Hi,I am a working professional taking an online part time course in data science.I have got a...

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Hi,I am a working professional taking an online part time course in data science.I have got a project due on Saturday morning Singapore time. It is about CART model and decision tree.Working code is already provided;however I am running into RStudio issues (the console keeps asking to terminate itself when I try to build the model). It is a small project which normally takes an hour or two to complete, and I've done similar assignments before but the issue in Rstudio never happened like this time.I've attached the error message and my working code before the message showed up for yourreference.Will appreciate any expert advice and help on this.Project -4  Personal Loan Campaign Problem                          Business Scenario   • The data provided is from a Personal Loans Campaign  executed by MyBank.        • 20000 customers were targeted with an offer of Personal  Loans at 10% interest rate.        • 2512 customers out of 20000 responded expressing their  need for Personal Loan; These customers are labelled as  Target = 1 and remaining customers are labelled as Target =  0  Data dictionary      Column Name Description    CUST_ID Customer ID - Unique ID         TARGET  Target Field - 1: Responder, 0: Non- Responder      AGE Age of the customer in years         GENDER Gender   BALANCE Average Monthly Balance         OCCUPATION Occupation   AGE_BKT Age Bucket         SCR Generic Marketing Score         HOLDING_PERIOD  Ability to hold money in the account (Range 0 - 31)      ACC_TYPE Account Type - Saving / Current         ACC_OP_DATE Account Open Date         LEN_OF_RLTN_IN_MNT Length of Relationship in Months    H     NO_OF_L_CR_TXNS No. of Credit Transactions         NO_OF_L_DR_TXNS No. of Debit Transactions         TOT_NO_OF_L_TXNS Total No. of Transaction         NO_OF_BR_CSH_WDL_D No. of Branch Cash Withdrawal Transactions    R_TXNS     NO_OF_ATM_DR_TXNS No. of ATM Debit Transactions         NO_OF_NET_DR_TXNS No. of Net Debit Transactions         NO_OF_MOB_DR_TXNS No. of Mobile Banking Debit Transactions            Column Name Description    FLG_HAS_CC Has Credit Card - 1: Yes, 0: No         AMT_ATM_DR Amount Withdrawn from ATM         AMT_BR_CSH_WDL_DR Amount cash withdrawn from Branch         AMT_CHQ_DR Amount debited by Cheque Transactions   AMT_NET_DR Amount debited by Net Transactions         AMT_MOB_DR  Amount debited by Mobile Banking Transactions      AMT_L_DR Total Amount Debited         FLG_HAS_ANY_CHGS Has any banking charges   AMT_OTH_BK_ATM_US Amount charged by way of the Other Bank    G_CHGS ATM usage    AMT_MIN_BAL_NMC_C Amount charged by way Minimum Balance    HGS not maintained    NO_OF_IW_CHQ_BNC_T Amount charged by way Inward Cheque    XNS Bounce    NO_OF_OW_CHQ_BNC_ Amount charged by way Outward Cheque    TXNS Bounce    AVG_AMT_PER_ATM_TX Avg. Amt withdrawn per ATM Transaction    N     AVG_AMT_PER_CSH_W Avg. Amt withdrawn per Cash Withdrawal    DL_TXN Transaction    AVG_AMT_PER_CHQ_TX Avg. Amt debited per Cheque Transaction    N     AVG_AMT_PER_NET_TX Avg. Amt debited per Net Transaction    N     AVG_AMT_PER_MOB_T Avg. Amt debited per Mobile Banking          Part 1 - Classification Tree      • Split data into Development (70%) and Hold-out (30%)  Sample   • Build Classification Tree using CART technique  • Do necessary pruning   • Measure Model Performance on Development  Sample   • Test Model Performance on Hold Out Sample  • Ensure the model is not an overfit model      Part 2 - Random Forest      • Split data into Development (70%) and Hold-out  (30%) Sample   • Build Model using Random Forest technique   • Measure Model Performance on Development  Sample   • Test Model Performance on Hold Out Sample  • Ensure the model is not an overfit model     •  Compare the 2 Models’ Performance  – CART  – Random Forest       • Ensemble Model – Create Ensemble Model  based on the output of the above 3 models       • Compare the Ensemble Model performance  with individual model.     --- title: "Bank_Personal_Loan" author: "Neha Tyagi" date: "June 14, 2019" output:   word_document: default   pdf_document: default   html_document:     df_print: paged --- ## Bank_Personal_Loan_Modelling Context:  bank (Thera Bank) which has a growing customer base.  Liability customers (depositors) with varying size of deposits - majority Asset customers  (borrowers) - small Objective:  Convert liability customers to personal loan customers (while retaining them as depositors).  Task: Build a model identifying the potential customers who have higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign. Historical Data: A campaign from last year- liability customers showed a healthy conversion rate of over 9% success.  Data on 5000 customers including    customer demographic information (age, income, etc.),    customer's relationship with the bank (mortgage, securities account, etc.)   customer response to the last personal loan campaign (Personal Loan).  Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign. # Understanding the attributes - Find relationship between different attributes (Independent variables) and choose carefully which all attributes have to be a part of the analysis and why #  Some Charts and Graphs to show case the relationship between Independent and Dependent Variables # Exploratory Data Analysis #  Splitting data in Train and Test dataset # Model Development (Any one of the below techniques to be used) #   o Random Forest #   o CART # Model Performance Measures # Validation of Model # Model Performance on Hold Out Sample # STEP1: IMPORT AND PRERARE DATA ```{r} library(readxl) data %    dplyr::rename(     Income = `Income (in K/month)` ,     Age = `Age (in years)`,     Experience = `Experience (in years)`,     ZIP.Code = `ZIP Code`,     Family.members = `Family members`,     Personal.Loan = `Personal Loan` ,     CD.Account = `CD Account`,     Securities.Account = `Securities Account`,     Credit.Card = `CreditCard`     ) names(thera.data) # Method 2 names(thera.data)[names(thera.data)=="Age..in.years."] =0) # Lets check summary again summary(thera.data) ``` # Check for Zero variance/Near Zero variance ```{r} # install.packages("caret") library(caret) nsv

Hi, I am a working professional taking an online part time course in data science.I have got a project due on Saturday morning Singapore time. It is about CART model and decision tree. Working code is...

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