNew.R
+# 1.10 26/11/2020 Changed "discreet" to "discrete" [woops!]
+# ************************************************
+# For lab 4
+# ************************************************
+
+# clears all objects in "global environment"
+rm(list=ls())
+
+# ************************************************
+# Global Environment variables
+# - i.e. available to all functions
+
+# ************************************************
+# Good practice to place constants in variables
+# I use UPPERCASE to identify these in my code
+
+DATASET_FILENAME <- "UCI-G.csv" #Name of input dataset file
+OUTPUT_FIELD <- "Status" # Field name of the output class to predict
+
+# These are the data preparation values
+
+HOLDOUT <- 70 # % split to create TRAIN dataset
+
+# Cutoff values - you can experiment with these
+
+CUTOFF_OUTLIER <- 0.99 # Confidence p-value for outlier detection
+ # negative = analyse but do not replace outliers
+CUTOFF_DISCRETE <- 6 # Number of empty bins to determine discrete
+CUTOFF_REDUNDANT <- 0.95 # Linear correlation coefficient cut-off
+
+# Indicates the type of each field
+
+TYPE_DISCRETE <- "DISCRETE" # field is discrete (numeric)
+TYPE_ORDINAL <- "ORDINAL" # field is continuous numeric
+TYPE_SYMBOLIC <- "SYMBOLIC" # field is a string
+TYPE_NUMERIC <- "NUMERIC" # field is initially a numeric
+TYPE_IGNORE <- "IGNORE" # field is not encoded
+
+MAX_LITERALS <- 55 # Maximum numner of 1-hot-encoding fields
+
+# These are the supervised model constants
+
+PDF_FILENAME <- "tree.pdf" # Name of PDF with graphical tree diagram
+RULES_FILENAME <- "rules.txt" # Name of text file with rules saved
+RESULTS_FILENAME <- "results.csv" # Name of the CSV results file
+NODE_LEVEL <- 1 # The number is the node level of the tree to print
+BOOST <- 20 # Number of boosting iterations. 1=single model
+FOREST_SIZE <- 1000 # Number of trees in the forest
+SCALE_DATASET <- TRUE # Set to true to scale dataset before ML stage
+
+BASICNN_HIDDEN <- 5 # 10 hidden layer neurons
+BASICNN_EPOCHS <- 100 # Maximum number of training epocs
+
+# See https://cran.r-project.org/web/packages/h2o/h2o.pdf
+
+DEEP_HIDDEN <- c(5,5) # Number of neurons in each layer
+DEEP_STOPPING <- 2 # Number of times no improvement before stop
+DEEP_TOLERANCE <- 0.01 # Error threshold
+DEEP_ACTIVATION <- "TanhWithDropout" # Non-linear activation function
+DEEP_REPRODUCABLE <- TRUE # Set to TRUE to test training is same for each run
+
+# Define and then load the libraries used in this project
+
+# Library from CRAN Version
+# pacman 0.5.1
+# outliers 0.14
+# corrplot 0.84
+# MASS 7.3.53
+# formattable 0.2.0.1
+# stats 4.0.3
+# PerformanceAnalytics 2.0.4
+# stringr 1.4.0
+# partykit 1.2.8
+# C50 0.1.3.1
+# randomForest 4.6.14
+# h2o 3.32.0.1
+# keras 2.3.0.0
+
+MYLIBRARIES<-c("outliers",
+ "corrplot",
+ "MASS",
+ "formattable",
+ "stats",
+ "caret",
+ "PerformanceAnalytics",
+ "stringr",
+ "partykit",
+ "C50",
+ "randomForest",
+ "keras",
+ "h2o")
+
+# User defined functions are next
+# ************************************************
+# simpleDT() :
+#
+# Create C5 Decision Tree on the raw dataset
+# A decision tree may not need the dataset to be pre-processed
+#
+# INPUT :
+# Data Frame - train - original train dataset
+# Data Frame - test - original test dataset
+# boolean - plot - TRUE = plot charts
+#
+# OUTPUT :
+# : Data Frame - measures - performance metrics
+# **
+simpleDT<-function(train,test,plot=TRUE){
+
+ positionClassOutput<-which(names(train)==OUTPUT_FIELD)
+ tree<-C50::C5.0(x=train[,-positionClassOutput],
+ y=factor(train[,positionClassOutput]),
+ rules=TRUE,
+ trials=1)
+ return(tree)
+} #endof simpleDT()
+
+
+
+
+# ************************************************
+# main() :
+#
+# Entry point to execute your data analytics
+#
+# INPUT: None
+#
+# OUTPUT :None
+# ************************************************
+main<-function(){
+
+ loans<-NreadDataset(DATASET_FILENAME)
+ original<-NConvertClass(loans)
+ original<-NPREPROCESSING_splitdataset(original)
+ measures<-simpleDT(original$train,original$test)
+ print(summary(measures))
+
+ allResults<-NULL
+ # write the code to answer the questions in 03 Lab4.docx
+
+ # Write the code to read in a CSV file with the name given in DATASET_FILENAME
+ # German Credit Score dataset
+
+
+} #end of main()
+
+
+print("Tree plotted in PDF file")
+
+# ************************************************
+# This is where R starts execution
+
+gc() # garbage collection to automatically release memory
+
+# clear plots and other graphics
+if(!is.null(dev.list())) dev.off()
+graphics.off()
+
+# clears the console area
+cat("\014")
+
+print("START Supervised Machine Learning")
+
+library(pacman)
+pacman::p_load(char=MYLIBRARIES,install=TRUE,character.only=TRUE)
+
+#Load additional R script files provide for this lab
+source("lab4DataPrepNew.R")
+source("4labFunctions.R")
+
+set.seed(123)
+
+# ************************************************
+main()
+
+print("end")
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..fdddb29aa445bf3d6a5d843d6dd77e10a9f99657
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,24 @@
+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or
+distribute this software, either in source code form or as a compiled
+binary, for any purpose, commercial or non-commercial, and by any
+means.
+
+In jurisdictions that recognize copyright laws, the author or authors
+of this software dedicate any and all copyright interest in the
+software to the public domain. We make this dedication for the benefit
+of the public at large and to the detriment of our heirs and
+successors. We intend this dedication to be an overt act of
+relinquishment in perpetuity of all present and future rights to this
+software under copyright law.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
+
+For more information, please refer to <https://unlicense.org>
diff --git a/My_code/.gitkeep b/My_code/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/New_file b/New_file
new file mode 100644
index 0000000000000000000000000000000000000000..8f400a07355720395dfa73ebaafdd7d53b9593ba
--- /dev/null
+++ b/New_file
@@ -0,0 +1,4 @@
+welcome to
+advance web technology lab
+@university of surrey
+hi there is an updation