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1. Perform a descriptive analysis of the data. (10 marks) 1. Use columns that contain numerical data, except sr no and ID. (7 marks) 2. Take up 3 non-numerical columns and share your inferences on the distribution of data. (3 marks) 2. Analyse the relationship between the type of insurance, payment type and months since the last delinquency. (10 Marks) 1. Creating a table that shows the relationship between the three variables. (6 marks) 2. Inferences from the table. (2*2 marks) 3. Analyse the relationship between policy lapsed, loan amount, type of insurance and annual income (10 Marks) 1. Creating (a) table(s) that shows the relationship between the four variables. (6 marks) 2. Inferences from the table. (4+1 marks) 4. Relationship between policy lapsed, payment type and city. (10 Marks 1. Creating a table that shows the relationship between the three variables. (6 marks) 2. Inferences from the table. (2*2 marks) The next segment contains the submission template. Make sure you compile all your files in the Equip Tape of insurance Months Since Last Delienquency Annual Income (In lakhs) Number of Children City SR mo Gender Married Loan Availed Loan Amount Credit Score Currently in Job ID 11500001 Child Mumbai 489 37533821 Y N Equip Mumbai 16849231 Y IN 2110000 453 10 Whole life N Y 32121327|M 0 Banglore 5031 1400000 3820000 Whole life Y Y 4251978 M 4020000 2 Pune 6200000 461Y Term 5170808 M IN Y Pune 10 5490000 675 Y 61327810 M Y Child 504 Y 8200000 5010000 Pune Term N 79516876|M O Mumbai 8500000 IY 5300000 538 N 82330817 F 3300000 Y 530 N 4 Equip 4290000 4 Banglore 96243008 M N Y 618 Y 4620000 4 Equip 4 Delhi 106276215 F Y N 0 610 4380000 2 Pune 21 Term 111178615|M Y Y 8100000 596 3700000 4 Delhi Whole life 129310813F N 9800000 5521 5330000 0 Delhi 4 Whole life 13 5299380 M Y Y 8100000 641 5860000 4 Delhi ol Term 147782633|M Y JY 600000 556 N 4130000 4 Delhi 4 Equip 15 3557816|M N IN 0 620 5440000 Mumbai 0 Term 16 4311014 Y IN 10 644 2380000 Delhi Whole life 178577438 M IY Y 1200000 665N 4790000 Pune 6 Equip 18 1059058 F Y Y 2500000 661 N 4150000 Pune 4 Term 19 7635453 M Y Y 4100000 592 N 14600001 Den 4 Term 20 2495652 M IN N 10 4144 2040000 0 Dell Z Equip 21 569082 M IN 2900000 5171 4510000 0 Delhi O Equip 22 9361166 F N 5300000 641 N 3430000 0 Pune 23 9957238 M IN 8900000 636 N 1980000 0 Banglore Term 24 3868540 M Y Y 1700000 456 N 3440000 4 Delhi Whole life 25 486458 0 528 N 4570000 1 Delhi CHID 26 50493230 IN IN 0 447Y 5780000 0 Mumbai Whole life 27 93982421 Y IN 0 698 2870000 3 Pune Child 28 7634354 M IN N 0 446 N 2630000 0 Pune 29 3534631 F 1 Equip Y Y 3900000 4831 4190000 4 Banglore 30 8955376 M N N 10 529 3920000 0 Pune Sl Whole life 31 5160928 M IN IY 8300000 4221 3240000 01 Delhi 52 4535170 M IN 6300000 8 Equip 626| N 48000001 0 Delhi 33 6006086E IN 2200000 Whole life 420 N 3700000 34 4280548 F 0 Delhi IN Y 600000 Whole life 423 N 4220000 0 Pune 35 4426961 M Y sl whole life IN 0 520 N 1790000 366139217 N Mumbai Y IN 10 Term 435 Y 2430000 37 181477OE IN Pune Y 3900000 621 N Equip 3010000 38 781029 M IN 3800000 Banglore 663Y 8 Whole life 39 1957047 M 1380000 TY 1000000 Pune 627 N 4 Term 1360000 Pune 40 2345081F 8000000 591 Y 2370000 Tern 41556305716 N Pune IN 10 Child 429907591 M 2810000 IN Mumbai 3000000 5681N 2430000 Term 435959971M Y 5600000 o Delhi 676 N 6 Whole life 443096095M Pune IN 10 513 Y 1390000 SEquip 457124070 M JY Banglore 433 N Whole life 466874280 F 2730000 Delhi 8800000 6481 5640000 477122405E N 686 Banglore 1690000 6 Whole life 48 8887435 M IN 7800000 2 Mumbal 589 19 3431839 F 4 Whole life 1710000 IN 0 Delhi 0 525 35200001 4 Banglore 1 Equip 4. Term Tenure Payment type Policy Initiated Date Policy amount Policy lapa 17000000 YES 6/3/2015 15 Monthly 1/25/2015 57000000 NO 8 Monthly 133000000 NO 10/28/2018 70 Yearly 9/24/2014 53000000 NO 70 Monthly 7/5/2010 211000000 YES 75 Yearly 15 Monthly 4/12/2018 127000000 NO 249000000 NO 10/1/2015 77|Quaterly 14 Yearly 11/17/2018 205000000 YES 48000000 YES 2/27/2019 15 Yearly 80 Yearly 11/2014 209000000|NO 8/8/2017 75 Monthl 27000001 YES 80 Monthly 12/13/2014 125000000 YES 82 Quaterle 6/22/2016 205000000 YES 10/5/2013 187000000 NO 13 Yearly 76 Yearly 7722/2017 44000000 NO 65 Quaterly 4/1/2017 97000000 YES 11 Yearly 3/7/2010 111000000 NO 78 Monthly 3/12/2011 46000000 NO 82 Monthly 10/20/2017 41000000 YES 12 Monthly 11:18/2019 88000000 YES 9 Yearly 9/30/2014 31000000 YES 11 Monthly 10/8/2019 141000000 YES 79 Yearly 5/16/2018 88000000 NO 70 Monthly 6/24/2013 168000000/YES 18 Monthly 2/18/2019 155000000 NO 60 Monthly 17/2011 136000000 NO 16 Yearly 5/29/2011 49000000) NO 10 Quaterly 11/10/2010 68000000) YES 13 Quater 11/3/2010 57000000 NO 65 Yearly 4/2/2018 195000000 YES 11 Monthly 12/19/2016 147000000 NO 60 Monthly 7717/2010 97000000 YES 80 Yearly 7/27/2019 25000000 YES 70 Quaterbo 5/19/2015 181000000 NO 76 Yearly 314/2012 25000000 YES 14 Yearly 4/15/2015 67000000 NO 65 Quaterly 12/212019 36000000 NO 81 Yearly 12/14/2013 26000000 Na 75 Quaterly 2/11/2015 28000000 YES 17 Monthly 5/23/2016 76000000 YES 76 Yearly 9/14/2017 101000000 NO 60 Quaterly 4717/2012 57000000 YES 8/20/2015 52000000 NO 65 Quaterly 7/24/2014 19000000 YES 6 Monthly 2/11 2012 45000000 YES 60 Quaterbi 2/13/2010 278000000 YES 65 Quater 1/29/2011 69000000 YES 8/17/2016 63000000 NO 80 Quaterly 2/28/2014 82000000 NO 3 Equip 579 N Y 5930000 8 Quaterly 5 Equip Yearly Type here to search 34C Save Data Review View Formulas Help Home Insert Draw Page Layout Insert X U A A General $ % Calibri 9X Delete v 11 Conditional Formatting Format as Table Cell Styles ||||| Format Paste IM . 40 00 000 AXP ernal Cells Voltage Encryption Voltage Number IS Styles Font K 2 Alignment 2 Sifications Clipboard X fx Pune 19 R M J 1 H F G 0 6300000 129/2019 525 489 590 N 555 4421N 10 5045 6700000 10 700000 0 7700000 0 3520000 2360000 4500000 4130000 4840000 1040000 5500000 39400001 4970000 3500000 5240000 1490000 2430000 1110000 1650000 3500000 3340000 2100000 4 Banglore Delhi 0 Banglore 0 Mumbai 4 Mumbai o Mumbai Den Mumbai Mumbai 2 Mumbai 0 Pune O Banglore OBShalore 1 Pune Banglore 0 Banglore 4 Delhi 2 Pune o Den 41 Term 0 Child 1 Term | Equip 8 Whole life Term Child Whole life Whole life Child Whole life Term Whole life Team Equip 800000 8800000 Whole life 4600000 2000000 2110000 3100000 3 Pune 10 3880000 5800000 1600000 . 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Format Paste 20 B I U V AXP Internal Voltage Encryption Cells Number Styles Font Alignment 2 Classifications Voltage Clipboard X . fo Pune R $ T K19 U M G H F 9400000 0 689 Y 421 605 584 570 5351 633 N Banglore Banglore Banglore Delhi Banglote 3400000 4 Banglore 3400000 5900000 5010000 2480000 1840000 2320000 4220000 4130000 16000001 1610000 2940000 1070000 4860000 5760000 1380000 481 N N O 14 Quaterly 17 Yearly 9 Quatedly 16 Quaterly 7 Quaterly 77 Quaters Yeally 17 Quaterly 18 Quaterlo 9 Monthly 10 Yearly 75 Yeah 60 Monthly 10 Monthly 15 Quatre 16 Monthly Yearly $6 Month 4300000 8000000 6800000 Banglore Delhi 1 Mumba O Banglore Pune Delhi Pune 678|N 4441 473Y Equip Child Equip Chid Equip 2 Term 0 Equip Child Child EQUIP Equip 6 Term B Whole life 2 Equip SIEU Chile Tern Whole e Term Term Equip 509 N 2000000 2750000 18700000 Ol Yearbe 4800000 10 9900000 4900000 8700000 3400000 BA00000 510 N 6235 537 N 6051 652 N 617 438 565 508 N 632 N 465 N 4031 569 5250 5261 4700000 2630000 1360000 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2260000 3900000 P 5/22/2015 258000000 NO 379/2017 66000000 YES 1917/2013 120000000 NG 2/18/2018 84000000 YE 2/17/2012 98000000 YES 6/12/2019 109000000 YES 9/22/2016 150000000 NO 10/30/2010 78000000 YES 7716/2017 31000000 NO 7/12/2019 25000000 NG 2/10/2012 7000000 NO 11/5/2018 103000000 NO 312712019 241000000 YES 9/16/2014 42000000 YES 10/6/2019 21000000 YE 127212016 107000000 YE 6/14/2013 124000000 YEE 571/2013 42000000 NO 5124/2014 76000000 NO 7/30/2017 55000000 NC 8/7/20191 197000000 YES 525/2019 13000000 NO 1/1/2020 201000000 YES 1126/2011 47000000 YES 8/24/2012 127000000 YE 7730/2013 40000000 YE 5/23/2016 26000000 NO 9/17/2013 17000000 YES 8/24/2011 209000000 YES 12/2015 49000000 NC V29/2016 50000000 NO 3/11/2016 138000000 NO 37000000 YE 12/7/2016 20000000 N 05000000 YES 3/19/2014 71000000 NO 4/8/2011 18000000 VE 203000000 YE 7/03/2018 49000000 YE YE 771201 TOO0000 NO 2/27/2013 239000000 NG 12/5/2010 1000000 3737200 20000000 YE 12/2016 20000000 YE TOV200 210000000 YE 11/5/2016 SODODO NO 7212017 SODODO NO 19/2017 20000000 YE 7/16/2011 DE0000 YES 8/8/2010 NO 724/2010 151000000 NE 05/200 2000 Om 2000OOOTING 1 Delhi O Banglore Mumbai Pune | Bangloe 4 Pune Delhi 0 Banglot 4 Pune 10 85 Ouatory EN 7800000 545 N 6971 512 N 656N 574 20151 8300000 3700000 Mba Team Term Equip 4 Equip 3 Term Whole Whole e Term 6 Whole life 622N 9700000 3160000 1940000 232E 489Y 534 V 1600000 Mumbai Banglore Mumba Banglore 0 Banglore Delhi Banglore 10 10 10 V13/2017 SON 77 Monti 77 Yearly 78 Quaters 12 Monthly 10 Monthly 75 Month 25 Yearby 80 Quately 00 Monte 30 Quater 64 Quaters 65 Monthly 65 Yey 05 Quatu 6 Monthly Yearly 78 Year 79 Month 7 Quatery MON Year 11 Month 15 Quatarly 5240000 4750000 5730000 2840000 5310000 5390000 D00000 27000001 5700000 4900000 O Mumbai 101 121776 Vhole luke Vhotels Term Equip Whole Term 4 Pune Banglore Pune 7700000 9100000 145 435 N 4201 463 6490 SON 654 N 400 4151 638 41 N SON 536N 441N 9Y SSA 0000000 O Mumbai Mumba OD Equip DEU 1320000 10 3100000 1350000 Bangla 10000000 2 Banglore TO Y 17300000 Wholela TADO TOT Sheet1 + Ready 34C D RI C Type here to search Formulas Data Review Insert Draw Insert Page Layout Home File General v 2X Delete ' ' v 11 = Calibri $ v % , Conditional Formatting Format as Table A Cell Styles V Format V V ML 60 00 A Paste V Cells AXP Internal Voltage Encryption Styles Number 2 Alignment Font Classifications Voltage Clipboard fx Pune M K19 J G F 0 10 0 7300000 10 0 2200000 8200000 1800000 8500000 2600000 7400000 10 7700000 10 10 3900000 900000 9500000 12800000 4600000 D E 149 148 120034 M N IN 150 149 9466922M Y N 151 1502910442 M Y N 152 151 38962201F Y Y 153 152, 5823969 F N N 154 153 8251701 M N N 154 874264 156 155 24856761 IN 157 156 54170381 Y 158 157 3371281 M Y 153 158 6726748 IY 160 159 125176 Y 161 150 8048119 M Y N 162 161) 5571919 Y N 163 162 1278691 Y N 164 163 875180 M Y N 165 1646676783 IN N 166 165 3511219 IN 167 156 1220568 M N IN 160 167) 7340144 M N 165 168 4584953 M N Y 170 16956717706 Y Y 171 170) 1598592F Y 112 171 6002059 M IN 179 1721 7723699 M IN 114 1731 4023784 M IN N 115 174 9045411 M N N ITE 175 8574680F Y IN 176 838703F 14 11 1771389469 M Y 113 17831154697 IN 180 1799487391 Y IN 151 1804398591 M IN 182 181) 8316331F Y N 1828773550 M Y IY 104 183 2751389 N 185 184 141640E Y IN 156 1856289730 F Y 196 9398332 M Y N 18 1879972539 M 199 1887479233 N 130 189 1336482 M N 130 7354223 F N 192 191 9557368 M Y Y 192 4983970 F IN Y 154 193996381 M Y 195 1944648469F N N 13 135758641 M N Y 1ST 195 2125360M Y Y 19728088827 IN 139 94511521M N N 200 19953392500 N 201 2003141065 M IN H 418 N 560 N 636 N 441 N 489 Y 554 Y 621 Y 594 N 622 N 550 N 62314 427 451 N 505Y 479 5981 570 645 504 N 659 501 679 591 528 453 644 620 6231 5964 5137 685 N 632) 423 N 650 N 441N 524 Y 536 N 629 650 Y 449 N 532 N 652 N 646 555 N 445 N 576 N 589 N 638 N 4241 526 Y 619 N 475 N 683Y DELLA ALLERLEI 3100000 3350000 2530000 4640000 4830000 1700000 1500000 4550000 2410000 3700000 4070000 5210000 2620000 3080000 4180000 5210000 1010000 5920000 3220000 1840000 2270000 2550000 5610000 5110000 5020000 4560000 1150000 4210000 4000000 4720000 1100000 3160000 4080000 1740000 2110000 3320000 2270000 5190000 2490000 5760000 4370000 3140000 5450000 5450000 4200000 4280000 4710000 3560000 3730000 1960000 3540000 4780000 5390000 Delhi 4 Banglore 2 Banglore 1 Pune 0 Delhi 0 Banglore 1 Pune 0 Pune 4 Delhi 4 Banglore umbal 4 Pune 1 Mumbai Mumbai 4 Pune 2 Banglore 0 Mumbai 0 Banglore 01 Delhi Mumbai 0 Pune Pune Banglore 01 Delhi Pune Pune 0 Banglore Pune Delhi Banglore Pune Pune Pune Punc 2 Pune 0 Delhi 4 Pune 1 Mumbai Murnal 21 Delhi 0 Pune 0 Equip 0 Term 8 Equip 4 Child 4 Whole life 5 Whole life 6 Child Equip o Whole life O Child Whole life Term 8 Equip Equip 4 Child 2 Whole life 6 Term Equip 6 Equip 5 Term 5 Whole life 8 Whole life 3 Equip Term Term Term 0 Whole life o Whole life 8 Whole life & Whole life Whole life Term 2 Child 5 Child 6 Child 8 Term Child 3 Equip 6 Term Whole life Term Term 2 Term 8) Term Term Whole life 4 Whole life 6 Equip 1 Term 4 Equip 6 Whole life 3 Whole life N 0 15 Monthly 81 Yearly 11 Monthly 15 Quaterly 70 Yearly 70 Yearly 17 Quaterly 7 Monthly 65 Quaterly 18 Monthly 60 Monthly 75 Quaterly 10 Yearly 12 Monthly 15 Yearly 60 Yearly 82 Yearly 6 Quaterly 5 Quaterly 83 Quaterly 80 Monthly 75 Quaterly 14 Yearly 82 Quaterly 82 Quaterly 77 Monthly 80 Monthl 70 Monthly 80 Quaterly 80 Quaterly 70 Quaterly 76 Quaterly 15 Yearly 17 Quaterli! 15 Yearly 85 Monthly 16 Yearly 8 Yearly 76 Yearly 80 Monthly 85 Quaterly 85 Monthly 76 Yearly 82 Quaterly 81 Yearly 60 Monthly 65 Quaterly 6 Yearly 75 Monthly 13 Quaterly 60 Monthly 65 Quaterlig TI Quaterly P 9/9/2016 28000000 YES 7/16/2011 131000000 YES 8/9/2018 40000000 NO 3124/2010 151000000 NO 3/5/2013 212000000 NO 874/2011 20000000 NO 1/13/2016 44000000 NO 1/28/2016 159000000 YES 8/4/2015 30000000 YES 9/13/2014 72000000 NO 2/14/2015 181000000 YES 3/5/2019 28000000 NO 1/25/2015 360000 YES 817/2018 144000000 YES 11/13/2018 134000000 YES 1/16/2011 40000000 YES 9/9/2010 33000000 YES 9/16/2014 257000000 YES 6716/2017 20000000 NO 7/16/2014 78000000 YES 19/2/2013 92000000 YES 9/22/2014 71000000 NO 5/14/2011 56000000 NO 11/8/2014 239000000 YES 8/24/2015 150000000 YES 7/21/2015 149000000 NO 8/26/2018 19000000 NO 417/2018 188000000 YES 8214/2011 95000000 NO 11/2/2019 175000000 YES 4/22/2011 19000000 NO 6/17/2018 26000000 YES 1/20/2011 201000000 NO 3/2/2016 28000000 YES 57272016 15000000 NO 3/29/2013 61000000 NO 2/23/2016 57000000 YES 7717/2012 197000000 NO 5/19/2014 98000000 NO 4/12/2014 111000000 NO 12/10/2015 91000000 YES 6/20/2012 62000000 NO 975/2011 237000000 YES 4/2/2015 44000000 YES 2/12/2016 23000000 YES 9/10/2016 61000000 YES 776/2012 179000000 NO 3/23/2013 85000000 NO 18/2019 129000000 NO 4729/2014 79000000 YES 1712014 152000000 NO 123/2013) 206000000 NO 7/17/2014 215000000 NO 8900000 4600000 10 4500000 3600000 2400000 Det 6000000 2700000 4900000 2200000 2300000 3300000 o Delhi 4 Delhi O Delhi 4 Banglore o Delhi Banglore Mumbai Mumbai Delhi Mumba 0 Mumbai 1000000 5900000 6500000 6600000 2800000 Sheet1 E You are part of the analytics team for InsuranceCo. that deals with insurance products. The company offers four types of insurance: Child, Equip, Term and Whole Life, each customised according to the customers' needs. The customisation depends on multiple factors such as marital status, income, credit score and the city the customer is currently residing in. Now, you have been tasked with understanding the relationships between the data points. The attached file contains the data points that you will use in your analysis. The file also contains the data dictionary that highlights the column details. X Insurance Data + Now, based on the data that you ha been provided, address the following 1. Perform a descriptive analysis of the data. (10 marks) 1. Use columns that contain numerical data, except sr no and ID. (7 marks) 2. Take up 3 non-numerical columns and share your inferences on the distribution of data. (3 marks) 2. Analyse the relationship between the type of insurance, payment type and months since the last delinquency. (10 Marks) 1. Creating a table that shows the relationship between the three variables. (6 marks) 2. Inferences from the table. (2*2 marks) 3. Analyse the relationship between policy lapsed, loan amount, type of insurance and annual income (10 Marks) 1. Creating (a) table(s) that shows the relationship between the four variables. (6 marks) 2. Inferences from the table. (4+1 marks) 4. Relationship between policy lapsed, payment type and city. (10 Marks 1. Creating a table that shows the relationship between the three variables. (6 marks) 2. Inferences from the table. (2*2 marks) The next segment contains the submission template. Make sure you compile all your files in the Equip Tape of insurance Months Since Last Delienquency Annual Income (In lakhs) Number of Children City SR mo Gender Married Loan Availed Loan Amount Credit Score Currently in Job ID 11500001 Child Mumbai 489 37533821 Y N Equip Mumbai 16849231 Y IN 2110000 453 10 Whole life N Y 32121327|M 0 Banglore 5031 1400000 3820000 Whole life Y Y 4251978 M 4020000 2 Pune 6200000 461Y Term 5170808 M IN Y Pune 10 5490000 675 Y 61327810 M Y Child 504 Y 8200000 5010000 Pune Term N 79516876|M O Mumbai 8500000 IY 5300000 538 N 82330817 F 3300000 Y 530 N 4 Equip 4290000 4 Banglore 96243008 M N Y 618 Y 4620000 4 Equip 4 Delhi 106276215 F Y N 0 610 4380000 2 Pune 21 Term 111178615|M Y Y 8100000 596 3700000 4 Delhi Whole life 129310813F N 9800000 5521 5330000 0 Delhi 4 Whole life 13 5299380 M Y Y 8100000 641 5860000 4 Delhi ol Term 147782633|M Y JY 600000 556 N 4130000 4 Delhi 4 Equip 15 3557816|M N IN 0 620 5440000 Mumbai 0 Term 16 4311014 Y IN 10 644 2380000 Delhi Whole life 178577438 M IY Y 1200000 665N 4790000 Pune 6 Equip 18 1059058 F Y Y 2500000 661 N 4150000 Pune 4 Term 19 7635453 M Y Y 4100000 592 N 14600001 Den 4 Term 20 2495652 M IN N 10 4144 2040000 0 Dell Z Equip 21 569082 M IN 2900000 5171 4510000 0 Delhi O Equip 22 9361166 F N 5300000 641 N 3430000 0 Pune 23 9957238 M IN 8900000 636 N 1980000 0 Banglore Term 24 3868540 M Y Y 1700000 456 N 3440000 4 Delhi Whole life 25 486458 0 528 N 4570000 1 Delhi CHID 26 50493230 IN IN 0 447Y 5780000 0 Mumbai Whole life 27 93982421 Y IN 0 698 2870000 3 Pune Child 28 7634354 M IN N 0 446 N 2630000 0 Pune 29 3534631 F 1 Equip Y Y 3900000 4831 4190000 4 Banglore 30 8955376 M N N 10 529 3920000 0 Pune Sl Whole life 31 5160928 M IN IY 8300000 4221 3240000 01 Delhi 52 4535170 M IN 6300000 8 Equip 626| N 48000001 0 Delhi 33 6006086E IN 2200000 Whole life 420 N 3700000 34 4280548 F 0 Delhi IN Y 600000 Whole life 423 N 4220000 0 Pune 35 4426961 M Y sl whole life IN 0 520 N 1790000 366139217 N Mumbai Y IN 10 Term 435 Y 2430000 37 181477OE IN Pune Y 3900000 621 N Equip 3010000 38 781029 M IN 3800000 Banglore 663Y 8 Whole life 39 1957047 M 1380000 TY 1000000 Pune 627 N 4 Term 1360000 Pune 40 2345081F 8000000 591 Y 2370000 Tern 41556305716 N Pune IN 10 Child 429907591 M 2810000 IN Mumbai 3000000 5681N 2430000 Term 435959971M Y 5600000 o Delhi 676 N 6 Whole life 443096095M Pune IN 10 513 Y 1390000 SEquip 457124070 M JY Banglore 433 N Whole life 466874280 F 2730000 Delhi 8800000 6481 5640000 477122405E N 686 Banglore 1690000 6 Whole life 48 8887435 M IN 7800000 2 Mumbal 589 19 3431839 F 4 Whole life 1710000 IN 0 Delhi 0 525 35200001 4 Banglore 1 Equip 4. Term Tenure Payment type Policy Initiated Date Policy amount Policy lapa 17000000 YES 6/3/2015 15 Monthly 1/25/2015 57000000 NO 8 Monthly 133000000 NO 10/28/2018 70 Yearly 9/24/2014 53000000 NO 70 Monthly 7/5/2010 211000000 YES 75 Yearly 15 Monthly 4/12/2018 127000000 NO 249000000 NO 10/1/2015 77|Quaterly 14 Yearly 11/17/2018 205000000 YES 48000000 YES 2/27/2019 15 Yearly 80 Yearly 11/2014 209000000|NO 8/8/2017 75 Monthl 27000001 YES 80 Monthly 12/13/2014 125000000 YES 82 Quaterle 6/22/2016 205000000 YES 10/5/2013 187000000 NO 13 Yearly 76 Yearly 7722/2017 44000000 NO 65 Quaterly 4/1/2017 97000000 YES 11 Yearly 3/7/2010 111000000 NO 78 Monthly 3/12/2011 46000000 NO 82 Monthly 10/20/2017 41000000 YES 12 Monthly 11:18/2019 88000000 YES 9 Yearly 9/30/2014 31000000 YES 11 Monthly 10/8/2019 141000000 YES 79 Yearly 5/16/2018 88000000 NO 70 Monthly 6/24/2013 168000000/YES 18 Monthly 2/18/2019 155000000 NO 60 Monthly 17/2011 136000000 NO 16 Yearly 5/29/2011 49000000) NO 10 Quaterly 11/10/2010 68000000) YES 13 Quater 11/3/2010 57000000 NO 65 Yearly 4/2/2018 195000000 YES 11 Monthly 12/19/2016 147000000 NO 60 Monthly 7717/2010 97000000 YES 80 Yearly 7/27/2019 25000000 YES 70 Quaterbo 5/19/2015 181000000 NO 76 Yearly 314/2012 25000000 YES 14 Yearly 4/15/2015 67000000 NO 65 Quaterly 12/212019 36000000 NO 81 Yearly 12/14/2013 26000000 Na 75 Quaterly 2/11/2015 28000000 YES 17 Monthly 5/23/2016 76000000 YES 76 Yearly 9/14/2017 101000000 NO 60 Quaterly 4717/2012 57000000 YES 8/20/2015 52000000 NO 65 Quaterly 7/24/2014 19000000 YES 6 Monthly 2/11 2012 45000000 YES 60 Quaterbi 2/13/2010 278000000 YES 65 Quater 1/29/2011 69000000 YES 8/17/2016 63000000 NO 80 Quaterly 2/28/2014 82000000 NO 3 Equip 579 N Y 5930000 8 Quaterly 5 Equip Yearly Type here to search 34C Save Data Review View Formulas Help Home Insert Draw Page Layout Insert X U A A General $ % Calibri 9X Delete v 11 Conditional Formatting Format as Table Cell Styles ||||| Format Paste IM . 40 00 000 AXP ernal Cells Voltage Encryption Voltage Number IS Styles Font K 2 Alignment 2 Sifications Clipboard X fx Pune 19 R M J 1 H F G 0 6300000 129/2019 525 489 590 N 555 4421N 10 5045 6700000 10 700000 0 7700000 0 3520000 2360000 4500000 4130000 4840000 1040000 5500000 39400001 4970000 3500000 5240000 1490000 2430000 1110000 1650000 3500000 3340000 2100000 4 Banglore Delhi 0 Banglore 0 Mumbai 4 Mumbai o Mumbai Den Mumbai Mumbai 2 Mumbai 0 Pune O Banglore OBShalore 1 Pune Banglore 0 Banglore 4 Delhi 2 Pune o Den 41 Term 0 Child 1 Term | Equip 8 Whole life Term Child Whole life Whole life Child Whole life Term Whole life Team Equip 800000 8800000 Whole life 4600000 2000000 2110000 3100000 3 Pune 10 3880000 5800000 1600000 . D E 498431839 F Y IN 50 1531886 M Y 511 556431 F N IN 52 357152 IN N 53 88228827 N 548643265 M IN 56 55 5578430 M Y IN 51 56 9990317 M IN Y 58 57 8697978 E IN N 1433213 M Y 60 59 4022570 M IN N 61 60 5357894M IN IN 61 23895226 N 65 62 457473 M Y 64 63 2899738 M N N 65 64 8415060F N 56 65 377082M 66 16506201 IN 67 8545245 N IN ES 68260145 M Y IN TO 69 1284791 M IN 707768323F IN 12 718308962M N 15 723608630 N TA 737621083 N 75 74 25573537 N T6 75 1691582 M 1Y TT 761869500M IN TS 77 17851591F IN Y 13 78 217905511 IN IN 792068094 IY IN 01 804335029 M N 31 8753331F 62 52356281 34 89 3246390 M IN N 24 223802 M N 86 852873864 OT 88 2729039M IN 8 87 35704337 3012151001 IN 30 893610972 M IN 31 90305342 M IY 91 544992 IN N 923896873 M 34 332245020 F 94 6463367 M N IN 36 957106552M 96 325519 M IN 31 5789209M S6 4224007 M IN 100 994478540 M IN NO 100 2221450 M 101283242010 Sheet1 466 N 430 N 6524 504 N 5341 658 534 N 595Y 636 419 Y 585 N 522 N 476 N 519 N 498Y 485 N 430 N 409 N 6331 478 5238 506 N 6541 400 N 50414 517 416 Y 542N 440 N 559 6871 625 N 596 Y 471 N 692Y 548 N 523 Y Equip Term Equip Schild Equip Term Term Term Equip Vhole life Vhole lube Whole life Term Term Term Term Whole life N 0 80 Quaterly 15 Yearly 83 Yearly 14 Yearly 60 Yearly 76 Monthly 17 Quaterlu 70 Quaterly 65 Quaterly 17 Monthly 80 Quaterly 84 Quaterte 60 Monthly 75 Yearly 8 Monthly 70 Quater 12 Yearby 83 Quatern 5 Monthly 5 Monthly 5 Monthly 79 Quatele 81 Monthly 19 Yearly 9 Quater 65 Yearly! 65 Quately 65 Qusterly 84 Monthly 82 Really 78 Quaterly 79 Quately 65 Monthly 81 Quatere 75 Yearby 65 Yearly 12 Month 70 Quaterly 60 Yeatly 15 Monthly 65 Monthly 50 Quaterly 14 Yearly 83 Quaterly 78 Yearly 82 Quaterly 32 Yearly 12 Quaterly 14 Monthly 80 Yearly 10 Monthly 78 Qustebi 14 Guately 3810000 3510000 1940000 5100000 1830000 4380000 5630000 5740000 3450000 1540000 2820000 3580000 2630000 5030000 4890000 4290000 5660000 5690000 5350000 1310000 3540000 6300000 9500000 4500000 9800000 4400000 P 2/28/2014 92000000 NO 2/13/2010 63000000 YES 2/26/2019 35000000 YES 31000000 YES 9/1/2015 232000000 NO 8/6/2019 36000000 YES 12/28/2019 224000000 NO 3/20/2014 99000000 YES 3/4/2013 68000000 NO 3/11/2015 157000000 NG 7/26/2018 184000000 NO 10/28/2018 21000000 YES 10/9/2019 101000000 NO 11/2/2011 17000000 YES 7/18/2015 63000000 YES 6/15/2012 133000000 NO 144/2012 84000000 YES 4122/2011 71000000 NO 4214/2014 78000000 NO 10/27/2010 41000000 NO 13/2018 172000000 NO 9/24/2017 141000000 YES 2/6/2013 132000000 NO 1715/2013 94000000 NO 8/18/2017 101000000 NO 473/2015 52000000 NG 12/2/2018 174000000 YES 7/18/2015 122000000 NO 10/8/2010 220000000 NO 5122/2013 168000000 NO 7/15/2016 24000000 NO 37000000 NO 9/27/2016 13000000 NG 7/6/2015 99000000 NO 11/11/2017 155000000 NG 9/26/2018 H2000000 YE 3/3/2017 74000000 NO 2/4/2015 204000000 YES 6/17/2017 263000000 YES 7126/2011 74000000 NO 10/30/2018 7000000 NO 131201 163000000 YES 8/25/2018 79000000 VEE 12/12/2015 92000000 VE 118000000 NO 8/5/2018 42000000 NO 6/15/2016 155000000 YES THP9/2010 35000000 YES 1114/2016 112000000 ND 114/2017 157000000 YE W/2016 33000000 NO 776/2015 12000000 YE 5/22/2015 250000000 NG 92/22/2010 1 Delhi O Banalore Mumbai Banglore Mumbai Pune Mumbai Pune Pune Banglor Banglore Pund Banalote Delhi Banglore 0 Delhi Pune Delhi o Mumbai Delhi Pune Delhi 0 Pune 0 Banglote 0 Delhi Banglore De 1 Banglore 4 Delhi O Banglore O Banglore O Banglore 10 9500000 3900000 10 6800000 900000 B000000 6800000 2100000 Whole Ne Whole life Equip Whole life Whole life Equip Whole life Whole life Equip Term Term 4 Term 6 Tem Equip 2350000 2/11/20 2910000 557 N 7300000 2 565 15700000 BIES 1330000 3590000 5020000 42800001 4200000 1790000 5640000 588 N 407Y 6754 5630 472 Y 5344 689 8400000 Vholelle Z Equip 5 Temm Equip 1200000 3700000 10 5900000 + A Ready EN 34C Type here to search Data Formulas Review View Help Home Draw File Insert Page Layout Insert General Sx Delete WES Calibri 11 Conditional Formatting Format as Table Cell Styles $ V % 9 Format Paste 20 B I U V AXP Internal Voltage Encryption Cells Number Styles Font Alignment 2 Classifications Voltage Clipboard X . fo Pune R $ T K19 U M G H F 9400000 0 689 Y 421 605 584 570 5351 633 N Banglore Banglore Banglore Delhi Banglote 3400000 4 Banglore 3400000 5900000 5010000 2480000 1840000 2320000 4220000 4130000 16000001 1610000 2940000 1070000 4860000 5760000 1380000 481 N N O 14 Quaterly 17 Yearly 9 Quatedly 16 Quaterly 7 Quaterly 77 Quaters Yeally 17 Quaterly 18 Quaterlo 9 Monthly 10 Yearly 75 Yeah 60 Monthly 10 Monthly 15 Quatre 16 Monthly Yearly $6 Month 4300000 8000000 6800000 Banglore Delhi 1 Mumba O Banglore Pune Delhi Pune 678|N 4441 473Y Equip Child Equip Chid Equip 2 Term 0 Equip Child Child EQUIP Equip 6 Term B Whole life 2 Equip SIEU Chile Tern Whole e Term Term Equip 509 N 2000000 2750000 18700000 Ol Yearbe 4800000 10 9900000 4900000 8700000 3400000 BA00000 510 N 6235 537 N 6051 652 N 617 438 565 508 N 632 N 465 N 4031 569 5250 5261 4700000 2630000 1360000 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2260000 3900000 P 5/22/2015 258000000 NO 379/2017 66000000 YES 1917/2013 120000000 NG 2/18/2018 84000000 YE 2/17/2012 98000000 YES 6/12/2019 109000000 YES 9/22/2016 150000000 NO 10/30/2010 78000000 YES 7716/2017 31000000 NO 7/12/2019 25000000 NG 2/10/2012 7000000 NO 11/5/2018 103000000 NO 312712019 241000000 YES 9/16/2014 42000000 YES 10/6/2019 21000000 YE 127212016 107000000 YE 6/14/2013 124000000 YEE 571/2013 42000000 NO 5124/2014 76000000 NO 7/30/2017 55000000 NC 8/7/20191 197000000 YES 525/2019 13000000 NO 1/1/2020 201000000 YES 1126/2011 47000000 YES 8/24/2012 127000000 YE 7730/2013 40000000 YE 5/23/2016 26000000 NO 9/17/2013 17000000 YES 8/24/2011 209000000 YES 12/2015 49000000 NC V29/2016 50000000 NO 3/11/2016 138000000 NO 37000000 YE 12/7/2016 20000000 N 05000000 YES 3/19/2014 71000000 NO 4/8/2011 18000000 VE 203000000 YE 7/03/2018 49000000 YE YE 771201 TOO0000 NO 2/27/2013 239000000 NG 12/5/2010 1000000 3737200 20000000 YE 12/2016 20000000 YE TOV200 210000000 YE 11/5/2016 SODODO NO 7212017 SODODO NO 19/2017 20000000 YE 7/16/2011 DE0000 YES 8/8/2010 NO 724/2010 151000000 NE 05/200 2000 Om 2000OOOTING 1 Delhi O Banglore Mumbai Pune | Bangloe 4 Pune Delhi 0 Banglot 4 Pune 10 85 Ouatory EN 7800000 545 N 6971 512 N 656N 574 20151 8300000 3700000 Mba Team Term Equip 4 Equip 3 Term Whole Whole e Term 6 Whole life 622N 9700000 3160000 1940000 232E 489Y 534 V 1600000 Mumbai Banglore Mumba Banglore 0 Banglore Delhi Banglore 10 10 10 V13/2017 SON 77 Monti 77 Yearly 78 Quaters 12 Monthly 10 Monthly 75 Month 25 Yearby 80 Quately 00 Monte 30 Quater 64 Quaters 65 Monthly 65 Yey 05 Quatu 6 Monthly Yearly 78 Year 79 Month 7 Quatery MON Year 11 Month 15 Quatarly 5240000 4750000 5730000 2840000 5310000 5390000 D00000 27000001 5700000 4900000 O Mumbai 101 121776 Vhole luke Vhotels Term Equip Whole Term 4 Pune Banglore Pune 7700000 9100000 145 435 N 4201 463 6490 SON 654 N 400 4151 638 41 N SON 536N 441N 9Y SSA 0000000 O Mumbai Mumba OD Equip DEU 1320000 10 3100000 1350000 Bangla 10000000 2 Banglore TO Y 17300000 Wholela TADO TOT Sheet1 + Ready 34C D RI C Type here to search Formulas Data Review Insert Draw Insert Page Layout Home File General v 2X Delete ' ' v 11 = Calibri $ v % , Conditional Formatting Format as Table A Cell Styles V Format V V ML 60 00 A Paste V Cells AXP Internal Voltage Encryption Styles Number 2 Alignment Font Classifications Voltage Clipboard fx Pune M K19 J G F 0 10 0 7300000 10 0 2200000 8200000 1800000 8500000 2600000 7400000 10 7700000 10 10 3900000 900000 9500000 12800000 4600000 D E 149 148 120034 M N IN 150 149 9466922M Y N 151 1502910442 M Y N 152 151 38962201F Y Y 153 152, 5823969 F N N 154 153 8251701 M N N 154 874264 156 155 24856761 IN 157 156 54170381 Y 158 157 3371281 M Y 153 158 6726748 IY 160 159 125176 Y 161 150 8048119 M Y N 162 161) 5571919 Y N 163 162 1278691 Y N 164 163 875180 M Y N 165 1646676783 IN N 166 165 3511219 IN 167 156 1220568 M N IN 160 167) 7340144 M N 165 168 4584953 M N Y 170 16956717706 Y Y 171 170) 1598592F Y 112 171 6002059 M IN 179 1721 7723699 M IN 114 1731 4023784 M IN N 115 174 9045411 M N N ITE 175 8574680F Y IN 176 838703F 14 11 1771389469 M Y 113 17831154697 IN 180 1799487391 Y IN 151 1804398591 M IN 182 181) 8316331F Y N 1828773550 M Y IY 104 183 2751389 N 185 184 141640E Y IN 156 1856289730 F Y 196 9398332 M Y N 18 1879972539 M 199 1887479233 N 130 189 1336482 M N 130 7354223 F N 192 191 9557368 M Y Y 192 4983970 F IN Y 154 193996381 M Y 195 1944648469F N N 13 135758641 M N Y 1ST 195 2125360M Y Y 19728088827 IN 139 94511521M N N 200 19953392500 N 201 2003141065 M IN H 418 N 560 N 636 N 441 N 489 Y 554 Y 621 Y 594 N 622 N 550 N 62314 427 451 N 505Y 479 5981 570 645 504 N 659 501 679 591 528 453 644 620 6231 5964 5137 685 N 632) 423 N 650 N 441N 524 Y 536 N 629 650 Y 449 N 532 N 652 N 646 555 N 445 N 576 N 589 N 638 N 4241 526 Y 619 N 475 N 683Y DELLA ALLERLEI 3100000 3350000 2530000 4640000 4830000 1700000 1500000 4550000 2410000 3700000 4070000 5210000 2620000 3080000 4180000 5210000 1010000 5920000 3220000 1840000 2270000 2550000 5610000 5110000 5020000 4560000 1150000 4210000 4000000 4720000 1100000 3160000 4080000 1740000 2110000 3320000 2270000 5190000 2490000 5760000 4370000 3140000 5450000 5450000 4200000 4280000 4710000 3560000 3730000 1960000 3540000 4780000 5390000 Delhi 4 Banglore 2 Banglore 1 Pune 0 Delhi 0 Banglore 1 Pune 0 Pune 4 Delhi 4 Banglore umbal 4 Pune 1 Mumbai Mumbai 4 Pune 2 Banglore 0 Mumbai 0 Banglore 01 Delhi Mumbai 0 Pune Pune Banglore 01 Delhi Pune Pune 0 Banglore Pune Delhi Banglore Pune Pune Pune Punc 2 Pune 0 Delhi 4 Pune 1 Mumbai Murnal 21 Delhi 0 Pune 0 Equip 0 Term 8 Equip 4 Child 4 Whole life 5 Whole life 6 Child Equip o Whole life O Child Whole life Term 8 Equip Equip 4 Child 2 Whole life 6 Term Equip 6 Equip 5 Term 5 Whole life 8 Whole life 3 Equip Term Term Term 0 Whole life o Whole life 8 Whole life & Whole life Whole life Term 2 Child 5 Child 6 Child 8 Term Child 3 Equip 6 Term Whole life Term Term 2 Term 8) Term Term Whole life 4 Whole life 6 Equip 1 Term 4 Equip 6 Whole life 3 Whole life N 0 15 Monthly 81 Yearly 11 Monthly 15 Quaterly 70 Yearly 70 Yearly 17 Quaterly 7 Monthly 65 Quaterly 18 Monthly 60 Monthly 75 Quaterly 10 Yearly 12 Monthly 15 Yearly 60 Yearly 82 Yearly 6 Quaterly 5 Quaterly 83 Quaterly 80 Monthly 75 Quaterly 14 Yearly 82 Quaterly 82 Quaterly 77 Monthly 80 Monthl 70 Monthly 80 Quaterly 80 Quaterly 70 Quaterly 76 Quaterly 15 Yearly 17 Quaterli! 15 Yearly 85 Monthly 16 Yearly 8 Yearly 76 Yearly 80 Monthly 85 Quaterly 85 Monthly 76 Yearly 82 Quaterly 81 Yearly 60 Monthly 65 Quaterly 6 Yearly 75 Monthly 13 Quaterly 60 Monthly 65 Quaterlig TI Quaterly P 9/9/2016 28000000 YES 7/16/2011 131000000 YES 8/9/2018 40000000 NO 3124/2010 151000000 NO 3/5/2013 212000000 NO 874/2011 20000000 NO 1/13/2016 44000000 NO 1/28/2016 159000000 YES 8/4/2015 30000000 YES 9/13/2014 72000000 NO 2/14/2015 181000000 YES 3/5/2019 28000000 NO 1/25/2015 360000 YES 817/2018 144000000 YES 11/13/2018 134000000 YES 1/16/2011 40000000 YES 9/9/2010 33000000 YES 9/16/2014 257000000 YES 6716/2017 20000000 NO 7/16/2014 78000000 YES 19/2/2013 92000000 YES 9/22/2014 71000000 NO 5/14/2011 56000000 NO 11/8/2014 239000000 YES 8/24/2015 150000000 YES 7/21/2015 149000000 NO 8/26/2018 19000000 NO 417/2018 188000000 YES 8214/2011 95000000 NO 11/2/2019 175000000 YES 4/22/2011 19000000 NO 6/17/2018 26000000 YES 1/20/2011 201000000 NO 3/2/2016 28000000 YES 57272016 15000000 NO 3/29/2013 61000000 NO 2/23/2016 57000000 YES 7717/2012 197000000 NO 5/19/2014 98000000 NO 4/12/2014 111000000 NO 12/10/2015 91000000 YES 6/20/2012 62000000 NO 975/2011 237000000 YES 4/2/2015 44000000 YES 2/12/2016 23000000 YES 9/10/2016 61000000 YES 776/2012 179000000 NO 3/23/2013 85000000 NO 18/2019 129000000 NO 4729/2014 79000000 YES 1712014 152000000 NO 123/2013) 206000000 NO 7/17/2014 215000000 NO 8900000 4600000 10 4500000 3600000 2400000 Det 6000000 2700000 4900000 2200000 2300000 3300000 o Delhi 4 Delhi O Delhi 4 Banglore o Delhi Banglore Mumbai Mumbai Delhi Mumba 0 Mumbai 1000000 5900000 6500000 6600000 2800000 Sheet1 E You are part of the analytics team for InsuranceCo. that deals with insurance products. The company offers four types of insurance: Child, Equip, Term and Whole Life, each customised according to the customers' needs. The customisation depends on multiple factors such as marital status, income, credit score and the city the customer is currently residing in. Now, you have been tasked with understanding the relationships between the data points. The attached file contains the data points that you will use in your analysis. The file also contains the data dictionary that highlights the column details. X Insurance Data + Now, based on the data that you ha been provided, address the following