Credit Score Predictions notebook (1).json

{"paragraphs":[{"title":"","text":"%md\n### Targeting Likely Good Credit Customers using Oracle Machine Learning's (OMS) Classification Models\n\nHeather has spent most of her time over the past couple of years extracting and preparing data for analysis.  The large volumes of data need extracting and processing mean she spends most of her time waiting for jobs to finish and very little of her time analyzing the data.  Demands from marketing are forcing a new approach whereby the data remains in the data warehouse and is processed there.  The alternative cloud solution is more complex, and has no direct out of the box processes to analyze the data in place.  She started taking a look at Oracle, and found the simple SQL commands in ADWC are familiar, and execute extremely fast, leveraging all the performance features of the platform.  Further once she is done can can apply the learning models to incoming data on the fly, and allow end user analysts to immediately see mining results.  This drastically reduces the cycle of data preparation, analysis, and publishing.  It also means there is no change to analysis/reporting Data Visualization toolset that users are familiar with.\n\nScroll down this notebook and learn how to use OML to create predictive perspectives on data in ADWC, WITHOUT moving it.  We will process a small 100k data set, but could use a 100M or billion row data set without worrying about processing time.\n\nThis is an extract of Alphaoffice customer information.  We will first get acclimated to Apache Zepplin, the open source interface for interactive collaboration in a team environment.\n\n### The Business Problem:   \n#### Increase Sales by  Targeting our Best Customers;  Good Credit Customers!\n\nHeather has a hunch that weakening sales may be due to the company selling to non-optimal customers;  customers who perhaps have poor credit and fail to make their payments for their purchases.   Heather has over 100 variables to consder so wants to first explore her data using simple charts and graphs, but then mover onto using Oracle Machine Learning's powerful algorithms to automtically sift through her data to find patterns, new insights and to make predictions that target her best customers--those who have good credit.","user":"CHARLIE","dateUpdated":"2018-07-16T21:06:17+0000","config":{"colWidth":8,"editorMode":"ace/mode/markdown","editorHide":true,"title":false,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":255.39999999999998},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1068538924","id":"20180717-165545_919966576","result":{"msg":"<h3>Targeting Likely Good Credit Customers using Oracle Machine Learning's (OMS) Classification Models</h3>\n<p>Heather has spent most of her time over the past couple of years extracting and preparing data for analysis.  The large volumes of data need extracting and processing mean she spends most of her time waiting for jobs to finish and very little of her time analyzing the data.  Demands from marketing are forcing a new approach whereby the data remains in the data warehouse and is processed there.  The alternative cloud solution is more complex, and has no direct out of the box processes to analyze the data in place.  She started taking a look at Oracle, and found the simple SQL commands in ADWC are familiar, and execute extremely fast, leveraging all the performance features of the platform.  Further once she is done can can apply the learning models to incoming data on the fly, and allow end user analysts to immediately see mining results.  This drastically reduces the cycle of data preparation, analysis, and publishing.  It also means there is no change to analysis/reporting Data Visualization toolset that users are familiar with.</p>\n<p>Scroll down this notebook and learn how to use OML to create predictive perspectives on data in ADWC, WITHOUT moving it.  We will process a small 100k data set, but could use a 100M or billion row data set without worrying about processing time.</p>\n<p>This is an extract of Alphaoffice customer information.  We will first get acclimated to Apache Zepplin, the open source interface for interactive collaboration in a team environment.</p>\n<h3>The Business Problem:</h3>\n<h4>Increase Sales by  Targeting our Best Customers;  Good Credit Customers!</h4>\n<p>Heather has a hunch that weakening sales may be due to the company selling to non-optimal customers;  customers who perhaps have poor credit and fail to make their payments for their purchases.   Heather has over 100 variables to consder so wants to first explore her data using simple charts and graphs, but then mover onto using Oracle Machine Learning's powerful algorithms to automtically sift through her data to find patterns, new insights and to make predictions that target her best customers&ndash;those who have good credit.</p>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-02-04T02:47:49+0000","dateStarted":"2018-07-16T21:06:17+0000","dateFinished":"2018-07-16T21:06:18+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:117"},{"text":"%md ![tiny arrow](http://www.oracle.com//technetwork/database/options/advanced-analytics/creditscore60-5013102.jpg  \"tiny arrow\") ","user":"CHARLIE","dateUpdated":"2018-07-16T16:58:40+0000","config":{"colWidth":4,"editorMode":"ace/mode/markdown","editorHide":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-272697361","id":"20180717-165545_1319898206","result":{"msg":"<p><img src=\"http://www.oracle.com//technetwork/database/options/advanced-analytics/creditscore60-5013102.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /></p>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-07-08T20:20:22+0000","dateStarted":"2018-07-16T16:58:40+0000","dateFinished":"2018-07-16T16:58:40+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:118"},{"text":"%md\n###Links to Additional Information\n\n#### Oracle Autonomous Data Warehouse Cloud product information:  \n#####  <https://cloud.oracle.com/en_US/datawarehouse>\n#### Oracle Machine Learning product information:  \n#####  <http://www.oracle.com/technetwork/database/options/oml/overview/index.html>\n#### Getting Started with Oracle Machine Learning Documentation and Quickstart Tutorials: \n#####  <https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/getting-started-oracle-machine-learning1.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA>\n#### Oracle Data Mining Basics:\n##### <https://docs.oracle.com/en/database/oracle/oracle-database/18/dmcon/what-is-data-mining.html#GUID-8232ABAD-E6B9-4C70-B227-E00738040932>\n##### <https://docs.oracle.com/en/database/oracle/oracle-database/18/dmcon/data-mining-basics.html#GUID-2116E665-721E-4EBA-AFE1-A30D6E8078C6>","user":"CHARLIE","dateUpdated":"2018-07-16T16:58:44+0000","config":{"tableHide":false,"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_94772852","id":"20180717-165545_865123445","result":{"msg":"<h3>Links to Additional Information</h3>\n<h4>Oracle Autonomous Data Warehouse Cloud product information:</h4>\n<h5><a href=\"https://cloud.oracle.com/en_US/datawarehouse\">https://cloud.oracle.com/en_US/datawarehouse</a></h5>\n<h4>Oracle Machine Learning product information:</h4>\n<h5><a href=\"http://www.oracle.com/technetwork/database/options/oml/overview/index.html\">http://www.oracle.com/technetwork/database/options/oml/overview/index.html</a></h5>\n<h4>Getting Started with Oracle Machine Learning Documentation and Quickstart Tutorials:</h4>\n<h5><a href=\"https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/getting-started-oracle-machine-learning1.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA\">https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/getting-started-oracle-machine-learning1.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA</a></h5>\n<h4>Oracle Data Mining Basics:</h4>\n<h5><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/18/dmcon/what-is-data-mining.html#GUID-8232ABAD-E6B9-4C70-B227-E00738040932\">https://docs.oracle.com/en/database/oracle/oracle-database/18/dmcon/what-is-data-mining.html#GUID-8232ABAD-E6B9-4C70-B227-E00738040932</a></h5>\n<h5><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/18/dmcon/data-mining-basics.html#GUID-2116E665-721E-4EBA-AFE1-A30D6E8078C6\">https://docs.oracle.com/en/database/oracle/oracle-database/18/dmcon/data-mining-basics.html#GUID-2116E665-721E-4EBA-AFE1-A30D6E8078C6</a></h5>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-06-27T21:12:18+0000","dateStarted":"2018-07-16T16:58:44+0000","dateFinished":"2018-07-16T16:58:44+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:119"},{"text":"%md \n#### **STEP 1: Review Zepplin Basics**\n\n- Note the page is made up of paragraphs such as this one where the text is sitting See [Apache Zepplin Documentation](https://zeppelin.apache.org/contribution/documentation.html).  Also note that are four icons in the top left of this panel.  Click on each icon to see what happens.  Note that the first icon (arrow) runs the panel.  This one is %md, which is markdown, or formatted text.  Other panels will be of different types (%sql for SQL, #script for PL/SQL).\n- Also see [Oracle Machine Learning documentation](http://www.oracle.com/technetwork/database/options/oml/overview/index.html )\n- Scroll down to the next step.","user":"ADWC_WS","dateUpdated":"2018-07-11T18:00:31+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1782112685","id":"20180717-165545_1716081713","result":{"msg":"<h4><strong>STEP 1: Review Zepplin Basics</strong></h4>\n<ul>\n<li>Note the page is made up of paragraphs such as this one where the text is sitting See <a href=\"https://zeppelin.apache.org/contribution/documentation.html\">Apache Zepplin Documentation</a>.  Also note that are four icons in the top left of this panel.  Click on each icon to see what happens.  Note that the first icon (arrow) runs the panel.  This one is %md, which is markdown, or formatted text.  Other panels will be of different types (%sql for SQL, #script for PL/SQL).</li>\n<li>Also see <a href=\"http://www.oracle.com/technetwork/database/options/oml/overview/index.html\">Oracle Machine Learning documentation</a></li>\n<li>Scroll down to the next step.</li>\n</ul>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-07-11T15:51:46+0000","dateStarted":"2018-07-11T17:07:41+0000","dateFinished":"2018-07-11T17:07:41+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:120"},{"title":"STEP 2:  Prepare the Data","text":"%script\n\n-- Click on the arrow to the right to execute this.\n\nBEGIN\n\nexecute immediate 'create or replace view credit_scoring_100k_v as select * from credit_scoring_100k';\nexecute immediate 'create or replace view credit_scoring_new_cust_v as select * from credit_scoring_100k sample (10)';\n\nend;\n","user":"CHARLIE","dateUpdated":"2018-07-16T21:37:07+0000","config":{"colWidth":12,"editorMode":"ace/mode/plsql","editorHide":false,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-170200692","id":"20180717-165545_2082276763","result":{"msg":"\nPL/SQL procedure successfully completed.\n\n","code":"SUCCESS","type":"TEXT"},"dateCreated":"2018-07-11T19:55:56+0000","dateStarted":"2018-07-16T21:37:07+0000","dateFinished":"2018-07-16T21:37:07+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:121"},{"title":"STEP 2: Show the available tables","text":"%sql\n\n-- Click on the arrow in the upper right to run a query that shows the tables and views owned by ADWC_WS user (the current one we are using).\n\nSelect table_name from user_tables where table_name not like ('DM$%')\nUNION\nselect view_name from user_views where view_name not like ('DM$%')\n","user":"CHARLIE","dateUpdated":"2018-07-16T21:36:01+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","editorHide":false,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[{"name":"TABLE_NAME","index":0,"aggr":"sum"}],"values":[],"scatter":{"xAxis":{"name":"TABLE_NAME","index":0,"aggr":"sum"}},"groups":[],"height":189},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_778550194","id":"20180717-165545_1392326842","result":{"msg":"TABLE_NAME\nAI_EXPLAIN_OUTPUT\nAI_EXPLAIN_OUTPUT_CREDIT_SCORE_BIN\nAI_EXPLAIN_OUTPUT_MAX_CC_SPENT_AMOUNT\nBIN_NUM_TBL\nCARS\nCREDIT_SCORING_100K\nCREDIT_SCORING_100K_NEW\nCREDIT_SCORING_100K_ORIGINAL\nCREDIT_SCORING_100K_V\nCREDIT_SCORING_NEW_CUST_V\nCUST_INSUR_LTV\nCUST_INSUR_LTV_APPLY\nMINING_DATA_BUILD\nMINING_DATA_VIEW\nN1_APPLYT_RESULT\nN1_APPLY_RESULT\nN1_BUILDT_SETTINGS\nN1_BUILD_SETTINGS\nN1_LIFTT_TABLE\nN1_LIFT_TABLE\nN1_TESTT_DATA\nN1_TEST_DATA\nN1_TRAIN_DATA\nSUPPLEMENTARY_DEMOGRAPHICS2\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-06-25T18:52:30+0000","dateStarted":"2018-07-16T16:59:43+0000","dateFinished":"2018-07-16T16:59:43+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:122"},{"title":"STEP 3:  Show the Potential Variables We Will Use","text":"%sql \n\n-- Execute this (arrow upper right)\n\nselect column_name from user_tab_cols where table_name='CREDIT_SCORING_100K_V';","user":"CHARLIE","dateUpdated":"2018-07-16T16:59:50+0000","config":{"tableHide":false,"colWidth":4,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[{"name":"COLUMN_NAME","index":0,"aggr":"sum"}],"values":[],"scatter":{"xAxis":{"name":"COLUMN_NAME","index":0,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_853386580","id":"20180717-165545_1681413508","result":{"msg":"COLUMN_NAME\nLIFECYCLE_STAGE\nNO_OF_FOLLOWERS_ON_TWITTER\nLOAN_TYPE\nGENDER\nHAS_COLLATERAL\nPERCENTAGE_MASTERS\nCUSTOMER_DMG_SEGMENT\nPERCENTAGE_OTHER\nMOST_POPULAR_POST_CATEGORY\nCREDIT_SCORE_BIN\nCURRENT_ADDRESS_DURATION\nRESIDENTAL_STATUS\nSECOND_MOST_SPENDING_TYPE\nJOB_FRIENDS_PERCENTAGE\nAVERAGE_NO_OF_RETWEETS\nIS_POSTED_STH_WITHIN_A_MONTH\nCUSTOMER_VALUE_SEGMENT\nFAMILY_SIZE\nFATHERS_JOB\nCREDIT_HISTORY\nFIXED_INCOME_RATE\nNEW_BANKRUPTCY\nNO_OF_RECRUITERS_ON_LINKEDIN\nDEBTOR_GUARANTORS\nCHURN_RATE_OF_ACCOUNT_NO1\nLOAN_AMOUNT\nINCOME\nHAS_CHECKING_ACCOUNT\nMOST_SPENDING_TYPE\nCHURN_RATE_OF_CCN\nCHURN_RATE_OF_ACCOUNT_NON\nCUSTOMER_DEPTH\nHAS_OWN_PHONE_NO\nMAX_CC_SPENT_AMOUNT_PREV\nCHURN_RATE__OF_ACCOUNT_NO2\nPERCENTAGE_UG\nCUSTOMER_LIFETIME_VALUE\nCUSTOMER_ID\nNUMBER_OF_CURRENT_ACCOUNTS\nNUMBER_OF_SAVING_ACCOUNTS\nNUMBER_OF_INACTIVE_ACCOUNTS\nCREDIT_CARD_UTILIZATION_RATE\nMAX_CC_SPENT_AMOUNT\nPRESENT_EMPLOYMENT_SINCE\nEARNINGS\nNUMBER_OF_LIABLES\nNUMBER_OF_PRIOR_LOANS\nTHIRD_MOST_SPENDING_TYPE\nLOAN_LENGTH\nREGION\nOCCUPATION\nNUMBER_OF_COLLECTIONS\nCITY_SIZE\nPERCENTAGE_PHD_ON_LINKEDIN\nCHURN_RATE_OF_CC1\nMARITAL_STATUS\nMOTHERS_JOB\nAVERAGE_JOB_CHANGING_PERIOD\nHEALTH_SCORE\nEDUCATION_LEVEL\nNUMBER_OF_CLOSED_ACCOUNTS\nNUMBER_OF_PROTESTOR_LIKES\nUNEMPLOYMENT_INDEX\nNUMBER_OF_INQUIRIES\nCONSUMER_FINDEX_SCORE\nFACEBOOK_INFLUENCE_SCORE\nCHURN_RATE_OF_CC2\nTENURE\nHAS_SAME_PHONE_NO_SINCE\nNUMBER_OF_OPEN_ACCOUNTS\nDELINQUENCY_STATUS\nNO_OF_DEBTORS_ON_FB\nINTEREST_RATE\nHOUSING_INDEX\nMODE_JOB_OF_CONTACTS\nPERCENTAGE_HIGH_SCHOOL\nPRODUCTION_INDEX\nSCHOOL_FRIENDS_PERCENTAGE\nHIGHEST_CREDIT_CARD_LIMIT\nNO_OF_PROTESTOR_COMMENTS\nAGE\nWEALTH\nNO_OF_LINKEDIN_CONTACTS\nNO_OF_TOTAL_ENDORSEMENTS\nCONSUMER_CONFIDENCE_INDEX\nIS_FOREIGN_WORKER\nINFLATION_RATE\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-07-11T17:12:32+0000","dateStarted":"2018-07-16T16:59:50+0000","dateFinished":"2018-07-16T16:59:50+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:123"},{"text":"%sql \nupdate CREDIT_SCORING_100k set credit_score_bin = ","dateUpdated":"2018-07-16T21:12:55+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1906924944","id":"20180717-165545_1224506862","dateCreated":"2018-07-16T21:11:22+0000","status":"READY","progressUpdateIntervalMs":500,"commited":false,"$$hashKey":"object:124"},{"title":"STEP 4: Review Credit Scoring Data","text":"%sql\n\n-- This shows the credit scoring data we will use historical data to predict the likelihood of a customer having good credit.\n\nSelect * from credit_scoring_100k_v where rownum < 100","user":"CHARLIE","dateUpdated":"2018-07-16T21:25:13+0000","config":{"tableHide":false,"colWidth":8,"editorMode":"ace/mode/osql","editorHide":false,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}],"values":[{"name":"AGE","index":1,"aggr":"sum"}],"scatter":{"yAxis":{"name":"AGE","index":1,"aggr":"sum"},"xAxis":{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}},"groups":[],"height":299},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1624478919","id":"20180717-165545_1549371575","result":{"msg":"CUSTOMER_ID\tAGE\tINCOME\tMARITAL_STATUS\tNUMBER_OF_LIABLES\tWEALTH\tEDUCATION_LEVEL\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tLOAN_LENGTH\tGENDER\tREGION\tCURRENT_ADDRESS_DURATION\tRESIDENTAL_STATUS\tNUMBER_OF_PRIOR_LOANS\tNUMBER_OF_CURRENT_ACCOUNTS\tNUMBER_OF_SAVING_ACCOUNTS\tOCCUPATION\tHAS_CHECKING_ACCOUNT\tCREDIT_HISTORY\tPRESENT_EMPLOYMENT_SINCE\tFIXED_INCOME_RATE\tDEBTOR_GUARANTORS\tHAS_OWN_PHONE_NO\tHAS_SAME_PHONE_NO_SINCE\tIS_FOREIGN_WORKER\tNUMBER_OF_OPEN_ACCOUNTS\tNUMBER_OF_CLOSED_ACCOUNTS\tNUMBER_OF_INACTIVE_ACCOUNTS\tNUMBER_OF_INQUIRIES\tHIGHEST_CREDIT_CARD_LIMIT\tCREDIT_CARD_UTILIZATION_RATE\tDELINQUENCY_STATUS\tNEW_BANKRUPTCY\tNUMBER_OF_COLLECTIONS\tCONSUMER_FINDEX_SCORE\tMAX_CC_SPENT_AMOUNT\tMAX_CC_SPENT_AMOUNT_PREV\tHAS_COLLATERAL\tFAMILY_SIZE\tCITY_SIZE\tFATHERS_JOB\tMOTHERS_JOB\tMOST_SPENDING_TYPE\tSECOND_MOST_SPENDING_TYPE\tTHIRD_MOST_SPENDING_TYPE\tSCHOOL_FRIENDS_PERCENTAGE\tJOB_FRIENDS_PERCENTAGE\tNUMBER_OF_PROTESTOR_LIKES\tNO_OF_PROTESTOR_COMMENTS\tNO_OF_LINKEDIN_CONTACTS\tAVERAGE_JOB_CHANGING_PERIOD\tNO_OF_DEBTORS_ON_FB\tNO_OF_RECRUITERS_ON_LINKEDIN\tNO_OF_TOTAL_ENDORSEMENTS\tNO_OF_FOLLOWERS_ON_TWITTER\tMODE_JOB_OF_CONTACTS\tAVERAGE_NO_OF_RETWEETS\tFACEBOOK_INFLUENCE_SCORE\tPERCENTAGE_PHD_ON_LINKEDIN\tPERCENTAGE_MASTERS\tPERCENTAGE_UG\tPERCENTAGE_HIGH_SCHOOL\tPERCENTAGE_OTHER\tIS_POSTED_STH_WITHIN_A_MONTH\tMOST_POPULAR_POST_CATEGORY\tINTEREST_RATE\tEARNINGS\tUNEMPLOYMENT_INDEX\tPRODUCTION_INDEX\tHOUSING_INDEX\tCONSUMER_CONFIDENCE_INDEX\tINFLATION_RATE\tCUSTOMER_VALUE_SEGMENT\tCUSTOMER_DMG_SEGMENT\tCUSTOMER_LIFETIME_VALUE\tCHURN_RATE_OF_CC1\tCHURN_RATE_OF_CC2\tCHURN_RATE_OF_CCN\tCHURN_RATE_OF_ACCOUNT_NO1\tCHURN_RATE__OF_ACCOUNT_NO2\tCHURN_RATE_OF_ACCOUNT_NON\tHEALTH_SCORE\tCUSTOMER_DEPTH\tLIFECYCLE_STAGE\tCREDIT_SCORE_BIN\n108\t24\t2150\tMarried\t3\tPoor\tBachelor's Degree\t19\t\t20000\t24\tFemale\tCentral Anatolia\t99\tTenant\t2\t1\t0\tTechnician\tNo\tNot Taken\tUnemployed\t0.9\tGuarantor\tNo\t81\tNo\t1\t2\t0\t1\t6450\t0\tFour Months Late\tUnknown\t1\t1438\t5805\t4515\tYes\t4\tMedium\tRetired\tProfessional\tBills\tHealth\tElectronic\t0.4\t0.2\t2\t0\t57\t1\t1\t2\t5\t21\tProduction\t2\t0.3\t7\t19\t25\t39\t10\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t275415\t0.6\t0.5\t0.55\t0.5\t0.6\t0.55\t8\t2\t1\tOther Credit\n109\t45\t2750\tSingle\t1\tAverage\tMaster's Degree\t27\tAuto\t25000\t12\tMale\tAegean\t8\tTenant\t1\t2\t0\tProfessional\tYes\tDelay\tLess Than One Year\t0.7\tNone\tNo\t3\t\t2\t2\t0\t2\t8250\t0.7\tOne Month Late\tNo\t0\t926\t3300\t5775\tYes\t2\tMedium\tFarmer\tUnemployed\tEating and Drinking\tTransportation\tHealth\t0.1\t0.2\t9\t1\t570\t30\t1\t22\t57\t176\tTransportation\t17\t0.6\t7\t3\t1\t0\t89\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t231000\t0.9\t0.5\t0.7\t1\t0.5\t0.75\t6\t3\t3\tOther Credit\n110\t63\t2150\tSingle\t5\tPoor\tBachelor's Degree\t11\tAuto\t20000\t48\tFemale\tMediterranean\t55\tTenant\t0\t5\t2\tProfessional\tNo\t\tLess Than Four Years\t0.8\tCo Applicant\tNo\t18\tNo\t2\t1\t1\t2\t6450\t0.3\tNormal\tNo\t0\t1315\t3225\t5805\tYes\t6\tSmall\tArmy\tUnemployed\tEating and Drinking\tBills\tHealth\t0.5\t0.1\t6\t0\t20\t24\t2\t0\t2\t51\tEnergy\t5\t0.3\t14\t5\t27\t24\t30\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t127710\t0.5\t0.7\t0.6\t0.2\t0.5\t0.35\t4\t8\t4\tOther Credit\n111\t59\t2750\tMarried\t6\tPoor\tBachelor's Degree\t10\tHousing\t45000\t12\tMale\tEastern Anatolia\t18\tTenant\t0\t3\t0\tArmy\tYes\tNot Taken\tLess Than One Year\t1\tNone\tYes\t51\tNo\t2\t1\t1\t2\t8250\t0.2\tOne Month Late\tNo\t0\t1202\t5775\t1650\tYes\t7\tBig\tManager\tRetired\tMarket\tBills\t\t0.5\t0.2\t0\t0\t78\t24\t2\t3\t7\t266\tDesign\t26\t0.7\t14\t12\t24\t11\t39\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t42900\t0.5\t0.1\t0.3\t0.2\t0.4\t0.3\t5\t7\t4\tOther Credit\n112\t20\t2750\tDivorced\t2\tAverage\tMaster's Degree\t9\tEducation\t15000\t24\tMale\tMediterranean\t85\tTenant\t0\t4\t0\tWorker\tNo\tDelay\tUnemployed\t1\tCo Applicant\tYes\t30\tNo\t3\t0\t1\t0\t8250\t0.4\tFour Months Late\tNo\t0\t1029\t8250\t2475\tYes\t3\tSmall\tProfessional\tTechnician\tEating and Drinking\tOther\tTransportation\t0.2\t0.1\t6\t1\t693\t2\t0\t27\t69\t78\tHealth\t7\t0.4\t5\t1\t12\t14\t68\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t160875\t0.5\t0.1\t0.3\t\t0.4\t0.45\t8\t7\t1\tOther Credit\n113\t71\t3750\tMarried\t5\tAverage\tMaster's Degree\t12\tHousing\t40000\t24\tFemale\tMediterranean\t97\tTenant\t\t2\t0\tManager\tYes\tDuly Past\tUnemployed\t1\tCo Applicant\tYes\t10\tNo\t2\t0\t0\t2\t11250\t0.3\tThree Months Late\tNo\t0\t1066\t11250\t3375\tNo\t6\tMedium\tRetired\tRetired\tEating and Drinking\tMarket\tTransportation\t0.2\t0.1\t2\t1\t288\t8\t1\t11\t28\t47\tEnergy\t4\t0.1\t5\t11\t22\t18\t44\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t47250\t0.8\t0.3\t0.55\t0.1\t0.2\t0.15\t3\t6\t5\tOther Credit\n114\t35\t2750\tMarried\t3\tPoor\tBachelor's Degree\t38\tHousing\t70000\t24\tFemale\tAegean\t35\tTenant\t3\t1\t0\tArmy\t\tDuly Now\tLess Than One Year\t0.9\tCo Applicant\tNo\t35\tNo\t1\t2\t0\t1\t8250\t0.1\tMore Than Four Months\tNo\t0\t1342\t8250\t5775\tYes\t4\tSmall\tRetired\tUnemployed\tEating and Drinking\tHealth\tMarket\t0.5\t0.2\t3\t5\t368\t2\t1\t14\t36\t4\tHealth\t0\t0.3\t14\t10\t5\t17\t54\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t288750\t0.9\t0.9\t0.9\t0.6\t0.5\t0.55\t7\t1\t2\tOther Credit\n115\t63\t500\tSingle\t2\tVery Poor\tPrimary School Grad\t5\tHousing\t85000\t144\tMale\tBlack Sea\t94\tTenant\t0\t6\t2\tArmy\tNo\tRisky\tLess Than Seven Years\t0.7\tNone\tYes\t\tNo\t3\t0\t1\t0\t1500\t0.2\tFour Months Late\tUnknown\t0\t1822\t600\t600\tNo\t3\tMedium\tManager\tUnemployed\tEating and Drinking\tTransportation\tMarket\t0.1\t0.2\t8\t1\t455\t1\t4\t18\t45\t261\tHealth\t26\t0.8\t6\t18\t14\t27\t35\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t13200\t0.8\t0.9\t0.85\t0.1\t0.4\t0.25\t4\t4\t4\tOther Credit\n116\t60\t3750\tMarried\t2\tRich\tDoctorate Degree\t6\tNeed\t20000\t36\tMale\tAegean\t51\tOwner\t1\t4\t0\tProfessional\tYes\tDuly Past\tUnemployed\t0.5\tCo Applicant\tYes\t74\tNo\t3\t1\t1\t0\t11250\t0.2\tNormal\tNo\t\t573\t9000\t5625\tNo\t3\tSmall\tProfessional\tRetired\tBills\tOther\tEating and Drinking\t0.1\t0.2\t8\t4\t665\t1\t0\t26\t66\t19\tLaw\t1\t0.3\t1\t11\t16\t7\t65\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t140625\t0.7\t0.5\t0.6\t0.4\t0.5\t0.45\t4\t6\t4\tOther Credit\n117\t55\t2750\tSingle\t3\tPoor\tBachelor's Degree\t8\tEducation\t20000\t24\tFemale\tMarmara\t75\tTenant\t1\t5\t\tArmy\tNo\tDuly Now\tLess Than Seven Years\t0.5\tNone\tNo\t37\tNo\t3\t0\t0\t2\t8250\t0.4\tTwo Months Late\tNo\t0\t1464\t825\t1650\tNo\t4\tMedium\tUnemployed\tFarmer\tMarket\tHealth\tElectronic\t0.1\t0.1\t2\t4\t30\t2\t0\t1\t3\t47\tFinance\t4\t0.3\t12\t8\t10\t17\t53\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t49500\t0.3\t0.2\t0.25\t0.4\t0.2\t0.3\t5\t5\t4\tOther Credit\n118\t62\t4250\tMarried\t3\tRich\tBachelor's Degree\t27\t\t40000\t12\tMale\tBlack Sea\t30\tOwner\t3\t3\t1\tManager\tYes\tRisky\tLess Than Four Years\t0.5\tCo Applicant\tNo\t3\tNo\t2\t1\t0\t2\t12750\t0\tOne Month Late\tUnknown\t1\t655\t12750\t7650\tYes\t4\tMedium\tManager\tUnemployed\tBills\tHealth\tOther\t0.4\t0.2\t1\t0\t96\t2\t1\t3\t9\t89\tScience\t8\t0.4\t11\t8\t3\t17\t61\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t175950\t0.6\t0.7\t0.65\t0.1\t0.3\t0.2\t4\t3\t4\tOther Credit\n119\t67\t4250\tDivorced\t6\tRich\tBachelor's Degree\t6\tAuto\t10000\t24\tMale\tEastern Anatolia\t7\tOwner\t1\t3\t2\tProfessional\tNo\tNot Taken\tLess Than Seven Years\t0.7\tNone\tYes\t30\tNo\t1\t1\t0\t2\t12750\t0.7\tNormal\tNo\t1\t565\t7650\t11475\tNo\t7\tSmall\tRetired\tClerical\tEating and Drinking\tOther\tBills\t0.3\t0.2\t0\t6\t32\t9\t2\t1\t3\t66\t\t6\t0.3\t14\t16\t17\t19\t34\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t206550\t0.7\t0.7\t0.7\t0.4\t0.1\t0.25\t4\t1\t5\tGood Credit\n120\t23\t2750\tSingle\t2\tAverage\tMaster's Degree\t26\tNeed\t20000\t36\tFemale\tBlack Sea\t58\tTenant\t2\t5\t2\tArmy\tNo\tDelay\tUnemployed\t0.8\tGuarantor\tYes\t52\tNo\t3\t2\t0\t0\t8250\t0\tOne Month Late\tUnknown\t1\t769\t6600\t5775\tYes\t3\tMedium\tWorker\tArmy\tEating and Drinking\tTransportation\tElectronic\t0.3\t0.1\t8\t0\t63\t9\t3\t2\t6\t56\tLaw\t5\t0.3\t2\t5\t6\t8\t79\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t358050\t0.6\t0.9\t0.75\t0.3\t0.3\t0.3\t8\t\t1\tOther Credit\n121\t43\t500\tMarried\t3\tVery Poor\tPrimary School Grad\t15\tAuto\t55000\t12\tMale\tMarmara\t55\tFamily Home\t0\t6\t2\tArmy\tYes\tDuly Past\tLess Than One Year\t0.7\tCo Applicant\tNo\t70\tNo\t3\t1\t1\t0\t1500\t0.5\tFour Months Late\tNo\t0\t1561\t150\t600\tYes\t4\tSmall\tProfessional\tProfessional\tMarket\tElectronic\tBills\t0.1\t0.2\t3\t1\t675\t6\t4\t27\t67\t13\tConsulting\t1\t0.2\t6\t7\t5\t5\t77\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t25200\t0.4\t0.8\t0.6\t0.1\t0.3\t0.2\t6\t\t3\tOther Credit\n122\t49\t2150\tSingle\t6\tPoor\tBachelor's Degree\t14\tNeed\t25000\t36\tFemale\tMarmara\t12\tTenant\t3\t5\t1\tTechnician\tNo\tDuly Past\tUnemployed\t0.8\tCo Applicant\tNo\t90\tNo\t3\t0\t1\t2\t6450\t0.5\tTwo Months Late\tNo\t0\t1178\t3870\t2580\tNo\t7\tMedium\tUnemployed\tWorker\tEating and Drinking\tHealth\tOther\t0.1\t0.2\t1\t0\t17\t27\t4\t0\t1\t19\tTransportation\t1\t0.2\t1\t12\t21\t34\t32\tYes\tPolitics\t7.5\t4.8\t10.1\t\t187.86\t0.65\t7.15\tBronze\tSegment4\t92880\t0.9\t0.7\t0.8\t0.2\t0.1\t0.15\t6\t4\t3\tOther Credit\n123\t51\t6250\tSingle\t2\tVery Rich\tBachelor's Degree\t18\tEducation\t10000\t12\tMale\tCentral Anatolia\t21\tOwner\t3\t5\t2\t\tYes\tDuly Now\tLess Than One Year\t0.6\tGuarantor\tNo\t11\tNo\t2\t1\t1\t0\t18750\t0.8\tOne Month Late\tYes\t1\t227\t5625\t9375\tYes\t3\tMedium\tArmy\tRetired\tEating and Drinking\tElectronic\tOther\t0.2\t0.2\t2\t0\t1\t9\t3\t0\t0\t91\tTourism\t9\t0.4\t0\t8\t17\t1\t74\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t318750\t0.2\t0.7\t0.45\t0.5\t0.2\t0.35\t5\t6\t3\tGood Credit\n124\t42\t3750\tSingle\t6\tAverage\tMaster's Degree\t15\tNeed\t15000\t36\tMale\tCentral Anatolia\t98\tTenant\t2\t6\t2\tManager\tYes\tDuly Now\tLess Than One Year\t0.9\tNone\tYes\t6\tNo\t3\t0\t1\t1\t11250\t0.6\tTwo Months Late\tNo\t0\t1018\t2250\t9000\tNo\t7\tSmall\tTechnician\tProfessional\tMarket\tHealth\tOther\t0.4\t0.2\t10\t4\t216\t21\t0\t8\t21\t64\tDesign\t6\t0.5\t4\t6\t26\t36\t28\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t387000\t0.1\t1\t\t0.2\t0.3\t0.25\t6\t5\t3\tOther Credit\n125\t41\t5250\tMarried\t3\tRich\tBachelor's Degree\t9\tNeed\t15000\t12\tMale\tMediterranean\t35\tTenant\t2\t3\t2\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.5\tGuarantor\tNo\t9\tNo\t1\t1\t0\t2\t15750\t0\tOne Month Late\tNo\t0\t503\t6300\t14175\tYes\t4\tBig\tArmy\tRetired\tEating and Drinking\tElectronic\tBills\t0.1\t0.1\t6\t1\t72\t21\t2\t2\t7\t80\tTransportation\t8\t0.4\t11\t11\t26\t17\t35\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t623700\t0.5\t0.9\t\t0.4\t0.4\t0.4\t6\t2\t3\tGood Credit\n127\t60\t2150\tSingle\t\tPoor\tBachelor's Degree\t11\tNeed\t45000\t24\tFemale\tSoutheast Anatolia\t15\tTenant\t1\t5\t2\tProfessional\tYes\tDuly Past\tLess Than One Year\t0.9\tCo Applicant\tYes\t25\tNo\t3\t0\t0\t1\t6450\t0.1\tTwo Months Late\tYes\t3\t1458\t5805\t645\tNo\t5\tBig\tUnemployed\tRetired\tEating and Drinking\tMarket\tBills\t0.2\t0.2\t0\t1\t28\t27\t4\t1\t2\t30\tDesign\t3\t0.3\t10\t10\t13\t1\t66\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t16125\t0.8\t0.4\t0.6\t0.1\t0.1\t0.1\t4\t8\t4\tOther Credit\n131\t42\t2150\tMarried\t3\tPoor\tBachelor's Degree\t9\tEducation\t10000\t24\tMale\tMarmara\t99\tTenant\t3\t5\t1\tClerical\tNo\tDelay\tLess Than Four Years\t0.5\tNone\tYes\t10\tNo\t3\t0\t1\t2\t6450\t\tFour Months Late\tNo\t0\t1278\t4515\t5160\tNo\t4\tMedium\tWorker\tManager\tMarket\tTransportation\tEating and Drinking\t0.2\t0.1\t10\t1\t30\t1\t2\t1\t3\t17\tProduction\t1\t0.3\t4\t11\t28\t31\t26\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t221880\t0.9\t0.6\t0.75\t0.3\t0.2\t0.25\t6\t8\t3\tOther Credit\n136\t59\t2150\tMarried\t3\tPoor\tBachelor's Degree\t21\tHousing\t105000\t60\tMale\tMediterranean\t91\tTenant\t2\t3\t0\tFarmer\tYes\tRisky\tLess Than One Year\t0.8\tGuarantor\tNo\t6\tNo\t2\t0\t1\t2\t6450\t0.7\tTwo Months Late\tUnknown\t1\t\t3870\t5160\tYes\t4\tMedium\tUnemployed\tTechnician\tEating and Drinking\tMarket\tHealth\t0.3\t0.2\t8\t6\t44\t7\t1\t1\t4\t37\tConsulting\t3\t0.2\t9\t17\t10\t1\t63\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t134160\t0.5\t0.8\t0.65\t0.3\t0.1\t0.2\t5\t7\t4\tOther Credit\n141\t43\t6250\tSingle\t5\tRich\tBachelor's Degree\t20\tHousing\t65000\t264\tFemale\tMediterranean\t20\tTenant\t2\t\t2\tManager\tYes\tDelay\tLess Than Four Years\t1\tCo Applicant\tNo\t59\tNo\t1\t1\t0\t2\t18750\t0.8\tNormal\tUnknown\t0\t463\t11250\t15000\tYes\t6\tBig\tRetired\tRetired\tBills\tElectronic\tHealth\t0.3\t0.2\t8\t0\t10\t24\t3\t0\t1\t87\tHealth\t8\t0.4\t8\t17\t3\t17\t55\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t630000\t0.9\t1\t0.95\t0.1\t0.5\t0.3\t6\t2\t3\tGood Credit\n145\t64\t5250\tSingle\t3\tRich\tBachelor's Degree\t25\tAuto\t20000\t36\tMale\tBlack Sea\t67\tOwner\t3\t2\t0\tProfessional\tYes\tDuly Past\tLess Than Seven Years\t0.9\tGuarantor\tNo\t3\tNo\t2\t2\t0\t0\t15750\t0\tNormal\tNo\t0\t750\t6300\t11025\tYes\t4\tMedium\tFarmer\tUnemployed\tMarket\tTransportation\tElectronic\t0.5\t0.1\t8\t5\t69\t2\t3\t2\t6\t\tDesign\t7\t0.3\t5\t2\t8\t18\t67\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t231525\t0.9\t0.6\t0.75\t1\t0.6\t0.8\t4\t1\t4\tOther Credit\n149\t32\t500\tMarried\t1\tVery Poor\tPrimary School Grad\t11\tHousing\t80000\t108\tMale\tBlack Sea\t49\tFamily Home\t2\t3\t1\tManager\tNo\tDuly Now\tLess Than Seven Years\t0.9\tNone\tYes\t59\tNo\t1\t2\t1\t2\t1500\t0.6\tMore Than Four Months\tNo\t0\t1759\t1350\t600\tNo\t2\tSmall\tUnemployed\tManager\tMarket\tHealth\tElectronic\t0.3\t0.1\t0\t6\t21\t9\t0\t0\t2\t63\tScience\t6\t0.3\t8\t19\t0\t11\t62\tYes\tPolitics\t7.5\t\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t31800\t1\t0.9\t0.95\t0.5\t0.8\t0.65\t7\t1\t2\tOther Credit\n153\t71\t4250\tMarried\t2\tVery Rich\tBachelor's Degree\t21\tNeed\t40000\t24\tFemale\tMediterranean\t72\tOwner\t2\t5\t2\tManager\tNo\tRisky\tLess Than One Year\t0.5\tCo Applicant\tYes\t37\tNo\t3\t2\t0\t2\t12750\t0.3\tOne Month Late\tNo\t0\t205\t7650\t2550\tYes\t3\tSmall\tArmy\tRetired\tMarket\tBills\tOther\t0.2\t0.2\t1\t1\t356\t1\t4\t14\t35\t2\tTourism\t0\t0.3\t1\t17\t6\t25\t51\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t\tGold\tSegment1\t35700\t0.5\t0.2\t0.35\t0.4\t0.4\t0.4\t3\t8\t5\tGood Credit\n158\t49\t2750\tMarried\t3\tPoor\tBachelor's Degree\t22\tNeed\t15000\t36\tFemale\tMediterranean\t\tTenant\t2\t5\t2\tWorker\tYes\tNot Taken\tMore Than Seven Years\t0.5\tNone\tNo\t33\tNo\t3\t0\t0\t2\t8250\t0.3\tMore Than Four Months\tUnknown\t1\t1261\t825\t3300\tYes\t4\tBig\tRetired\tTechnician\tEating and Drinking\tElectronic\tOther\t0.5\t0.1\t8\t0\t51\t8\t1\t2\t5\t79\tHealth\t7\t0.4\t13\t14\t29\t27\t17\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t118800\t0.2\t0.6\t0.4\t0.1\t0.5\t0.3\t6\t6\t3\tOther Credit\n162\t62\t2150\tSingle\t1\tAverage\tMaster's Degree\t19\tHousing\t100000\t264\tFemale\tCentral Anatolia\t78\tTenant\t2\t3\t2\tFarmer\tNo\tDuly Past\tMore Than Seven Years\t0.9\tNone\tNo\t43\tNo\t1\t0\t0\t1\t6450\t0.3\tMore Than Four Months\tNo\t0\t830\t3870\t645\tYes\t2\tBig\tRetired\tUnemployed\tBills\tEating and Drinking\tElectronic\t0.4\t0.2\t9\t1\t54\t9\t4\t2\t5\t33\tTourism\t3\t0.1\t4\t14\t21\t30\t31\tYes\tFuture\t\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t14835\t0.8\t0.3\t0.55\t0.5\t0.4\t0.45\t4\t8\t4\tOther Credit\n167\t33\t6250\tDivorced\t2\tVery Rich\tBachelor's Degree\t10\tHousing\t45000\t192\tMale\tMarmara\t43\tOwner\t2\t5\t1\tManager\tNo\tRisky\tLess Than One Year\t0.8\tGuarantor\tNo\t6\tNo\t3\t2\t1\t1\t18750\t0.7\tNormal\tNo\t0\t241\t7500\t7500\tYes\t3\tMedium\tUnemployed\tRetired\tEating and Drinking\tTransportation\tMarket\t0.2\t0.2\t2\t0\t75\t9\t0\t3\t7\t\tFinance\t2\t0.1\t2\t11\t26\t37\t24\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t390000\t0.6\t1\t0.8\t0.4\t0.4\t0.4\t7\t2\t2\tGood Credit\n168\t45\t500\tSingle\t2\tVery Poor\tPrimary School Grad\t11\tAuto\t50000\t12\tFemale\tMarmara\t48\tTenant\t0\t4\t1\tProfessional\tNo\tRisky\tUnemployed\t0.9\tNone\tNo\t1\tNo\t3\t2\t0\t1\t1500\t0.7\tFour Months Late\tNo\t0\t1777\t1050\t1350\tNo\t3\tMedium\tManager\tUnemployed\tEating and Drinking\tTransportation\tBills\t0.4\t0.2\t6\t0\t184\t6\t0\t\t18\t52\tDesign\t5\t0.5\t12\t6\t6\t33\t43\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t54000\t0.7\t0.8\t0.75\t0.4\t0.4\t0.4\t6\t3\t3\tOther Credit\n169\t31\t6250\tMarried\t4\tRich\tBachelor's Degree\t82\tNeed\t45000\t24\tMale\tMediterranean\t83\tTenant\t0\t3\t1\tManager\tYes\tDelay\tUnemployed\t\tCo Applicant\tNo\t92\tNo\t1\t0\t1\t2\t18750\t0.5\tNormal\tNo\t0\t759\t3750\t7500\tYes\t5\tMedium\tRetired\tRetired\tBills\tOther\tHealth\t0.2\t0.2\t9\t0\t6\t6\t3\t0\t0\t53\tEnergy\t5\t0.3\t2\t13\t12\t14\t59\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t405000\t0.4\t0.8\t0.6\t0.4\t0.4\t0.4\t7\t6\t2\tOther Credit\n3\t70\t2750\tMarried\t3\tPoor\tBachelor's Degree\t12\tEducation\t10000\t24\tMale\tBlack Sea\t5\tTenant\t1\t3\t0\tManager\tNo\tRisky\tUnemployed\t1\tCo Applicant\tNo\t22\tNo\t3\t1\t0\t2\t8250\t0.4\tThree Months Late\tNo\t0\t1159\t4125\t5775\tYes\t4\tSmall\tManager\tRetired\tEating and Drinking\tTransportation\tOther\t0.2\t0.1\t3\t0\t\t24\t2\t17\t42\t194\tHealth\t19\t0.5\t6\t1\t20\t10\t63\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t86625\t0.5\t0.6\t0.55\t0.5\t0.4\t0.45\t3\t6\t5\tOther Credit\n7\t25\t4250\tMarried\t3\tRich\tBachelor's Degree\t11\tNeed\t25000\t12\tMale\tMediterranean\t7\tTenant\t0\t2\t1\tManager\tNo\tDuly Now\tUnemployed\t0.8\tCo Applicant\tYes\t48\tNo\t1\t1\t0\t2\t12750\t0.7\tNormal\tUnknown\t0\t581\t5100\t3825\tNo\t4\tMedium\tRetired\tRetired\tEating and Drinking\tTransportation\tMarket\t0.2\t0.1\t8\t0\t20\t7\t2\t0\t2\t27\tFinance\t2\t0.2\t7\t2\t1\t37\t53\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t229500\t0.7\t0.4\t0.55\t0.4\t0.5\t0.45\t8\t3\t\tOther Credit\n12\t56\t5250\tSingle\t2\tVery Rich\tBachelor's Degree\t14\tNeed\t40000\t24\tFemale\tAegean\t45\tOwner\t3\t5\t1\t\tNo\tDelay\tUnemployed\t0.7\tNone\tYes\t98\tNo\t3\t0\t1\t1\t15750\t0.7\tNormal\tNo\t0\t377\t11025\t11025\tNo\t3\tMedium\tRetired\tTechnician\tEating and Drinking\tMarket\tBills\t0.2\t0.1\t8\t1\t42\t2\t1\t1\t4\t51\tHealth\t5\t0.5\t8\t1\t11\t26\t54\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t319725\t1\t1\t1\t0.3\t0.5\t0.4\t5\t2\t4\tGood Credit\n16\t23\t500\tDivorced\t3\tVery Poor\tPrimary School Grad\t15\tAuto\t55000\t36\tMale\tBlack Sea\t95\tFamily Home\t0\t\t1\tManager\tNo\tRisky\tUnemployed\t0.5\tNone\tNo\t7\tNo\t3\t0\t1\t2\t1500\t0.2\tOne Month Late\tNo\t0\t1619\t1350\t1350\tYes\t4\tMedium\tManager\tFarmer\tEating and Drinking\tElectronic\tMarket\t0.2\t0.1\t6\t0\t99\t30\t1\t3\t9\t4\tEnergy\t0\t0.1\t6\t0\t8\t33\t53\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t83700\t0.6\t0.5\t0.55\t0.1\t0.4\t0.25\t8\t6\t1\tOther Credit\n21\t40\t2150\tSingle\t4\tPoor\tBachelor's Degree\t18\tNeed\t10000\t36\tFemale\tEastern Anatolia\t77\tTenant\t2\t3\t0\tManager\tYes\tDuly Now\tMore Than Seven Years\t0.5\tCo Applicant\tNo\t78\tNo\t3\t2\t0\t0\t6450\t0.7\tMore Than Four Months\tNo\t0\t1376\t645\t1935\tNo\t5\tMedium\tRetired\tWorker\tEating and Drinking\tMarket\tOther\t0.1\t0.1\t1\t0\t\t2\t1\t35\t89\t77\tDesign\t7\t0.5\t2\t1\t26\t27\t44\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t87075\t0.2\t0.1\t0.15\t1\t0.6\t0.8\t6\t6\t3\tOther Credit\n25\t67\t500\tMarried\t5\tVery Poor\tPrimary School Grad\t14\tEducation\t15000\t12\tFemale\tAegean\t2\tTenant\t0\t6\t2\tManager\tNo\tRisky\tUnemployed\t0.9\tCo Applicant\tNo\t71\tNo\t3\t1\t1\t1\t1500\t0.9\tTwo Months Late\tNo\t0\t1596\t750\t150\tNo\t6\tMedium\tTechnician\tFarmer\tBills\tMarket\tHealth\t0.2\t0.1\t2\t0\t570\t30\t4\t22\t57\t22\tTourism\t\t0.3\t8\t4\t4\t21\t63\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t2700\t0.6\t0.3\t0.45\t0.3\t0.5\t0.4\t4\t7\t5\tOther Credit\n29\t22\t2750\tMarried\t5\tPoor\tBachelor's Degree\t8\tAuto\t55000\t24\tFemale\tMarmara\t63\tTenant\t2\t5\t2\tFarmer\tNo\tDelay\tUnemployed\t0.5\tCo Applicant\tNo\t35\tNo\t2\t0\t1\t2\t8250\t0.7\tThree Months Late\tNo\t0\t1164\t6600\t825\tYes\t6\tMedium\tFarmer\tManager\tEating and Drinking\tOther\tHealth\t0.5\t0.1\t6\t1\t90\t2\t2\t3\t9\t37\tEnergy\t3\t0.2\t11\t0\t4\t26\t59\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t51975\t0.7\t0.5\t0.6\t0.5\t0.4\t0.45\t8\t\t1\tOther Credit\n34\t48\t2150\tDivorced\t4\tPoor\tBachelor's Degree\t94\tEducation\t15000\t24\tFemale\tMediterranean\t65\tTenant\t3\t5\t2\tArmy\tYes\tDelay\tLess Than Seven Years\t0.8\tCo Applicant\tNo\t39\tNo\t2\t0\t1\t0\t6450\t0.5\tFour Months Late\tUnknown\t1\t1278\t1935\t3870\tYes\t5\tMedium\tFarmer\tManager\tEating and Drinking\tElectronic\tHealth\t0.3\t0.2\t8\t4\t2\t27\t4\t0\t0\t39\tFinance\t3\t0.3\t7\t15\t24\t9\t45\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t143190\t0.4\t0.8\t0.6\t0.4\t0.3\t\t6\t5\t3\tOther Credit\n39\t34\t3750\tMarried\t1\tRich\tDoctorate Degree\t26\tAuto\t50000\t12\tMale\tSoutheast Anatolia\t74\tTenant\t1\t1\t0\tProfessional\tNo\tDuly Now\tLess Than Four Years\t0.7\tNone\tYes\t85\tNo\t1\t1\t0\t1\t11250\t0.9\tNormal\tUnknown\t1\t476\t9000\t1125\tYes\t2\tMedium\tUnemployed\tRetired\tEating and Drinking\tHealth\tOther\t0.2\t0.1\t10\t5\t160\t21\t0\t6\t16\t149\tTourism\t14\t0.6\t6\t17\t14\t\t60\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t57375\t0.6\t0.3\t0.45\t0.5\t0.7\t0.6\t7\t3\t2\tGood Credit\n43\t45\t3750\tMarried\t3\tAverage\tMaster's Degree\t15\tAuto\t35000\t\tMale\tBlack Sea\t59\tTenant\t3\t3\t0\tWorker\tYes\tNot Taken\tMore Than Seven Years\t0.5\tGuarantor\tYes\t64\tNo\t3\t1\t0\t0\t11250\t0.1\tTwo Months Late\tYes\t2\t875\t9000\t1125\tYes\t4\tMedium\tUnemployed\tUnemployed\tEating and Drinking\tHealth\tElectronic\t0.4\t0.2\t7\t6\t700\t6\t3\t28\t70\t44\tHealth\t4\t0.1\t11\t0\t24\t5\t60\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t45000\t0.5\t0.3\t0.4\t0.1\t0.2\t0.15\t6\t5\t3\tOther Credit\n44\t36\t2150\tMarried\t3\tPoor\tBachelor's Degree\t7\tHousing\t45000\t264\tMale\tCentral Anatolia\t94\tTenant\t0\t3\t0\tArmy\tYes\tDuly Now\tLess Than Four Years\t\tNone\tYes\t83\tNo\t2\t0\t1\t2\t6450\t0.3\tOne Month Late\tNo\t0\t1337\t4515\t2580\tYes\t4\tBig\tRetired\tWorker\tEating and Drinking\tTransportation\tBills\t0.5\t0.1\t3\t1\t675\t30\t4\t27\t67\t18\tTourism\t1\t0.2\t4\t10\t13\t20\t53\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t126420\t0.6\t0.5\t0.55\t0.1\t0.4\t0.25\t7\t8\t2\tOther Credit\n45\t19\t2750\tMarried\t1\tAverage\tMaster's Degree\t19\tEducation\t20000\t24\tMale\tCentral Anatolia\t21\tTenant\t1\t2\t0\tFarmer\tYes\tDuly Now\tUnemployed\t0.7\tNone\tYes\t56\tNo\t1\t2\t1\t0\t8250\t0.1\tTwo Months Late\tNo\t0\t1130\t7425\t6600\tYes\t2\tMedium\tClerical\tFarmer\tEating and Drinking\tElectronic\tOther\t0.1\t0.2\t0\t4\t408\t30\t0\t16\t40\t2\tTourism\t0\t0.1\t9\t13\t5\t14\t59\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t\t0.65\t7.15\tSilver\tSegment3\t435600\t0.8\t0.6\t0.7\t0.9\t0.8\t0.85\t9\t1\t1\tOther Credit\n48\t21\t2750\tMarried\t6\tPoor\tBachelor's Degree\t35\tNeed\t35000\t12\tFemale\tCentral Anatolia\t93\tTenant\t3\t3\t1\tClerical\tNo\tDuly Now\tMore Than Seven Years\t1\tCo Applicant\tYes\t72\tNo\t2\t0\t0\t1\t8250\t0.4\tFour Months Late\tUnknown\t0\t1144\t4125\t7425\tNo\t7\tMedium\tFarmer\tUnemployed\tMarket\tHealth\tOther\t0.3\t0.2\t1\t0\t15\t9\t1\t0\t1\t13\tProduction\t1\t0.1\t\t17\t11\t25\t46\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t475200\t0.9\t0.6\t0.75\t0.4\t0.3\t0.35\t8\t5\t1\tOther Credit\n53\t36\t4250\tSingle\t5\tRich\tBachelor's Degree\t15\tNeed\t40000\t36\tMale\tEastern Anatolia\t73\tTenant\t2\t5\t1\tManager\t\tNot Taken\tMore Than Seven Years\t0.9\tCo Applicant\tYes\t65\tNo\t3\t0\t1\t1\t12750\t0\tOne Month Late\tNo\t1\t578\t2550\t1275\tNo\t6\tBig\tWorker\tClerical\tEating and Drinking\tTransportation\tBills\t0.3\t0.1\t0\t6\t4\t21\t3\t0\t0\t24\tEducation\t2\t0.1\t0\t1\t16\t31\t52\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t62475\t0.3\t0.1\t0.2\t0.5\t0.2\t0.35\t7\t8\t2\tOther Credit\n57\t29\t500\tMarried\t5\tVery Poor\tPrimary School Grad\t11\tHousing\t85000\t108\tFemale\tSoutheast Anatolia\t50\tFamily Home\t2\t1\t0\tWorker\tNo\tRisky\tMore Than Seven Years\t0.5\tGuarantor\tNo\t68\t\t1\t0\t0\t2\t1500\t0.3\tNormal\tNo\t0\t1720\t900\t300\tYes\t6\tSmall\tUnemployed\tClerical\tEating and Drinking\tBills\tOther\t0.4\t0.2\t2\t1\t400\t27\t2\t16\t40\t25\tConsulting\t2\t0.2\t4\t12\t21\t6\t57\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t16800\t1\t0.2\t0.6\t0.1\t0.4\t0.25\t8\t1\t2\tOther Credit\n61\t54\t2150\tMarried\t1\tAverage\tMaster's Degree\t33\tAuto\t55000\t48\tMale\tCentral Anatolia\t9\tTenant\t0\t1\t0\tClerical\tYes\tDelay\tUnemployed\t1\tCo Applicant\tYes\t57\tNo\t1\t0\t0\t2\t6450\t0\tFour Months Late\tUnknown\t0\t1061\t3225\t1935\tNo\t2\tSmall\tRetired\tProfessional\tEating and Drinking\tHealth\tBills\t0.1\t0.2\t6\t0\t360\t6\t1\t14\t36\t37\tEnergy\t3\t0.3\t9\t5\t27\t17\t42\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t\tSilver\tSegment3\t59985\t0.9\t0.4\t0.65\t0.3\t0.3\t0.3\t5\t1\t3\tOther Credit\n66\t34\t500\tSingle\t3\tVery Poor\tPrimary School Grad\t57\tHousing\t35000\t228\tMale\tMarmara\t82\t\t1\t6\t2\tWorker\tYes\tDelay\tLess Than Seven Years\t0.7\tGuarantor\tYes\t12\tNo\t3\t0\t1\t1\t1500\t0.2\tOne Month Late\tNo\t0\t1780\t150\t900\tYes\t4\tMedium\tRetired\tTechnician\tBills\tElectronic\tEating and Drinking\t0.4\t0.1\t8\t0\t504\t8\t1\t20\t50\t48\tConsulting\t4\t0.2\t9\t8\t3\t40\t40\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t45900\t0.4\t0.7\t0.55\t0.4\t0.2\t0.3\t7\t8\t2\tOther Credit\n70\t47\t2750\tMarried\t3\tPoor\tBachelor's Degree\t6\tNeed\t30000\t12\tFemale\tCentral Anatolia\t78\tTenant\t3\t3\t0\tWorker\tNo\tRisky\tLess Than Seven Years\t0.9\tCo Applicant\tNo\t15\tNo\t2\t2\t1\t1\t8250\t0.8\tNormal\tUnknown\t\t1457\t5775\t825\tYes\t4\tMedium\tRetired\tRetired\tEating and Drinking\tTransportation\tBills\t0.5\t0.2\t2\t0\t23\t27\t3\t0\t2\t53\tLaw\t5\t0.4\t11\t1\t11\t0\t77\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t31350\t0.8\t0.3\t0.55\t1\t0.7\t0.85\t6\t1\t3\tOther Credit\n75\t50\t2750\tMarried\t4\tPoor\tBachelor's Degree\t10\tHousing\t55000\t276\tMale\tMarmara\t36\tTenant\t2\t3\t1\tManager\tNo\tDuly Now\tLess Than Four Years\t0.5\tGuarantor\tNo\t60\tNo\t1\t0\t1\t1\t8250\t0.6\tMore Than Four Months\tNo\t1\t1152\t7425\t6600\tNo\t5\tMedium\tClerical\tRetired\tBills\tTransportation\tOther\t0.5\t0.1\t7\t6\t390\t7\t3\t15\t39\t287\tEnergy\t28\t0.6\t14\t18\t0\t35\t33\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\t\t231000\t0.5\t0.5\t0.5\t0.5\t0.5\t0.5\t5\t7\t3\tOther Credit\n79\t22\t6250\t\t2\tVery Rich\tBachelor's Degree\t34\tAuto\t40000\t24\tFemale\tMediterranean\t32\tOwner\t1\t6\t2\tManager\tNo\tRisky\tMore Than Seven Years\t0.5\tCo Applicant\tYes\t98\tNo\t3\t0\t1\t2\t18750\t0\tNormal\tNo\t0\t222\t18750\t7500\tYes\t3\tBig\tUnemployed\tTechnician\tMarket\tHealth\tElectronic\t0.2\t0.2\t0\t0\t46\t8\t1\t1\t4\t12\tFinance\t1\t0.2\t9\t1\t23\t6\t61\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t472500\t1\t0.8\t0.9\t0.5\t0.5\t0.5\t8\t3\t1\tGood Credit\n83\t27\t5250\tMarried\t2\tVery Rich\tBachelor's Degree\t9\tHousing\t45000\t168\tMale\tAegean\t55\tOwner\t2\t4\t1\tProfessional\tNo\tRisky\tLess Than One Year\t0.7\tNone\tNo\t8\tNo\t2\t1\t1\t1\t15750\t0.4\tNormal\tUnknown\t0\t310\t9450\t9450\tYes\t3\tSmall\tProfessional\tUnemployed\tEating and Drinking\tTransportation\tBills\t0.1\t0.1\t7\t1\t40\t30\t3\t1\t4\t15\tTransportation\t1\t0.1\t11\t14\t25\t15\t35\tYes\tSocial\t7.5\t\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t548100\t0.5\t0.7\t0.6\t0.2\t0.1\t0.15\t8\t7\t2\tGood Credit\n87\t42\t6250\tMarried\t6\tRich\tBachelor's Degree\t19\tAuto\t35000\t24\tFemale\tMarmara\t91\tTenant\t2\t4\t0\tManager\tYes\tRisky\tLess Than Seven Years\t0.7\tNone\tYes\t98\tNo\t3\t2\t1\t0\t18750\t0.5\tNormal\tUnknown\t0\t601\t1875\t13125\tYes\t7\tMedium\tRetired\tManager\tMarket\tBills\tElectronic\t0.5\t0.2\t8\t1\t34\t6\t1\t1\t3\t22\tDesign\t2\t0.2\t1\t9\t20\t25\t45\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t\t0.65\t7.15\tSilver\tSegment2\t564375\t0.3\t1\t0.65\t1\t0.8\t0.9\t6\t4\t3\tOther Credit\n88\t40\t2750\tMarried\t5\tPoor\tBachelor's Degree\t5\tNeed\t30000\t24\tFemale\tAegean\t49\tTenant\t3\t3\t2\tTechnician\tNo\tRisky\tLess Than One Year\t1\tGuarantor\tNo\t66\tNo\t1\t0\t0\t2\t8250\t0.2\tOne Month Late\tNo\t0\t1290\t4125\t825\tNo\t6\tMedium\tUnemployed\tArmy\tMarket\tBills\tHealth\t0.5\t0.1\t0\t0\t500\t21\t3\t\t50\t240\tScience\t24\t0.6\t14\t1\t2\t12\t71\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t37125\t0.9\t0.4\t0.65\t0.4\t0.5\t0.45\t6\t8\t3\tOther Credit\n89\t31\t6250\tDivorced\t\tRich\tBachelor's Degree\t14\tNeed\t40000\t24\tMale\tMediterranean\t83\tTenant\t1\t5\t2\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.8\tNone\tNo\t92\tNo\t2\t1\t1\t0\t18750\t0.3\tNormal\tNo\t0\t729\t13125\t3750\tYes\t7\tMedium\tArmy\tClerical\tEating and Drinking\tHealth\tBills\t0.2\t0.2\t0\t4\t288\t6\t3\t11\t28\t61\tTransportation\t6\t0.5\t7\t11\t7\t34\t41\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t202500\t0.8\t0.4\t0.6\t0.4\t0.2\t0.3\t7\t6\t2\tOther Credit\n90\t21\t2750\tSingle\t1\tAverage\tMaster's Degree\t7\tAuto\t35000\t24\tFemale\tMarmara\t91\tTenant\t0\t4\t1\tManager\tNo\tNot Taken\tMore Than Seven Years\t0.9\tGuarantor\tYes\t65\tNo\t3\t2\t0\t2\t8250\t0.8\tThree Months Late\tUnknown\t1\t784\t4950\t825\tNo\t2\tMedium\tRetired\tWorker\tEating and Drinking\tTransportation\tHealth\t0.3\t0.1\t0\t0\t72\t\t2\t2\t7\t176\tEducation\t17\t0.6\t6\t14\t14\t22\t44\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t52800\t0.5\t0.4\t0.45\t0.1\t0.2\t0.15\t8\t8\t1\tOther Credit\n91\t65\t2150\tSingle\t3\tPoor\tBachelor's Degree\t9\tEducation\t15000\t12\tFemale\tBlack Sea\t70\tTenant\t0\t3\t0\tTechnician\tYes\tNot Taken\tUnemployed\t0.7\tNone\tNo\t\tNo\t3\t2\t0\t2\t6450\t0.9\tFour Months Late\tYes\t2\t1211\t5160\t2580\tYes\t4\tBig\tUnemployed\tUnemployed\tMarket\tEating and Drinking\tElectronic\t0.3\t0.2\t0\t1\t282\t30\t1\t11\t28\t171\tHealth\t17\t0.5\t4\t0\t6\t2\t88\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t51600\t0.8\t0.8\t0.8\t0.9\t0.8\t0.85\t4\t3\t5\tOther Credit\n92\t36\t3750\tSingle\t2\tRich\tDoctorate Degree\t13\tNeed\t25000\t12\tMale\tBlack Sea\t15\tTenant\t1\t3\t1\tManager\tYes\tRisky\tMore Than Seven Years\t0.5\tCo Applicant\tNo\t89\tNo\t1\t2\t1\t1\t11250\t0.3\tNormal\tNo\t0\t468\t4500\t3375\tYes\t3\tMedium\tUnemployed\tWorker\tEating and Drinking\tHealth\tElectronic\t0.3\t0.2\t10\t0\t42\t6\t0\t1\t4\t2\tLaw\t0\t0.2\t4\t7\t14\t28\t47\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t165375\t0.8\t0.5\t0.65\t0.9\t1\t0.95\t7\t2\t\tGood Credit\n93\t18\t5250\tMarried\t3\tRich\tBachelor's Degree\t5\t\t90000\t60\tFemale\tMarmara\t71\tOwner\t2\t4\t2\tProfessional\tYes\tRisky\tMore Than Seven Years\t0.9\tCo Applicant\tYes\t56\tNo\t2\t2\t0\t0\t15750\t0.5\tNormal\tNo\t0\t652\t6300\t4725\tNo\t4\tMedium\tUnemployed\tClerical\tEating and Drinking\tHealth\tMarket\t0.4\t0.2\t1\t0\t154\t6\t0\t6\t15\t3\tConsulting\t0\t0.2\t12\t5\t26\t1\t56\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t316575\t1\t0.2\t0.6\t0.4\t0.2\t0.3\t9\t3\t1\tOther Credit\n94\t46\t4250\tMarried\t3\tRich\tBachelor's Degree\t10\tNeed\t45000\t36\tMale\tSoutheast Anatolia\t71\tOwner\t0\t4\t1\tManager\tNo\tRisky\tLess Than Four Years\t1\tCo Applicant\tYes\t15\tNo\t2\t1\t1\t1\t12750\t0.8\tNormal\tNo\t0\t435\t12750\t11475\tYes\t4\tMedium\tRetired\tUnemployed\tMarket\tOther\tHealth\t0.3\t0.1\t8\t4\t20\t8\t2\t0\t2\t20\tEnergy\t2\t0.2\t6\t15\t6\t\t68\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t447525\t0.8\t0.7\t0.75\t0.5\t0.5\t0.5\t6\t3\t3\tGood Credit\n95\t71\t2750\tSingle\t3\tPoor\tBachelor's Degree\t21\tAuto\t55000\t12\tFemale\tSoutheast Anatolia\t16\tTenant\t3\t\t0\tManager\tNo\tDelay\tLess Than Seven Years\t1\tGuarantor\tNo\t5\tNo\t1\t2\t1\t1\t8250\t0.1\tOne Month Late\tNo\t0\t1378\t6600\t5775\tYes\t4\tSmall\tProfessional\tFarmer\tMarket\tBills\tEating and Drinking\t0.2\t0.2\t10\t5\t40\t2\t0\t1\t4\t28\tProduction\t2\t0.2\t2\t17\t20\t10\t51\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t80850\t0.8\t0.6\t0.7\t1\t0.6\t0.8\t3\t3\t5\tOther Credit\n96\t39\t2750\tMarried\t3\tPoor\tBachelor's Degree\t2\tNeed\t35000\t36\tFemale\tEastern Anatolia\t95\tTenant\t2\t3\t1\tWorker\tYes\tDelay\tLess Than Seven Years\t0.9\tGuarantor\tYes\t79\tNo\t2\t0\t0\t1\t8250\t0.4\tNormal\tNo\t1\t1154\t1650\t6600\tYes\t4\tBig\tUnemployed\tManager\tEating and Drinking\tOther\tMarket\t0.5\t0.1\t0\t5\t960\t27\t3\t38\t96\t158\tEducation\t15\t0.7\t10\t5\t29\t18\t38\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t303600\t0.5\t0.9\t0.7\t0.2\t0.3\t0.25\t\t3\t2\tOther Credit\n97\t70\t4250\tSingle\t3\tRich\tBachelor's Degree\t5\tHousing\t80000\t48\tMale\tAegean\t89\tOwner\t3\t2\t1\tManager\tNo\tRisky\tLess Than Four Years\t0.9\tCo Applicant\tYes\t49\tNo\t1\t0\t0\t\t12750\t0.8\tNormal\tNo\t1\t736\t8925\t5100\tYes\t4\tBig\tManager\tManager\tBills\tElectronic\tTransportation\t0.3\t0.1\t8\t1\t21\t6\t2\t0\t2\t116\tFinance\t11\t0.6\t11\t16\t9\t40\t24\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t76500\t1\t0.7\t0.85\t0.3\t0.4\t0.35\t3\t1\t5\tOther Credit\n98\t20\t3750\tDivorced\t2\tRich\tDoctorate Degree\t8\tNeed\t25000\t36\tFemale\tMarmara\t67\tTenant\t3\t4\t0\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.5\tCo Applicant\tYes\t14\tNo\t3\t0\t1\t0\t11250\t0.6\tNormal\tNo\t0\t694\t11250\t4500\tNo\t3\tBig\tRetired\tUnemployed\tEating and Drinking\tBills\tMarket\t0.2\t0.1\t0\t1\t12\t7\t1\t0\t1\t42\tScience\t4\t0.2\t9\t10\t10\t44\t\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t292500\t1\t0.5\t0.75\t0.2\t0.1\t0.15\t8\t8\t1\tOther Credit\n99\t39\t4250\tSingle\t2\tVery Rich\tBachelor's Degree\t12\tNeed\t20000\t12\tFemale\tCentral Anatolia\t61\tOwner\t1\t5\t2\tManager\tYes\tNot Taken\tUnemployed\t0.5\tNone\tNo\t31\tNo\t3\t1\t0\t2\t12750\t0.3\tNormal\tNo\t0\t222\t12750\t11475\tNo\t3\tBig\tUnemployed\tUnemployed\tMarket\tHealth\tOther\t0.4\t0.1\t1\t1\t52\t2\t\t2\t5\t70\tTransportation\t7\t0.3\t7\t10\t10\t39\t34\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t527850\t0.6\t0.6\t0.6\t0.1\t0.5\t0.3\t7\t5\t2\tGood Credit\n100\t47\t2150\tMarried\t4\tPoor\tBachelor's Degree\t12\tEducation\t10000\t24\tFemale\tBlack Sea\t38\tTenant\t3\t4\t2\tManager\tNo\tNot Taken\tUnemployed\t0.6\tGuarantor\tNo\t7\tNo\t2\t1\t0\t0\t6450\t0.3\tMore Than Four Months\tNo\t0\t1512\t1290\t5160\tYes\t5\tMedium\tManager\tWorker\tEating and Drinking\tHealth\tElectronic\t0.3\t0.1\t2\t1\t104\t1\t2\t4\t10\t43\tTourism\t4\t0.3\t8\t8\t18\t25\t41\tYes\tThemselves\t7.5\t\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t196080\t0.4\t0.7\t0.55\t0.3\t0.2\t0.25\t6\t7\t3\tOther Credit\n101\t53\t2150\tMarried\t5\tPoor\tBachelor's Degree\t33\tAuto\t10000\t36\tFemale\tSoutheast Anatolia\t34\tTenant\t1\t3\t2\tTechnician\tNo\tNot Taken\tLess Than Seven Years\t0.5\tGuarantor\tNo\t71\tNo\t1\t1\t0\t0\t6450\t0.8\tTwo Months Late\tUnknown\t1\t1409\t5160\t1935\tNo\t6\tSmall\tRetired\tRetired\tMarket\tBills\tTransportation\t0.2\t0.1\t9\t4\t462\t30\t2\t18\t46\t270\tProduction\t27\t0.8\t0\t8\t17\t44\t31\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t61920\t0.7\t0.5\t0.6\t0.3\t0.1\t\t5\t3\t3\tOther Credit\n102\t38\t5250\tSingle\t6\tRich\tBachelor's Degree\t35\tNeed\t45000\t12\tFemale\tMediterranean\t13\tTenant\t3\t5\t1\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.6\tNone\tYes\t53\tNo\t3\t0\t1\t1\t15750\t0.3\tNormal\tNo\t0\t627\t14175\t6300\tYes\t7\tMedium\tManager\tManager\tBills\tOther\tTransportation\t0.3\t0.1\t9\t0\t72\t21\t0\t2\t7\t92\tFinance\t9\t0.5\t5\t4\t22\t17\t52\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t\tSilver\tSegment2\t296100\t0.9\t0.5\t0.7\t0.2\t0.5\t0.35\t7\t8\t2\tOther Credit\n103\t46\t2750\tMarried\t6\tPoor\tBachelor's Degree\t5\tAuto\t35000\t12\tMale\tMediterranean\t73\tTenant\t3\t1\t0\tTechnician\tNo\tDuly Now\tLess Than Four Years\t0.6\tCo Applicant\tNo\t96\tNo\t1\t0\t0\t0\t8250\t0\tThree Months Late\tUnknown\t0\t1226\t1650\t2475\tYes\t7\tSmall\tTechnician\tTechnician\tEating and Drinking\tBills\tElectronic\t\t0.2\t6\t1\t568\t27\t0\t22\t56\t31\tTransportation\t3\t0.3\t13\t17\t29\t20\t21\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t96525\t0.1\t0.3\t0.2\t0.5\t0.1\t0.3\t6\t4\t3\tOther Credit\n104\t64\t5250\tMarried\t3\tRich\t\t82\tNeed\t30000\t24\tFemale\tSoutheast Anatolia\t82\tOwner\t2\t4\t1\tProfessional\tNo\tRisky\tMore Than Seven Years\t1\tGuarantor\tYes\t67\tNo\t3\t1\t0\t2\t15750\t0.8\tOne Month Late\tNo\t0\t705\t14175\t6300\tYes\t4\tMedium\tClerical\tTechnician\tEating and Drinking\tBills\tMarket\t0.3\t0.1\t7\t6\t32\t8\t3\t1\t3\t99\tTransportation\t9\t0.4\t4\t9\t9\t11\t67\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t132300\t0.6\t0.5\t0.55\t0.5\t0.1\t0.3\t4\t8\t4\tOther Credit\n105\t27\t500\tMarried\t2\tVery Poor\t\t39\tHousing\t85000\t120\tMale\tEastern Anatolia\t49\tFamily Home\t1\t3\t1\tFarmer\tYes\tRisky\tLess Than Seven Years\t0.9\tGuarantor\tNo\t31\tNo\t1\t2\t1\t0\t1500\t0.6\tOne Month Late\tNo\t0\t1814\t900\t450\tNo\t3\tMedium\tClerical\tFarmer\tEating and Drinking\tOther\tTransportation\t0.2\t0.1\t9\t0\t360\t30\t2\t14\t36\t281\tTourism\t28\t0.7\t13\t14\t7\t37\t29\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t26100\t1\t0.1\t0.55\t0.5\t0.8\t0.65\t8\t1\t2\tOther Credit\n106\t19\t4250\tSingle\t2\tVery Rich\tBachelor's Degree\t27\tAuto\t40000\t24\tFemale\tMediterranean\t24\tTenant\t2\t4\t2\t\tNo\tRisky\tLess Than Four Years\t0.7\tNone\tYes\t51\tNo\t1\t1\t1\t1\t12750\t0.9\tOne Month Late\tNo\t0\t346\t3825\t3825\tYes\t3\tMedium\tManager\tRetired\tEating and Drinking\tHealth\tMarket\t0.2\t0.1\t0\t0\t66\t27\t2\t2\t6\t46\tTourism\t4\t0.3\t7\t14\t13\t23\t43\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t252450\t0.2\t0.1\t0.15\t0.3\t0.5\t0.4\t9\t5\t1\tGood Credit\n107\t18\t3750\tMarried\t1\tRich\tDoctorate Degree\t39\tHousing\t15000\t144\tMale\tBlack Sea\t16\tTenant\t1\t3\t2\tManager\tYes\tNot Taken\tUnemployed\t1\tCo Applicant\tNo\t35\tNo\t1\t2\t0\t1\t11250\t0.1\tNormal\tUnknown\t0\t414\t3375\t3375\tYes\t2\tMedium\tWorker\tWorker\tMarket\tBills\tElectronic\t0.4\t0.2\t7\t0\t860\t30\t3\t34\t86\t9\tConsulting\t0\t0.1\t4\t8\t29\t40\t19\t\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t226125\t0.4\t0.4\t0.4\t0.1\t0.1\t0.1\t9\t3\t1\tGood Credit\n372\t\t5250\tSingle\t3\tRich\tBachelor's Degree\t2\tNeed\t15000\t24\tFemale\tEastern Anatolia\t32\tOwner\t2\t3\t1\tProfessional\tNo\tDuly Now\tUnemployed\t0.8\tCo Applicant\tNo\t11\tNo\t2\t2\t0\t2\t15750\t0.7\tNormal\tNo\t0\t560\t7875\t1575\tYes\t4\tSmall\tManager\tRetired\tBills\tHealth\tOther\t0.5\t0.1\t7\t6\t90\t30\t3\t3\t9\t22\tEnergy\t2\t0.1\t4\t15\t17\t27\t37\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t33075\t0.8\t0.3\t0.55\t0.9\t0.6\t0.75\t4\t1\t4\tGood Credit\n375\t67\t6250\tMarried\t2\tVery Rich\tBachelor's Degree\t26\tNeed\t40000\t36\tFemale\tCentral Anatolia\t72\tOwner\t2\t3\t1\tManager\tNo\tDuly Past\t\t0.7\tNone\tYes\t27\tNo\t2\t2\t0\t1\t18750\t0.8\tNormal\tUnknown\t1\t351\t13125\t5625\tYes\t3\tBig\tRetired\tArmy\tEating and Drinking\tOther\tMarket\t0.3\t0.1\t1\t0\t560\t21\t4\t22\t56\t28\tDesign\t2\t0.3\t12\t10\t15\t11\t52\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t101250\t1\t0.1\t0.55\t1\t0.8\t0.9\t4\t1\t5\tGood Credit\n379\t27\t4250\tMarried\t2\tVery Rich\tBachelor's Degree\t17\tHousing\t105000\t240\tFemale\tMarmara\t81\tOwner\t3\t3\t2\tProfessional\tYes\tDelay\tUnemployed\t0.7\tCo Applicant\tYes\t73\tNo\t1\t0\t0\t0\t12750\t0.7\tNormal\tNo\t0\t\t6375\t5100\tNo\t3\tMedium\tClerical\tWorker\tEating and Drinking\tMarket\tOther\t0.4\t0.1\t1\t0\t5\t7\t2\t0\t0\t29\tScience\t2\t0.3\t9\t18\t17\t10\t46\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t295800\t0.9\t0.6\t0.75\t0.4\t0.5\t0.45\t8\t4\t2\tGood Credit\n383\t47\t2750\tMarried\t3\tPoor\tBachelor's Degree\t59\tNeed\t40000\t24\tMale\tBlack Sea\t78\tTenant\t3\t2\t1\tTechnician\tNo\tRisky\tLess Than Four Years\t0.6\tCo Applicant\tNo\t64\tNo\t1\t1\t0\t1\t8250\t0.1\tFour Months Late\tUnknown\t1\t1294\t2475\t3300\t\t4\tBig\tWorker\tProfessional\tBills\tElectronic\tHealth\t0.3\t0.2\t9\t1\t30\t27\t2\t1\t3\t40\tFinance\t4\t0.2\t1\t2\t4\t35\t58\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t125400\t0.2\t0.8\t0.5\t0.5\t0.3\t0.4\t6\t2\t3\tOther Credit\n388\t59\t2750\tDivorced\t\tAverage\tMaster's Degree\t2\tNeed\t10000\t24\tMale\tAegean\t50\tTenant\t3\t3\t0\tFarmer\tNo\tNot Taken\tLess Than One Year\t0.6\tNone\tNo\t92\tNo\t3\t2\t0\t1\t8250\t0.2\tThree Months Late\tUnknown\t1\t880\t3300\t1650\tYes\t2\tMedium\tUnemployed\tArmy\tBills\tElectronic\tOther\t0.1\t0.2\t9\t1\t60\t27\t3\t2\t6\t66\tEnergy\t6\t0.5\t8\t3\t11\t18\t60\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t42900\t0.6\t0.2\t0.4\t0.8\t0.9\t0.85\t5\t2\t4\tOther Credit\n392\t44\t2750\tMarried\t2\tAverage\tMaster's Degree\t70\tNeed\t20000\t12\tMale\tBlack Sea\t34\tTenant\t1\t1\t0\tProfessional\tNo\tDelay\tLess Than Seven Years\t0.9\tGuarantor\t\t22\tNo\t1\t2\t0\t0\t8250\t0.2\tFour Months Late\tNo\t0\t1091\t6600\t7425\tNo\t3\tSmall\tArmy\tProfessional\tEating and Drinking\tBills\tTransportation\t0.4\t0.2\t3\t1\t13\t21\t2\t0\t1\t69\tTransportation\t6\t0.3\t4\t5\t19\t37\t35\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t304425\t0.8\t0.9\t0.85\t1\t0.9\t0.95\t6\t2\t3\tOther Credit\n396\t50\t500\tDivorced\t2\tVery Poor\tPrimary School Grad\t15\tAuto\t30000\t12\tMale\tMarmara\t80\tTenant\t0\t5\t2\tTechnician\tNo\tRisky\tLess Than Four Years\t0.7\tNone\tYes\t7\tNo\t3\t1\t0\t2\t1500\t0.6\tTwo Months Late\tNo\t0\t1608\t1050\t600\tYes\t3\tMedium\tTechnician\tRetired\tEating and Drinking\tMarket\tHealth\t0.1\t0.2\t6\t0\t30\t1\t3\t1\t3\t32\tConsulting\t3\t0.3\t12\t2\t24\t29\t33\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t\t7.15\tBronze\tSegment5\t21000\t0.9\t0.5\t0.7\t0.2\t0.1\t0.15\t5\t5\t3\tOther Credit\n400\t62\t2750\tSingle\t2\tAverage\tMaster's Degree\t21\tNeed\t\t24\tMale\tMediterranean\t17\tTenant\t0\t4\t1\tClerical\tNo\tDuly Now\tMore Than Seven Years\t0.7\tNone\tNo\t43\tNo\t3\t0\t0\t2\t8250\t0.5\tMore Than Four Months\tNo\t0\t940\t6600\t4125\tYes\t3\tMedium\tArmy\tManager\tEating and Drinking\tMarket\tOther\t0.1\t0.1\t0\t0\t205\t9\t2\t8\t20\t23\tProduction\t2\t0.1\t8\t8\t23\t7\t54\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t94875\t0.9\t0.6\t0.75\t0.2\t0.2\t0.2\t4\t7\t4\tOther Credit\n403\t68\t2750\tDivorced\t1\tAverage\tMaster's Degree\t13\tAuto\t50000\t24\tMale\tCentral Anatolia\t86\tTenant\t2\t4\t1\tArmy\tNo\tRisky\tLess Than Seven Years\t0.7\tGuarantor\tNo\t69\tNo\t2\t0\t1\t2\t8250\t0.2\tTwo Months Late\tUnknown\t0\t787\t6600\t3300\tYes\t2\tMedium\tManager\tRetired\tEating and Drinking\tOther\tElectronic\t0.3\t0.1\t3\t1\t600\t21\t2\t24\t60\t16\tTransportation\t1\t0.2\t8\t3\t\t27\t55\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t56100\t0.8\t0.7\t0.75\t0.4\t0.2\t0.3\t4\t6\t5\tOther Credit\n404\t68\t2150\tMarried\t3\tPoor\tBachelor's Degree\t9\tNeed\t35000\t12\tFemale\tMarmara\t56\tTenant\t3\t4\t2\tProfessional\tNo\tNot Taken\tLess Than Four Years\t0.9\tGuarantor\tNo\t36\tNo\t\t2\t0\t1\t6450\t0.7\tNormal\tNo\t0\t1242\t3225\t3225\tYes\t4\tSmall\tTechnician\tRetired\tEating and Drinking\tHealth\tMarket\t0.3\t0.2\t7\t1\t864\t7\t1\t34\t86\t89\tConsulting\t8\t0.4\t11\t5\t1\t40\t43\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t54825\t0.8\t0.6\t0.7\t0.5\t0.3\t0.4\t4\t1\t5\tOther Credit\n405\t63\t6250\tMarried\t1\tVery Rich\tBachelor's Degree\t49\tNeed\t30000\t36\tMale\tSoutheast Anatolia\t44\tOwner\t3\t2\t1\tManager\tYes\tRisky\tLess Than One Year\t1\tNone\tNo\t46\tNo\t1\t2\t0\t0\t18750\t0.5\tNormal\tUnknown\t0\t266\t3750\t1875\tYes\t2\tSmall\tClerical\tUnemployed\tMarket\tHealth\tEating and Drinking\t0.2\t0.1\t10\t5\t31\t6\t1\t1\t3\t64\tConsulting\t6\t0.5\t9\t16\t3\t38\t34\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t41250\t0.1\t0.5\t0.3\t0.5\t\t0.7\t4\t2\t4\tGood Credit\n406\t28\t6250\tMarried\t2\tVery Rich\tBachelor's Degree\t9\tEducation\t20000\t12\tFemale\tCentral Anatolia\t66\tOwner\t3\t3\t0\tManager\tYes\tNot Taken\tLess Than Four Years\t0.5\tNone\tNo\t83\tNo\t3\t1\t0\t1\t18750\t0.4\tOne Month Late\tUnknown\t1\t342\t1875\t5625\t\t3\tSmall\tClerical\tUnemployed\tMarket\tHealth\tElectronic\t0.4\t0.2\t10\t1\t344\t24\t3\t13\t34\t137\tScience\t13\t0.6\t3\t2\t3\t17\t75\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t320625\t0.1\t0.1\t0.1\t0.5\t0.5\t0.5\t8\t8\t2\tGood Credit\n407\t42\t2150\tMarried\t3\tPoor\tBachelor's Degree\t39\tHousing\t60000\t168\tMale\tMediterranean\t16\tTenant\t2\t3\t2\tProfessional\tNo\tDuly Past\tUnemployed\t0.5\tNone\tNo\t73\tNo\t1\t0\t0\t2\t6450\t0.7\tThree Months Late\tUnknown\t0\t1238\t3870\t4515\tYes\t4\tMedium\tManager\tTechnician\tEating and Drinking\tBills\tTransportation\t0.3\t0.2\t0\t0\t700\t2\t4\t28\t70\t43\t\t4\t0.3\t1\t15\t9\t43\t32\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t194145\t0.5\t0.7\t0.6\t0.4\t0.3\t0.35\t6\t8\t3\tOther Credit\n409\t67\t2150\tMarried\t3\tPoor\tBachelor's Degree\t4\tAuto\t50000\t48\tMale\tCentral Anatolia\t86\tTenant\t2\t2\t1\tWorker\tYes\tNot Taken\tUnemployed\t1\tCo Applicant\tYes\t66\tNo\t1\t0\t0\t1\t6450\t0.6\tMore Than Four Months\tUnknown\t1\t1163\t5160\t3870\t\t4\tBig\tUnemployed\tManager\tEating and Drinking\tBills\tElectronic\t0.1\t0.2\t2\t1\t63\t2\t3\t2\t6\t75\tConsulting\t7\t0.5\t3\t7\t18\t30\t42\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t69660\t0.7\t1\t0.85\t0.2\t0.3\t0.25\t4\t3\t5\tOther Credit\n414\t33\t2750\tMarried\t1\tAverage\tMaster's Degree\t4\tAuto\t40000\t12\tMale\tEastern Anatolia\t21\tTenant\t3\t1\t0\tWorker\tYes\tNot Taken\tUnemployed\t0.8\tNone\tNo\t34\tNo\t1\t2\t0\t1\t8250\t0.3\tOne Month Late\tNo\t0\t855\t2475\t1650\tYes\t2\tSmall\tUnemployed\tUnemployed\tEating and Drinking\tBills\tOther\t0.1\t0.1\t1\t1\t20\t24\t2\t0\t2\t14\tEnergy\t1\t0.1\t14\t3\t7\t29\t47\tNo\tNo Post\t\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t85800\t0.2\t0.1\t0.15\t0.8\t1\t0.9\t7\t2\t2\tOther Credit\n418\t33\t2750\tSingle\t2\tAverage\tMaster's Degree\t5\tHousing\t20000\t168\tFemale\tMediterranean\t68\tTenant\t1\t4\t2\tProfessional\tNo\tDuly Now\tUnemployed\t0.8\tNone\tYes\t82\tNo\t2\t2\t0\t1\t8250\t0.4\tFour Months Late\tNo\t0\t813\t1650\t825\tYes\t3\tSmall\tArmy\tProfessional\tMarket\tBills\tTransportation\t0.1\t0.2\t2\t0\t72\t2\t2\t2\t7\t91\tFinance\t9\t0.3\t11\t8\t1\t13\t67\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t\t7.15\tSilver\tSegment3\t42900\t0.2\t0.5\t0.35\t0.1\t0.2\t0.15\t7\t6\t2\tOther Credit\n422\t25\t2750\tDivorced\t1\tAverage\tMaster's Degree\t8\tNeed\t40000\t36\tMale\tMarmara\t58\tTenant\t3\t5\t1\tTechnician\tYes\tRisky\tUnemployed\t0.5\tNone\tNo\t39\tNo\t3\t0\t1\t1\t8250\t0.4\tMore Than Four Months\tUnknown\t1\t883\t6600\t6600\tNo\t2\tSmall\tProfessional\tWorker\tEating and Drinking\tTransportation\tHealth\t0.3\t0.2\t1\t6\t490\t6\t1\t19\t49\t29\tEnergy\t2\t0.1\t8\t5\t28\t44\t15\tYes\t\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t396000\t1\t1\t1\t0.5\t0.1\t0.3\t8\t3\t2\tOther Credit\n427\t30\t4250\tMarried\t6\tRich\tBachelor's Degree\t11\tAuto\t45000\t24\tFemale\tAegean\t97\tTenant\t2\t4\t1\tManager\tNo\tDelay\tLess Than Four Years\t0.7\tCo Applicant\tNo\t32\tNo\t2\t2\t1\t2\t12750\t0.3\tNormal\tUnknown\t1\t705\t\t10200\tYes\t7\tMedium\tFarmer\tManager\tEating and Drinking\tMarket\tTransportation\t0.1\t0.2\t3\t5\t330\t21\t3\t13\t33\t39\tScience\t3\t0.3\t6\t1\t21\t44\t28\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t561000\t1\t0.8\t0.9\t0.5\t0.5\t0.5\t7\t2\t2\tOther Credit\n431\t43\t2150\tSingle\t4\t\tBachelor's Degree\t9\tNeed\t45000\t36\tMale\tBlack Sea\t42\tTenant\t1\t5\t2\tClerical\tYes\tDelay\tLess Than One Year\t0.6\tNone\tYes\t59\tNo\t3\t1\t0\t1\t6450\t0.5\tFour Months Late\tNo\t0\t1223\t5805\t5160\tYes\t5\tSmall\tManager\tRetired\tEating and Drinking\tMarket\tHealth\t0.1\t0.2\t3\t5\t400\t7\t0\t16\t40\t78\tDesign\t7\t0.3\t7\t15\t10\t33\t35\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t216720\t0.6\t0.8\t0.7\t0.2\t0.2\t0.2\t6\t7\t3\tOther Credit\n435\t49\t2750\tMarried\t2\tAverage\tMaster's Degree\t6\tNeed\t25000\t12\tMale\tCentral Anatolia\t91\tTenant\t1\t5\t2\tFarmer\tYes\tNot Taken\tLess Than Seven Years\t0.5\tGuarantor\tNo\t71\tNo\t3\t0\t0\t2\t8250\t0.3\tMore Than Four Months\tNo\t0\t983\t4950\t5775\tNo\t3\tSmall\tArmy\tTechnician\tMarket\tHealth\tBills\t0.4\t0.2\t0\t0\t9\t27\t3\t0\t0\t36\tScience\t3\t0.1\t4\t17\t23\t2\t54\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t207900\t0.9\t0.9\t0.9\t0.4\t\t0.3\t6\t1\t3\tOther Credit\n440\t67\t2750\tSingle\t2\tAverage\tMaster's Degree\t84\tNeed\t10000\t36\tFemale\tCentral Anatolia\t76\tTenant\t2\t5\t2\tArmy\tYes\tDuly Now\tMore Than Seven Years\t0.6\tCo Applicant\tYes\t82\tNo\t2\t1\t1\t0\t8250\t0.4\tFour Months Late\t\t0\t768\t2475\t4125\tNo\t3\tSmall\tUnemployed\tClerical\tMarket\tTransportation\tElectronic\t0.5\t0.2\t3\t1\t255\t24\t0\t10\t25\t14\tLaw\t1\t0.2\t7\t5\t22\t10\t56\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t74250\t0.4\t0.7\t0.55\t0.2\t0.3\t0.25\t4\t8\t5\tOther Credit\n444\t56\t3750\tDivorced\t2\tRich\tDoctorate Degree\t14\tEducation\t15000\t24\tFemale\tBlack Sea\t93\tOwner\t1\t4\t2\t\tNo\tDelay\tLess Than Four Years\t0.8\tCo Applicant\tNo\t87\tNo\t1\t2\t1\t0\t11250\t0.7\tNormal\tNo\t0\t659\t1125\t10125\tYes\t3\tMedium\tUnemployed\tTechnician\tBills\tElectronic\tMarket\t0.1\t0.1\t3\t1\t272\t1\t4\t10\t27\t24\tDesign\t2\t0.2\t13\t8\t6\t32\t41\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t293625\t0.1\t1\t0.55\t0.1\t0.3\t0.2\t5\t6\t4\tOther Credit\n448\t36\t2750\tSingle\t2\tAverage\tMaster's Degree\t5\tHousing\t30000\t132\tFemale\tMediterranean\t26\tTenant\t2\t\t2\tManager\tYes\tNot Taken\tLess Than Four Years\t0.5\tGuarantor\tNo\t12\tNo\t1\t0\t0\t0\t8250\t0.5\tThree Months Late\tUnknown\t1\t808\t4950\t3300\tNo\t3\tMedium\tManager\tArmy\tEating and Drinking\tTransportation\tBills\t0.4\t0.2\t1\t1\t30\t1\t2\t1\t3\t9\tTransportation\t0\t0.2\t0\t0\t19\t22\t59\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t161700\t1\t1\t1\t0.5\t0.1\t0.3\t7\t3\t2\tOther Credit\n449\t31\t2150\tMarried\t3\tPoor\tBachelor's Degree\t15\tNeed\t10000\t24\tMale\tBlack Sea\t75\tTenant\t1\t2\t0\tClerical\tYes\tDelay\tLess Than Four Years\t0.5\tGuarantor\tNo\t56\tNo\t1\t1\t1\t2\t6450\t0.2\tFour Months Late\tNo\t0\t1377\t5160\t1290\tYes\t4\tMedium\tWorker\tRetired\tEating and Drinking\tOther\t\t0.2\t0.2\t1\t0\t15\t21\t4\t0\t1\t38\tDesign\t3\t0.1\t11\t0\t24\t20\t45\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t69660\t1\t0.3\t0.65\t0.8\t0.8\t0.8\t7\t3\t2\tOther Credit\n450\t40\t2150\tDivorced\t2\tAverage\tMaster's Degree\t18\tNeed\t45000\t36\tFemale\tSoutheast Anatolia\t76\tTenant\t0\t4\t2\tClerical\tNo\tNot Taken\tLess Than One Year\t0.7\tNone\tYes\t56\tNo\t2\t1\t0\t1\t6450\t0.5\tFour Months Late\tYes\t3\t1089\t3225\t4515\tYes\t3\tBig\tManager\t\tEating and Drinking\tBills\tOther\t0.2\t0.2\t9\t6\t117\t2\t0\t4\t11\t51\tScience\t5\t0.5\t7\t19\t24\t28\t22\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t203175\t0.9\t0.9\t0.9\t0.1\t0.3\t0.2\t6\t1\t3\tOther Credit\n451\t45\t500\tMarried\t3\tVery Poor\tPrimary School Grad\t39\tNeed\t45000\t24\tFemale\tEastern Anatolia\t98\tTenant\t3\t1\t0\tArmy\tNo\tDuly Past\tUnemployed\t0.9\tNone\tYes\t93\tNo\t1\t1\t0\t1\t1500\t0.2\tThree Months Late\tNo\t0\t1761\t300\t1050\tYes\t4\t\tRetired\tArmy\tEating and Drinking\tOther\tTransportation\t0.4\t0.1\t8\t4\t686\t1\t2\t27\t68\t25\tHealth\t2\t0.2\t11\t11\t29\t4\t45\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t42000\t0.2\t1\t0.6\t1\t0.6\t0.8\t6\t1\t3\tOther Credit\n455\t65\t6250\tSingle\t2\tVery Rich\tBachelor's Degree\t6\tNeed\t15000\t36\tFemale\tMediterranean\t59\tOwner\t0\t5\t2\tManager\tYes\tRisky\tMore Than Seven Years\t0.8\tNone\tNo\t15\tNo\t3\t2\t0\t0\t18750\t0.2\tOne Month Late\tNo\t0\t327\t1875\t5625\tYes\t3\tMedium\tWorker\tTechnician\tMarket\tOther\tTransportation\t0.1\t0.1\t10\t6\t672\t21\t3\t26\t67\t1\tDesign\t0\t0.3\t11\t\t29\t28\t23\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t112500\t0.4\t0.4\t0.4\t0.3\t0.5\t0.4\t4\t7\t5\tGood Credit\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-06-27T18:49:20+0000","dateStarted":"2018-07-16T21:22:51+0000","dateFinished":"2018-07-16T21:22:52+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:125"},{"title":"Good Credit Customers are Hard to Find!","text":"%sql \nSelect * from credit_scoring_100k_v where rownum < 100","user":"CHARLIE","dateUpdated":"2018-07-16T21:25:02+0000","config":{"colWidth":4,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"multiBarChart","optionOpen":false,"keys":[{"name":"CREDIT_SCORE_BIN","index":86,"aggr":"sum"}],"values":[{"name":"CUSTOMER_ID","index":0,"aggr":"count"}],"scatter":{"yAxis":{"name":"AGE","index":1,"aggr":"sum"},"xAxis":{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1104857655","id":"20180717-165545_1742361454","result":{"msg":"CUSTOMER_ID\tAGE\tINCOME\tMARITAL_STATUS\tNUMBER_OF_LIABLES\tWEALTH\tEDUCATION_LEVEL\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tLOAN_LENGTH\tGENDER\tREGION\tCURRENT_ADDRESS_DURATION\tRESIDENTAL_STATUS\tNUMBER_OF_PRIOR_LOANS\tNUMBER_OF_CURRENT_ACCOUNTS\tNUMBER_OF_SAVING_ACCOUNTS\tOCCUPATION\tHAS_CHECKING_ACCOUNT\tCREDIT_HISTORY\tPRESENT_EMPLOYMENT_SINCE\tFIXED_INCOME_RATE\tDEBTOR_GUARANTORS\tHAS_OWN_PHONE_NO\tHAS_SAME_PHONE_NO_SINCE\tIS_FOREIGN_WORKER\tNUMBER_OF_OPEN_ACCOUNTS\tNUMBER_OF_CLOSED_ACCOUNTS\tNUMBER_OF_INACTIVE_ACCOUNTS\tNUMBER_OF_INQUIRIES\tHIGHEST_CREDIT_CARD_LIMIT\tCREDIT_CARD_UTILIZATION_RATE\tDELINQUENCY_STATUS\tNEW_BANKRUPTCY\tNUMBER_OF_COLLECTIONS\tCONSUMER_FINDEX_SCORE\tMAX_CC_SPENT_AMOUNT\tMAX_CC_SPENT_AMOUNT_PREV\tHAS_COLLATERAL\tFAMILY_SIZE\tCITY_SIZE\tFATHERS_JOB\tMOTHERS_JOB\tMOST_SPENDING_TYPE\tSECOND_MOST_SPENDING_TYPE\tTHIRD_MOST_SPENDING_TYPE\tSCHOOL_FRIENDS_PERCENTAGE\tJOB_FRIENDS_PERCENTAGE\tNUMBER_OF_PROTESTOR_LIKES\tNO_OF_PROTESTOR_COMMENTS\tNO_OF_LINKEDIN_CONTACTS\tAVERAGE_JOB_CHANGING_PERIOD\tNO_OF_DEBTORS_ON_FB\tNO_OF_RECRUITERS_ON_LINKEDIN\tNO_OF_TOTAL_ENDORSEMENTS\tNO_OF_FOLLOWERS_ON_TWITTER\tMODE_JOB_OF_CONTACTS\tAVERAGE_NO_OF_RETWEETS\tFACEBOOK_INFLUENCE_SCORE\tPERCENTAGE_PHD_ON_LINKEDIN\tPERCENTAGE_MASTERS\tPERCENTAGE_UG\tPERCENTAGE_HIGH_SCHOOL\tPERCENTAGE_OTHER\tIS_POSTED_STH_WITHIN_A_MONTH\tMOST_POPULAR_POST_CATEGORY\tINTEREST_RATE\tEARNINGS\tUNEMPLOYMENT_INDEX\tPRODUCTION_INDEX\tHOUSING_INDEX\tCONSUMER_CONFIDENCE_INDEX\tINFLATION_RATE\tCUSTOMER_VALUE_SEGMENT\tCUSTOMER_DMG_SEGMENT\tCUSTOMER_LIFETIME_VALUE\tCHURN_RATE_OF_CC1\tCHURN_RATE_OF_CC2\tCHURN_RATE_OF_CCN\tCHURN_RATE_OF_ACCOUNT_NO1\tCHURN_RATE__OF_ACCOUNT_NO2\tCHURN_RATE_OF_ACCOUNT_NON\tHEALTH_SCORE\tCUSTOMER_DEPTH\tLIFECYCLE_STAGE\tCREDIT_SCORE_BIN\n108\t24\t2150\tMarried\t3\tPoor\tBachelor's Degree\t19\t\t20000\t24\tFemale\tCentral Anatolia\t99\tTenant\t2\t1\t0\tTechnician\tNo\tNot Taken\tUnemployed\t0.9\tGuarantor\tNo\t81\tNo\t1\t2\t0\t1\t6450\t0\tFour Months Late\tUnknown\t1\t1438\t5805\t4515\tYes\t4\tMedium\tRetired\tProfessional\tBills\tHealth\tElectronic\t0.4\t0.2\t2\t0\t57\t1\t1\t2\t5\t21\tProduction\t2\t0.3\t7\t19\t25\t39\t10\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t275415\t0.6\t0.5\t0.55\t0.5\t0.6\t0.55\t8\t2\t1\tOther Credit\n109\t45\t2750\tSingle\t1\tAverage\tMaster's Degree\t27\tAuto\t25000\t12\tMale\tAegean\t8\tTenant\t1\t2\t0\tProfessional\tYes\tDelay\tLess Than One Year\t0.7\tNone\tNo\t3\t\t2\t2\t0\t2\t8250\t0.7\tOne Month Late\tNo\t0\t926\t3300\t5775\tYes\t2\tMedium\tFarmer\tUnemployed\tEating and Drinking\tTransportation\tHealth\t0.1\t0.2\t9\t1\t570\t30\t1\t22\t57\t176\tTransportation\t17\t0.6\t7\t3\t1\t0\t89\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t231000\t0.9\t0.5\t0.7\t1\t0.5\t0.75\t6\t3\t3\tOther Credit\n110\t63\t2150\tSingle\t5\tPoor\tBachelor's Degree\t11\tAuto\t20000\t48\tFemale\tMediterranean\t55\tTenant\t0\t5\t2\tProfessional\tNo\t\tLess Than Four Years\t0.8\tCo Applicant\tNo\t18\tNo\t2\t1\t1\t2\t6450\t0.3\tNormal\tNo\t0\t1315\t3225\t5805\tYes\t6\tSmall\tArmy\tUnemployed\tEating and Drinking\tBills\tHealth\t0.5\t0.1\t6\t0\t20\t24\t2\t0\t2\t51\tEnergy\t5\t0.3\t14\t5\t27\t24\t30\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t127710\t0.5\t0.7\t0.6\t0.2\t0.5\t0.35\t4\t8\t4\tOther Credit\n111\t59\t2750\tMarried\t6\tPoor\tBachelor's Degree\t10\tHousing\t45000\t12\tMale\tEastern Anatolia\t18\tTenant\t0\t3\t0\tArmy\tYes\tNot Taken\tLess Than One Year\t1\tNone\tYes\t51\tNo\t2\t1\t1\t2\t8250\t0.2\tOne Month Late\tNo\t0\t1202\t5775\t1650\tYes\t7\tBig\tManager\tRetired\tMarket\tBills\t\t0.5\t0.2\t0\t0\t78\t24\t2\t3\t7\t266\tDesign\t26\t0.7\t14\t12\t24\t11\t39\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t42900\t0.5\t0.1\t0.3\t0.2\t0.4\t0.3\t5\t7\t4\tOther Credit\n112\t20\t2750\tDivorced\t2\tAverage\tMaster's Degree\t9\tEducation\t15000\t24\tMale\tMediterranean\t85\tTenant\t0\t4\t0\tWorker\tNo\tDelay\tUnemployed\t1\tCo Applicant\tYes\t30\tNo\t3\t0\t1\t0\t8250\t0.4\tFour Months Late\tNo\t0\t1029\t8250\t2475\tYes\t3\tSmall\tProfessional\tTechnician\tEating and Drinking\tOther\tTransportation\t0.2\t0.1\t6\t1\t693\t2\t0\t27\t69\t78\tHealth\t7\t0.4\t5\t1\t12\t14\t68\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t160875\t0.5\t0.1\t0.3\t\t0.4\t0.45\t8\t7\t1\tOther Credit\n113\t71\t3750\tMarried\t5\tAverage\tMaster's Degree\t12\tHousing\t40000\t24\tFemale\tMediterranean\t97\tTenant\t\t2\t0\tManager\tYes\tDuly Past\tUnemployed\t1\tCo Applicant\tYes\t10\tNo\t2\t0\t0\t2\t11250\t0.3\tThree Months Late\tNo\t0\t1066\t11250\t3375\tNo\t6\tMedium\tRetired\tRetired\tEating and Drinking\tMarket\tTransportation\t0.2\t0.1\t2\t1\t288\t8\t1\t11\t28\t47\tEnergy\t4\t0.1\t5\t11\t22\t18\t44\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t47250\t0.8\t0.3\t0.55\t0.1\t0.2\t0.15\t3\t6\t5\tOther Credit\n114\t35\t2750\tMarried\t3\tPoor\tBachelor's Degree\t38\tHousing\t70000\t24\tFemale\tAegean\t35\tTenant\t3\t1\t0\tArmy\t\tDuly Now\tLess Than One Year\t0.9\tCo Applicant\tNo\t35\tNo\t1\t2\t0\t1\t8250\t0.1\tMore Than Four Months\tNo\t0\t1342\t8250\t5775\tYes\t4\tSmall\tRetired\tUnemployed\tEating and Drinking\tHealth\tMarket\t0.5\t0.2\t3\t5\t368\t2\t1\t14\t36\t4\tHealth\t0\t0.3\t14\t10\t5\t17\t54\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t288750\t0.9\t0.9\t0.9\t0.6\t0.5\t0.55\t7\t1\t2\tOther Credit\n115\t63\t500\tSingle\t2\tVery Poor\tPrimary School Grad\t5\tHousing\t85000\t144\tMale\tBlack Sea\t94\tTenant\t0\t6\t2\tArmy\tNo\tRisky\tLess Than Seven Years\t0.7\tNone\tYes\t\tNo\t3\t0\t1\t0\t1500\t0.2\tFour Months Late\tUnknown\t0\t1822\t600\t600\tNo\t3\tMedium\tManager\tUnemployed\tEating and Drinking\tTransportation\tMarket\t0.1\t0.2\t8\t1\t455\t1\t4\t18\t45\t261\tHealth\t26\t0.8\t6\t18\t14\t27\t35\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t13200\t0.8\t0.9\t0.85\t0.1\t0.4\t0.25\t4\t4\t4\tOther Credit\n116\t60\t3750\tMarried\t2\tRich\tDoctorate Degree\t6\tNeed\t20000\t36\tMale\tAegean\t51\tOwner\t1\t4\t0\tProfessional\tYes\tDuly Past\tUnemployed\t0.5\tCo Applicant\tYes\t74\tNo\t3\t1\t1\t0\t11250\t0.2\tNormal\tNo\t\t573\t9000\t5625\tNo\t3\tSmall\tProfessional\tRetired\tBills\tOther\tEating and Drinking\t0.1\t0.2\t8\t4\t665\t1\t0\t26\t66\t19\tLaw\t1\t0.3\t1\t11\t16\t7\t65\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t140625\t0.7\t0.5\t0.6\t0.4\t0.5\t0.45\t4\t6\t4\tOther Credit\n117\t55\t2750\tSingle\t3\tPoor\tBachelor's Degree\t8\tEducation\t20000\t24\tFemale\tMarmara\t75\tTenant\t1\t5\t\tArmy\tNo\tDuly Now\tLess Than Seven Years\t0.5\tNone\tNo\t37\tNo\t3\t0\t0\t2\t8250\t0.4\tTwo Months Late\tNo\t0\t1464\t825\t1650\tNo\t4\tMedium\tUnemployed\tFarmer\tMarket\tHealth\tElectronic\t0.1\t0.1\t2\t4\t30\t2\t0\t1\t3\t47\tFinance\t4\t0.3\t12\t8\t10\t17\t53\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t49500\t0.3\t0.2\t0.25\t0.4\t0.2\t0.3\t5\t5\t4\tOther Credit\n118\t62\t4250\tMarried\t3\tRich\tBachelor's Degree\t27\t\t40000\t12\tMale\tBlack Sea\t30\tOwner\t3\t3\t1\tManager\tYes\tRisky\tLess Than Four Years\t0.5\tCo Applicant\tNo\t3\tNo\t2\t1\t0\t2\t12750\t0\tOne Month Late\tUnknown\t1\t655\t12750\t7650\tYes\t4\tMedium\tManager\tUnemployed\tBills\tHealth\tOther\t0.4\t0.2\t1\t0\t96\t2\t1\t3\t9\t89\tScience\t8\t0.4\t11\t8\t3\t17\t61\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t175950\t0.6\t0.7\t0.65\t0.1\t0.3\t0.2\t4\t3\t4\tOther Credit\n119\t67\t4250\tDivorced\t6\tRich\tBachelor's Degree\t6\tAuto\t10000\t24\tMale\tEastern Anatolia\t7\tOwner\t1\t3\t2\tProfessional\tNo\tNot Taken\tLess Than Seven Years\t0.7\tNone\tYes\t30\tNo\t1\t1\t0\t2\t12750\t0.7\tNormal\tNo\t1\t565\t7650\t11475\tNo\t7\tSmall\tRetired\tClerical\tEating and Drinking\tOther\tBills\t0.3\t0.2\t0\t6\t32\t9\t2\t1\t3\t66\t\t6\t0.3\t14\t16\t17\t19\t34\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t206550\t0.7\t0.7\t0.7\t0.4\t0.1\t0.25\t4\t1\t5\tGood Credit\n120\t23\t2750\tSingle\t2\tAverage\tMaster's Degree\t26\tNeed\t20000\t36\tFemale\tBlack Sea\t58\tTenant\t2\t5\t2\tArmy\tNo\tDelay\tUnemployed\t0.8\tGuarantor\tYes\t52\tNo\t3\t2\t0\t0\t8250\t0\tOne Month Late\tUnknown\t1\t769\t6600\t5775\tYes\t3\tMedium\tWorker\tArmy\tEating and Drinking\tTransportation\tElectronic\t0.3\t0.1\t8\t0\t63\t9\t3\t2\t6\t56\tLaw\t5\t0.3\t2\t5\t6\t8\t79\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t358050\t0.6\t0.9\t0.75\t0.3\t0.3\t0.3\t8\t\t1\tOther Credit\n121\t43\t500\tMarried\t3\tVery Poor\tPrimary School Grad\t15\tAuto\t55000\t12\tMale\tMarmara\t55\tFamily Home\t0\t6\t2\tArmy\tYes\tDuly Past\tLess Than One Year\t0.7\tCo Applicant\tNo\t70\tNo\t3\t1\t1\t0\t1500\t0.5\tFour Months Late\tNo\t0\t1561\t150\t600\tYes\t4\tSmall\tProfessional\tProfessional\tMarket\tElectronic\tBills\t0.1\t0.2\t3\t1\t675\t6\t4\t27\t67\t13\tConsulting\t1\t0.2\t6\t7\t5\t5\t77\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t25200\t0.4\t0.8\t0.6\t0.1\t0.3\t0.2\t6\t\t3\tOther Credit\n122\t49\t2150\tSingle\t6\tPoor\tBachelor's Degree\t14\tNeed\t25000\t36\tFemale\tMarmara\t12\tTenant\t3\t5\t1\tTechnician\tNo\tDuly Past\tUnemployed\t0.8\tCo Applicant\tNo\t90\tNo\t3\t0\t1\t2\t6450\t0.5\tTwo Months Late\tNo\t0\t1178\t3870\t2580\tNo\t7\tMedium\tUnemployed\tWorker\tEating and Drinking\tHealth\tOther\t0.1\t0.2\t1\t0\t17\t27\t4\t0\t1\t19\tTransportation\t1\t0.2\t1\t12\t21\t34\t32\tYes\tPolitics\t7.5\t4.8\t10.1\t\t187.86\t0.65\t7.15\tBronze\tSegment4\t92880\t0.9\t0.7\t0.8\t0.2\t0.1\t0.15\t6\t4\t3\tOther Credit\n123\t51\t6250\tSingle\t2\tVery Rich\tBachelor's Degree\t18\tEducation\t10000\t12\tMale\tCentral Anatolia\t21\tOwner\t3\t5\t2\t\tYes\tDuly Now\tLess Than One Year\t0.6\tGuarantor\tNo\t11\tNo\t2\t1\t1\t0\t18750\t0.8\tOne Month Late\tYes\t1\t227\t5625\t9375\tYes\t3\tMedium\tArmy\tRetired\tEating and Drinking\tElectronic\tOther\t0.2\t0.2\t2\t0\t1\t9\t3\t0\t0\t91\tTourism\t9\t0.4\t0\t8\t17\t1\t74\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t318750\t0.2\t0.7\t0.45\t0.5\t0.2\t0.35\t5\t6\t3\tGood Credit\n124\t42\t3750\tSingle\t6\tAverage\tMaster's Degree\t15\tNeed\t15000\t36\tMale\tCentral Anatolia\t98\tTenant\t2\t6\t2\tManager\tYes\tDuly Now\tLess Than One Year\t0.9\tNone\tYes\t6\tNo\t3\t0\t1\t1\t11250\t0.6\tTwo Months Late\tNo\t0\t1018\t2250\t9000\tNo\t7\tSmall\tTechnician\tProfessional\tMarket\tHealth\tOther\t0.4\t0.2\t10\t4\t216\t21\t0\t8\t21\t64\tDesign\t6\t0.5\t4\t6\t26\t36\t28\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t387000\t0.1\t1\t\t0.2\t0.3\t0.25\t6\t5\t3\tOther Credit\n125\t41\t5250\tMarried\t3\tRich\tBachelor's Degree\t9\tNeed\t15000\t12\tMale\tMediterranean\t35\tTenant\t2\t3\t2\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.5\tGuarantor\tNo\t9\tNo\t1\t1\t0\t2\t15750\t0\tOne Month Late\tNo\t0\t503\t6300\t14175\tYes\t4\tBig\tArmy\tRetired\tEating and Drinking\tElectronic\tBills\t0.1\t0.1\t6\t1\t72\t21\t2\t2\t7\t80\tTransportation\t8\t0.4\t11\t11\t26\t17\t35\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t623700\t0.5\t0.9\t\t0.4\t0.4\t0.4\t6\t2\t3\tGood Credit\n127\t60\t2150\tSingle\t\tPoor\tBachelor's Degree\t11\tNeed\t45000\t24\tFemale\tSoutheast Anatolia\t15\tTenant\t1\t5\t2\tProfessional\tYes\tDuly Past\tLess Than One Year\t0.9\tCo Applicant\tYes\t25\tNo\t3\t0\t0\t1\t6450\t0.1\tTwo Months Late\tYes\t3\t1458\t5805\t645\tNo\t5\tBig\tUnemployed\tRetired\tEating and Drinking\tMarket\tBills\t0.2\t0.2\t0\t1\t28\t27\t4\t1\t2\t30\tDesign\t3\t0.3\t10\t10\t13\t1\t66\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t16125\t0.8\t0.4\t0.6\t0.1\t0.1\t0.1\t4\t8\t4\tOther Credit\n131\t42\t2150\tMarried\t3\tPoor\tBachelor's Degree\t9\tEducation\t10000\t24\tMale\tMarmara\t99\tTenant\t3\t5\t1\tClerical\tNo\tDelay\tLess Than Four Years\t0.5\tNone\tYes\t10\tNo\t3\t0\t1\t2\t6450\t\tFour Months Late\tNo\t0\t1278\t4515\t5160\tNo\t4\tMedium\tWorker\tManager\tMarket\tTransportation\tEating and Drinking\t0.2\t0.1\t10\t1\t30\t1\t2\t1\t3\t17\tProduction\t1\t0.3\t4\t11\t28\t31\t26\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t221880\t0.9\t0.6\t0.75\t0.3\t0.2\t0.25\t6\t8\t3\tOther Credit\n136\t59\t2150\tMarried\t3\tPoor\tBachelor's Degree\t21\tHousing\t105000\t60\tMale\tMediterranean\t91\tTenant\t2\t3\t0\tFarmer\tYes\tRisky\tLess Than One Year\t0.8\tGuarantor\tNo\t6\tNo\t2\t0\t1\t2\t6450\t0.7\tTwo Months Late\tUnknown\t1\t\t3870\t5160\tYes\t4\tMedium\tUnemployed\tTechnician\tEating and Drinking\tMarket\tHealth\t0.3\t0.2\t8\t6\t44\t7\t1\t1\t4\t37\tConsulting\t3\t0.2\t9\t17\t10\t1\t63\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t134160\t0.5\t0.8\t0.65\t0.3\t0.1\t0.2\t5\t7\t4\tOther Credit\n141\t43\t6250\tSingle\t5\tRich\tBachelor's Degree\t20\tHousing\t65000\t264\tFemale\tMediterranean\t20\tTenant\t2\t\t2\tManager\tYes\tDelay\tLess Than Four Years\t1\tCo Applicant\tNo\t59\tNo\t1\t1\t0\t2\t18750\t0.8\tNormal\tUnknown\t0\t463\t11250\t15000\tYes\t6\tBig\tRetired\tRetired\tBills\tElectronic\tHealth\t0.3\t0.2\t8\t0\t10\t24\t3\t0\t1\t87\tHealth\t8\t0.4\t8\t17\t3\t17\t55\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t630000\t0.9\t1\t0.95\t0.1\t0.5\t0.3\t6\t2\t3\tGood Credit\n145\t64\t5250\tSingle\t3\tRich\tBachelor's Degree\t25\tAuto\t20000\t36\tMale\tBlack Sea\t67\tOwner\t3\t2\t0\tProfessional\tYes\tDuly Past\tLess Than Seven Years\t0.9\tGuarantor\tNo\t3\tNo\t2\t2\t0\t0\t15750\t0\tNormal\tNo\t0\t750\t6300\t11025\tYes\t4\tMedium\tFarmer\tUnemployed\tMarket\tTransportation\tElectronic\t0.5\t0.1\t8\t5\t69\t2\t3\t2\t6\t\tDesign\t7\t0.3\t5\t2\t8\t18\t67\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t231525\t0.9\t0.6\t0.75\t1\t0.6\t0.8\t4\t1\t4\tOther Credit\n149\t32\t500\tMarried\t1\tVery Poor\tPrimary School Grad\t11\tHousing\t80000\t108\tMale\tBlack Sea\t49\tFamily Home\t2\t3\t1\tManager\tNo\tDuly Now\tLess Than Seven Years\t0.9\tNone\tYes\t59\tNo\t1\t2\t1\t2\t1500\t0.6\tMore Than Four Months\tNo\t0\t1759\t1350\t600\tNo\t2\tSmall\tUnemployed\tManager\tMarket\tHealth\tElectronic\t0.3\t0.1\t0\t6\t21\t9\t0\t0\t2\t63\tScience\t6\t0.3\t8\t19\t0\t11\t62\tYes\tPolitics\t7.5\t\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t31800\t1\t0.9\t0.95\t0.5\t0.8\t0.65\t7\t1\t2\tOther Credit\n153\t71\t4250\tMarried\t2\tVery Rich\tBachelor's Degree\t21\tNeed\t40000\t24\tFemale\tMediterranean\t72\tOwner\t2\t5\t2\tManager\tNo\tRisky\tLess Than One Year\t0.5\tCo Applicant\tYes\t37\tNo\t3\t2\t0\t2\t12750\t0.3\tOne Month Late\tNo\t0\t205\t7650\t2550\tYes\t3\tSmall\tArmy\tRetired\tMarket\tBills\tOther\t0.2\t0.2\t1\t1\t356\t1\t4\t14\t35\t2\tTourism\t0\t0.3\t1\t17\t6\t25\t51\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t\tGold\tSegment1\t35700\t0.5\t0.2\t0.35\t0.4\t0.4\t0.4\t3\t8\t5\tGood Credit\n158\t49\t2750\tMarried\t3\tPoor\tBachelor's Degree\t22\tNeed\t15000\t36\tFemale\tMediterranean\t\tTenant\t2\t5\t2\tWorker\tYes\tNot Taken\tMore Than Seven Years\t0.5\tNone\tNo\t33\tNo\t3\t0\t0\t2\t8250\t0.3\tMore Than Four Months\tUnknown\t1\t1261\t825\t3300\tYes\t4\tBig\tRetired\tTechnician\tEating and Drinking\tElectronic\tOther\t0.5\t0.1\t8\t0\t51\t8\t1\t2\t5\t79\tHealth\t7\t0.4\t13\t14\t29\t27\t17\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t118800\t0.2\t0.6\t0.4\t0.1\t0.5\t0.3\t6\t6\t3\tOther Credit\n162\t62\t2150\tSingle\t1\tAverage\tMaster's Degree\t19\tHousing\t100000\t264\tFemale\tCentral Anatolia\t78\tTenant\t2\t3\t2\tFarmer\tNo\tDuly Past\tMore Than Seven Years\t0.9\tNone\tNo\t43\tNo\t1\t0\t0\t1\t6450\t0.3\tMore Than Four Months\tNo\t0\t830\t3870\t645\tYes\t2\tBig\tRetired\tUnemployed\tBills\tEating and Drinking\tElectronic\t0.4\t0.2\t9\t1\t54\t9\t4\t2\t5\t33\tTourism\t3\t0.1\t4\t14\t21\t30\t31\tYes\tFuture\t\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t14835\t0.8\t0.3\t0.55\t0.5\t0.4\t0.45\t4\t8\t4\tOther Credit\n167\t33\t6250\tDivorced\t2\tVery Rich\tBachelor's Degree\t10\tHousing\t45000\t192\tMale\tMarmara\t43\tOwner\t2\t5\t1\tManager\tNo\tRisky\tLess Than One Year\t0.8\tGuarantor\tNo\t6\tNo\t3\t2\t1\t1\t18750\t0.7\tNormal\tNo\t0\t241\t7500\t7500\tYes\t3\tMedium\tUnemployed\tRetired\tEating and Drinking\tTransportation\tMarket\t0.2\t0.2\t2\t0\t75\t9\t0\t3\t7\t\tFinance\t2\t0.1\t2\t11\t26\t37\t24\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t390000\t0.6\t1\t0.8\t0.4\t0.4\t0.4\t7\t2\t2\tGood Credit\n168\t45\t500\tSingle\t2\tVery Poor\tPrimary School Grad\t11\tAuto\t50000\t12\tFemale\tMarmara\t48\tTenant\t0\t4\t1\tProfessional\tNo\tRisky\tUnemployed\t0.9\tNone\tNo\t1\tNo\t3\t2\t0\t1\t1500\t0.7\tFour Months Late\tNo\t0\t1777\t1050\t1350\tNo\t3\tMedium\tManager\tUnemployed\tEating and Drinking\tTransportation\tBills\t0.4\t0.2\t6\t0\t184\t6\t0\t\t18\t52\tDesign\t5\t0.5\t12\t6\t6\t33\t43\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t54000\t0.7\t0.8\t0.75\t0.4\t0.4\t0.4\t6\t3\t3\tOther Credit\n169\t31\t6250\tMarried\t4\tRich\tBachelor's Degree\t82\tNeed\t45000\t24\tMale\tMediterranean\t83\tTenant\t0\t3\t1\tManager\tYes\tDelay\tUnemployed\t\tCo Applicant\tNo\t92\tNo\t1\t0\t1\t2\t18750\t0.5\tNormal\tNo\t0\t759\t3750\t7500\tYes\t5\tMedium\tRetired\tRetired\tBills\tOther\tHealth\t0.2\t0.2\t9\t0\t6\t6\t3\t0\t0\t53\tEnergy\t5\t0.3\t2\t13\t12\t14\t59\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t405000\t0.4\t0.8\t0.6\t0.4\t0.4\t0.4\t7\t6\t2\tOther Credit\n3\t70\t2750\tMarried\t3\tPoor\tBachelor's Degree\t12\tEducation\t10000\t24\tMale\tBlack Sea\t5\tTenant\t1\t3\t0\tManager\tNo\tRisky\tUnemployed\t1\tCo Applicant\tNo\t22\tNo\t3\t1\t0\t2\t8250\t0.4\tThree Months Late\tNo\t0\t1159\t4125\t5775\tYes\t4\tSmall\tManager\tRetired\tEating and Drinking\tTransportation\tOther\t0.2\t0.1\t3\t0\t\t24\t2\t17\t42\t194\tHealth\t19\t0.5\t6\t1\t20\t10\t63\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t86625\t0.5\t0.6\t0.55\t0.5\t0.4\t0.45\t3\t6\t5\tOther Credit\n7\t25\t4250\tMarried\t3\tRich\tBachelor's Degree\t11\tNeed\t25000\t12\tMale\tMediterranean\t7\tTenant\t0\t2\t1\tManager\tNo\tDuly Now\tUnemployed\t0.8\tCo Applicant\tYes\t48\tNo\t1\t1\t0\t2\t12750\t0.7\tNormal\tUnknown\t0\t581\t5100\t3825\tNo\t4\tMedium\tRetired\tRetired\tEating and Drinking\tTransportation\tMarket\t0.2\t0.1\t8\t0\t20\t7\t2\t0\t2\t27\tFinance\t2\t0.2\t7\t2\t1\t37\t53\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t229500\t0.7\t0.4\t0.55\t0.4\t0.5\t0.45\t8\t3\t\tOther Credit\n12\t56\t5250\tSingle\t2\tVery Rich\tBachelor's Degree\t14\tNeed\t40000\t24\tFemale\tAegean\t45\tOwner\t3\t5\t1\t\tNo\tDelay\tUnemployed\t0.7\tNone\tYes\t98\tNo\t3\t0\t1\t1\t15750\t0.7\tNormal\tNo\t0\t377\t11025\t11025\tNo\t3\tMedium\tRetired\tTechnician\tEating and Drinking\tMarket\tBills\t0.2\t0.1\t8\t1\t42\t2\t1\t1\t4\t51\tHealth\t5\t0.5\t8\t1\t11\t26\t54\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t319725\t1\t1\t1\t0.3\t0.5\t0.4\t5\t2\t4\tGood Credit\n16\t23\t500\tDivorced\t3\tVery Poor\tPrimary School Grad\t15\tAuto\t55000\t36\tMale\tBlack Sea\t95\tFamily Home\t0\t\t1\tManager\tNo\tRisky\tUnemployed\t0.5\tNone\tNo\t7\tNo\t3\t0\t1\t2\t1500\t0.2\tOne Month Late\tNo\t0\t1619\t1350\t1350\tYes\t4\tMedium\tManager\tFarmer\tEating and Drinking\tElectronic\tMarket\t0.2\t0.1\t6\t0\t99\t30\t1\t3\t9\t4\tEnergy\t0\t0.1\t6\t0\t8\t33\t53\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t83700\t0.6\t0.5\t0.55\t0.1\t0.4\t0.25\t8\t6\t1\tOther Credit\n21\t40\t2150\tSingle\t4\tPoor\tBachelor's Degree\t18\tNeed\t10000\t36\tFemale\tEastern Anatolia\t77\tTenant\t2\t3\t0\tManager\tYes\tDuly Now\tMore Than Seven Years\t0.5\tCo Applicant\tNo\t78\tNo\t3\t2\t0\t0\t6450\t0.7\tMore Than Four Months\tNo\t0\t1376\t645\t1935\tNo\t5\tMedium\tRetired\tWorker\tEating and Drinking\tMarket\tOther\t0.1\t0.1\t1\t0\t\t2\t1\t35\t89\t77\tDesign\t7\t0.5\t2\t1\t26\t27\t44\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t87075\t0.2\t0.1\t0.15\t1\t0.6\t0.8\t6\t6\t3\tOther Credit\n25\t67\t500\tMarried\t5\tVery Poor\tPrimary School Grad\t14\tEducation\t15000\t12\tFemale\tAegean\t2\tTenant\t0\t6\t2\tManager\tNo\tRisky\tUnemployed\t0.9\tCo Applicant\tNo\t71\tNo\t3\t1\t1\t1\t1500\t0.9\tTwo Months Late\tNo\t0\t1596\t750\t150\tNo\t6\tMedium\tTechnician\tFarmer\tBills\tMarket\tHealth\t0.2\t0.1\t2\t0\t570\t30\t4\t22\t57\t22\tTourism\t\t0.3\t8\t4\t4\t21\t63\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t2700\t0.6\t0.3\t0.45\t0.3\t0.5\t0.4\t4\t7\t5\tOther Credit\n29\t22\t2750\tMarried\t5\tPoor\tBachelor's Degree\t8\tAuto\t55000\t24\tFemale\tMarmara\t63\tTenant\t2\t5\t2\tFarmer\tNo\tDelay\tUnemployed\t0.5\tCo Applicant\tNo\t35\tNo\t2\t0\t1\t2\t8250\t0.7\tThree Months Late\tNo\t0\t1164\t6600\t825\tYes\t6\tMedium\tFarmer\tManager\tEating and Drinking\tOther\tHealth\t0.5\t0.1\t6\t1\t90\t2\t2\t3\t9\t37\tEnergy\t3\t0.2\t11\t0\t4\t26\t59\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t51975\t0.7\t0.5\t0.6\t0.5\t0.4\t0.45\t8\t\t1\tOther Credit\n34\t48\t2150\tDivorced\t4\tPoor\tBachelor's Degree\t94\tEducation\t15000\t24\tFemale\tMediterranean\t65\tTenant\t3\t5\t2\tArmy\tYes\tDelay\tLess Than Seven Years\t0.8\tCo Applicant\tNo\t39\tNo\t2\t0\t1\t0\t6450\t0.5\tFour Months Late\tUnknown\t1\t1278\t1935\t3870\tYes\t5\tMedium\tFarmer\tManager\tEating and Drinking\tElectronic\tHealth\t0.3\t0.2\t8\t4\t2\t27\t4\t0\t0\t39\tFinance\t3\t0.3\t7\t15\t24\t9\t45\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t143190\t0.4\t0.8\t0.6\t0.4\t0.3\t\t6\t5\t3\tOther Credit\n39\t34\t3750\tMarried\t1\tRich\tDoctorate Degree\t26\tAuto\t50000\t12\tMale\tSoutheast Anatolia\t74\tTenant\t1\t1\t0\tProfessional\tNo\tDuly Now\tLess Than Four Years\t0.7\tNone\tYes\t85\tNo\t1\t1\t0\t1\t11250\t0.9\tNormal\tUnknown\t1\t476\t9000\t1125\tYes\t2\tMedium\tUnemployed\tRetired\tEating and Drinking\tHealth\tOther\t0.2\t0.1\t10\t5\t160\t21\t0\t6\t16\t149\tTourism\t14\t0.6\t6\t17\t14\t\t60\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t57375\t0.6\t0.3\t0.45\t0.5\t0.7\t0.6\t7\t3\t2\tGood Credit\n43\t45\t3750\tMarried\t3\tAverage\tMaster's Degree\t15\tAuto\t35000\t\tMale\tBlack Sea\t59\tTenant\t3\t3\t0\tWorker\tYes\tNot Taken\tMore Than Seven Years\t0.5\tGuarantor\tYes\t64\tNo\t3\t1\t0\t0\t11250\t0.1\tTwo Months Late\tYes\t2\t875\t9000\t1125\tYes\t4\tMedium\tUnemployed\tUnemployed\tEating and Drinking\tHealth\tElectronic\t0.4\t0.2\t7\t6\t700\t6\t3\t28\t70\t44\tHealth\t4\t0.1\t11\t0\t24\t5\t60\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t45000\t0.5\t0.3\t0.4\t0.1\t0.2\t0.15\t6\t5\t3\tOther Credit\n44\t36\t2150\tMarried\t3\tPoor\tBachelor's Degree\t7\tHousing\t45000\t264\tMale\tCentral Anatolia\t94\tTenant\t0\t3\t0\tArmy\tYes\tDuly Now\tLess Than Four Years\t\tNone\tYes\t83\tNo\t2\t0\t1\t2\t6450\t0.3\tOne Month Late\tNo\t0\t1337\t4515\t2580\tYes\t4\tBig\tRetired\tWorker\tEating and Drinking\tTransportation\tBills\t0.5\t0.1\t3\t1\t675\t30\t4\t27\t67\t18\tTourism\t1\t0.2\t4\t10\t13\t20\t53\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t126420\t0.6\t0.5\t0.55\t0.1\t0.4\t0.25\t7\t8\t2\tOther Credit\n45\t19\t2750\tMarried\t1\tAverage\tMaster's Degree\t19\tEducation\t20000\t24\tMale\tCentral Anatolia\t21\tTenant\t1\t2\t0\tFarmer\tYes\tDuly Now\tUnemployed\t0.7\tNone\tYes\t56\tNo\t1\t2\t1\t0\t8250\t0.1\tTwo Months Late\tNo\t0\t1130\t7425\t6600\tYes\t2\tMedium\tClerical\tFarmer\tEating and Drinking\tElectronic\tOther\t0.1\t0.2\t0\t4\t408\t30\t0\t16\t40\t2\tTourism\t0\t0.1\t9\t13\t5\t14\t59\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t\t0.65\t7.15\tSilver\tSegment3\t435600\t0.8\t0.6\t0.7\t0.9\t0.8\t0.85\t9\t1\t1\tOther Credit\n48\t21\t2750\tMarried\t6\tPoor\tBachelor's Degree\t35\tNeed\t35000\t12\tFemale\tCentral Anatolia\t93\tTenant\t3\t3\t1\tClerical\tNo\tDuly Now\tMore Than Seven Years\t1\tCo Applicant\tYes\t72\tNo\t2\t0\t0\t1\t8250\t0.4\tFour Months Late\tUnknown\t0\t1144\t4125\t7425\tNo\t7\tMedium\tFarmer\tUnemployed\tMarket\tHealth\tOther\t0.3\t0.2\t1\t0\t15\t9\t1\t0\t1\t13\tProduction\t1\t0.1\t\t17\t11\t25\t46\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t475200\t0.9\t0.6\t0.75\t0.4\t0.3\t0.35\t8\t5\t1\tOther Credit\n53\t36\t4250\tSingle\t5\tRich\tBachelor's Degree\t15\tNeed\t40000\t36\tMale\tEastern Anatolia\t73\tTenant\t2\t5\t1\tManager\t\tNot Taken\tMore Than Seven Years\t0.9\tCo Applicant\tYes\t65\tNo\t3\t0\t1\t1\t12750\t0\tOne Month Late\tNo\t1\t578\t2550\t1275\tNo\t6\tBig\tWorker\tClerical\tEating and Drinking\tTransportation\tBills\t0.3\t0.1\t0\t6\t4\t21\t3\t0\t0\t24\tEducation\t2\t0.1\t0\t1\t16\t31\t52\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t62475\t0.3\t0.1\t0.2\t0.5\t0.2\t0.35\t7\t8\t2\tOther Credit\n57\t29\t500\tMarried\t5\tVery Poor\tPrimary School Grad\t11\tHousing\t85000\t108\tFemale\tSoutheast Anatolia\t50\tFamily Home\t2\t1\t0\tWorker\tNo\tRisky\tMore Than Seven Years\t0.5\tGuarantor\tNo\t68\t\t1\t0\t0\t2\t1500\t0.3\tNormal\tNo\t0\t1720\t900\t300\tYes\t6\tSmall\tUnemployed\tClerical\tEating and Drinking\tBills\tOther\t0.4\t0.2\t2\t1\t400\t27\t2\t16\t40\t25\tConsulting\t2\t0.2\t4\t12\t21\t6\t57\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t16800\t1\t0.2\t0.6\t0.1\t0.4\t0.25\t8\t1\t2\tOther Credit\n61\t54\t2150\tMarried\t1\tAverage\tMaster's Degree\t33\tAuto\t55000\t48\tMale\tCentral Anatolia\t9\tTenant\t0\t1\t0\tClerical\tYes\tDelay\tUnemployed\t1\tCo Applicant\tYes\t57\tNo\t1\t0\t0\t2\t6450\t0\tFour Months Late\tUnknown\t0\t1061\t3225\t1935\tNo\t2\tSmall\tRetired\tProfessional\tEating and Drinking\tHealth\tBills\t0.1\t0.2\t6\t0\t360\t6\t1\t14\t36\t37\tEnergy\t3\t0.3\t9\t5\t27\t17\t42\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t\tSilver\tSegment3\t59985\t0.9\t0.4\t0.65\t0.3\t0.3\t0.3\t5\t1\t3\tOther Credit\n66\t34\t500\tSingle\t3\tVery Poor\tPrimary School Grad\t57\tHousing\t35000\t228\tMale\tMarmara\t82\t\t1\t6\t2\tWorker\tYes\tDelay\tLess Than Seven Years\t0.7\tGuarantor\tYes\t12\tNo\t3\t0\t1\t1\t1500\t0.2\tOne Month Late\tNo\t0\t1780\t150\t900\tYes\t4\tMedium\tRetired\tTechnician\tBills\tElectronic\tEating and Drinking\t0.4\t0.1\t8\t0\t504\t8\t1\t20\t50\t48\tConsulting\t4\t0.2\t9\t8\t3\t40\t40\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t45900\t0.4\t0.7\t0.55\t0.4\t0.2\t0.3\t7\t8\t2\tOther Credit\n70\t47\t2750\tMarried\t3\tPoor\tBachelor's Degree\t6\tNeed\t30000\t12\tFemale\tCentral Anatolia\t78\tTenant\t3\t3\t0\tWorker\tNo\tRisky\tLess Than Seven Years\t0.9\tCo Applicant\tNo\t15\tNo\t2\t2\t1\t1\t8250\t0.8\tNormal\tUnknown\t\t1457\t5775\t825\tYes\t4\tMedium\tRetired\tRetired\tEating and Drinking\tTransportation\tBills\t0.5\t0.2\t2\t0\t23\t27\t3\t0\t2\t53\tLaw\t5\t0.4\t11\t1\t11\t0\t77\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t31350\t0.8\t0.3\t0.55\t1\t0.7\t0.85\t6\t1\t3\tOther Credit\n75\t50\t2750\tMarried\t4\tPoor\tBachelor's Degree\t10\tHousing\t55000\t276\tMale\tMarmara\t36\tTenant\t2\t3\t1\tManager\tNo\tDuly Now\tLess Than Four Years\t0.5\tGuarantor\tNo\t60\tNo\t1\t0\t1\t1\t8250\t0.6\tMore Than Four Months\tNo\t1\t1152\t7425\t6600\tNo\t5\tMedium\tClerical\tRetired\tBills\tTransportation\tOther\t0.5\t0.1\t7\t6\t390\t7\t3\t15\t39\t287\tEnergy\t28\t0.6\t14\t18\t0\t35\t33\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\t\t231000\t0.5\t0.5\t0.5\t0.5\t0.5\t0.5\t5\t7\t3\tOther Credit\n79\t22\t6250\t\t2\tVery Rich\tBachelor's Degree\t34\tAuto\t40000\t24\tFemale\tMediterranean\t32\tOwner\t1\t6\t2\tManager\tNo\tRisky\tMore Than Seven Years\t0.5\tCo Applicant\tYes\t98\tNo\t3\t0\t1\t2\t18750\t0\tNormal\tNo\t0\t222\t18750\t7500\tYes\t3\tBig\tUnemployed\tTechnician\tMarket\tHealth\tElectronic\t0.2\t0.2\t0\t0\t46\t8\t1\t1\t4\t12\tFinance\t1\t0.2\t9\t1\t23\t6\t61\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t472500\t1\t0.8\t0.9\t0.5\t0.5\t0.5\t8\t3\t1\tGood Credit\n83\t27\t5250\tMarried\t2\tVery Rich\tBachelor's Degree\t9\tHousing\t45000\t168\tMale\tAegean\t55\tOwner\t2\t4\t1\tProfessional\tNo\tRisky\tLess Than One Year\t0.7\tNone\tNo\t8\tNo\t2\t1\t1\t1\t15750\t0.4\tNormal\tUnknown\t0\t310\t9450\t9450\tYes\t3\tSmall\tProfessional\tUnemployed\tEating and Drinking\tTransportation\tBills\t0.1\t0.1\t7\t1\t40\t30\t3\t1\t4\t15\tTransportation\t1\t0.1\t11\t14\t25\t15\t35\tYes\tSocial\t7.5\t\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t548100\t0.5\t0.7\t0.6\t0.2\t0.1\t0.15\t8\t7\t2\tGood Credit\n87\t42\t6250\tMarried\t6\tRich\tBachelor's Degree\t19\tAuto\t35000\t24\tFemale\tMarmara\t91\tTenant\t2\t4\t0\tManager\tYes\tRisky\tLess Than Seven Years\t0.7\tNone\tYes\t98\tNo\t3\t2\t1\t0\t18750\t0.5\tNormal\tUnknown\t0\t601\t1875\t13125\tYes\t7\tMedium\tRetired\tManager\tMarket\tBills\tElectronic\t0.5\t0.2\t8\t1\t34\t6\t1\t1\t3\t22\tDesign\t2\t0.2\t1\t9\t20\t25\t45\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t\t0.65\t7.15\tSilver\tSegment2\t564375\t0.3\t1\t0.65\t1\t0.8\t0.9\t6\t4\t3\tOther Credit\n88\t40\t2750\tMarried\t5\tPoor\tBachelor's Degree\t5\tNeed\t30000\t24\tFemale\tAegean\t49\tTenant\t3\t3\t2\tTechnician\tNo\tRisky\tLess Than One Year\t1\tGuarantor\tNo\t66\tNo\t1\t0\t0\t2\t8250\t0.2\tOne Month Late\tNo\t0\t1290\t4125\t825\tNo\t6\tMedium\tUnemployed\tArmy\tMarket\tBills\tHealth\t0.5\t0.1\t0\t0\t500\t21\t3\t\t50\t240\tScience\t24\t0.6\t14\t1\t2\t12\t71\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t37125\t0.9\t0.4\t0.65\t0.4\t0.5\t0.45\t6\t8\t3\tOther Credit\n89\t31\t6250\tDivorced\t\tRich\tBachelor's Degree\t14\tNeed\t40000\t24\tMale\tMediterranean\t83\tTenant\t1\t5\t2\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.8\tNone\tNo\t92\tNo\t2\t1\t1\t0\t18750\t0.3\tNormal\tNo\t0\t729\t13125\t3750\tYes\t7\tMedium\tArmy\tClerical\tEating and Drinking\tHealth\tBills\t0.2\t0.2\t0\t4\t288\t6\t3\t11\t28\t61\tTransportation\t6\t0.5\t7\t11\t7\t34\t41\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t202500\t0.8\t0.4\t0.6\t0.4\t0.2\t0.3\t7\t6\t2\tOther Credit\n90\t21\t2750\tSingle\t1\tAverage\tMaster's Degree\t7\tAuto\t35000\t24\tFemale\tMarmara\t91\tTenant\t0\t4\t1\tManager\tNo\tNot Taken\tMore Than Seven Years\t0.9\tGuarantor\tYes\t65\tNo\t3\t2\t0\t2\t8250\t0.8\tThree Months Late\tUnknown\t1\t784\t4950\t825\tNo\t2\tMedium\tRetired\tWorker\tEating and Drinking\tTransportation\tHealth\t0.3\t0.1\t0\t0\t72\t\t2\t2\t7\t176\tEducation\t17\t0.6\t6\t14\t14\t22\t44\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t52800\t0.5\t0.4\t0.45\t0.1\t0.2\t0.15\t8\t8\t1\tOther Credit\n91\t65\t2150\tSingle\t3\tPoor\tBachelor's Degree\t9\tEducation\t15000\t12\tFemale\tBlack Sea\t70\tTenant\t0\t3\t0\tTechnician\tYes\tNot Taken\tUnemployed\t0.7\tNone\tNo\t\tNo\t3\t2\t0\t2\t6450\t0.9\tFour Months Late\tYes\t2\t1211\t5160\t2580\tYes\t4\tBig\tUnemployed\tUnemployed\tMarket\tEating and Drinking\tElectronic\t0.3\t0.2\t0\t1\t282\t30\t1\t11\t28\t171\tHealth\t17\t0.5\t4\t0\t6\t2\t88\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t51600\t0.8\t0.8\t0.8\t0.9\t0.8\t0.85\t4\t3\t5\tOther Credit\n92\t36\t3750\tSingle\t2\tRich\tDoctorate Degree\t13\tNeed\t25000\t12\tMale\tBlack Sea\t15\tTenant\t1\t3\t1\tManager\tYes\tRisky\tMore Than Seven Years\t0.5\tCo Applicant\tNo\t89\tNo\t1\t2\t1\t1\t11250\t0.3\tNormal\tNo\t0\t468\t4500\t3375\tYes\t3\tMedium\tUnemployed\tWorker\tEating and Drinking\tHealth\tElectronic\t0.3\t0.2\t10\t0\t42\t6\t0\t1\t4\t2\tLaw\t0\t0.2\t4\t7\t14\t28\t47\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t165375\t0.8\t0.5\t0.65\t0.9\t1\t0.95\t7\t2\t\tGood Credit\n93\t18\t5250\tMarried\t3\tRich\tBachelor's Degree\t5\t\t90000\t60\tFemale\tMarmara\t71\tOwner\t2\t4\t2\tProfessional\tYes\tRisky\tMore Than Seven Years\t0.9\tCo Applicant\tYes\t56\tNo\t2\t2\t0\t0\t15750\t0.5\tNormal\tNo\t0\t652\t6300\t4725\tNo\t4\tMedium\tUnemployed\tClerical\tEating and Drinking\tHealth\tMarket\t0.4\t0.2\t1\t0\t154\t6\t0\t6\t15\t3\tConsulting\t0\t0.2\t12\t5\t26\t1\t56\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t316575\t1\t0.2\t0.6\t0.4\t0.2\t0.3\t9\t3\t1\tOther Credit\n94\t46\t4250\tMarried\t3\tRich\tBachelor's Degree\t10\tNeed\t45000\t36\tMale\tSoutheast Anatolia\t71\tOwner\t0\t4\t1\tManager\tNo\tRisky\tLess Than Four Years\t1\tCo Applicant\tYes\t15\tNo\t2\t1\t1\t1\t12750\t0.8\tNormal\tNo\t0\t435\t12750\t11475\tYes\t4\tMedium\tRetired\tUnemployed\tMarket\tOther\tHealth\t0.3\t0.1\t8\t4\t20\t8\t2\t0\t2\t20\tEnergy\t2\t0.2\t6\t15\t6\t\t68\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t447525\t0.8\t0.7\t0.75\t0.5\t0.5\t0.5\t6\t3\t3\tGood Credit\n95\t71\t2750\tSingle\t3\tPoor\tBachelor's Degree\t21\tAuto\t55000\t12\tFemale\tSoutheast Anatolia\t16\tTenant\t3\t\t0\tManager\tNo\tDelay\tLess Than Seven Years\t1\tGuarantor\tNo\t5\tNo\t1\t2\t1\t1\t8250\t0.1\tOne Month Late\tNo\t0\t1378\t6600\t5775\tYes\t4\tSmall\tProfessional\tFarmer\tMarket\tBills\tEating and Drinking\t0.2\t0.2\t10\t5\t40\t2\t0\t1\t4\t28\tProduction\t2\t0.2\t2\t17\t20\t10\t51\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t80850\t0.8\t0.6\t0.7\t1\t0.6\t0.8\t3\t3\t5\tOther Credit\n96\t39\t2750\tMarried\t3\tPoor\tBachelor's Degree\t2\tNeed\t35000\t36\tFemale\tEastern Anatolia\t95\tTenant\t2\t3\t1\tWorker\tYes\tDelay\tLess Than Seven Years\t0.9\tGuarantor\tYes\t79\tNo\t2\t0\t0\t1\t8250\t0.4\tNormal\tNo\t1\t1154\t1650\t6600\tYes\t4\tBig\tUnemployed\tManager\tEating and Drinking\tOther\tMarket\t0.5\t0.1\t0\t5\t960\t27\t3\t38\t96\t158\tEducation\t15\t0.7\t10\t5\t29\t18\t38\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t303600\t0.5\t0.9\t0.7\t0.2\t0.3\t0.25\t\t3\t2\tOther Credit\n97\t70\t4250\tSingle\t3\tRich\tBachelor's Degree\t5\tHousing\t80000\t48\tMale\tAegean\t89\tOwner\t3\t2\t1\tManager\tNo\tRisky\tLess Than Four Years\t0.9\tCo Applicant\tYes\t49\tNo\t1\t0\t0\t\t12750\t0.8\tNormal\tNo\t1\t736\t8925\t5100\tYes\t4\tBig\tManager\tManager\tBills\tElectronic\tTransportation\t0.3\t0.1\t8\t1\t21\t6\t2\t0\t2\t116\tFinance\t11\t0.6\t11\t16\t9\t40\t24\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t76500\t1\t0.7\t0.85\t0.3\t0.4\t0.35\t3\t1\t5\tOther Credit\n98\t20\t3750\tDivorced\t2\tRich\tDoctorate Degree\t8\tNeed\t25000\t36\tFemale\tMarmara\t67\tTenant\t3\t4\t0\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.5\tCo Applicant\tYes\t14\tNo\t3\t0\t1\t0\t11250\t0.6\tNormal\tNo\t0\t694\t11250\t4500\tNo\t3\tBig\tRetired\tUnemployed\tEating and Drinking\tBills\tMarket\t0.2\t0.1\t0\t1\t12\t7\t1\t0\t1\t42\tScience\t4\t0.2\t9\t10\t10\t44\t\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t292500\t1\t0.5\t0.75\t0.2\t0.1\t0.15\t8\t8\t1\tOther Credit\n99\t39\t4250\tSingle\t2\tVery Rich\tBachelor's Degree\t12\tNeed\t20000\t12\tFemale\tCentral Anatolia\t61\tOwner\t1\t5\t2\tManager\tYes\tNot Taken\tUnemployed\t0.5\tNone\tNo\t31\tNo\t3\t1\t0\t2\t12750\t0.3\tNormal\tNo\t0\t222\t12750\t11475\tNo\t3\tBig\tUnemployed\tUnemployed\tMarket\tHealth\tOther\t0.4\t0.1\t1\t1\t52\t2\t\t2\t5\t70\tTransportation\t7\t0.3\t7\t10\t10\t39\t34\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t527850\t0.6\t0.6\t0.6\t0.1\t0.5\t0.3\t7\t5\t2\tGood Credit\n100\t47\t2150\tMarried\t4\tPoor\tBachelor's Degree\t12\tEducation\t10000\t24\tFemale\tBlack Sea\t38\tTenant\t3\t4\t2\tManager\tNo\tNot Taken\tUnemployed\t0.6\tGuarantor\tNo\t7\tNo\t2\t1\t0\t0\t6450\t0.3\tMore Than Four Months\tNo\t0\t1512\t1290\t5160\tYes\t5\tMedium\tManager\tWorker\tEating and Drinking\tHealth\tElectronic\t0.3\t0.1\t2\t1\t104\t1\t2\t4\t10\t43\tTourism\t4\t0.3\t8\t8\t18\t25\t41\tYes\tThemselves\t7.5\t\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t196080\t0.4\t0.7\t0.55\t0.3\t0.2\t0.25\t6\t7\t3\tOther Credit\n101\t53\t2150\tMarried\t5\tPoor\tBachelor's Degree\t33\tAuto\t10000\t36\tFemale\tSoutheast Anatolia\t34\tTenant\t1\t3\t2\tTechnician\tNo\tNot Taken\tLess Than Seven Years\t0.5\tGuarantor\tNo\t71\tNo\t1\t1\t0\t0\t6450\t0.8\tTwo Months Late\tUnknown\t1\t1409\t5160\t1935\tNo\t6\tSmall\tRetired\tRetired\tMarket\tBills\tTransportation\t0.2\t0.1\t9\t4\t462\t30\t2\t18\t46\t270\tProduction\t27\t0.8\t0\t8\t17\t44\t31\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t61920\t0.7\t0.5\t0.6\t0.3\t0.1\t\t5\t3\t3\tOther Credit\n102\t38\t5250\tSingle\t6\tRich\tBachelor's Degree\t35\tNeed\t45000\t12\tFemale\tMediterranean\t13\tTenant\t3\t5\t1\tManager\tNo\tNot Taken\tLess Than Seven Years\t0.6\tNone\tYes\t53\tNo\t3\t0\t1\t1\t15750\t0.3\tNormal\tNo\t0\t627\t14175\t6300\tYes\t7\tMedium\tManager\tManager\tBills\tOther\tTransportation\t0.3\t0.1\t9\t0\t72\t21\t0\t2\t7\t92\tFinance\t9\t0.5\t5\t4\t22\t17\t52\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t\tSilver\tSegment2\t296100\t0.9\t0.5\t0.7\t0.2\t0.5\t0.35\t7\t8\t2\tOther Credit\n103\t46\t2750\tMarried\t6\tPoor\tBachelor's Degree\t5\tAuto\t35000\t12\tMale\tMediterranean\t73\tTenant\t3\t1\t0\tTechnician\tNo\tDuly Now\tLess Than Four Years\t0.6\tCo Applicant\tNo\t96\tNo\t1\t0\t0\t0\t8250\t0\tThree Months Late\tUnknown\t0\t1226\t1650\t2475\tYes\t7\tSmall\tTechnician\tTechnician\tEating and Drinking\tBills\tElectronic\t\t0.2\t6\t1\t568\t27\t0\t22\t56\t31\tTransportation\t3\t0.3\t13\t17\t29\t20\t21\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t96525\t0.1\t0.3\t0.2\t0.5\t0.1\t0.3\t6\t4\t3\tOther Credit\n104\t64\t5250\tMarried\t3\tRich\t\t82\tNeed\t30000\t24\tFemale\tSoutheast Anatolia\t82\tOwner\t2\t4\t1\tProfessional\tNo\tRisky\tMore Than Seven Years\t1\tGuarantor\tYes\t67\tNo\t3\t1\t0\t2\t15750\t0.8\tOne Month Late\tNo\t0\t705\t14175\t6300\tYes\t4\tMedium\tClerical\tTechnician\tEating and Drinking\tBills\tMarket\t0.3\t0.1\t7\t6\t32\t8\t3\t1\t3\t99\tTransportation\t9\t0.4\t4\t9\t9\t11\t67\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t132300\t0.6\t0.5\t0.55\t0.5\t0.1\t0.3\t4\t8\t4\tOther Credit\n105\t27\t500\tMarried\t2\tVery Poor\t\t39\tHousing\t85000\t120\tMale\tEastern Anatolia\t49\tFamily Home\t1\t3\t1\tFarmer\tYes\tRisky\tLess Than Seven Years\t0.9\tGuarantor\tNo\t31\tNo\t1\t2\t1\t0\t1500\t0.6\tOne Month Late\tNo\t0\t1814\t900\t450\tNo\t3\tMedium\tClerical\tFarmer\tEating and Drinking\tOther\tTransportation\t0.2\t0.1\t9\t0\t360\t30\t2\t14\t36\t281\tTourism\t28\t0.7\t13\t14\t7\t37\t29\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t26100\t1\t0.1\t0.55\t0.5\t0.8\t0.65\t8\t1\t2\tOther Credit\n106\t19\t4250\tSingle\t2\tVery Rich\tBachelor's Degree\t27\tAuto\t40000\t24\tFemale\tMediterranean\t24\tTenant\t2\t4\t2\t\tNo\tRisky\tLess Than Four Years\t0.7\tNone\tYes\t51\tNo\t1\t1\t1\t1\t12750\t0.9\tOne Month Late\tNo\t0\t346\t3825\t3825\tYes\t3\tMedium\tManager\tRetired\tEating and Drinking\tHealth\tMarket\t0.2\t0.1\t0\t0\t66\t27\t2\t2\t6\t46\tTourism\t4\t0.3\t7\t14\t13\t23\t43\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t252450\t0.2\t0.1\t0.15\t0.3\t0.5\t0.4\t9\t5\t1\tGood Credit\n107\t18\t3750\tMarried\t1\tRich\tDoctorate Degree\t39\tHousing\t15000\t144\tMale\tBlack Sea\t16\tTenant\t1\t3\t2\tManager\tYes\tNot Taken\tUnemployed\t1\tCo Applicant\tNo\t35\tNo\t1\t2\t0\t1\t11250\t0.1\tNormal\tUnknown\t0\t414\t3375\t3375\tYes\t2\tMedium\tWorker\tWorker\tMarket\tBills\tElectronic\t0.4\t0.2\t7\t0\t860\t30\t3\t34\t86\t9\tConsulting\t0\t0.1\t4\t8\t29\t40\t19\t\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t226125\t0.4\t0.4\t0.4\t0.1\t0.1\t0.1\t9\t3\t1\tGood Credit\n372\t\t5250\tSingle\t3\tRich\tBachelor's Degree\t2\tNeed\t15000\t24\tFemale\tEastern Anatolia\t32\tOwner\t2\t3\t1\tProfessional\tNo\tDuly Now\tUnemployed\t0.8\tCo Applicant\tNo\t11\tNo\t2\t2\t0\t2\t15750\t0.7\tNormal\tNo\t0\t560\t7875\t1575\tYes\t4\tSmall\tManager\tRetired\tBills\tHealth\tOther\t0.5\t0.1\t7\t6\t90\t30\t3\t3\t9\t22\tEnergy\t2\t0.1\t4\t15\t17\t27\t37\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t33075\t0.8\t0.3\t0.55\t0.9\t0.6\t0.75\t4\t1\t4\tGood Credit\n375\t67\t6250\tMarried\t2\tVery Rich\tBachelor's Degree\t26\tNeed\t40000\t36\tFemale\tCentral Anatolia\t72\tOwner\t2\t3\t1\tManager\tNo\tDuly Past\t\t0.7\tNone\tYes\t27\tNo\t2\t2\t0\t1\t18750\t0.8\tNormal\tUnknown\t1\t351\t13125\t5625\tYes\t3\tBig\tRetired\tArmy\tEating and Drinking\tOther\tMarket\t0.3\t0.1\t1\t0\t560\t21\t4\t22\t56\t28\tDesign\t2\t0.3\t12\t10\t15\t11\t52\tYes\tSocial\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t101250\t1\t0.1\t0.55\t1\t0.8\t0.9\t4\t1\t5\tGood Credit\n379\t27\t4250\tMarried\t2\tVery Rich\tBachelor's Degree\t17\tHousing\t105000\t240\tFemale\tMarmara\t81\tOwner\t3\t3\t2\tProfessional\tYes\tDelay\tUnemployed\t0.7\tCo Applicant\tYes\t73\tNo\t1\t0\t0\t0\t12750\t0.7\tNormal\tNo\t0\t\t6375\t5100\tNo\t3\tMedium\tClerical\tWorker\tEating and Drinking\tMarket\tOther\t0.4\t0.1\t1\t0\t5\t7\t2\t0\t0\t29\tScience\t2\t0.3\t9\t18\t17\t10\t46\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t295800\t0.9\t0.6\t0.75\t0.4\t0.5\t0.45\t8\t4\t2\tGood Credit\n383\t47\t2750\tMarried\t3\tPoor\tBachelor's Degree\t59\tNeed\t40000\t24\tMale\tBlack Sea\t78\tTenant\t3\t2\t1\tTechnician\tNo\tRisky\tLess Than Four Years\t0.6\tCo Applicant\tNo\t64\tNo\t1\t1\t0\t1\t8250\t0.1\tFour Months Late\tUnknown\t1\t1294\t2475\t3300\t\t4\tBig\tWorker\tProfessional\tBills\tElectronic\tHealth\t0.3\t0.2\t9\t1\t30\t27\t2\t1\t3\t40\tFinance\t4\t0.2\t1\t2\t4\t35\t58\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t125400\t0.2\t0.8\t0.5\t0.5\t0.3\t0.4\t6\t2\t3\tOther Credit\n388\t59\t2750\tDivorced\t\tAverage\tMaster's Degree\t2\tNeed\t10000\t24\tMale\tAegean\t50\tTenant\t3\t3\t0\tFarmer\tNo\tNot Taken\tLess Than One Year\t0.6\tNone\tNo\t92\tNo\t3\t2\t0\t1\t8250\t0.2\tThree Months Late\tUnknown\t1\t880\t3300\t1650\tYes\t2\tMedium\tUnemployed\tArmy\tBills\tElectronic\tOther\t0.1\t0.2\t9\t1\t60\t27\t3\t2\t6\t66\tEnergy\t6\t0.5\t8\t3\t11\t18\t60\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t42900\t0.6\t0.2\t0.4\t0.8\t0.9\t0.85\t5\t2\t4\tOther Credit\n392\t44\t2750\tMarried\t2\tAverage\tMaster's Degree\t70\tNeed\t20000\t12\tMale\tBlack Sea\t34\tTenant\t1\t1\t0\tProfessional\tNo\tDelay\tLess Than Seven Years\t0.9\tGuarantor\t\t22\tNo\t1\t2\t0\t0\t8250\t0.2\tFour Months Late\tNo\t0\t1091\t6600\t7425\tNo\t3\tSmall\tArmy\tProfessional\tEating and Drinking\tBills\tTransportation\t0.4\t0.2\t3\t1\t13\t21\t2\t0\t1\t69\tTransportation\t6\t0.3\t4\t5\t19\t37\t35\tYes\tEducation\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t304425\t0.8\t0.9\t0.85\t1\t0.9\t0.95\t6\t2\t3\tOther Credit\n396\t50\t500\tDivorced\t2\tVery Poor\tPrimary School Grad\t15\tAuto\t30000\t12\tMale\tMarmara\t80\tTenant\t0\t5\t2\tTechnician\tNo\tRisky\tLess Than Four Years\t0.7\tNone\tYes\t7\tNo\t3\t1\t0\t2\t1500\t0.6\tTwo Months Late\tNo\t0\t1608\t1050\t600\tYes\t3\tMedium\tTechnician\tRetired\tEating and Drinking\tMarket\tHealth\t0.1\t0.2\t6\t0\t30\t1\t3\t1\t3\t32\tConsulting\t3\t0.3\t12\t2\t24\t29\t33\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t\t7.15\tBronze\tSegment5\t21000\t0.9\t0.5\t0.7\t0.2\t0.1\t0.15\t5\t5\t3\tOther Credit\n400\t62\t2750\tSingle\t2\tAverage\tMaster's Degree\t21\tNeed\t\t24\tMale\tMediterranean\t17\tTenant\t0\t4\t1\tClerical\tNo\tDuly Now\tMore Than Seven Years\t0.7\tNone\tNo\t43\tNo\t3\t0\t0\t2\t8250\t0.5\tMore Than Four Months\tNo\t0\t940\t6600\t4125\tYes\t3\tMedium\tArmy\tManager\tEating and Drinking\tMarket\tOther\t0.1\t0.1\t0\t0\t205\t9\t2\t8\t20\t23\tProduction\t2\t0.1\t8\t8\t23\t7\t54\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t94875\t0.9\t0.6\t0.75\t0.2\t0.2\t0.2\t4\t7\t4\tOther Credit\n403\t68\t2750\tDivorced\t1\tAverage\tMaster's Degree\t13\tAuto\t50000\t24\tMale\tCentral Anatolia\t86\tTenant\t2\t4\t1\tArmy\tNo\tRisky\tLess Than Seven Years\t0.7\tGuarantor\tNo\t69\tNo\t2\t0\t1\t2\t8250\t0.2\tTwo Months Late\tUnknown\t0\t787\t6600\t3300\tYes\t2\tMedium\tManager\tRetired\tEating and Drinking\tOther\tElectronic\t0.3\t0.1\t3\t1\t600\t21\t2\t24\t60\t16\tTransportation\t1\t0.2\t8\t3\t\t27\t55\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t56100\t0.8\t0.7\t0.75\t0.4\t0.2\t0.3\t4\t6\t5\tOther Credit\n404\t68\t2150\tMarried\t3\tPoor\tBachelor's Degree\t9\tNeed\t35000\t12\tFemale\tMarmara\t56\tTenant\t3\t4\t2\tProfessional\tNo\tNot Taken\tLess Than Four Years\t0.9\tGuarantor\tNo\t36\tNo\t\t2\t0\t1\t6450\t0.7\tNormal\tNo\t0\t1242\t3225\t3225\tYes\t4\tSmall\tTechnician\tRetired\tEating and Drinking\tHealth\tMarket\t0.3\t0.2\t7\t1\t864\t7\t1\t34\t86\t89\tConsulting\t8\t0.4\t11\t5\t1\t40\t43\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t54825\t0.8\t0.6\t0.7\t0.5\t0.3\t0.4\t4\t1\t5\tOther Credit\n405\t63\t6250\tMarried\t1\tVery Rich\tBachelor's Degree\t49\tNeed\t30000\t36\tMale\tSoutheast Anatolia\t44\tOwner\t3\t2\t1\tManager\tYes\tRisky\tLess Than One Year\t1\tNone\tNo\t46\tNo\t1\t2\t0\t0\t18750\t0.5\tNormal\tUnknown\t0\t266\t3750\t1875\tYes\t2\tSmall\tClerical\tUnemployed\tMarket\tHealth\tEating and Drinking\t0.2\t0.1\t10\t5\t31\t6\t1\t1\t3\t64\tConsulting\t6\t0.5\t9\t16\t3\t38\t34\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t41250\t0.1\t0.5\t0.3\t0.5\t\t0.7\t4\t2\t4\tGood Credit\n406\t28\t6250\tMarried\t2\tVery Rich\tBachelor's Degree\t9\tEducation\t20000\t12\tFemale\tCentral Anatolia\t66\tOwner\t3\t3\t0\tManager\tYes\tNot Taken\tLess Than Four Years\t0.5\tNone\tNo\t83\tNo\t3\t1\t0\t1\t18750\t0.4\tOne Month Late\tUnknown\t1\t342\t1875\t5625\t\t3\tSmall\tClerical\tUnemployed\tMarket\tHealth\tElectronic\t0.4\t0.2\t10\t1\t344\t24\t3\t13\t34\t137\tScience\t13\t0.6\t3\t2\t3\t17\t75\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t320625\t0.1\t0.1\t0.1\t0.5\t0.5\t0.5\t8\t8\t2\tGood Credit\n407\t42\t2150\tMarried\t3\tPoor\tBachelor's Degree\t39\tHousing\t60000\t168\tMale\tMediterranean\t16\tTenant\t2\t3\t2\tProfessional\tNo\tDuly Past\tUnemployed\t0.5\tNone\tNo\t73\tNo\t1\t0\t0\t2\t6450\t0.7\tThree Months Late\tUnknown\t0\t1238\t3870\t4515\tYes\t4\tMedium\tManager\tTechnician\tEating and Drinking\tBills\tTransportation\t0.3\t0.2\t0\t0\t700\t2\t4\t28\t70\t43\t\t4\t0.3\t1\t15\t9\t43\t32\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t194145\t0.5\t0.7\t0.6\t0.4\t0.3\t0.35\t6\t8\t3\tOther Credit\n409\t67\t2150\tMarried\t3\tPoor\tBachelor's Degree\t4\tAuto\t50000\t48\tMale\tCentral Anatolia\t86\tTenant\t2\t2\t1\tWorker\tYes\tNot Taken\tUnemployed\t1\tCo Applicant\tYes\t66\tNo\t1\t0\t0\t1\t6450\t0.6\tMore Than Four Months\tUnknown\t1\t1163\t5160\t3870\t\t4\tBig\tUnemployed\tManager\tEating and Drinking\tBills\tElectronic\t0.1\t0.2\t2\t1\t63\t2\t3\t2\t6\t75\tConsulting\t7\t0.5\t3\t7\t18\t30\t42\tNo\tNo Post\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t69660\t0.7\t1\t0.85\t0.2\t0.3\t0.25\t4\t3\t5\tOther Credit\n414\t33\t2750\tMarried\t1\tAverage\tMaster's Degree\t4\tAuto\t40000\t12\tMale\tEastern Anatolia\t21\tTenant\t3\t1\t0\tWorker\tYes\tNot Taken\tUnemployed\t0.8\tNone\tNo\t34\tNo\t1\t2\t0\t1\t8250\t0.3\tOne Month Late\tNo\t0\t855\t2475\t1650\tYes\t2\tSmall\tUnemployed\tUnemployed\tEating and Drinking\tBills\tOther\t0.1\t0.1\t1\t1\t20\t24\t2\t0\t2\t14\tEnergy\t1\t0.1\t14\t3\t7\t29\t47\tNo\tNo Post\t\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t85800\t0.2\t0.1\t0.15\t0.8\t1\t0.9\t7\t2\t2\tOther Credit\n418\t33\t2750\tSingle\t2\tAverage\tMaster's Degree\t5\tHousing\t20000\t168\tFemale\tMediterranean\t68\tTenant\t1\t4\t2\tProfessional\tNo\tDuly Now\tUnemployed\t0.8\tNone\tYes\t82\tNo\t2\t2\t0\t1\t8250\t0.4\tFour Months Late\tNo\t0\t813\t1650\t825\tYes\t3\tSmall\tArmy\tProfessional\tMarket\tBills\tTransportation\t0.1\t0.2\t2\t0\t72\t2\t2\t2\t7\t91\tFinance\t9\t0.3\t11\t8\t1\t13\t67\tYes\tBusiness\t7.5\t4.8\t10.1\t127.2\t187.86\t\t7.15\tSilver\tSegment3\t42900\t0.2\t0.5\t0.35\t0.1\t0.2\t0.15\t7\t6\t2\tOther Credit\n422\t25\t2750\tDivorced\t1\tAverage\tMaster's Degree\t8\tNeed\t40000\t36\tMale\tMarmara\t58\tTenant\t3\t5\t1\tTechnician\tYes\tRisky\tUnemployed\t0.5\tNone\tNo\t39\tNo\t3\t0\t1\t1\t8250\t0.4\tMore Than Four Months\tUnknown\t1\t883\t6600\t6600\tNo\t2\tSmall\tProfessional\tWorker\tEating and Drinking\tTransportation\tHealth\t0.3\t0.2\t1\t6\t490\t6\t1\t19\t49\t29\tEnergy\t2\t0.1\t8\t5\t28\t44\t15\tYes\t\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t396000\t1\t1\t1\t0.5\t0.1\t0.3\t8\t3\t2\tOther Credit\n427\t30\t4250\tMarried\t6\tRich\tBachelor's Degree\t11\tAuto\t45000\t24\tFemale\tAegean\t97\tTenant\t2\t4\t1\tManager\tNo\tDelay\tLess Than Four Years\t0.7\tCo Applicant\tNo\t32\tNo\t2\t2\t1\t2\t12750\t0.3\tNormal\tUnknown\t1\t705\t\t10200\tYes\t7\tMedium\tFarmer\tManager\tEating and Drinking\tMarket\tTransportation\t0.1\t0.2\t3\t5\t330\t21\t3\t13\t33\t39\tScience\t3\t0.3\t6\t1\t21\t44\t28\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t561000\t1\t0.8\t0.9\t0.5\t0.5\t0.5\t7\t2\t2\tOther Credit\n431\t43\t2150\tSingle\t4\t\tBachelor's Degree\t9\tNeed\t45000\t36\tMale\tBlack Sea\t42\tTenant\t1\t5\t2\tClerical\tYes\tDelay\tLess Than One Year\t0.6\tNone\tYes\t59\tNo\t3\t1\t0\t1\t6450\t0.5\tFour Months Late\tNo\t0\t1223\t5805\t5160\tYes\t5\tSmall\tManager\tRetired\tEating and Drinking\tMarket\tHealth\t0.1\t0.2\t3\t5\t400\t7\t0\t16\t40\t78\tDesign\t7\t0.3\t7\t15\t10\t33\t35\tYes\tFuture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t216720\t0.6\t0.8\t0.7\t0.2\t0.2\t0.2\t6\t7\t3\tOther Credit\n435\t49\t2750\tMarried\t2\tAverage\tMaster's Degree\t6\tNeed\t25000\t12\tMale\tCentral Anatolia\t91\tTenant\t1\t5\t2\tFarmer\tYes\tNot Taken\tLess Than Seven Years\t0.5\tGuarantor\tNo\t71\tNo\t3\t0\t0\t2\t8250\t0.3\tMore Than Four Months\tNo\t0\t983\t4950\t5775\tNo\t3\tSmall\tArmy\tTechnician\tMarket\tHealth\tBills\t0.4\t0.2\t0\t0\t9\t27\t3\t0\t0\t36\tScience\t3\t0.1\t4\t17\t23\t2\t54\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t207900\t0.9\t0.9\t0.9\t0.4\t\t0.3\t6\t1\t3\tOther Credit\n440\t67\t2750\tSingle\t2\tAverage\tMaster's Degree\t84\tNeed\t10000\t36\tFemale\tCentral Anatolia\t76\tTenant\t2\t5\t2\tArmy\tYes\tDuly Now\tMore Than Seven Years\t0.6\tCo Applicant\tYes\t82\tNo\t2\t1\t1\t0\t8250\t0.4\tFour Months Late\t\t0\t768\t2475\t4125\tNo\t3\tSmall\tUnemployed\tClerical\tMarket\tTransportation\tElectronic\t0.5\t0.2\t3\t1\t255\t24\t0\t10\t25\t14\tLaw\t1\t0.2\t7\t5\t22\t10\t56\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t74250\t0.4\t0.7\t0.55\t0.2\t0.3\t0.25\t4\t8\t5\tOther Credit\n444\t56\t3750\tDivorced\t2\tRich\tDoctorate Degree\t14\tEducation\t15000\t24\tFemale\tBlack Sea\t93\tOwner\t1\t4\t2\t\tNo\tDelay\tLess Than Four Years\t0.8\tCo Applicant\tNo\t87\tNo\t1\t2\t1\t0\t11250\t0.7\tNormal\tNo\t0\t659\t1125\t10125\tYes\t3\tMedium\tUnemployed\tTechnician\tBills\tElectronic\tMarket\t0.1\t0.1\t3\t1\t272\t1\t4\t10\t27\t24\tDesign\t2\t0.2\t13\t8\t6\t32\t41\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment2\t293625\t0.1\t1\t0.55\t0.1\t0.3\t0.2\t5\t6\t4\tOther Credit\n448\t36\t2750\tSingle\t2\tAverage\tMaster's Degree\t5\tHousing\t30000\t132\tFemale\tMediterranean\t26\tTenant\t2\t\t2\tManager\tYes\tNot Taken\tLess Than Four Years\t0.5\tGuarantor\tNo\t12\tNo\t1\t0\t0\t0\t8250\t0.5\tThree Months Late\tUnknown\t1\t808\t4950\t3300\tNo\t3\tMedium\tManager\tArmy\tEating and Drinking\tTransportation\tBills\t0.4\t0.2\t1\t1\t30\t1\t2\t1\t3\t9\tTransportation\t0\t0.2\t0\t0\t19\t22\t59\tYes\tProduct\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t161700\t1\t1\t1\t0.5\t0.1\t0.3\t7\t3\t2\tOther Credit\n449\t31\t2150\tMarried\t3\tPoor\tBachelor's Degree\t15\tNeed\t10000\t24\tMale\tBlack Sea\t75\tTenant\t1\t2\t0\tClerical\tYes\tDelay\tLess Than Four Years\t0.5\tGuarantor\tNo\t56\tNo\t1\t1\t1\t2\t6450\t0.2\tFour Months Late\tNo\t0\t1377\t5160\t1290\tYes\t4\tMedium\tWorker\tRetired\tEating and Drinking\tOther\t\t0.2\t0.2\t1\t0\t15\t21\t4\t0\t1\t38\tDesign\t3\t0.1\t11\t0\t24\t20\t45\tYes\tThemselves\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment4\t69660\t1\t0.3\t0.65\t0.8\t0.8\t0.8\t7\t3\t2\tOther Credit\n450\t40\t2150\tDivorced\t2\tAverage\tMaster's Degree\t18\tNeed\t45000\t36\tFemale\tSoutheast Anatolia\t76\tTenant\t0\t4\t2\tClerical\tNo\tNot Taken\tLess Than One Year\t0.7\tNone\tYes\t56\tNo\t2\t1\t0\t1\t6450\t0.5\tFour Months Late\tYes\t3\t1089\t3225\t4515\tYes\t3\tBig\tManager\t\tEating and Drinking\tBills\tOther\t0.2\t0.2\t9\t6\t117\t2\t0\t4\t11\t51\tScience\t5\t0.5\t7\t19\t24\t28\t22\tYes\tCulture\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tSilver\tSegment3\t203175\t0.9\t0.9\t0.9\t0.1\t0.3\t0.2\t6\t1\t3\tOther Credit\n451\t45\t500\tMarried\t3\tVery Poor\tPrimary School Grad\t39\tNeed\t45000\t24\tFemale\tEastern Anatolia\t98\tTenant\t3\t1\t0\tArmy\tNo\tDuly Past\tUnemployed\t0.9\tNone\tYes\t93\tNo\t1\t1\t0\t1\t1500\t0.2\tThree Months Late\tNo\t0\t1761\t300\t1050\tYes\t4\t\tRetired\tArmy\tEating and Drinking\tOther\tTransportation\t0.4\t0.1\t8\t4\t686\t1\t2\t27\t68\t25\tHealth\t2\t0.2\t11\t11\t29\t4\t45\tYes\tEconomics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tBronze\tSegment5\t42000\t0.2\t1\t0.6\t1\t0.6\t0.8\t6\t1\t3\tOther Credit\n455\t65\t6250\tSingle\t2\tVery Rich\tBachelor's Degree\t6\tNeed\t15000\t36\tFemale\tMediterranean\t59\tOwner\t0\t5\t2\tManager\tYes\tRisky\tMore Than Seven Years\t0.8\tNone\tNo\t15\tNo\t3\t2\t0\t0\t18750\t0.2\tOne Month Late\tNo\t0\t327\t1875\t5625\tYes\t3\tMedium\tWorker\tTechnician\tMarket\tOther\tTransportation\t0.1\t0.1\t10\t6\t672\t21\t3\t26\t67\t1\tDesign\t0\t0.3\t11\t\t29\t28\t23\tYes\tPolitics\t7.5\t4.8\t10.1\t127.2\t187.86\t0.65\t7.15\tGold\tSegment1\t112500\t0.4\t0.4\t0.4\t0.3\t0.5\t0.4\t4\t7\t5\tGood Credit\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-07-16T21:23:28+0000","dateStarted":"2018-07-16T21:23:46+0000","dateFinished":"2018-07-16T21:23:46+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:126"},{"title":"STEP 5: Review Data by Mode of Job Contacts and Income","text":"%sql \n\n-- This is a basic example of a chart visualization in Zepplin.  This particular one is a column graph.  Click on the 'settings' link below.  That will show you the fields in the query that were used to create the chart.  After you review the settings you can click on the link again to hide the settings.\n\nselect customer_id, age, income, tenure, loan_type, loan_amount, occupation, number_of_current_accounts, max_cc_spent_amount, mode_job_of_contacts from credit_scoring_100k_v where rownum < 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6: Review Data by Occupation","text":"%sql \n-- This shows an alternative presentation style - a pie chart.  Note that Zepplin visualizations are limited.  In lab 400 we will use Oracle Data Visualization to create more more interesting perspectives.\n\nselect customer_id, age, income, tenure, loan_type, loan_amount, occupation, marital_status \nfrom credit_scoring_100k_v where rownum < 1000","user":"CHARLIE","dateUpdated":"2018-07-16T17:00:07+0000","config":{"colWidth":6,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"pieChart","optionOpen":false,"keys":[{"name":"OCCUPATION","index":6,"aggr":"sum"}],"values":[{"name":"CUSTOMER_ID","index":0,"aggr":"count"}],"scatter":{"xAxis":{"name":"AGE","index":1,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-215146147","id":"20180717-165545_1783670954","result":{"msg":"CUSTOMER_ID\tAGE\tINCOME\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tOCCUPATION\tMARITAL_STATUS\n108\t24\t2150\t19\t\t20000\tTechnician\tMarried\n109\t45\t2750\t27\tAuto\t25000\tProfessional\tSingle\n110\t63\t2150\t11\tAuto\t20000\tProfessional\tSingle\n111\t59\t2750\t10\tHousing\t45000\tArmy\tMarried\n112\t20\t2750\t9\tEducation\t15000\tWorker\tDivorced\n113\t71\t3750\t12\tHousing\t40000\tManager\tMarried\n114\t35\t2750\t38\tHousing\t70000\tArmy\tMarried\n115\t63\t500\t5\tHousing\t85000\tArmy\tSingle\n116\t60\t3750\t6\tNeed\t20000\tProfessional\tMarried\n117\t55\t2750\t8\tEducation\t20000\tArmy\tSingle\n118\t62\t4250\t27\t\t40000\tManager\tMarried\n119\t67\t4250\t6\tAuto\t10000\tProfessional\tDivorced\n120\t23\t2750\t26\tNeed\t20000\tArmy\tSingle\n121\t43\t500\t15\tAuto\t55000\tArmy\tMarried\n122\t49\t2150\t14\tNeed\t25000\tTechnician\tSingle\n123\t51\t6250\t18\tEducation\t10000\t\tSingle\n124\t42\t3750\t15\tNeed\t15000\tManager\tSingle\n125\t41\t5250\t9\tNeed\t15000\tManager\tMarried\n127\t60\t2150\t11\tNeed\t45000\tProfessional\tSingle\n131\t42\t2150\t9\tEducation\t10000\tClerical\tMarried\n136\t59\t2150\t21\tHousing\t105000\tFarmer\tMarried\n141\t43\t6250\t20\tHousing\t65000\tManager\tSingle\n145\t64\t5250\t25\tAuto\t20000\tProfessional\tSingle\n149\t32\t500\t11\tHousing\t80000\tManager\tMarried\n153\t71\t4250\t21\tNeed\t40000\tManager\tMarried\n158\t49\t2750\t22\tNeed\t15000\tWorker\tMarried\n162\t62\t2150\t19\tHousing\t100000\tFarmer\tSingle\n167\t33\t6250\t10\tHousing\t45000\tManager\tDivorced\n168\t45\t500\t11\tAuto\t50000\tProfessional\tSingle\n169\t31\t6250\t82\tNeed\t45000\tManager\tMarried\n3\t70\t2750\t12\tEducation\t10000\tManager\tMarried\n7\t25\t4250\t11\tNeed\t25000\tManager\tMarried\n12\t56\t5250\t14\tNeed\t40000\t\tSingle\n16\t23\t500\t15\tAuto\t55000\tManager\tDivorced\n21\t40\t2150\t18\tNeed\t10000\tManager\tSingle\n25\t67\t500\t14\tEducation\t15000\tManager\tMarried\n29\t22\t2750\t8\tAuto\t55000\tFarmer\tMarried\n34\t48\t2150\t94\tEducation\t15000\tArmy\tDivorced\n39\t34\t3750\t26\tAuto\t50000\tProfessional\tMarried\n43\t45\t3750\t15\tAuto\t35000\tWorker\tMarried\n44\t36\t2150\t7\tHousing\t45000\tArmy\tMarried\n45\t19\t2750\t19\tEducation\t20000\tFarmer\tMarried\n48\t21\t2750\t35\tNeed\t35000\tClerical\tMarried\n53\t36\t4250\t15\tNeed\t40000\tManager\tSingle\n57\t29\t500\t11\tHousing\t85000\tWorker\tMarried\n61\t54\t2150\t33\tAuto\t55000\tClerical\tMarried\n66\t34\t500\t57\tHousing\t35000\tWorker\tSingle\n70\t47\t2750\t6\tNeed\t30000\tWorker\tMarried\n75\t50\t2750\t10\tHousing\t55000\tManager\tMarried\n79\t22\t6250\t34\tAuto\t40000\tManager\t\n83\t27\t5250\t9\tHousing\t45000\tProfessional\tMarried\n87\t42\t6250\t19\tAuto\t35000\tManager\tMarried\n88\t40\t2750\t5\tNeed\t30000\tTechnician\tMarried\n89\t31\t6250\t14\tNeed\t40000\tManager\tDivorced\n90\t21\t2750\t7\tAuto\t35000\tManager\tSingle\n91\t65\t2150\t9\tEducation\t15000\tTechnician\tSingle\n92\t36\t3750\t13\tNeed\t25000\tManager\tSingle\n93\t18\t5250\t5\t\t90000\tProfessional\tMarried\n94\t46\t4250\t10\tNeed\t45000\tManager\tMarried\n95\t71\t2750\t21\tAuto\t55000\tManager\tSingle\n96\t39\t2750\t2\tNeed\t35000\tWorker\tMarried\n97\t70\t4250\t5\tHousing\t80000\tManager\tSingle\n98\t20\t3750\t8\tNeed\t25000\tManager\tDivorced\n99\t39\t4250\t12\tNeed\t20000\tManager\tSingle\n100\t47\t2150\t12\tEducation\t10000\tManager\tMarried\n101\t53\t2150\t33\tAuto\t10000\tTechnician\tMarried\n102\t38\t5250\t35\tNeed\t45000\tManager\tSingle\n103\t46\t2750\t5\tAuto\t35000\tTechnician\tMarried\n104\t64\t5250\t82\tNeed\t30000\tProfessional\tMarried\n105\t27\t500\t39\tHousing\t85000\tFarmer\tMarried\n106\t19\t4250\t27\tAuto\t40000\t\tSingle\n107\t18\t3750\t39\tHousing\t15000\tManager\tMarried\n372\t\t5250\t2\tNeed\t15000\tProfessional\tSingle\n375\t67\t6250\t26\tNeed\t40000\tManager\tMarried\n379\t27\t4250\t17\tHousing\t105000\tProfessional\tMarried\n383\t47\t2750\t59\tNeed\t40000\tTechnician\tMarried\n388\t59\t2750\t2\tNeed\t10000\tFarmer\tDivorced\n392\t44\t2750\t70\tNeed\t20000\tProfessional\tMarried\n396\t50\t500\t15\tAuto\t30000\tTechnician\tDivorced\n400\t62\t2750\t21\tNeed\t\tClerical\tSingle\n403\t68\t2750\t13\tAuto\t50000\tArmy\tDivorced\n404\t68\t2150\t9\tNeed\t35000\tProfessional\tMarried\n405\t63\t6250\t49\tNeed\t30000\tManager\tMarried\n406\t28\t6250\t9\tEducation\t20000\tManager\tMarried\n407\t42\t2150\t39\tHousing\t60000\tProfessional\tMarried\n409\t67\t2150\t4\tAuto\t50000\tWorker\tMarried\n414\t33\t2750\t4\tAuto\t40000\tWorker\tMarri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Pie Chart Example","text":"%sql \n-- This shows an alternative presentation style - a pie chart.  Note that Zepplin visualizations are limited.  In lab 400 we will use Oracle Data Visualization to create more more interesting perspectives.\n\nselect customer_id, age, income, tenure, loan_type, loan_amount, occupation, marital_status \nfrom credit_scoring_100k_v where rownum < 1000","user":"CHARLIE","dateUpdated":"2018-07-16T17:00:11+0000","config":{"colWidth":6,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"pieChart","optionOpen":false,"keys":[{"name":"LOAN_TYPE","index":4,"aggr":"sum"}],"values":[{"name":"CUSTOMER_ID","index":0,"aggr":"count"}],"scatter":{"yAxis":{"name":"AGE","index":1,"aggr":"sum"},"xAxis":{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-206259281","id":"20180717-165545_2061277543","result":{"msg":"CUSTOMER_ID\tAGE\tINCOME\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tOCCUPATION\tMARITAL_STATUS\n108\t24\t2150\t19\t\t20000\tTechnician\tMarried\n109\t45\t2750\t27\tAuto\t25000\tProfessional\tSingle\n110\t63\t2150\t11\tAuto\t20000\tProfessional\tSingle\n111\t59\t2750\t10\tHousing\t45000\tArmy\tMarried\n112\t20\t2750\t9\tEducation\t15000\tWorker\tDivorced\n113\t71\t3750\t12\tHousing\t40000\tManager\tMarried\n114\t35\t2750\t38\tHousing\t70000\tArmy\tMarried\n115\t63\t500\t5\tHousing\t85000\tArmy\tSingle\n116\t60\t3750\t6\tNeed\t20000\tProfessional\tMarried\n117\t55\t2750\t8\tEducation\t20000\tArmy\tSingle\n118\t62\t4250\t27\t\t40000\tManager\tMarried\n119\t67\t4250\t6\tAuto\t10000\tProfessional\tDivorced\n120\t23\t2750\t26\tNeed\t20000\tArmy\tSingle\n121\t43\t500\t15\tAuto\t55000\tArmy\tMarried\n122\t49\t2150\t14\tNeed\t25000\tTechnician\tSingle\n123\t51\t6250\t18\tEducation\t10000\t\tSingle\n124\t42\t3750\t15\tNeed\t15000\tManager\tSingle\n125\t41\t5250\t9\tNeed\t15000\tManager\tMarried\n127\t60\t2150\t11\tNeed\t45000\tProfessional\tSingle\n131\t42\t2150\t9\tEducation\t10000\tClerical\tMarried\n136\t59\t2150\t21\tHousing\t105000\tFarmer\tMarried\n141\t43\t6250\t20\tHousing\t65000\tManager\tSingle\n145\t64\t5250\t25\tAuto\t20000\tProfessional\tSingle\n149\t32\t500\t11\tHousing\t80000\tManager\tMarried\n153\t71\t4250\t21\tNeed\t40000\tManager\tMarried\n158\t49\t2750\t22\tNeed\t15000\tWorker\tMarried\n162\t62\t2150\t19\tHousing\t100000\tFarmer\tSingle\n167\t33\t6250\t10\tHousing\t45000\tManager\tDivorced\n168\t45\t500\t11\tAuto\t50000\tProfessional\tSingle\n169\t31\t6250\t82\tNeed\t45000\tManager\tMarried\n3\t70\t2750\t12\tEducation\t10000\tManager\tMarried\n7\t25\t4250\t11\tNeed\t25000\tManager\tMarried\n12\t56\t5250\t14\tNeed\t40000\t\tSingle\n16\t23\t500\t15\tAuto\t55000\tManager\tDivorced\n21\t40\t2150\t18\tNeed\t10000\tManager\tSingle\n25\t67\t500\t14\tEducation\t15000\tManager\tMarried\n29\t22\t2750\t8\tAuto\t55000\tFarmer\tMarried\n34\t48\t2150\t94\tEducation\t15000\tArmy\tDivorced\n39\t34\t3750\t26\tAuto\t50000\tProfessional\tMarried\n43\t45\t3750\t15\tAuto\t35000\tWorker\tMarried\n44\t36\t2150\t7\tHousing\t45000\tArmy\tMarried\n45\t19\t2750\t19\tEducation\t20000\tFarmer\tMarried\n48\t21\t2750\t35\tNeed\t35000\tClerical\tMarried\n53\t36\t4250\t15\tNeed\t40000\tManager\tSingle\n57\t29\t500\t11\tHousing\t85000\tWorker\tMarried\n61\t54\t2150\t33\tAuto\t55000\tClerical\tMarried\n66\t34\t500\t57\tHousing\t35000\tWorker\tSingle\n70\t47\t2750\t6\tNeed\t30000\tWorker\tMarried\n75\t50\t2750\t10\tHousing\t55000\tManager\tMarried\n79\t22\t6250\t34\tAuto\t40000\tManager\t\n83\t27\t5250\t9\tHousing\t45000\tProfessional\tMarried\n87\t42\t6250\t19\tAuto\t35000\tManager\tMarried\n88\t40\t2750\t5\tNeed\t30000\tTechnician\tMarried\n89\t31\t6250\t14\tNeed\t40000\tManager\tDivorced\n90\t21\t2750\t7\tAuto\t35000\tManager\tSingle\n91\t65\t2150\t9\tEducation\t15000\tTechnician\tSingle\n92\t36\t3750\t13\tNeed\t25000\tManager\tSingle\n93\t18\t5250\t5\t\t90000\tProfessional\tMarried\n94\t46\t4250\t10\tNeed\t45000\tManager\tMarried\n95\t71\t2750\t21\tAuto\t55000\tManager\tSingle\n96\t39\t2750\t2\tNeed\t35000\tWorker\tMarried\n97\t70\t4250\t5\tHousing\t80000\tManager\tSingle\n98\t20\t3750\t8\tNeed\t25000\tManager\tDivorced\n99\t39\t4250\t12\tNeed\t20000\tManager\tSingle\n100\t47\t2150\t12\tEducation\t10000\tManager\tMarried\n101\t53\t2150\t33\tAuto\t10000\tTechnician\tMarried\n102\t38\t5250\t35\tNeed\t45000\tManager\tSingle\n103\t46\t2750\t5\tAuto\t35000\tTechnician\tMarried\n104\t64\t5250\t82\tNeed\t30000\tProfessional\tMarried\n105\t27\t500\t39\tHousing\t85000\tFarmer\tMarried\n106\t19\t4250\t27\tAuto\t40000\t\tSingle\n107\t18\t3750\t39\tHousing\t15000\tManager\tMarried\n372\t\t5250\t2\tNeed\t15000\tProfessional\tSingle\n375\t67\t6250\t26\tNeed\t40000\tManager\tMarried\n379\t27\t4250\t17\tHousing\t105000\tProfessional\tMarried\n383\t47\t2750\t59\tNeed\t40000\tTechnician\tMarried\n388\t59\t2750\t2\tNeed\t10000\tFarmer\tDivorced\n392\t44\t2750\t70\tNeed\t20000\tProfessional\tMarried\n396\t50\t500\t15\tAuto\t30000\tTechnician\tDivorced\n400\t62\t2750\t21\tNeed\t\tClerical\tSingle\n403\t68\t2750\t13\tAuto\t50000\tArmy\tDivorced\n404\t68\t2150\t9\tNeed\t35000\tProfessional\tMarried\n405\t63\t6250\t49\tNeed\t30000\tManager\tMarried\n406\t28\t6250\t9\tEducation\t20000\tManager\tMarried\n407\t42\t2150\t39\tHousing\t60000\tProfessional\tMarried\n409\t67\t2150\t4\tAuto\t50000\tWorker\t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7: Review Income by Age, Tenure, Grouped by Loan Type","text":"%sql \nselect customer_id, age, income, tenure, loan_type, loan_amount, occupation, education_level from credit_scoring_100k_v where rownum < 40","user":"CHARLIE","dateUpdated":"2018-07-16T17:00:34+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"scatterChart","optionOpen":false,"keys":[{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}],"values":[{"name":"AGE","index":1,"aggr":"sum"}],"scatter":{"yAxis":{"name":"TENURE","index":3,"aggr":"sum"},"xAxis":{"name":"AGE","index":1,"aggr":"sum"},"size":{"name":"INCOME","index":2,"aggr":"sum"},"group":{"name":"LOAN_TYPE","index":4,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1044798447","id":"20180717-165545_1336472689","result":{"msg":"CUSTOMER_ID\tAGE\tINCOME\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tOCCUPATION\tEDUCATION_LEVEL\n108\t24\t2150\t19\t\t20000\tTechnician\tBachelor's Degree\n109\t45\t2750\t27\tAuto\t25000\tProfessional\tMaster's Degree\n110\t63\t2150\t11\tAuto\t20000\tProfessional\tBachelor's Degree\n111\t59\t2750\t10\tHousing\t45000\tArmy\tBachelor's Degree\n112\t20\t2750\t9\tEducation\t15000\tWorker\tMaster's Degree\n113\t71\t3750\t12\tHousing\t40000\tManager\tMaster's Degree\n114\t35\t2750\t38\tHousing\t70000\tArmy\tBachelor's Degree\n115\t63\t500\t5\tHousing\t85000\tArmy\tPrimary School Grad\n116\t60\t3750\t6\tNeed\t20000\tProfessional\tDoctorate Degree\n117\t55\t2750\t8\tEducation\t20000\tArmy\tBachelor's Degree\n118\t62\t4250\t27\t\t40000\tManager\tBachelor's Degree\n119\t67\t4250\t6\tAuto\t10000\tProfessional\tBachelor's Degree\n120\t23\t2750\t26\tNeed\t20000\tArmy\tMaster's Degree\n121\t43\t500\t15\tAuto\t55000\tArmy\tPrimary School Grad\n122\t49\t2150\t14\tNeed\t25000\tTechnician\tBachelor's Degree\n123\t51\t6250\t18\tEducation\t10000\t\tBachelor's Degree\n124\t42\t3750\t15\tNeed\t15000\tManager\tMaster's Degree\n125\t41\t5250\t9\tNeed\t15000\tManager\tBachelor's Degree\n127\t60\t2150\t11\tNeed\t45000\tProfessional\tBachelor's Degree\n131\t42\t2150\t9\tEducation\t10000\tClerical\tBachelor's Degree\n136\t59\t2150\t21\tHousing\t105000\tFarmer\tBachelor's Degree\n141\t43\t6250\t20\tHousing\t65000\tManager\tBachelor's Degree\n145\t64\t5250\t25\tAuto\t20000\tProfessional\tBachelor's Degree\n149\t32\t500\t11\tHousing\t80000\tManager\tPrimary School Grad\n153\t71\t4250\t21\tNeed\t40000\tManager\tBachelor's Degree\n158\t49\t2750\t22\tNeed\t15000\tWorker\tBachelor's Degree\n162\t62\t2150\t19\tHousing\t100000\tFarmer\tMaster's Degree\n167\t33\t6250\t10\tHousing\t45000\tManager\tBachelor's Degree\n168\t45\t500\t11\tAuto\t50000\tProfessional\tPrimary School Grad\n169\t31\t6250\t82\tNeed\t45000\tManager\tBachelor's Degree\n3\t70\t2750\t12\tEducation\t10000\tManager\tBachelor's Degree\n7\t25\t4250\t11\tNeed\t25000\tManager\tBachelor's Degree\n12\t56\t5250\t14\tNeed\t40000\t\tBachelor's Degree\n16\t23\t500\t15\tAuto\t55000\tManager\tPrimary School Grad\n21\t40\t2150\t18\tNeed\t10000\tManager\tBachelor's Degree\n25\t67\t500\t14\tEducation\t15000\tManager\tPrimary School Grad\n29\t22\t2750\t8\tAuto\t55000\tFarmer\tBachelor's Degree\n34\t48\t2150\t94\tEducation\t15000\tArmy\tBachelor's Degree\n39\t34\t3750\t26\tAuto\t50000\tProfessional\tDoctorate Degree\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-06-29T16:45:24+0000","dateStarted":"2018-07-16T17:00:34+0000","dateFinished":"2018-07-16T17:00:34+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:130"},{"title":"STEP 8: Bin The Variables","text":"%script\n\n-- execute this procedure to create a view that bins all the numeric columns for you.  We will review the results to the right.\n\nBEGIN\n\nBEGIN \nEXECUTE IMMEDIATE 'DROP TABLE bin_num_tbl';\nEXCEPTION WHEN OTHERS THEN NULL; \nEND; \n\nBEGIN\nEXECUTE IMMEDIATE 'DROP VIEW mining_data_bin_view';\nEXCEPTION WHEN OTHERS THEN NULL; \nEND; \n\n   dbms_data_mining_transform.create_bin_num(\n           bin_table_name     => 'bin_num_tbl');\n\n   dbms_data_mining_transform.insert_autobin_num_eqwidth(\n           bin_table_name     => 'bin_num_tbl',\n           data_table_name    => 'CREDIT_SCORING_100K_V',\n           bin_num            => 5,\n           max_bin_num        => 10,\n           exclude_list       => dbms_data_mining_transform.COLUMN_LIST('CUSTOMER_ID'));\n\n  dbms_data_mining_transform.xform_bin_num(\n           bin_table_name      => 'bin_num_tbl',\n           data_table_name     => 'CREDIT_SCORING_100K_V',\n           xform_view_name     => 'mining_data_bin_view');\nEND;","user":"CHARLIE","dateUpdated":"2018-07-16T21:25:43+0000","config":{"colWidth":4,"editorMode":"ace/mode/plsql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1988448312","id":"20180717-165545_1261922414","result":{"msg":"\nPL/SQL procedure successfully completed.\n\n","code":"SUCCESS","type":"TEXT"},"dateCreated":"2018-07-11T17:34:44+0000","dateStarted":"2018-07-16T21:25:43+0000","dateFinished":"2018-07-16T21:25:46+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:131"},{"title":"Show Histogram of Age (All Numeric Variables are Binned, This is an Example)","text":"%sql\nselect customer_id, age, income, tenure, loan_type, loan_amount, occupation, education_level from mining_data_bin_view;\n\n-- Note:  Typically, data scientists should explolre their data to make sure it makes sense and matches the data they believe they are working with.   .....","user":"CHARLIE","dateUpdated":"2018-07-16T21:26:01+0000","config":{"colWidth":8,"editorMode":"ace/mode/osql","editorHide":false,"title":true,"graph":{"mode":"multiBarChart","optionOpen":false,"keys":[{"name":"AGE","index":1,"aggr":"sum"}],"values":[{"name":"CUSTOMER_ID","index":0,"aggr":"count"}],"scatter":{"yAxis":{"name":"AGE","index":1,"aggr":"sum"},"xAxis":{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}},"groups":[],"height":418},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1623674831","id":"20180717-165545_609066074","result":{"msg":"CUSTOMER_ID\tAGE\tINCOME\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tOCCUPATION\tEDUCATION_LEVEL\n108\t2\t3\t2\t\t2\tTechnician\tBachelor's Degree\n109\t6\t4\t3\tAuto\t2\tProfessional\tMaster's Degree\n110\t9\t3\t2\tAuto\t2\tProfessional\tBachelor's Degree\n111\t8\t4\t1\tHousing\t4\tArmy\tBachelor's Degree\n112\t1\t4\t1\tEducation\t1\tWorker\tMaster's Degree\n113\t10\t6\t2\tHousing\t4\tManager\tMaster's Degree\n114\t4\t4\t4\tHousing\t7\tArmy\tBachelor's Degree\n115\t9\t1\t1\tHousing\t8\tArmy\tPrimary School Grad\n116\t8\t6\t1\tNeed\t2\tProfessional\tDoctorate Degree\n117\t7\t4\t1\tEducation\t2\tArmy\tBachelor's Degree\n118\t9\t7\t3\t\t4\tManager\tBachelor's Degree\n119\t10\t7\t1\tAuto\t1\tProfessional\tBachelor's Degree\n120\t1\t4\t3\tNeed\t2\tArmy\tMaster's Degree\n121\t5\t1\t2\tAuto\t5\tArmy\tPrimary School Grad\n122\t6\t3\t2\tNeed\t2\tTechnician\tBachelor's Degree\n123\t7\t10\t2\tEducation\t1\t\tBachelor's Degree\n124\t5\t6\t2\tNeed\t1\tManager\tMaster's Degree\n125\t5\t9\t1\tNeed\t1\tManager\tBachelor's Degree\n127\t8\t3\t2\tNeed\t4\tProfessional\tBachelor's Degree\n131\t5\t3\t1\tEducation\t1\tClerical\tBachelor's Degree\n136\t8\t3\t3\tHousing\t10\tFarmer\tBachelor's Degree\n141\t5\t10\t3\tHousing\t6\tManager\tBachelor's Degree\n145\t9\t9\t3\tAuto\t2\tProfessional\tBachelor's 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Degree\n97\t10\t7\t1\tHousing\t8\tManager\tBachelor's Degree\n98\t1\t6\t1\tNeed\t2\tManager\tDoctorate Degree\n99\t4\t7\t2\tNeed\t2\tManager\tBachelor's Degree\n100\t6\t3\t2\tEducation\t1\tManager\tBachelor's Degree\n101\t7\t3\t4\tAuto\t1\tTechnician\tBachelor's Degree\n102\t4\t9\t4\tNeed\t4\tManager\tBachelor's Degree\n103\t6\t4\t1\tAuto\t3\tTechnician\tBachelor's Degree\n104\t9\t9\t9\tNeed\t3\tProfessional\t\n105\t2\t1\t5\tHousing\t8\tFarmer\t\n106\t1\t7\t3\tAuto\t4\t\tBachelor's Degree\n107\t1\t6\t5\tHousing\t1\tManager\tDoctorate Degree\n372\t\t9\t1\tNeed\t1\tProfessional\tBachelor's Degree\n375\t10\t10\t3\tNeed\t4\tManager\tBachelor's Degree\n379\t2\t7\t2\tHousing\t10\tProfessional\tBachelor's Degree\n383\t6\t4\t7\tNeed\t4\tTechnician\tBachelor's Degree\n388\t8\t4\t1\tNeed\t1\tFarmer\tMaster's Degree\n392\t5\t4\t8\tNeed\t2\tProfessional\tMaster's Degree\n396\t7\t1\t2\tAuto\t3\tTechnician\tPrimary School Grad\n400\t9\t4\t3\tNeed\t\tClerical\tMaster's Degree\n403\t10\t4\t2\tAuto\t5\tArmy\tMaster's Degree\n404\t10\t3\t1\tNeed\t3\tProfessional\tBachelor's Degree\n405\t9\t10\t6\tNeed\t3\tManager\tBachelor's Degree\n406\t2\t10\t1\tEducation\t2\tManager\tBachelor's Degree\n407\t5\t3\t5\tHousing\t6\tProfessional\tBachelor's Degree\n409\t10\t3\t1\tAuto\t5\tWorker\tBachelor's Degree\n414\t3\t4\t1\tAuto\t4\tWorker\tMaster's Degree\n418\t3\t4\t1\tHousing\t2\tProfessional\tMaster's Degree\n422\t2\t4\t1\tNeed\t4\tTechnician\tMaster's Degree\n427\t3\t7\t2\tAuto\t4\tManager\tBachelor's Degree\n431\t5\t3\t1\tNeed\t4\tClerical\tBachelor's Degree\n435\t6\t4\t1\tNeed\t2\tFarmer\tMaster's Degree\n440\t10\t4\t9\tNeed\t1\tArmy\tMaster's Degree\n444\t8\t6\t2\tEducation\t1\t\tDoctorate Degree\n448\t4\t4\t1\tHousing\t3\tManager\tMaster's Degree\n449\t3\t3\t2\tNeed\t1\tClerical\tBachelor's Degree\n450\t5\t3\t2\tNeed\t4\tClerical\tMaster's Degree\n451\t6\t1\t5\tNeed\t4\tArmy\tPrimary School Grad\n455\t9\t10\t1\tNeed\t1\tManager\tBachelor's 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Degree\n189\t10\t1\t1\tNeed\t1\tProfessional\t\n190\t6\t7\t2\tNeed\t2\tProfessional\tBachelor's Degree\n191\t1\t3\t1\tHousing\t10\tClerical\tMaster's Degree\n192\t1\t1\t7\tNeed\t2\tWorker\tPrimary School Grad\n193\t7\t1\t2\tHousing\t2\tClerical\tPrimary School Grad\n194\t5\t3\t1\tNeed\t4\tTechnician\tBachelor's Degree\n195\t1\t9\t1\tHousing\t6\tProfessional\tBachelor's Degree\n196\t7\t10\t1\tNeed\t4\tProfessional\tBachelor's Degree\n197\t5\t4\t1\tNeed\t1\tManager\tBachelor's Degree\n200\t4\t7\t2\tNeed\t1\tManager\tBachelor's Degree\n204\t4\t3\t8\tNeed\t3\tFarmer\tBachelor's Degree\n209\t4\t3\t3\tHousing\t6\tArmy\tBachelor's Degree\n212\t3\t1\t2\tHousing\t2\tArmy\tPrimary School Grad\n217\t4\t3\t2\tEducation\t2\tManager\tMaster's Degree\n221\t1\t4\t3\tNeed\t1\tArmy\tBachelor's Degree\n230\t5\t6\t3\tNeed\t4\tTechnician\tMaster's Degree\n234\t6\t4\t2\tNeed\t3\tManager\tBachelor's Degree\n239\t6\t4\t2\tEducation\t2\tClerical\tBachelor's Degree\n240\t6\t6\t1\tNeed\t1\tClerical\tMaster's 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Degree\n2659\t1\t10\t4\tHousing\t5\tProfessional\tBachelor's Degree\n2660\t3\t3\t1\tHousing\t7\tArmy\tBachelor's Degree\n2661\t10\t3\t4\tEducation\t1\tManager\tBachelor's Degree\n2662\t7\t4\t2\tNeed\t1\tTechnician\tBachelor's Degree\n2663\t1\t4\t2\tHousing\t8\tClerical\tBachelor's Degree\n2926\t3\t10\t2\tNeed\t2\tManager\tBachelor's Degree\n2930\t6\t\t1\tNeed\t4\tTechnician\tMaster's Degree\n2934\t10\t1\t1\tHousing\t10\tClerical\tPrimary School Grad\n2939\t3\t3\t2\tNeed\t3\tProfessional\tBachelor's Degree\n2943\t4\t3\t1\tNeed\t4\tManager\tBachelor's Degree\n2947\t4\t7\t1\tNeed\t2\tProfessional\tBachelor's Degree\n2952\t9\t6\t2\tHousing\t10\tManager\tDoctorate Degree\n2956\t1\t3\t4\tNeed\t1\tFarmer\tBachelor's Degree\n2960\t4\t7\t1\tNeed\t3\tManager\tBachelor's Degree\n2964\t5\t4\t1\tEducation\t1\tArmy\tMaster's Degree\n2965\t8\t6\t1\tHousing\t10\tManager\tDoctorate Degree\n2966\t1\t3\t1\tAuto\t3\tClerical\t\n2967\t5\t10\t1\tAuto\t1\tProfessional\tBachelor's Degree\n2968\t1\t3\t2\tNeed\t4\tClerical\tBachelor's Degree\n2969\t1\t4\t3\tNeed\t3\tClerical\tBachelor's Degree\n2970\t8\t4\t5\tAuto\t2\tClerical\tBachelor's Degree\n2971\t6\t1\t2\tAuto\t2\tClerical\tPrimary School Grad\n2972\t10\t9\t1\t\t5\tProfessional\tBachelor's Degree\n2973\t10\t9\t1\tHousing\t6\tProfessional\tBachelor's Degree\n2974\t1\t4\t2\tAuto\t2\tClerical\tBachelor's Degree\n2975\t4\t7\t1\tHousing\t2\tManager\tBachelor's Degree\n2976\t9\t4\t1\tAuto\t1\tWorker\tMaster's Degree\n2977\t2\t6\t3\tNeed\t1\tProfessional\tDoctorate Degree\n2978\t6\t9\t2\tNeed\t4\tManager\tBachelor's Degree\n2979\t6\t3\t4\tNeed\t1\tClerical\tBachelor's Degree\n2980\t5\t7\t2\tHousing\t10\tManager\tBachelor's Degree\n2981\t4\t7\t3\tNeed\t3\tManager\tBachelor's Degree\n2982\t1\t3\t1\tAuto\t3\tProfessional\tBachelor's Degree\n2983\t6\t3\t1\tNeed\t1\tWorker\tBachelor's Degree\n2984\t7\t9\t4\tAuto\t2\tProfessional\tBachelor's Degree\n2757\t3\t4\t1\tAuto\t\tProfessional\tMaster's Degree\n2761\t8\t1\t2\tNeed\t3\tWorker\tPrimary School Grad\n2766\t8\t4\t2\tHousing\t1\tWorker\tBachelor's Degree\n2770\t10\t3\t1\tHousing\t1\tFarmer\tBachelor's Degree\n2775\t1\t7\t2\tHousing\t3\tProfessional\tBachelor's Degree\n2779\t9\t7\t2\tNeed\t4\tManager\tBachelor's Degree\n2784\t5\t3\t2\tHousing\t8\tProfessional\tBachelor's Degree\n2788\t10\t3\t2\tHousing\t6\tClerical\tMaster's Degree\n2793\t8\t7\t1\tNeed\t1\tManager\tBachelor's Degree\n2796\t3\t4\t2\tAuto\t3\tTechnician\tMaster's Degree\n2797\t10\t7\t4\tNeed\t4\tProfessional\tBachelor's Degree\n2798\t6\t4\t1\tNeed\t2\tClerical\tMaster's Degree\n2799\t5\t7\t1\tHousing\t6\tManager\tBachelor's Degree\n2800\t8\t3\t3\tNeed\t3\tManager\tMaster's Degree\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-07-11T17:37:18+0000","dateStarted":"2018-07-16T21:25:48+0000","dateFinished":"2018-07-16T21:25:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:132"},{"title":"STEP 9: Enough with Simple Charting;  Let's Run Some OML Machine Learning Algorithms!","text":"%md\n\nNow we might have gotten lucky finding the solution to our problem by looking at simple pie, column and scatter plots, but in most real world problems, its not that easy.   Typically, we have lots of data in both the amount of records, but more interestingly, the number of possible variable that might have some correlation or influence on our specific problem.    In this business case, Healther is looking to discover which variables (also referred to as \"attributes\") have the strongest correlation with here business problem: Increase Sales by Targeting our Best Customers; Good Credit Customers! \n\nFirst, let's use Oracle Machine Learning's Attribute Importance algorithm and specifically the DBMS_Predictive_Analytics.Explain simplist version of it.  These OML Feature Selection machine learning algorithms automatically sift through all the input variables or attributes looking for those variables that have the strongest correlation with another key variable.   First, Heather is looking to find those variables most influence her finding Good Credit customers.   While Oracle Machine Learning's algorithms can build predictive modles using hundreds or thousands of \"big data\" variables, oftentimes it is extremely useful simply to find those fewer variables that are most important.   Once found, you can use that information to change business practices, create better reports and seek out more similar variables that may prove even more informative and useful\n\n#### For more information: \n##### <https://docs.oracle.com/en/database/oracle/oracle-database/18/dmapi/feature-selection-extraction.html#GUID-564D57DB-7AED-4130-A863-EA1698D6F4B4>\n##### <https://docs.oracle.com/en/database/oracle/oracle-database/18/dmapi/DBMS_PREDICTIVE_ANALYTICS.html#GUID-9DD29322-C9EC-4AE7-A1E7-92AFC4D800FF>","user":"CHARLIE","dateUpdated":"2018-07-16T21:47:54+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":false,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-1517038215","id":"20180717-165545_985939477","result":{"msg":"<p>Now we might have gotten lucky finding the solution to our problem by looking at simple pie, column and scatter plots, but in most real world problems, its not that easy.   Typically, we have lots of data in both the amount of records, but more interestingly, the number of possible variable that might have some correlation or influence on our specific problem.    In this business case, Healther is looking to discover which variables (also referred to as &ldquo;attributes&rdquo;) have the strongest correlation with here business problem: Increase Sales by Targeting our Best Customers; Good Credit Customers!</p>\n<p>First, let's use Oracle Machine Learning's Attribute Importance algorithm and specifically the DBMS_Predictive_Analytics.Explain simplist version of it.  These OML Feature Selection machine learning algorithms automatically sift through all the input variables or attributes looking for those variables that have the strongest correlation with another key variable.   First, Heather is looking to find those variables most influence her finding God Credit customers.   While Oracle Machine Learning's algorithms can build predictive modles using hundreds or thousands of &ldquo;big data&rdquo; variables, oftentimes it is extremely useful simply to find those fewer variables that are most important.   Once found, you can use that information to change business practices, create better reports and seek out more similar variables that may prove even more informative and useful</p>\n<h4>For more information:</h4>\n<h5><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/18/dmapi/feature-selection-extraction.html#GUID-564D57DB-7AED-4130-A863-EA1698D6F4B4\">https://docs.oracle.com/en/database/oracle/oracle-database/18/dmapi/feature-selection-extraction.html#GUID-564D57DB-7AED-4130-A863-EA1698D6F4B4</a></h5>\n<h5><a href=\"https://docs.oracle.com/en/database/oracle/oracle-database/18/dmapi/DBMS_PREDICTIVE_ANALYTICS.html#GUID-9DD29322-C9EC-4AE7-A1E7-92AFC4D800FF\">https://docs.oracle.com/en/database/oracle/oracle-database/18/dmapi/DBMS_PREDICTIVE_ANALYTICS.html#GUID-9DD29322-C9EC-4AE7-A1E7-92AFC4D800FF</a></h5>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-06-29T16:49:08+0000","dateStarted":"2018-07-16T21:10:24+0000","dateFinished":"2018-07-16T21:10:24+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:133"},{"title":"Create Attribute Importance Machine Learning Model for Good Credit Customers","text":"%script\n\n-- Find the importance of attributes that independently impact the target attribute:  CREDIT_SCORE_BIN\n\nDECLARE\nv_sql varchar2(100);\n\nBEGIN\nBEGIN EXECUTE IMMEDIATE 'DROP TABLE ai_explain_output_credit_score_bin';\nEXCEPTION WHEN OTHERS THEN NULL; \nEND;\n\nBEGIN\n  DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(\n    data_table_name     => 'CREDIT_SCORING_100K_V',\n    explain_column_name => 'CREDIT_SCORE_BIN',\n    result_table_name   => 'AI_EXPLAIN_OUTPUT_CREDIT_SCORE_BIN');\nEND;\n\nEnd;","user":"CHARLIE","dateUpdated":"2018-07-17T16:57:30+0000","config":{"colWidth":4,"editorMode":"ace/mode/plsql","editorHide":false,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":93.15},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-1702594762","id":"20180717-165545_851922496","result":{"code":"SUCCESS","type":"TEXT","msg":"\nPL/SQL procedure successfully completed.\n\n"},"dateCreated":"2018-06-29T16:50:20+0000","dateStarted":"2018-07-17T16:57:30+0000","dateFinished":"2018-07-17T16:57:48+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:134"},{"title":"Display the Top N Attributes for Good Credit Customers","text":"%sql\n\nSelect * from ai_explain_output_CREDIT_SCORE_BIN where rownum < 7;","user":"CHARLIE","dateUpdated":"2018-07-16T21:30:13+0000","config":{"colWidth":8,"editorMode":"ace/mode/osql","editorHide":false,"title":true,"graph":{"mode":"multiBarChart","optionOpen":false,"keys":[{"name":"ATTRIBUTE_NAME","index":0,"aggr":"sum"}],"values":[{"name":"EXPLANATORY_VALUE","index":2,"aggr":"sum"}],"scatter":{"yAxis":{"name":"ATTRIBUTE_SUBNAME","index":1,"aggr":"sum"},"xAxis":{"name":"ATTRIBUTE_NAME","index":0,"aggr":"sum"}},"groups":[],"height":261},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1762227082","id":"20180717-165545_1120085102","result":{"msg":"ATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tEXPLANATORY_VALUE\tRANK\nWEALTH\t\t6.39820169845722756654135240803042083968E-01\t1\nCUSTOMER_DMG_SEGMENT\t\t6.39539843558222673341224279586983867486E-01\t2\nINCOME\t\t5.07466680849800322196637921343739257745E-01\t3\nHIGHEST_CREDIT_CARD_LIMIT\t\t5.07378900187189108265917890149658811488E-01\t4\nCUSTOMER_VALUE_SEGMENT\t\t4.63324080121358226689825619991627104707E-01\t5\nRESIDENTAL_STATUS\t\t3.58431248186241132778138869117726725613E-01\t6\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-06-29T16:51:10+0000","dateStarted":"2018-07-16T21:30:13+0000","dateFinished":"2018-07-16T21:30:13+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:135"},{"title":"STEP 10: Create an Attribute Importance Model for MAX_CC_SPENT_AMOUNT","text":"%script \n\n-- Find the importance of attributes that independently impact the target attribute:  MAX_CC_SPENT_AMOUNT\n\nDECLARE\nv_sql varchar2(100);\n\nBEGIN\n\nBEGIN EXECUTE IMMEDIATE 'DROP TABLE ai_explain_output_MAX_CC_SPENT_AMOUNT';\nEXCEPTION WHEN OTHERS THEN NULL; \nEND;\n\nBEGIN\n  DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(\n    data_table_name     => 'Credit_Scoring_100k_v',\n    explain_column_name => 'MAX_CC_SPENT_AMOUNT',\n    result_table_name   => 'ai_explain_output_MAX_CC_SPENT_AMOUNT');\nEND;\n\nEnd;\n","user":"CHARLIE","dateUpdated":"2018-07-16T21:30:19+0000","config":{"colWidth":6,"editorMode":"ace/mode/plsql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-552234396","id":"20180717-165545_178573088","result":{"msg":"\nPL/SQL procedure successfully completed.\n\n","code":"SUCCESS","type":"TEXT"},"dateCreated":"2018-06-29T16:55:07+0000","dateStarted":"2018-07-16T21:30:20+0000","dateFinished":"2018-07-16T21:31:00+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:136"},{"title":"Display Top N Attributes for MAX_CC_SPENT_AMOUNT","text":"%sql \n\nSelect * from ai_explain_output_MAX_CC_SPENT_AMOUNT where rownum < 7;","user":"CHARLIE","dateUpdated":"2018-07-16T21:31:02+0000","config":{"colWidth":6,"editorMode":"ace/mode/osql","editorHide":false,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":238},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1022834576","id":"20180717-165545_294354167","result":{"msg":"ATTRIBUTE_NAME\tATTRIBUTE_SUBNAME\tEXPLANATORY_VALUE\tRANK\nINCOME\t\t8.84222960726802757427310996544661885952E-02\t1\nHIGHEST_CREDIT_CARD_LIMIT\t\t8.84086207651107592300019491735605569996E-02\t2\nCUSTOMER_DMG_SEGMENT\t\t7.17298726953317671232176065975823055083E-02\t3\nWEALTH\t\t7.1648190116805769507976645134724895537E-02\t4\nMAX_CC_SPENT_AMOUNT_PREV\t\t6.45251841924508379341195572351317276106E-02\t5\nCHURN_RATE_OF_CC1\t\t5.03608398919125202336305998664887568865E-02\t6\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-07-02T19:01:50+0000","dateStarted":"2018-07-16T21:31:02+0000","dateFinished":"2018-07-16T21:31:02+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:137"},{"title":"STEP 11:  Create Predictive Model to Target Good Credit Customers","text":"%md\nNow that Heather has found the key attributes that most influence findig more Good Credit customers and also making better Maximum Credit Card Amount decisions, she wants to leverage Oracle Machine Learning's powerful in-Database, parallelized algorithms to build predictive models that help her company to better target \"the right customers\" with the \"right offers\".\n\n\nLets' quickly review the machine learning process: \n\n**Problem Definition:**   Target Good Credit Customers\n**Data Gathering and Preparation:**  We've assembled 100K records with 100+ variable about each customer and have created a target field (Good Customer/Other Customer) so we can use OML's Supervised Algorithms, specifically let's start by using a decision tree algorithm.\n**Model Building and Evaluation:**   We'll create a randomly selected sample from our Credit_Scoring_100k historical data and use 60% as training data for the machine learning model building phase.   Then, we'll use the remaining 40% as a holdout sampple to test our model's accuracy using various model evaluation tools such as a \"lift chart\".\n##### Knowledge Deployment:  Once we're satisfied that we have a useful ML model that can predict with some accuracy which customers we should target (Good Credit customers), we want to apply our OML model to new customer data inside ADWC and then take a deeper look a them.   Lastly, we'll jump over to Oracle Analytics Cloud for a more interactive, exploratory data analysis experience but now focusing on our customers of interest (Good Credit customers).","user":"CHARLIE","dateUpdated":"2018-07-16T21:31:09+0000","config":{"colWidth":8,"editorMode":"ace/mode/markdown","editorHide":true,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_904658578","id":"20180717-165545_423007688","result":{"msg":"<p>Now that Heather has found the key attributes that most influence findig more Good Credit customers and also making better Maximum Credit Card Amount decisions, she wants to leverage Oracle Machine Learning's powerful in-Database, parallelized algorithms to build predictive models that help her company to better target &ldquo;the right customers&rdquo; with the &ldquo;right offers&rdquo;.</p>\n<p>Lets' quickly review the machine learning process:</p>\n<p><strong>Problem Definition:</strong>   Target Good Credit Customers\n<br  /><strong>Data Gathering and Preparation:</strong>  We've assembled 100K records with 100+ variable about each customer and have created a target field (Good Customer/Other Customer) so we can use OML's Supervised Algorithms, specifically let's start by using a decision tree algorithm.\n<br  /><strong>Model Building and Evaluation:</strong>   We'll create a randomly selected sample from our Credit_Scoring_100k historical data and use 60% as training data for the machine learning model building phase.   Then, we'll use the remaining 40% as a holdout sampple to test our model's accuracy using various model evaluation tools such as a &ldquo;lift chart&rdquo;.</p>\n<h5>Knowledge Deployment:  Once we're satisfied that we have a useful ML model that can predict with some accuracy which customers we should target (Good Credit customers), we want to apply our OML model to new customer data inside ADWC and then take a deeper look a them.   Lastly, we'll jump over to Oracle Analytics Cloud for a more interactive, exploratory data analysis experience but now focusing on our customers of interest (Good Credit customers).</h5>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-06-29T17:08:30+0000","dateStarted":"2018-07-16T21:31:09+0000","dateFinished":"2018-07-16T21:31:09+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:138"},{"title":"Data Mining and Machine Learning Process","text":"%md \n![tiny arrow](http://www.oracle.com///technetwork/database/options/advanced-analytics/dmprocess80-5013719.jpg \"tiny arrow\") ","user":"CHARLIE","dateUpdated":"2018-07-16T21:31:12+0000","config":{"colWidth":4,"editorMode":"ace/mode/markdown","editorHide":true,"title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-1579864513","id":"20180717-165545_1116549844","result":{"msg":"<p><img src=\"http://www.oracle.com///technetwork/database/options/advanced-analytics/dmprocess80-5013719.jpg\" alt=\"tiny arrow\" title=\"tiny arrow\" /></p>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-07-13T22:08:11+0000","dateStarted":"2018-07-16T21:31:12+0000","dateFinished":"2018-07-16T21:31:12+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:139"},{"title":"Preparatory Steps, Automation of Model Build and Test and Clean up using PL/SQL script","text":"%script\r\n\r\n-- Build a classification model and then generate a lift test result and an apply result.  Click on the arrow in the the upper right.\r\n\r\nDECLARE\r\nv_sql varchar2(100);\r\n\r\nBEGIN\r\n\r\n--------------------------------------------\r\n-- REMOVE PRIOR RUNS\r\n--------------------------------------------\r\n\r\n-- drop build settings\r\nBEGIN\r\nv_sql := 'DROP TABLE n1_build_settings PURGE';\r\nEXECUTE IMMEDIATE v_sql;\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': succeeded');\r\nEXCEPTION\r\nWHEN OTHERS THEN\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': drop unneccessary - no table exists');\r\nEND;\r\n\r\n-- drop model\r\nBEGIN\r\nv_sql := 'CALL DBMS_DATA_MINING.DROP_MODEL(''N1_CLASS_MODEL'')';\r\nEXECUTE IMMEDIATE v_sql;\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': succeeded');\r\nEXCEPTION\r\nWHEN OTHERS THEN\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': drop unneccessary - no model exists');\r\nEND;\r\n\r\n-- drop apply result\r\nBEGIN\r\nv_sql := 'DROP TABLE N1_APPLY_RESULT PURGE';\r\nEXECUTE IMMEDIATE v_sql;\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': succeeded');\r\nEXCEPTION\r\nWHEN OTHERS THEN\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': drop unneccessary - no table exists');\r\nEND;\r\n\r\n-- drop lift result\r\nBEGIN\r\nv_sql := 'DROP TABLE N1_LIFT_TABLE PURGE';\r\nEXECUTE IMMEDIATE v_sql;\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': succeeded');\r\nEXCEPTION\r\nWHEN OTHERS THEN\r\nDBMS_OUTPUT.PUT_LINE (v_sql ||': drop unneccessary - no table exists');\r\nEND;\r\n\r\n\r\n-- Split the Data into N1_TRAIN_DATA and N1_TEST_DATA\r\nEXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW N1_TRAIN_DATA AS SELECT * FROM CREDIT_SCORING_100K_v SAMPLE (60) SEED (1)';\r\nDBMS_OUTPUT.PUT_LINE ('Created N1_TRAIN_DATA');\r\nEXECUTE IMMEDIATE 'CREATE OR REPLACE VIEW N1_TEST_DATA AS SELECT * FROM CREDIT_SCORING_100K_v MINUS SELECT * FROM N1_TRAIN_DATA';\r\nDBMS_OUTPUT.PUT_LINE ('Created N1_TEST_DATA');\r\n\r\n-- Create a Build Setting (DT) for Model Build\r\n\r\nEXECUTE IMMEDIATE 'CREATE TABLE n1_build_settings (setting_name VARCHAR2(30),setting_value VARCHAR2(4000))';\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings (setting_name, setting_value) VALUES (''ALGO_NAME'', ''ALGO_DECISION_TREE'')';\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings (setting_name, setting_value) VALUES (''PREP_AUTO'', ''ON'')';\r\n\r\n\r\nDBMS_OUTPUT.PUT_LINE ('Created model build settings table: n1_build_settings ');\r\n\r\n/*\r\n-- Populate and Adjust Model Setting (DT) for Model Build\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings VALUES (''TREE_TERM_MAX_DEPTH'', 7)';\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings VALUES (''TREE_TERM_MINREC_SPLIT'', 20)';\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings VALUES (''TREE_TERM_MINPCT_SPLIT'', .1)';\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings VALUES (''TREE_TERM_MINREC_NODE'', 10)';\r\nEXECUTE IMMEDIATE 'INSERT INTO n1_build_settings VALUES (''TREE_TERM_MINPCT_NODE'', 0.05)';\r\n*/\r\n    \r\n-- Build a Classification Model\r\n\r\nEXECUTE IMMEDIATE 'CALL DBMS_DATA_MINING.CREATE_MODEL(''N1_CLASS_MODEL'', ''CLASSIFICATION'', ''N1_TRAIN_DATA'', ''CUSTOMER_ID'','' CREDIT_SCORE_BIN'', ''n1_build_settings'')';\r\nDBMS_OUTPUT.PUT_LINE ('Created model: N1_CLASS_MODEL ');\r\n\r\n-- Test the Model by generating a apply result and then create a lift result\r\n\r\nEXECUTE IMMEDIATE 'CALL DBMS_DATA_MINING.APPLY(''N1_CLASS_MODEL'',''N1_TEST_DATA'',''CUSTOMER_ID'',''N1_APPLY_RESULT'')';\r\nDBMS_OUTPUT.PUT_LINE ('Created apply result: N1_APPLY_RESULT ');\r\nEXECUTE IMMEDIATE 'CALL DBMS_DATA_MINING.COMPUTE_LIFT(''N1_APPLY_RESULT'',''N1_TEST_DATA'',''CUSTOMER_ID'',''CREDIT_SCORE_BIN'',''N1_LIFT_TABLE'',''Good Credit'',''PREDICTION'',''PROBABILITY'',100)';\r\nDBMS_OUTPUT.PUT_LINE ('Created lift result: N1_LIFT_TABLE ');\r\n\r\n\r\nEND;","user":"CHARLIE","dateUpdated":"2018-07-16T21:37:26+0000","config":{"colWidth":12,"editorMode":"ace/mode/plsql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-65168579","id":"20180717-165545_1862023274","result":{"msg":"DROP TABLE n1_build_settings PURGE: succeeded\nCALL DBMS_DATA_MINING.DROP_MODEL('N1_CLASS_MODEL'): succeeded\nDROP TABLE N1_APPLY_RESULT PURGE: succeeded\nDROP TABLE N1_LIFT_TABLE PURGE: succeeded\nCreated N1_TRAIN_DATA\nCreated N1_TEST_DATA\nCreated model build settings table: n1_build_settings \nCreated model: N1_CLASS_MODEL \nCreated apply result: N1_APPLY_RESULT \nCreated lift result: N1_LIFT_TABLE \n\n\nPL/SQL procedure successfully completed.\n\n","code":"SUCCESS","type":"TEXT"},"dateCreated":"2018-06-29T17:40:22+0000","dateStarted":"2018-07-16T21:37:26+0000","dateFinished":"2018-07-16T21:37:39+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:140"},{"title":"STEP 12:  View Model's Cummulative Gains Chart and decide if its a good model","text":"%sql\nSELECT QUANTILE_NUMBER, GAIN_CUMULATIVE from N1_LIFT_TABLE where rownum < 40","user":"CHARLIE","dateUpdated":"2018-07-16T21:32:12+0000","config":{"lineNumbers":false,"colWidth":12,"editorMode":"ace/mode/osql","editorHide":false,"title":true,"graph":{"mode":"multiBarChart","optionOpen":false,"keys":[{"name":"QUANTILE_NUMBER","index":0,"aggr":"sum"}],"values":[{"name":"GAIN_CUMULATIVE","index":1,"aggr":"avg"}],"scatter":{"yAxis":{"name":"GAIN_CUMULATIVE","index":1,"aggr":"sum"},"xAxis":{"name":"QUANTILE_NUMBER","index":0,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1043940261","id":"20180717-165545_609918342","result":{"msg":"QUANTILE_NUMBER\tGAIN_CUMULATIVE\n1\t4.29830830959250220604456210015442311935E-02\n2\t8.59661661918500441208912420030884623869E-02\n3\t1.28949256020088793293624531215530553717E-01\n4\t1.71932332383700088241782484006176924774E-01\n5\t2.14915422211938837414515773218619016104E-01\n6\t2.57898512040177586587249062431061107434E-01\n7\t3.00881588403788881535407015221707478491E-01\n8\t3.43864664767400176483564968012353849548E-01\n9\t3.86847768060266379880873593646591661152E-01\n10\t4.29830844423877674829031546437238032208E-01\n11\t4.72813947716743878226340172071475843812E-01\n12\t5.15797024080355173174498124862122214869E-01\n13\t5.43405919524183763512022942863445841606E-01\n14\t5.64984708491892786234281932495036399735E-01\n15\t5.86563497459601808956540922126626957865E-01\n16\t6.08142286427310831678799911758217515994E-01\n17\t6.29721075395019854401058901389808074123E-01\n18\t6.51299810504219060225016545334215751158E-01\n19\t6.72878599471928082947275534965806309287E-01\n20\t6.94457388439637105669534524597396867417E-01\n21\t7.16036177407346128391793514228987425546E-01\n22\t7.37614966375055151114052503860577983675E-01\n23\t7.59193755342764173836311493492168541805E-01\n24\t7.80012169868878226340172071475843812045E-01\n25\t8.0131019442491175821751599382307522612E-01\n26\t8.22608272839455106993161261857489521288E-01\n27\t8.43906297395488638870505184204720935363E-01\n28\t8.65204375810031987646150452239135230532E-01\n29\t8.86502400366065519523494374586366644606E-01\n30\t9.07800478780608868299139642620780939775E-01\n31\t9.30497424270626516655636443856165894551E-01\n32\t9.53238210587635120229428634458416060004E-01\n33\t9.7601982165508493271564085594529009486E-01\n34\t9.98798955231083167879991175821751599382E-01\n35\t1\n36\t1\n37\t1\n38\t1\n39\t1\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-02-04T03:06:50+0000","dateStarted":"2018-07-16T21:32:12+0000","dateFinished":"2018-07-16T21:32:12+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:141"},{"title":"STEP 13:  Apply the Oracle Machine Learning Model to New Customers to Show Customers Most Likely to Have Good Credit","text":"%sql \n\n-- This will show the top predictive attributes\n\nselect a.customer_id\n\t, a.prob_Credit_Score_Bin\n\t, b.age, b.income, b.tenure, b.loan_type, b.loan_amount, b.occupation, b.education_level, b.marital_status\nfrom (select * from (select Customer_id, round(prob_Credit_Score_Bin *100,2) prob_Credit_Score_Bin from (select Customer_ID, prediction_probability(N1_CLASS_MODEL, NULL using *) prob_Credit_Score_Bin from credit_scoring_new_cust_v))) a\n, credit_scoring_100k_v b\nwhere a.customer_id = b.customer_id \norder by a.prob_Credit_Score_Bin desc;","user":"CHARLIE","dateUpdated":"2018-07-16T21:38:38+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}],"values":[{"name":"PROB_CREDIT_SCORE_BIN","index":1,"aggr":"sum"}],"scatter":{"xAxis":{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1596841787","id":"20180717-165545_166708240","result":{"msg":"CUSTOMER_ID\tPROB_CREDIT_SCORE_BIN\tAGE\tINCOME\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tOCCUPATION\tEDUCATION_LEVEL\tMARITAL_STATUS\n127\t98.89\t60\t2150\t11\tNeed\t45000\tProfessional\tBachelor's Degree\tSingle\n34\t98.89\t48\t2150\t94\tEducation\t15000\tArmy\tBachelor's Degree\tDivorced\n90\t98.89\t21\t2750\t7\tAuto\t35000\tManager\tMaster's Degree\tSingle\n485\t98.89\t66\t2150\t79\tNeed\t20000\tTechnician\tBachelor's Degree\tMarried\n283\t98.89\t30\t2750\t17\tAuto\t55000\tClerical\tMaster's Degree\tSingle\n284\t98.89\t63\t2750\t8\tNeed\t35000\tArmy\tBachelor's Degree\tMarried\n356\t98.89\t35\t500\t8\tAuto\t55000\tTechnician\tPrimary School Grad\tMarried\n704\t98.89\t37\t2750\t2\tNeed\t35000\tArmy\tBachelor's Degree\tSingle\n526\t98.89\t49\t2750\t15\tNeed\t35000\tWorker\tMaster's Degree\tMarried\n552\t98.89\t47\t2750\t13\tAuto\t45000\tProfessional\tMaster's Degree\tMarried\n604\t98.89\t65\t2750\t9\tNeed\t20000\tTechnician\tBachelor's Degree\tSingle\n678\t98.89\t27\t2750\t17\tAuto\t30000\tFarmer\tBachelor's Degree\tMarried\n975\t98.89\t33\t2750\t3\tNeed\t25000\tProfessional\tBachelor's Degree\tSingle\n1011\t98.89\t68\t2150\t12\tHousing\t10000\tManager\tMaster's Degree\tMarried\n798\t98.89\t56\t500\t89\tNeed\t30000\tTechnician\tPrimary School Grad\tMarried\n807\t98.89\t18\t2150\t6\tHousing\t15000\tManager\tMaster's Degree\tMarried\n955\t98.89\t42\t2750\t1\tAuto\t25000\tFarmer\tBachelor's Degree\tMarried\n963\t98.89\t36\t2750\t13\tNeed\t15000\tFarmer\tBachelor's Degree\tSingle\n1319\t98.89\t36\t3750\t51\tAuto\t15000\tProfessional\tMaster's Degree\tMarried\n1321\t98.89\t61\t2750\t9\tHousing\t85000\tArmy\tBachelor's Degree\tSingle\n1343\t98.89\t70\t2150\t65\tNeed\t35000\tWorker\tBachelor's Degree\tMarried\n1131\t98.89\t55\t2750\t31\t\t10000\tProfessional\tBachelor's Degree\tSingle\n1135\t98.89\t46\t2150\t4\tHousing\t35000\tProfessional\tMaster's Degree\tMarried\n1145\t98.89\t36\t2150\t2\tHousing\t70000\tArmy\tMaster's Degree\tSingle\n1151\t98.89\t33\t2750\t4\tAuto\t30000\tArmy\tBachelor's Degree\tMarried\n1164\t98.89\t55\t2150\t21\tEducation\t15000\tWorker\tBachelor's Degree\tMarried\n1232\t98.89\t53\t2750\t20\tHousing\t90000\tProfessional\tMaster's Degree\tMarried\n1235\t98.89\t26\t2150\t16\tHousing\t55000\tProfessional\tMaster's Degree\tMarried\n1239\t98.89\t45\t2750\t81\tNeed\t15000\tWorker\tMaster's Degree\tSingle\n1271\t98.89\t68\t2750\t4\tHousing\t75000\tArmy\tMaster's Degree\tMarried\n1274\t98.89\t46\t2750\t7\tHousing\t100000\tClerical\tMaster's Degree\tMarried\n1551\t98.89\t51\t3750\t11\tNeed\t35000\tWorker\tMaster's Degree\tMarried\n1597\t98.89\t45\t2150\t1\tNeed\t40000\tManager\tBachelor's Degree\tMarried\n1614\t98.89\t63\t2150\t14\tNeed\t15000\tWorker\tMaster's Degree\tSingle\n1622\t98.89\t22\t2150\t29\tNeed\t25000\tWorker\tBachelor's Degree\tSingle\n1623\t98.89\t67\t500\t27\tHousing\t50000\tProfessional\tPrimary School Grad\tMarried\n1476\t98.89\t66\t2150\t15\tNeed\t15000\tWorker\tBachelor's Degree\tMarried\n1494\t98.89\t67\t500\t38\tNeed\t45000\tFarmer\tPrimary School Grad\tSingle\n1903\t98.89\t45\t2750\t8\tAuto\t30000\tManager\tMaster's Degree\tDivorced\n1693\t98.89\t37\t2150\t16\tHousing\t75000\tArmy\tMaster's Degree\tSingle\n1815\t98.89\t69\t2150\t18\tNeed\t10000\tManager\tBachelor's Degree\tSingle\n1829\t98.89\t51\t2750\t3\tAuto\t50000\tTechnician\tBachelor's Degree\tSingle\n1858\t98.89\t34\t2750\t5\tHousing\t95000\tClerical\tBachelor's Degree\tMarried\n2207\t98.89\t54\t500\t11\tEducation\t10000\tTechnician\tPrimary School Grad\tDivorced\n2214\t98.89\t46\t2150\t2\tHousing\t40000\tTechnician\tBachelor's Degree\tDivorced\n1999\t98.89\t54\t2750\t12\tNeed\t20000\tFarmer\tBachelor's Degree\tMarried\n2020\t98.89\t43\t2150\t12\tHousing\t60000\tManager\tBachelor's Degree\tMarried\n2406\t98.89\t58\t2750\t10\tHousing\t30000\tManager\tMaster's Degree\tMarried\n2247\t98.89\t34\t2150\t22\tNeed\t30000\tArmy\tBachelor's Degree\tSingle\n2313\t98.89\t56\t3750\t23\tNeed\t45000\tWorker\tMaster's Degree\tMarried\n2326\t98.89\t19\t2750\t4\tAuto\t50000\tArmy\tBachelor's Degree\tMarried\n2380\t98.89\t51\t2150\t17\tNeed\t10000\tTechnician\tBachelor's Degree\tSingle\n2664\t98.89\t37\t2750\t5\tHousing\t55000\tProfessional\tBachelor's Degree\tMarried\n2666\t98.89\t22\t2750\t9\tHousing\t70000\tFarmer\tBachelor's Degree\tMarried\n2682\t98.89\t36\t2750\t6\tHousing\t100000\tManager\tMaster's Degree\tMarried\n2754\t98.89\t66\t500\t38\t\t10000\tArmy\tPrimary School Grad\tSingle\n2492\t98.89\t70\t500\t52\tHousing\t45000\tClerical\tPrimary School Grad\tMarried\n2523\t98.89\t34\t\t9\tEducation\t10000\tClerical\tBachelor's Degree\tSingle\n2646\t98.89\t21\t500\t5\tAuto\t55000\tTechnician\tPrimary School Grad\tSingle\n3260\t98.89\t43\t2750\t37\tEducation\t15000\tFarmer\tBachelor's Degree\tMarried\n2995\t98.89\t45\t500\t7\tAuto\t10000\tClerical\tPrimary School Grad\tMarried\n3026\t98.89\t64\t3750\t31\tAuto\t20000\tManager\tMaster's Degree\tSingle\n3030\t98.89\t29\t2750\t36\tAuto\t55000\tWorker\tBachelor's Degree\tMarried\n3073\t98.89\t21\t\t36\tHousing\t100000\tProfessional\tPrimary School Grad\tMarried\n3074\t98.89\t25\t2150\t2\tHousing\t100000\tManager\tMaster's Degree\tSingle\n3119\t98.89\t63\t2750\t4\tAuto\t20000\tArmy\tBachelor's Degree\tSingle\n3135\t98.89\t19\t2750\t2\tNeed\t\tWorker\tBachelor's Degree\tMarried\n3148\t98.89\t69\t2750\t14\tNeed\t25000\tTechnician\tMaster's Degree\tDivorced\n3185\t98.89\t43\t2750\t1\tHousing\t85000\tManager\tBachelor's Degree\tSingle\n3563\t98.89\t62\t2150\t16\tHousing\t75000\tClerical\tMaster's Degree\tSingle\n3565\t98.89\t31\t2150\t12\tNeed\t20000\tClerical\tBachelor's Degree\tSingle\n3378\t98.89\t68\t2750\t31\tAuto\t35000\tArmy\tMaster's Degree\tSingle\n3391\t98.89\t51\t500\t13\tEducation\t15000\tManager\tPrimary School Grad\tMarried\n3445\t98.89\t21\t500\t15\tHousing\t60000\tWorker\tPrimary School Grad\tSingle\n3790\t98.89\t45\t2750\t6\tNeed\t20000\tFarmer\tMaster's Degree\tMarried\n3800\t98.89\t67\t2750\t30\t\t30000\tWorker\tBachelor's Degree\tSingle\n3802\t98.89\t61\t2750\t23\tNeed\t40000\tTechnician\tBachelor's Degree\tMarried\n3809\t98.89\t36\t2750\t5\tAuto\t15000\tProfessional\tBachelor's Degree\tMarried\n3810\t98.89\t37\t2150\t12\tNeed\t30000\tManager\tBachelor's 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Degree\tMarried\n37694\t98.89\t56\t2750\t22\tHousing\t55000\tProfessional\tMaster's Degree\tMarried\n37707\t98.89\t55\t2750\t23\tNeed\t30000\tTechnician\tBachelor's Degree\tDivorced\n38740\t98.89\t36\t2750\t34\tNeed\t45000\tArmy\tBachelor's Degree\t\n39431\t98.89\t27\t2750\t12\tHousing\t35000\tFarmer\tMaster's Degree\tMarried\n39436\t98.89\t45\t2150\t76\tHousing\t85000\tFarmer\tMaster's Degree\tMarried\n39439\t98.89\t28\t2750\t22\tAuto\t45000\tFarmer\tBachelor's Degree\tMarried\n37727\t98.89\t64\t500\t21\tEducation\t10000\tFarmer\tPrimary School Grad\tSingle\n37770\t98.89\t59\t2750\t6\tNeed\t10000\tManager\tBachelor's Degree\tDivorced\n38255\t98.89\t62\t2150\t16\tAuto\t30000\tArmy\tMaster's Degree\tMarried\n38831\t98.89\t49\t2750\t64\tNeed\t30000\tClerical\tMaster's Degree\tSingle\n39344\t98.89\t69\t2150\t28\tNeed\t30000\tClerical\tMaster's Degree\tMarried\n37818\t98.89\t55\t2150\t9\tNeed\t25000\tWorker\tBachelor's Degree\tSingle\n39449\t98.89\t54\t2750\t36\tHousing\t40000\tTechnician\tBachelor's Degree\tSingle\n37848\t98.89\t22\t2750\t2\tEducation\t10000\tTechnician\tBachelor's Degree\tMarried\n38320\t98.89\t23\t2750\t32\tNeed\t25000\tProfessional\tBachelor's Degree\tMarried\n38324\t98.89\t58\t2750\t24\tHousing\t45000\tManager\tBachelor's Degree\tMarried\n38325\t98.89\t49\t2150\t77\tHousing\t95000\tManager\tBachelor's Degree\tSingle\n39662\t98.89\t21\t2750\t37\tNeed\t35000\tWorker\tMaster's Degree\tMarried\n37893\t98.89\t33\t2750\t94\tNeed\t40000\tArmy\tMaster's Degree\tMarried\n37929\t98.89\t59\t2150\t25\tAuto\t45000\tManager\tBachelor's Degree\tMarried\n38939\t98.89\t66\t2150\t2\tEducation\t20000\tWorker\tMaster's Degree\tMarried\n39382\t98.89\t37\t500\t\tNeed\t20000\tProfessional\tPrimary School Grad\tMarried\n38370\t98.89\t33\t2750\t30\tAuto\t10000\tManager\tBachelor's Degree\tDivorced\n38384\t98.89\t48\t2750\t15\tHousing\t35000\tProfessional\tBachelor's Degree\tSingle\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-07-03T16:00:59+0000","dateStarted":"2018-07-16T21:38:39+0000","dateFinished":"2018-07-16T21:38:39+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:142"},{"title":"Drop Table CREDIT_SCORE_NEW_PREDICTIONS From Previous Run (If Exists)","text":"%script\nbegin\nexecute immediate 'drop table credit_score_new_predictions';\nDBMS_OUTPUT.PUT_LINE ('succeeded');\nEXCEPTION\nWHEN OTHERS THEN\nDBMS_OUTPUT.PUT_LINE ('drop unneccessary - no table exists');\nEND;","user":"CHARLIE","dateUpdated":"2018-07-16T21:38:51+0000","config":{"colWidth":12,"editorMode":"ace/mode/plsql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-16576111","id":"20180717-165545_576674923","result":{"msg":"drop unneccessary - no table exists\n\n\nPL/SQL procedure successfully completed.\n\n","code":"SUCCESS","type":"TEXT"},"dateCreated":"2018-07-12T20:12:46+0000","dateStarted":"2018-07-16T21:38:51+0000","dateFinished":"2018-07-16T21:38:51+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:143"},{"title":"STEP 16:  Create a New Table CREDIT_SCORE_NEW_PREDICTIONS to New Customers","text":"%sql \n\n-- create table with results above so we can view in Data Visualization\n\ncreate table credit_score_new_predictions as \nselect a.customer_id\n\t, a.prob_good_credit\n\t, b.age, b.income, b.tenure, b.loan_type, b.loan_amount, b.occupation, b.education_level, b.marital_status\nfrom (select * from (select Customer_id, round(prob_good_credit *100,2) prob_good_credit from (select Customer_ID, prediction_probability(N1_CLASS_MODEL, 'Good Credit' using *) prob_good_credit from credit_scoring_new_cust_v))) a\n, credit_scoring_100k_v b\nwhere a.customer_id = b.customer_id\n","user":"CHARLIE","dateUpdated":"2018-07-16T21:38:56+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1700159816","id":"20180717-165545_770641698","result":{"msg":"Updated 10710 row(s).","code":"SUCCESS","type":"TEXT"},"dateCreated":"2018-07-10T20:45:35+0000","dateStarted":"2018-07-16T21:38:56+0000","dateFinished":"2018-07-16T21:38:57+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:144"},{"title":"STEP 17:  Review the CREDIT_SCORING_NEW_PREDICTIONS Table and rank Good Customers Based on Prediction Probablity, and Other Factors","text":"%sql \n\n-- Score and rank new customers that are a professional, married, and borrowing for education (in this case).  You can substitute or add other filters.\n\nselect * from credit_score_new_predictions\norder by rank() over (order by prob_good_credit desc) ","user":"CHARLIE","dateUpdated":"2018-07-16T21:40:20+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}],"values":[{"name":"PROB_GOOD_CREDIT","index":1,"aggr":"sum"}],"scatter":{"xAxis":{"name":"CUSTOMER_ID","index":0,"aggr":"sum"}},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1655659274","id":"20180717-165545_1562968500","result":{"msg":"CUSTOMER_ID\tPROB_GOOD_CREDIT\tAGE\tINCOME\tTENURE\tLOAN_TYPE\tLOAN_AMOUNT\tOCCUPATION\tEDUCATION_LEVEL\tMARITAL_STATUS\n56565\t98.75\t34\t5250\t33\tNeed\t\tManager\tBachelor's Degree\tSingle\n52771\t98.75\t34\t6250\t91\tHousing\t\tManager\tBachelor's Degree\tMarried\n90056\t98.75\t65\t5250\t\tEducation\t10000\tManager\tBachelor's Degree\tSingle\n19292\t98.75\t20\t6250\t1\tEducation\t10000\tManager\tBachelor's Degree\tSingle\n82328\t98.75\t49\t5250\t1\tNeed\t10000\tManager\tBachelor's Degree\tMarried\n54979\t98.75\t\t6250\t10\tEducation\t10000\tManager\tBachelor's Degree\tMarried\n7769\t98.75\t21\t4250\t10\tAuto\t10000\tManager\tBachelor's Degree\tSingle\n62646\t98.75\t27\t4250\t10\tEducation\t10000\tManager\tBachelor's Degree\tMarried\n22414\t98.75\t33\t4250\t10\tEducation\t10000\tManager\tBachelor's Degree\tMarried\n96621\t98.75\t36\t6250\t10\tAuto\t10000\tManager\tBachelor's Degree\tMarried\n37273\t98.75\t56\t5250\t10\tHousing\t10000\tManager\tBachelor's Degree\tMarried\n23820\t98.75\t71\t5250\t10\tNeed\t10000\tManager\tBachelor's Degree\tMarried\n7427\t98.75\t25\t4250\t11\tAuto\t10000\tManager\tBachelor's Degree\tDivorced\n82807\t98.75\t71\t4250\t11\tAuto\t10000\tManager\tBachelor's 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prediction_probability(N1_CLASS_MODEL, 'Good Credit' \n  USING 'Rich' as WEALTH, 2000 as income, 'Gold' as customer_value_segment) Prediction_Probability\nfrom dual;","user":"CHARLIE","dateUpdated":"2018-07-16T21:42:17+0000","config":{"colWidth":12,"editorMode":"ace/mode/osql","title":true,"graph":{"mode":"table","optionOpen":false,"keys":[{"name":"PREDICTION_PROBABILITY","index":0,"aggr":"sum"}],"values":[],"scatter":{"xAxis":{"name":"PREDICTION_PROBABILITY","index":0,"aggr":"sum"}},"groups":[],"height":84},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_-1208474658","id":"20180717-165545_705794373","result":{"msg":"PREDICTION_PROBABILITY\n0.9875318407293203\n","code":"SUCCESS","type":"TABLE","comment":""},"dateCreated":"2018-07-02T22:47:39+0000","dateStarted":"2018-07-16T21:42:05+0000","dateFinished":"2018-07-16T21:42:05+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:146"},{"text":"%md\n### Conclusions\n\nADWC's Machine Learning offers out of the box predictive capabilities far beyond competing cloud solutions that have little to no machine learning cabilities built into the database engine.  Predictive analytics in those cases must extract the data (extremely time consuming and complex) into a separate mining repository.  Oracle's ADWC can apply a wide variety of ML algorithms to data in ADWC where terrabytes can be processed in place to discover patterns and predict future outcomes.\n\nThis has vastly simplified Heather's analysis and created entirely new opportunties to process new data sources quickly without other tools that require data extraction.  She can also create applications to process incoming streaming data on the fly, and create a continuous adaptive process that refines her models as trends emerge.\n","user":"ADWC_WS","dateUpdated":"2018-07-11T21:48:25+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","editorHide":true,"graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_1402251963","id":"20180717-165545_1920281704","result":{"msg":"<h3>Conclusions</h3>\n<p>ADWC's Machine Learning offers out of the box predictive capabilities far beyond competing cloud solutions that have little to no machine learning cabilities built into the database engine.  Predictive analytics in those cases must extract the data (extremely time consuming and complex) into a separate mining repository.  Oracle's ADWC can apply a wide variety of ML algorithms to data in ADWC where terrabytes can be processed in place to discover patterns and predict future outcomes.</p>\n<p>This has vastly simplified Heather's analysis and created entirely new opportunties to process new data sources quickly without other tools that require data extraction.  She can also create applications to process incoming streaming data on the fly, and create a continuous adaptive process that refines her models as trends emerge.</p>\n","code":"SUCCESS","type":"HTML"},"dateCreated":"2018-06-29T17:37:16+0000","dateStarted":"2018-07-11T21:48:22+0000","dateFinished":"2018-07-11T21:48:22+0000","status":"FINISHED","progressUpdateIntervalMs":500,"commited":true,"$$hashKey":"object:147"},{"text":"%md ","user":"ADWC_WS","dateUpdated":"2018-07-11T21:48:01+0000","config":{"colWidth":12,"editorMode":"ace/mode/markdown","graph":{"mode":"table","optionOpen":false,"keys":[],"values":[],"scatter":{},"groups":[],"height":300},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1531846545866_2110159520","id":"20180717-165545_657898267","dateCreated":"2018-07-11T21:48:01+0000","status":"READY","progressUpdateIntervalMs":500,"commited":false,"$$hashKey":"object:148"}],"name":"Credit Score Predictions notebook","id":"53599","angularObjects":{"ORA81E9828272:shared_process":[],"MDW381939C745:shared_process":[]},"config":{"looknfeel":"default"},"info":{}}