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Brooklyn1.0 NN.json
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Brooklyn1.0 NN.json
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{
"paragraphs": [
{
"title": "Oracle Machine Learning AutoML UI - Experiment - Generated Notebook",
"text": "%md\n**Experiment name:** Brooklyn1.0\n \n**Workspace:** BROOKLYN Workspace\n \n**Project:** BROOKLYN Project\n \n**User:** BROOKLYN\n \n**Data:** BROOKLYN.BROOKLYN_VIEW_FINAL\n \n**Prediction type:** regression\n \n**Predict:** SALE_PRICE\n \n**Algorithm:** Neural Network\n \n**Timestamp:** 2022-07-02 00:29:39\n This notebook was automatically generated by Oracle Machine Learning AutoML UI\n ",
"user": "BROOKLYN",
"dateUpdated": "2022-07-12T17:10:54+0000",
"progress": 0,
"config": {
"editorSetting": {
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"editOnDblClick": false
},
"colWidth": 12,
"editorMode": "ace/mode/undefined",
"fontSize": 9,
"editorHide": true,
"title": true,
"results": {},
"graph": {
"mode": "table",
"height": 300,
"optionOpen": false,
"keys": [],
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},
"enabled": true
},
"settings": {
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"results": {
"code": "SUCCESS",
"msg": [
{
"type": "HTML",
"data": "<p><strong>Experiment name:</strong> Brooklyn1.0</p>\n<p><strong>Workspace:</strong> BROOKLYN Workspace</p>\n<p><strong>Project:</strong> BROOKLYN Project</p>\n<p><strong>User:</strong> BROOKLYN</p>\n<p><strong>Data:</strong> BROOKLYN.BROOKLYN_VIEW_FINAL</p>\n<p><strong>Prediction type:</strong> regression</p>\n<p><strong>Predict:</strong> SALE_PRICE</p>\n<p><strong>Algorithm:</strong> Neural Network</p>\n<p><strong>Timestamp:</strong> 2022-07-02 00:29:39</p>\n<pre><code> This notebook was automatically generated by Oracle Machine Learning AutoML UI\n</code></pre>\n"
}
]
},
"apps": [],
"interrupted": false,
"runtimeInfos": {},
"progressUpdateIntervalMs": 500,
"jobName": "paragraph_1656738605188_-57989048",
"id": "20220702-051005_817446576",
"dateCreated": "2022-07-02T00:29:39+0000",
"dateStarted": "2022-07-12T15:46:05+0000",
"dateFinished": "2022-07-12T15:46:06+0000",
"status": "FINISHED",
"focus": true,
"$$hashKey": "object:40"
},
{
"title": "Get proxy object for selected data",
"text": "%python\n\nimport oml\n\ncolumns = '\"YEAR\"' , '\"NEIGHBORHOOD\"' , '\"BLOCK\"' , '\"LAND_SQFT\"' , '\"GROSS_SQFT\"' , '\"SALE_PRICE\"'\nschema='\"BROOKLYN\"'\ntable='\"BROOKLYN_VIEW_FINAL\"'\n\ncolumn = ','.join(columns)\nquery = 'SELECT ' + column + ' FROM ' + schema + '.' + table + ' where ' + '\"SALE_PRICE\"' + ' is not null'\n\nbuild_data = oml.sync(query=query)\nz.show(build_data)",
"user": "BROOKLYN",
"dateUpdated": "2022-07-12T17:10:54+0000",
"progress": 0,
"config": {
"editorSetting": {
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"title": true,
"results": {
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"setting": {
"table": {
"tableGridState": {},
"tableColumnTypeState": {
"names": {
"YEAR": "number",
"NEIGHBORHOOD": "string",
"BLOCK": "number",
"LAND_SQFT": "number",
"GROSS_SQFT": "number",
"SALE_PRICE": "number"
},
"updated": false
},
"tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]",
"tableOptionValue": {
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},
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}
},
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}
},
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"results": {
"code": "SUCCESS",
"msg": [
{
"type": "TABLE",
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STUYVESANT\t1838\t2000\t3840\t662000\n2005\tBENSONHURST\t6170\t3017\t3430\t570000\n2005\tBUSHWICK\t3378\t2000\t3180\t530000\n2005\tBUSHWICK\t3382\t2000\t2700\t542000\n2005\tBENSONHURST\t6193\t1938\t3480\t1180000\n2005\tBENSONHURST\t6183\t1833\t1551\t565000\n2005\tBENSONHURST\t6312\t940\t2100\t426000\n2005\tBENSONHURST\t5776\t2133\t3600\t650000\n2005\tBENSONHURST\t6193\t2000\t1664\t468000\n2005\tBENSONHURST\t6161\t2000\t3870\t705500\n2005\tBENSONHURST\t5561\t1683\t1416\t500000\n2005\tBENSONHURST\t6235\t2040\t3340\t694500\n2005\tBUSHWICK\t3309\t1500\t2034\t485000\n2005\tBUSHWICK\t3285\t2000\t1920\t492550\n2005\tBUSHWICK\t3422\t1275\t3060\t475000\n2005\tBUSHWICK\t3418\t1975\t2844\t438780\n2005\tBUSHWICK\t3401\t2000\t2700\t465000\n2005\tBUSH TERMINAL\t813\t1753\t1857\t425000\n2005\tBUSHWICK\t3275\t1667\t1785\t413400\n2005\tBENSONHURST\t6310\t2383\t1960\t625000\n2005\tBENSONHURST\t5562\t2500\t2849\t860000\n2005\tBENSONHURST\t6260\t2000\t3372\t540000\n2005\tBENSONHURST\t5575\t4000\t4000\t700000\n2005\tBENSONHURST\t6197\t2500\t2480\t728000\n2005\tBROWNSVILLE\t3625\t2500\t2440\t430000\n2005\tBUSH 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STUYVESANT\t1775\t1900\t3129\t690000\n2005\tBEDFORD STUYVESANT\t1681\t1650\t1980\t504000\n2005\tBEDFORD STUYVESANT\t1770\t2000\t3200\t620000\n2005\tBEDFORD STUYVESANT\t1801\t1500\t2025\t675000\n2005\tBEDFORD STUYVESANT\t1855\t2000\t2536\t515000\n2014\tGRAVESEND\t6675\t1867\t1912\t699000\n2016\tFLATBUSH-EAST\t4887\t1600\t3300\t700000\n2007\tWINDSOR TERRACE\t5274\t1563\t1235\t700000\n2017\tOCEAN HILL\t1514\t2067\t1920\t700000\n2005\tBUSHWICK\t3342\t2013\t3750\t605858\n2005\tBUSHWICK\t3136\t2200\t2200\t435000\n2005\tBUSHWICK\t3415\t1800\t2430\t491000\n2005\tBUSHWICK\t3300\t1900\t3080\t545000\n2005\tBENSONHURST\t6247\t2186\t2440\t585000\n2005\tBENSONHURST\t6343\t2500\t2041\t570000\n2005\tBENSONHURST\t5554\t2500\t1962\t665000\n2005\tBENSONHURST\t5566\t1521\t2036\t588000\n2005\tBENSONHURST\t6264\t3000\t2640\t890000\n2005\tBENSONHURST\t6284\t1231\t3310\t550000\n2005\tBENSONHURST\t5562\t2833\t2320\t758000\n2005\tBENSONHURST\t6249\t3000\t3960\t885000\n2005\tBUSHWICK\t3397\t2000\t3600\t438000\n2005\tBUSHWICK\t3264\t1216\t2888\t425000\n2005\tBROWNSVILLE\t3625\t2000\t1820\t460000\n2005\tCLINTON HILL\t1947\t2000\t2616\t955500\n2005\tBEDFORD STUYVESANT\t1914\t5000\t0\t645070\n2005\tBEDFORD STUYVESANT\t1593\t0\t0\t790000\n2005\tBEDFORD STUYVESANT\t1593\t0\t0\t780000\n2005\tBEDFORD STUYVESANT\t1593\t0\t0\t780000\n2005\tBEDFORD STUYVESANT\t1593\t0\t0\t754000\n2005\tBEDFORD 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GARDEN\t4801\t2000\t2200\t700000\n2014\tMIDWOOD\t6737\t918\t1836\t700000\n2005\tBUSHWICK\t3418\t2000\t2700\t430000\n2005\tBUSHWICK\t3408\t1800\t3186\t645000\n2005\tBUSHWICK\t3431\t1658\t1791\t480000\n2005\tBUSHWICK\t3266\t2725\t1720\t583000\n2005\tBROWNSVILLE\t3633\t2500\t2200\t410000\n2005\tBUSHWICK\t3273\t2000\t2480\t670000\n2005\tBUSHWICK\t3366\t1400\t2775\t550000\n2005\tBUSHWICK\t3384\t1900\t2565\t490000\n2005\tBUSHWICK\t3421\t2100\t2850\t446000\n2005\tCANARSIE\t8226\t2000\t1560\t425000\n2005\tBEDFORD STUYVESANT\t1621\t2000\t4400\t477000\n2005\tBEDFORD STUYVESANT\t1990\t1875\t2520\t425500\n2005\tBEDFORD STUYVESANT\t1846\t2500\t3520\t450500\n2005\tBEDFORD STUYVESANT\t1864\t2000\t4080\t720000\n2005\tBEDFORD STUYVESANT\t1666\t2000\t3000\t540000\n2005\tBEDFORD STUYVESANT\t1667\t2600\t2100\t625000\n2005\tBUSHWICK\t3396\t1767\t3180\t620000\n2005\tBENSONHURST\t6323\t2475\t1840\t510000\n2005\tBENSONHURST\t6157\t2670\t2172\t899900\n2005\tBENSONHURST\t6205\t2500\t3072\t715000\n2005\tBENSONHURST\t6258\t2500\t1772\t685000\n2005\tBEDFORD STUYVESANT\t1871\t1782\t2720\t450000\n2014\tGRAVESEND\t6623\t2000\t1290\t699000\n2008\tBOROUGH PARK\t5444\t2225\t3488\t700000\n2014\tEAST NEW YORK\t3772\t1400\t3303\t700000\n2005\tCLINTON HILL\t1952\t1500\t2616\t850000\n2005\tCLINTON HILL\t1984\t2250\t4824\t896000\n2005\tCANARSIE\t8307\t2502\t3088\t654000\n2005\tBENSONHURST\t6260\t2000\t2592\t725000\n2005\tBUSHWICK\t3349\t2500\t2310\t467500\n2005\tBUSHWICK\t3370\t1800\t2700\t499000\n2005\tBUSHWICK\t3401\t2000\t2250\t490000\n2005\tBUSHWICK\t3357\t1500\t1800\t640000\n2005\tBUSHWICK\t3293\t2199\t3092\t415233\n2005\tBUSHWICK\t3383\t1800\t3240\t620000\n2005\tBUSHWICK\t3440\t1950\t2634\t450000\n2005\tBROWNSVILLE\t3625\t2500\t2120\t448000\n2005\tCANARSIE\t8211\t2500\t1600\t475000\n2005\tCANARSIE\t8033\t3000\t2384\t530000\n2005\tBEDFORD STUYVESANT\t1758\t1875\t3476\t750000\n2005\tBEDFORD STUYVESANT\t1593\t2006\t4300\t637000\n2005\tBEDFORD STUYVESANT\t1589\t2500\t4800\t725000\n2005\tBEDFORD STUYVESANT\t1589\t1440\t2430\t675000\n2005\tBEDFORD STUYVESANT\t1589\t1440\t2430\t550000\n2005\tBENSONHURST\t6229\t1992\t1920\t575000\n2005\tBROWNSVILLE\t3623\t2500\t2178\t465000\n2005\tBROWNSVILLE\t3557\t2500\t5200\t631315\n2005\tBUSHWICK\t3173\t2500\t2000\t550000\n2005\tBUSHWICK\t3396\t2000\t4000\t595000\n2005\tBUSHWICK\t3383\t1800\t3240\t600000\n2005\tBUSHWICK\t3401\t2000\t3300\t610000\n2005\tBUSHWICK\t3352\t1600\t2560\t460000\n2005\tBUSHWICK\t3297\t2000\t2000\t600000\n2005\tBUSHWICK\t3365\t1825\t3000\t588040\n2005\tBUSHWICK\t3387\t1800\t2700\t425000\n2005\tBEDFORD STUYVESANT\t1788\t2500\t3168\t445000\n2005\tBEDFORD STUYVESANT\t1736\t2000\t2400\t639000\n2005\tBEDFORD STUYVESANT\t1736\t2000\t2400\t499000\n2005\tBEDFORD STUYVESANT\t1869\t1530\t3240\t580000\n2005\tBEDFORD STUYVESANT\t1815\t1586\t2262\t520000\n2008\tBEDFORD STUYVESANT\t1658\t2000\t1920\t700000\n2014\tCROWN HEIGHTS\t1237\t1600\t2220\t700000\n2009\tGRAVESEND\t6622\t2667\t2304\t690000\n2017\tEAST NEW YORK\t4326\t2993\t3984\t700000\n2017\tEAST NEW YORK\t4003\t2217\t1296\t700000\n2016\tBUSHWICK\t3340\t1667\t2336\t700000\n2005\tCANARSIE\t8144\t3000\t3240\t460000\n2005\tBUSHWICK\t3374\t1900\t2736\t505000\n2005\tBENSONHURST\t6240\t2367\t1374\t590000\n2005\tBENSONHURST\t5553\t1872\t2692\t605000\n2005\tBENSONHURST\t6202\t2000\t2730\t437500\n2005\tBENSONHURST\t6170\t3750\t2734\t725000\n2005\tBENSONHURST\t6306\t2000\t3180\t580000\n2005\tBUSHWICK\t3415\t2000\t3000\t500000\n2005\tBUSHWICK\t3133\t2000\t2808\t500000\n2013\tBERGEN BEACH\t8362\t3092\t3892\t695000\n2014\tBUSHWICK\t3433\t1600\t3024\t690000\n2011\tBAY RIDGE\t6138\t2074\t1554\t690000\n2008\tKENSINGTON\t5330\t2015\t3300\t700000\n2017\tBENSONHURST\t6203\t1600\t1030\t697000\n2012\tBEDFORD STUYVESANT\t1678\t1800\t2430\t699999\n2014\tOCEAN HILL\t1534\t2500\t1500\t695000\n2007\tCROWN HEIGHTS\t1215\t3051\t4472\t700000\n2016\tMARINE PARK\t8482\t4000\t1572\t700000\n2012\tBATH BEACH\t6424\t2000\t1812\t699000\n2013\tSUNSET PARK\t757\t2180\t1760\t700000\n2014\tSUNSET PARK\t798\t2003\t4368\t698000\n2007\tEAST NEW YORK\t4004\t2883\t2823\t700000\n2010\tCROWN HEIGHTS\t1288\t2400\t2400\t696000\n2014\tMIDWOOD\t6756\t3000\t2111\t700000\n2007\tFLATBUSH-CENTRAL\t5236\t1220\t3240\t695000\n2011\tSUNSET PARK\t879\t1803\t1368\t690000\n2013\tBENSONHURST\t5558\t1458\t1633\t693000\n2014\tBERGEN BEACH\t8357\t4646\t3400\t690000\n2017\tMARINE PARK\t7718\t1800\t1092\t693000\n2014\tDYKER HEIGHTS\t5902\t2000\t2680\t700000\n2016\tBOROUGH PARK\t5511\t1200\t1760\t700000\n2010\tBENSONHURST\t5568\t2660\t1800\t690000\n2015\tBOROUGH PARK\t5494\t5148\t2080\t700000\n2015\tGRAVESEND\t6606\t2000\t2840\t700000\n2008\tMIDWOOD\t7645\t2850\t2038\t700000\n2013\tDYKER HEIGHTS\t6243\t1500\t1863\t690000\n2016\tCANARSIE\t7966\t1950\t2090\t699000\n2007\tBEDFORD STUYVESANT\t1633\t2500\t2700\t697000\n2007\tWILLIAMSBURG-EAST\t2747\t1667\t1800\t700000\n2017\tBEDFORD STUYVESANT\t1769\t1617\t1632\t700000\n2003\tMANHATTAN BEACH\t8753\t4000\t2898\t700000\n2017\tGRAVESEND\t6672\t2000\t2224\t700000\n2012\tCROWN HEIGHTS\t1339\t2000\t3760\t698700\n2008\tSHEEPSHEAD BAY\t7463\t3700\t3144\t700000\n2008\tCOBBLE HILL\t301\t1695\t3440\t700000\n2008\tKENSINGTON\t5339\t2017\t1704\t700000\n2014\tBAY RIDGE\t5939\t3406\t2240\t700000\n2013\tBEDFORD STUYVESANT\t1774\t2083\t2436\t696296\n2013\tGRAVESEND\t6678\t1926\t2038\t699000\n2016\tGRAVESEND\t7162\t5000\t1958\t700000\n2011\tMARINE PARK\t7712\t4000\t1374\t690000\n2012\tBATH BEACH\t6395\t2417\t2000\t690000\n2007\tBOROUGH PARK\t5300\t1600\t3000\t700000\n2015\tSUNSET PARK\t914\t2504\t2040\t700000\n2014\tWILLIAMSBURG-CENTRAL\t2231\t1600\t3046\t700000\n2016\tBUSHWICK\t3229\t2010\t2286\t700000\n2016\tCANARSIE\t8035\t2900\t3328\t695000\n2017\tMARINE PARK\t7714\t1453\t1683\t700000\n2014\tBATH BEACH\t6424\t2078\t1802\t695000\n2010\tBATH BEACH\t6480\t1740\t2052\t690000\n2007\tFLATBUSH-CENTRAL\t5145\t5000\t2363\t700000\n2015\tFLATBUSH-LEFFERTS GARDEN\t4819\t1393\t2480\t695000\n2016\tFLATBUSH-EAST\t5000\t4000\t1650\t700000\n2007\tBOROUGH PARK\t5584\t1903\t2352\t700000\n2017\tFLATLANDS\t7803\t2317\t3392\t700000\n2007\tFLATBUSH-CENTRAL\t5079\t2565\t2800\t700000\n2015\tBENSONHURST\t5563\t2300\t1600\t700000\n2015\tWILLIAMSBURG-EAST\t3037\t1983\t1910\t700000\n2007\tOCEAN HILL\t1481\t2000\t4300\t700000\n2012\tDYKER HEIGHTS\t6231\t2300\t2128\t697000\n2014\tBAY RIDGE\t5841\t1440\t1632\t700000\n2016\tFLATBUSH-EAST\t7619\t3000\t3540\t700000\n2016\tCROWN HEIGHTS\t1371\t3125\t3192\t700000\n2003\tBOROUGH PARK\t5487\t2003\t3453\t700000\n2017\tCYPRESS HILLS\t4107\t1800\t1860\t700000\n2003\tCARROLL GARDENS\t377\t2000\t2820\t700000\n2010\tGREENPOINT\t2833\t2500\t1536\t699000\n2014\tCROWN HEIGHTS\t1279\t2000\t2088\t700000\n2012\tBAY RIDGE\t6124\t2600\t1400\t690000\n2008\tGRAVESEND\t7101\t1800\t2187\t700000\n2012\tBEDFORD STUYVESANT\t1803\t1463\t2634\t694364\n2015\tBERGEN BEACH\t8448\t2717\t1665\t697000\n2010\tSUNSET PARK\t634\t1778\t2209\t690000\n2012\tDYKER HEIGHTS\t6022\t2000\t1302\t690000\n2016\tSHEEPSHEAD BAY\t7410\t2342\t3120\t700000\n2011\tBOERUM HILL\t179\t1710\t2565\t695000\n2014\tOCEAN PARKWAY-SOUTH\t7224\t4000\t1472\t700000\n2016\tCANARSIE\t8000\t1975\t2223\t695000\n2010\tBEDFORD STUYVESANT\t1797\t1667\t2100\t692410\n2015\tCROWN HEIGHTS\t1370\t2427\t2922\t700000\n2016\tCROWN HEIGHTS\t1370\t2427\t2922\t694650\n2017\tBOROUGH PARK\t5381\t2604\t2360\t700000\n2014\tMIDWOOD\t6749\t3000\t2999\t700000\n2007\tSUNSET PARK\t790\t2003\t3000\t691900\n2008\tOCEAN HILL\t1471\t1070\t3060\t695000\n2015\tOCEAN PARKWAY-NORTH\t5396\t2000\t1698\t700000\n2014\tBENSONHURST\t6311\t1800\t1800\t695000\n2007\tEAST NEW YORK\t4505\t2520\t3000\t699000\n2007\tFLATBUSH-NORTH\t4802\t1800\t4320\t700000\n2014\tMIDWOOD\t6697\t4000\t3696\t700000\n2014\tMARINE PARK\t8473\t3350\t1795\t700000\n2011\tSUNSET PARK\t846\t2003\t3090\t690000\n2007\tBEDFORD STUYVESANT\t1767\t1875\t2250\t695000\n2007\tFLATBUSH-LEFFERTS GARDEN\t5026\t1632\t2720\t697000\n2012\tBAY RIDGE\t5871\t1600\t2112\t692500\n2014\tDYKER HEIGHTS\t5765\t2000\t3600\t697500\n2014\tMADISON\t7295\t2000\t1250\t700000\n2012\tBAY RIDGE\t6093\t2585\t2722\t690000\n2008\tBENSONHURST\t6348\t2750\t2800\t700000\n2007\tMILL BASIN\t8598\t2683\t3339\t695000\n2008\tWILLIAMSBURG-EAST\t2745\t2519\t2496\t700000\n2003\tOCEAN PARKWAY-NORTH\t5495\t6313\t4500\t700000\n2007\tOLD MILL BASIN\t7854\t4000\t3068\t700000\n2014\tGRAVESEND\t6646\t2583\t1486\t699000\n2015\tFLATBUSH-NORTH\t1324\t1858\t1800\t700000\n2003\tMADISON\t7330\t2500\t4080\t699300\n2017\tBERGEN BEACH\t8366\t2659\t1600\t700000\n2008\tRED HOOK\t578\t3588\t1379\t700000\n2014\tBATH BEACH\t6464\t2127\t1687\t699000\n2016\tOCEAN PARKWAY-SOUTH\t7193\t2000\t2680\t700000\n2013\tPROSPECT HEIGHTS\t1138\t2200\t2140\t700000\n2017\tMILL BASIN\t8595\t2565\t3232\t700000\n2016\tBUSHWICK\t3314\t2500\t2400\t700000\n2003\tMIDWOOD\t6714\t4000\t0\t700000\n2016\tMARINE PARK\t6843\t2000\t1280\t700000\n2017\tEAST NEW YORK\t4046\t1595\t2120\t700000\n2007\tCANARSIE\t8307\t1680\t2967\t696809\n2009\tBEDFORD STUYVESANT\t1838\t1667\t3000\t690000\n2013\tGRAVESEND\t6646\t2700\t1486\t690000\n2008\tBOROUGH PARK\t5609\t2003\t3120\t700000\n2010\tBOROUGH PARK\t5603\t2003\t1920\t690000\n2016\tBENSONHURST\t6275\t2500\t1760\t700000\n2017\tBOROUGH PARK\t5712\t2003\t1640\t700000\n2016\tFLATBUSH-CENTRAL\t5076\t5808\t3270\t695000\n2016\tCANARSIE\t4747\t2200\t1701\t694500\n2007\tSUNSET PARK\t756\t2003\t2952\t700000\n2003\tBOROUGH PARK\t5661\t2454\t2520\t700000\n2011\tBENSONHURST\t6168\t4000\t3100\t690000\n2008\tBAY RIDGE\t6078\t1650\t2136\t700000\n2011\tGRAVESEND\t6619\t2000\t1840\t690000\n2017\tPARK SLOPE SOUTH\t1052\t2096\t2304\t700000\n2014\tBEDFORD STUYVESANT\t1813\t2000\t2800\t700000\n2008\tFLATBUSH-CENTRAL\t5223\t5000\t3195\t700000\n2009\tBEDFORD STUYVESANT\t1825\t1900\t2565\t699000\n2012\tBENSONHURST\t6300\t3500\t2240\t690000\n2003\tWINDSOR TERRACE\t5275\t2500\t1800\t700000\n2015\tSHEEPSHEAD BAY\t7374\t2500\t784\t692000\n2013\tGRAVESEND\t6621\t2225\t1552\t690000\n2013\tBROWNSVILLE\t3620\t2067\t1623\t690929\n2015\tWILLIAMSBURG-EAST\t2758\t1433\t1600\t700000\n2015\tCANARSIE\t8024\t5400\t2160\t700000\n2016\tMILL BASIN\t8595\t2500\t2120\t693600\n2015\tFLATBUSH-EAST\t4722\t2000\t2500\t700000\n2012\tBEDFORD STUYVESANT\t1775\t1740\t4785\t690000\n2017\tEAST NEW YORK\t3809\t2725\t2280\t693625\n2007\tOLD MILL BASIN\t8377\t2000\t3360\t695000\n2016\tFLATBUSH-CENTRAL\t5140\t1500\t1916\t700000\n2003\tCLINTON HILL\t1983\t2020\t3200\t700000\n2011\tBOROUGH PARK\t5691\t1669\t1736\t699000\n2017\tMADISON\t6837\t1800\t1890\t700000\n2014\tBUSH TERMINAL\t736\t1761\t1680\t700000\n2007\tBOROUGH PARK\t5501\t2003\t1920\t700000\n2007\tBUSHWICK\t3376\t2000\t2700\t695000\n2010\tOCEAN PARKWAY-NORTH\t6569\t2200\t1376\t690000\n2010\tBEDFORD STUYVESANT\t1840\t1875\t2532\t695000\n2007\tFLATBUSH-NORTH\t4663\t2516\t3708\t699000\n2017\tBRIGHTON BEACH\t8665\t1800\t925\t695000\n2008\tCANARSIE\t8240\t2000\t4280\t700000\n2017\tKENSINGTON\t5317\t2000\t2165\t700000\n2015\tFLATBUSH-NORTH\t4660\t2000\t3120\t695000\n2016\tBAY RIDGE\t5988\t2187\t1328\t700000\n2007\tBERGEN BEACH\t8343\t2200\t3146\t700000\n2012\tBAY RIDGE\t5942\t2000\t2930\t691000\n2007\tFLATBUSH-EAST\t7954\t2383\t3220\t700000\n2015\tKENSINGTON\t5303\t2363\t2717\t700000\n2008\tCANARSIE\t8108\t2000\t3450\t699000\n2013\tBOROUGH PARK\t5507\t2003\t2040\t691000\n2015\tSHEEPSHEAD BAY\t7475\t2000\t2225\t700000\n2008\tBEDFORD STUYVESANT\t1713\t1567\t1752\t695000\n2003\tMANHATTAN BEACH\t8731\t2320\t1760\t700000\n2013\tBEDFORD STUYVESANT\t1638\t1667\t2001\t692000\n2007\tWINDSOR TERRACE\t1114\t2000\t3192\t700000\n2016\tFLATBUSH-NORTH\t4805\t2300\t1960\t700000\n2003\tGREENPOINT\t2647\t2375\t4270\t700000\n2007\tCROWN HEIGHTS\t1357\t1700\t1530\t700000\n2008\tBRIGHTON BEACH\t8707\t2200\t1861\t693000\n2012\tSUNSET PARK\t645\t1720\t1530\t698340\n2014\tBUSH TERMINAL\t745\t1736\t1672\t695000\n2017\tOLD MILL BASIN\t8403\t2483\t1510\t699000\n2010\tSUNSET PARK\t658\t2000\t3192\t690000\n2013\tBEDFORD STUYVESANT\t1642\t1513\t2040\t699000\n2007\tMIDWOOD\t7608\t2058\t1472\t700000\n2012\tCLINTON HILL\t1892\t1667\t1536\t693500\n2016\tPARK SLOPE SOUTH\t1024\t1667\t2110\t700000\n2017\tCROWN HEIGHTS\t1261\t1600\t2040\t700000\n2008\tEAST NEW YORK\t4518\t2000\t2940\t697501\n2015\tBEDFORD STUYVESANT\t1796\t2000\t2520\t699225\n2008\tFLATBUSH-LEFFERTS GARDEN\t5033\t1967\t3036\t700000\n2010\tDYKER HEIGHTS\t6156\t2000\t3000\t690000\n2015\tGRAVESEND\t6598\t2000\t1400\t700000\n2016\tEAST NEW YORK\t4269\t3000\t3780\t700000\n2016\tBOROUGH PARK\t5540\t1179\t1305\t700000\n2008\tBRIGHTON BEACH\t8714\t1674\t1852\t700000\n2016\tMARINE PARK\t7698\t2100\t1472\t699000\n2003\tBOROUGH PARK\t5623\t2003\t2704\t700000\n2013\tOCEAN PARKWAY-NORTH\t6544\t3200\t2072\t695000\n2007\tFLATBUSH-CENTRAL\t5169\t2000\t2970\t690100\n2010\tMARINE PARK\t8535\t3125\t1404\t690000\n2010\tBEDFORD STUYVESANT\t1863\t1800\t3024\t695015\n2013\tBUSHWICK\t3457\t2000\t3180\t690500\n2014\tMIDWOOD\t7608\t2942\t2180\t700000\n2007\tCROWN HEIGHTS\t1376\t1626\t2465\t695000\n2008\tBAY RIDGE\t5863\t2000\t2722\t700000\n2016\tFLATLANDS\t7783\t3500\t2880\t700000\n2014\tBUSHWICK\t3296\t2500\t0\t700000\n2007\tGRAVESEND\t6605\t1848\t2880\t700000\n2008\tSHEEPSHEAD BAY\t7403\t2450\t2000\t695000\n2014\tSHEEPSHEAD BAY\t7381\t2500\t2892\t700000\n2007\tBERGEN BEACH\t8393\t3780\t3000\t692000\n2012\tOCEAN PARKWAY-SOUTH\t7200\t2400\t2720\t690000\n2016\tSUNSET PARK\t5792\t1666\t2360\t700000\n2015\tOCEAN PARKWAY-NORTH\t6566\t2000\t1536\t700000\n2017\tEAST NEW YORK\t4402\t1703\t1836\t700000\n2016\tFLATBUSH-EAST\t7616\t1800\t2080\t700000\n2009\tGRAVESEND\t6672\t2000\t3336\t690000\n2005\tBEDFORD STUYVESANT\t1757\t2000\t3240\t779000\n2005\tBEDFORD STUYVESANT\t1761\t1700\t5200\t699000\n2005\tBEDFORD STUYVESANT\t1841\t1683\t3500\t645000\n2005\tBEDFORD STUYVESANT\t1851\t2500\t5200\t810000\n2005\tBEDFORD STUYVESANT\t1791\t1925\t3312\t575000\n2005\tBENSONHURST\t6263\t2500\t2600\t510000\n2005\tBENSONHURST\t6305\t2000\t1800\t455000\n2005\tBENSONHURST\t6175\t2500\t2480\t680000\n2005\tBENSONHURST\t6175\t2500\t2480\t545000\n2005\tBENSONHURST\t6223\t3600\t3263\t860000\n2005\tBENSONHURST\t5563\t2300\t2480\t542000\n2005\tBENSONHURST\t6172\t1850\t2424\t515000\n2015\tBAY RIDGE\t6092\t1012\t1040\t700000\n2015\tFLATBUSH-EAST\t7612\t4200\t1750\t700000\n2008\tSUNSET PARK\t697\t1800\t1635\t700000\n2007\tBEDFORD STUYVESANT\t1871\t1240\t1920\t695000\n2015\tBAY RIDGE\t6107\t1072\t1088\t700000\n2008\tFLATBUSH-CENTRAL\t5056\t2437\t2064\t700000\n2015\tBAY RIDGE\t5988\t2187\t1280\t700000\n2005\tBEDFORD STUYVESANT\t1913\t0\t0\t607000\n2005\tBEDFORD STUYVESANT\t1661\t1667\t2184\t484600\n2005\tBEDFORD STUYVESANT\t1594\t2008\t4080\t406932\n2005\tBEDFORD STUYVESANT\t1645\t2000\t3600\t696280\n2005\tBEDFORD STUYVESANT\t1860\t1750\t3152\t712850\n2005\tBEDFORD STUYVESANT\t1817\t1510\t3200\t408642\n2005\tBEDFORD STUYVESANT\t1662\t1800\t2430\t585000\n2005\tBEDFORD STUYVESANT\t1839\t2000\t4000\t650000\n2005\tBEDFORD STUYVESANT\t1755\t2000\t2520\t590000\n2008\tMADISON\t7310\t2600\t1456\t700000\n2012\tDYKER HEIGHTS\t6322\t1567\t2340\t695000\n2007\tRED HOOK\t587\t1900\t3345\t700000\n2014\tFLATBUSH-EAST\t7651\t2767\t1600\t700000\n2013\tWILLIAMSBURG-EAST\t2755\t2500\t1112\t700000\n2009\tFLATBUSH-CENTRAL\t5154\t2280\t3000\t695000\n2005\tBEDFORD STUYVESANT\t1637\t2000\t3000\t540000\n2005\tBEDFORD STUYVESANT\t1662\t1400\t2364\t460000\n2005\tBEDFORD STUYVESANT\t1634\t1883\t3016\t550000\n2005\tBEDFORD STUYVESANT\t1996\t1967\t2736\t551000\n2005\tBEDFORD STUYVESANT\t1609\t1950\t3512\t470000\n2005\tBEDFORD STUYVESANT\t1655\t1867\t2988\t795000\n2005\tBEDFORD STUYVESANT\t1682\t2000\t2260\t455000\n2005\tBEDFORD STUYVESANT\t1768\t2000\t4160\t728000\n2005\tBEDFORD STUYVESANT\t1864\t2000\t4240\t720000\n2005\tBEDFORD STUYVESANT\t1828\t1800\t1920\t680000\n2005\tBEDFORD STUYVESANT\t1832\t2000\t3570\t640000\n2005\tBEDFORD STUYVESANT\t1594\t1917\t2971\t423000\n2005\tBEDFORD STUYVESANT\t1985\t2365\t3621\t810000\n2009\tMIDWOOD\t7646\t3000\t1462\t690000\n2017\tEAST NEW YORK\t4269\t2500\t2520\t700000\n2017\tCROWN HEIGHTS\t1380\t2405\t3340\t700000\n2009\tBEDFORD STUYVESANT\t1772\t2500\t1620\t699000\n2005\tBEDFORD STUYVESANT\t1772\t2000\t2940\t475000\n2005\tBEDFORD STUYVESANT\t1809\t1700\t2499\t650000\n2005\tBEDFORD STUYVESANT\t1758\t2017\t3388\t550000\n2005\tBEDFORD STUYVESANT\t1974\t2400\t3620\t725000\n2005\tBEDFORD STUYVESANT\t1782\t1623\t3600\t580000\n2005\tBEDFORD STUYVESANT\t1670\t2000\t3600\t620000\n2005\tBEDFORD STUYVESANT\t1822\t2000\t3376\t765000\n2005\tBEDFORD STUYVESANT\t1687\t1273\t2486\t540000\n2005\tBEDFORD STUYVESANT\t1971\t1900\t3420\t772500\n2005\tBEDFORD STUYVESANT\t1662\t1800\t2430\t533500\n2005\tBEDFORD STUYVESANT\t1853\t1800\t2550\t550000\n2005\tBEDFORD STUYVESANT\t1987\t2000\t3200\t778000\n2005\tBEDFORD STUYVESANT\t1596\t1850\t2220\t550000\n2005\tBEDFORD STUYVESANT\t1656\t1633\t2744\t455000\n2005\tBEDFORD STUYVESANT\t1990\t1667\t2296\t440000\n2005\tBEDFORD STUYVESANT\t1950\t1800\t2160\t476000\n2005\tBEDFORD STUYVESANT\t1676\t2000\t2880\t570000\n2005\tBEDFORD STUYVESANT\t1632\t2000\t3150\t657000\n2005\tBEDFORD STUYVESANT\t1632\t2083\t3249\t666410\n2005\tBEDFORD STUYVESANT\t1797\t1667\t2100\t435000\n2005\tBEDFORD STUYVESANT\t1989\t1600\t5440\t800000\n2005\tBEDFORD STUYVESANT\t1794\t1900\t3420\t689000\n2005\tBEDFORD STUYVESANT\t1789\t1875\t2592\t675000\n2005\tBEDFORD STUYVESANT\t1714\t2156\t3476\t640000\n2005\tBEDFORD STUYVESANT\t1762\t1767\t2336\t535000\n2005\tBEDFORD STUYVESANT\t1683\t1875\t2388\t550000\n2005\tBEDFORD STUYVESANT\t1673\t1800\t3178\t700000\n2005\tBEDFORD STUYVESANT\t1651\t2000\t2700\t580000\n2005\tBEDFORD STUYVESANT\t1852\t1400\t2124\t550000\n2005\tBEDFORD STUYVESANT\t1629\t2033\t2439\t685000\n2005\tBEDFORD STUYVESANT\t1792\t3000\t3600\t596185\n2005\tBEDFORD STUYVESANT\t1871\t1473\t3420\t442900\n2008\tFLATBUSH-EAST\t4869\t1550\t2880\t700000\n2005\tBEDFORD STUYVESANT\t1758\t2000\t2700\t750000\n2005\tBEDFORD STUYVESANT\t1647\t2900\t3352\t769000\n2005\tBEDFORD STUYVESANT\t1830\t1900\t3420\t540000\n2005\tBEDFORD STUYVESANT\t1871\t1240\t1920\t515000\n2005\tBEDFORD STUYVESANT\t1747\t1800\t3400\t667000\n2005\tBEDFORD STUYVESANT\t1586\t1600\t4000\t585000\n2005\tBEDFORD STUYVESANT\t1815\t2500\t3080\t670000\n2005\tBEDFORD STUYVESANT\t1912\t2650\t8654\t445000\n2005\tBEDFORD STUYVESANT\t1869\t1530\t3240\t480000\n2005\tBEDFORD STUYVESANT\t1783\t1717\t3776\t520000\n2005\tBEDFORD STUYVESANT\t1863\t1800\t3024\t722125\n2005\tBEDFORD STUYVESANT\t1593\t2000\t2880\t420000\n2005\tBEDFORD STUYVESANT\t1659\t1800\t3698\t660000\n2005\tBEDFORD STUYVESANT\t1612\t1866\t3337\t620000\n2005\tBEDFORD STUYVESANT\t1846\t1100\t2442\t670000\n2005\tBEDFORD STUYVESANT\t1792\t2000\t3360\t700000\n2005\tBEDFORD STUYVESANT\t1656\t1800\t2976\t550000\n2005\tBEDFORD STUYVESANT\t1665\t2000\t2508\t405000\n2005\tBEDFORD STUYVESANT\t1847\t1667\t3000\t550000\n2005\tBEDFORD STUYVESANT\t1774\t2500\t5200\t795253\n2005\tBEDFORD STUYVESANT\t1832\t2000\t2700\t772740\n2005\tBEDFORD STUYVESANT\t1990\t1667\t1722\t490000\n2005\tBEDFORD STUYVESANT\t1638\t1125\t2739\t610000\n2005\tBEDFORD STUYVESANT\t1926\t1375\t2960\t680000\n2005\tBEDFORD STUYVESANT\t1586\t2420\t5280\t610000\n2005\tBEDFORD STUYVESANT\t1593\t2000\t4000\t626300\n2005\tBEDFORD STUYVESANT\t1793\t2000\t3360\t700000\n2005\tBEDFORD STUYVESANT\t1867\t2150\t4300\t530400\n2005\tBEDFORD STUYVESANT\t1856\t2000\t3600\t475000\n2005\tBEDFORD STUYVESANT\t1646\t4000\t2400\t500000\n2005\tBEDFORD STUYVESANT\t1986\t1507\t2322\t775000\n2005\tBEDFORD STUYVESANT\t1780\t2000\t4400\t505000\n2011\tBEDFORD STUYVESANT\t1622\t4700\t3780\t695000\n2015\tOCEAN HILL\t1566\t1639\t1530\t699999\n2016\tSHEEPSHEAD BAY\t7477\t1950\t2232\t700000\n2012\tBEDFORD STUYVESANT\t1828\t2000\t4000\t696450\n2008\tRED HOOK\t596\t1667\t1792\t700000\n2015\tOCEAN PARKWAY-NORTH\t5378\t2000\t1376\t700000\n2009\tBENSONHURST\t6276\t4000\t2382\t699000\n2010\tBERGEN BEACH\t8392\t3100\t2910\t695000\n2017\tCYPRESS HILLS\t3888\t2200\t3300\t700000\n2016\tSHEEPSHEAD BAY\t7383\t2200\t1610\t697000\n2005\tBEDFORD STUYVESANT\t1757\t2075\t3240\t679000\n2005\tBEDFORD STUYVESANT\t1602\t1800\t3240\t638000\n2005\tBEDFORD STUYVESANT\t1990\t1875\t1404\t572000\n2005\tBEDFORD STUYVESANT\t1772\t1900\t3420\t525000\n2014\tSEAGATE\t6960\t6000\t4884\t700000\n2009\tCLINTON HILL\t1993\t1600\t3200\t695000\n2003\tFORT GREENE\t2046\t1250\t2430\t700000\n2014\tBAY RIDGE\t6052\t1665\t2542\t690000\n2007\tBENSONHURST\t5568\t1543\t1900\t700000\n2007\tGRAVESEND\t7098\t1668\t1900\t695000\n2016\tOCEAN PARKWAY-SOUTH\t7136\t1558\t1440\t700000\n2014\tBATH BEACH\t6360\t1547\t1428\t695000\n2014\tBOROUGH PARK\t5549\t2300\t2480\t700000\n2016\tSHEEPSHEAD BAY\t7410\t2042\t1400\t695000\n2009\tDYKER HEIGHTS\t5903\t2000\t2200\t690000\n2003\tBOROUGH PARK\t5591\t0\t0\t585000\n2003\tGRAVESEND\t6924\t0\t0\t585000\n2005\tBEDFORD STUYVESANT\t1648\t2000\t4800\t487000\n2005\tBEDFORD STUYVESANT\t1593\t1890\t4500\t641497\n2005\tBEDFORD STUYVESANT\t1999\t1470\t3270\t450000\n2005\tBEDFORD STUYVESANT\t1621\t1775\t2984\t684100\n2014\tBUSHWICK\t3411\t2000\t4000\t700000\n2014\tBEDFORD STUYVESANT\t1726\t2083\t2916\t690000\n2014\tBUSHWICK\t3411\t1800\t3240\t700000\n2008\tMADISON\t6832\t2589\t2687\t700000\n2017\tFLATBUSH-CENTRAL\t5108\t2835\t1116\t700000\n2015\tFLATBUSH-LEFFERTS GARDEN\t1329\t2050\t2952\t700000\n2005\tBEDFORD STUYVESANT\t1793\t2000\t3600\t425000\n2005\tBEDFORD STUYVESANT\t1593\t1778\t4240\t765000\n2005\tBEDFORD STUYVESANT\t1758\t1750\t3100\t653000\n2005\tBEDFORD STUYVESANT\t1675\t1667\t3000\t700000\n2005\tBEDFORD STUYVESANT\t1767\t2000\t3600\t523998\n2005\tBEDFORD STUYVESANT\t1815\t1683\t1902\t475000\n2005\tBEDFORD STUYVESANT\t1593\t1890\t4500\t650661\n2014\tBOROUGH PARK\t5290\t1520\t1900\t700000\n2007\tBOROUGH PARK\t5675\t1669\t2196\t700000\n2012\tSUNSET PARK\t764\t2003\t3087\t695000\n2011\tCROWN HEIGHTS\t1211\t2200\t2456\t699000\n2014\tBAY RIDGE\t5961\t2500\t1186\t700000\n2013\tBUSHWICK\t3150\t2500\t4200\t690000\n2017\tMIDWOOD\t7628\t2000\t1378\t700000\n2017\tSHEEPSHEAD BAY\t7403\t2275\t1786\t700000\n2007\tBROWNSVILLE\t3525\t1800\t3896\t692410\n2008\tBENSONHURST\t5561\t1667\t1320\t695000\n2005\tBEDFORD STUYVESANT\t1836\t1900\t4127\t585000\n2005\tBEDFORD STUYVESANT\t1926\t1533\t2127\t426000\n2005\tBEDFORD STUYVESANT\t1639\t1900\t2280\t545000\n2005\tBEDFORD STUYVESANT\t1605\t1900\t3496\t665000\n2005\tBEDFORD STUYVESANT\t1813\t2000\t5524\t669195\n2005\tBEDFORD STUYVESANT\t1659\t1783\t2064\t629000\n2005\tBEDFORD STUYVESANT\t1662\t1800\t3240\t708000\n2016\tCROWN HEIGHTS\t1413\t3162\t4158\t700000\n2017\tMILL BASIN\t8647\t2600\t1152\t695000\n2014\tBUSHWICK\t3380\t2000\t4000\t700000\n2013\tFLATBUSH-EAST\t7649\t3000\t2155\t690000\n2007\tMIDWOOD\t6715\t4000\t2392\t691000\n2017\tMIDWOOD\t7592\t2000\t1504\t699900\n2011\tCROWN HEIGHTS\t1154\t2730\t2916\t690000\n2015\tCYPRESS HILLS\t4101\t2738\t2248\t695000\n2014\tBOROUGH PARK\t5675\t2404\t2720\t700000\n2017\tMIDWOOD\t7628\t2000\t1378\t700000\n2005\tCANARSIE\t8179\t2250\t3450\t615000\n2005\tCANARSIE\t8167\t1480\t1072\t436141\n2005\tCANARSIE\t8079\t2400\t2412\t550000\n2005\tCANARSIE\t8219\t3081\t2300\t425000\n2005\tBEDFORD STUYVESANT\t1654\t1800\t2352\t510000\n2005\tBEDFORD STUYVESANT\t1871\t1473\t3268\t595000\n2005\tBEDFORD STUYVESANT\t1927\t1188\t1890\t465000\n2005\tBEDFORD STUYVESANT\t1830\t2000\t3760\t595000\n2005\tBEDFORD STUYVESANT\t1846\t1933\t2784\t585000\n2005\tBEDFORD STUYVESANT\t1594\t1917\t3961\t423000\n2005\tBENSONHURST\t5576\t0\t0\t800000\n2005\tBENSONHURST\t6184\t0\t0\t1275000\n2005\tBENSONHURST\t6224\t6000\t2793\t1100000\n2005\tBENSONHURST\t6261\t0\t0\t620000\n2005\tBENSONHURST\t6856\t0\t0\t998000\n2005\tBENSONHURST\t5762\t0\t0\t450000\n2005\tBENSONHURST\t6159\t0\t0\t610000\n2013\tOCEAN HILL\t1501\t1833\t2310\t690000\n2007\tBOROUGH PARK\t5588\t2003\t2352\t700000\n2014\tFLATBUSH-CENTRAL\t5229\t1999\t2441\t695000\n2014\tFLATBUSH-CENTRAL\t5223\t1910\t4060\t700000\n2014\tOCEAN PARKWAY-NORTH\t6583\t2240\t3000\t695000\n2014\tBEDFORD STUYVESANT\t1773\t2000\t2400\t695932\n2005\tCANARSIE\t8082\t2550\t3080\t610000\n2005\tCANARSIE\t8238\t1680\t1620\t465000\n2005\tBEDFORD STUYVESANT\t1852\t2520\t3864\t860000\n2005\tBEDFORD STUYVESANT\t1620\t1800\t2709\t699000\n2005\tBEDFORD STUYVESANT\t1594\t2020\t4166\t423000\n2005\tBEDFORD STUYVESANT\t1645\t1900\t3420\t750000\n2005\tBEDFORD STUYVESANT\t1594\t1958\t2277\t423000\n2005\tBEDFORD 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STUYVESANT\t1678\t1600\t3084\t575000\n2005\tBENSONHURST\t6250\t2000\t1408\t570000\n2005\tBENSONHURST\t6246\t2450\t3310\t660000\n2005\tBENSONHURST\t6293\t1425\t1440\t555000\n2005\tBENSONHURST\t6184\t2000\t2800\t700000\n2005\tBENSONHURST\t5564\t2400\t2832\t637000\n2005\tBENSONHURST\t5762\t2000\t1840\t635000\n2005\tBENSONHURST\t6185\t2500\t2400\t840000\n2005\tBENSONHURST\t6225\t2050\t3267\t550000\n2017\tCANARSIE\t7958\t2342\t2256\t700000\n2007\tBERGEN BEACH\t8456\t2333\t2030\t691391\n2017\tMIDWOOD\t7674\t2000\t1280\t690500\n2016\tBOROUGH PARK\t5701\t1352\t1672\t700000\n2016\tBEDFORD STUYVESANT\t1596\t1983\t2676\t700000\n2015\tOCEAN PARKWAY-SOUTH\t6681\t1240\t1734\t700000\n2016\tSHEEPSHEAD BAY\t7441\t1600\t1526\t700000\n2008\tSHEEPSHEAD BAY\t7381\t2120\t2904\t700000\n2015\tBUSHWICK\t3376\t1750\t1839\t695000\n2005\tCANARSIE\t8250\t1927\t1585\t415000\n2005\tCANARSIE\t8198\t2295\t1620\t500000\n2005\tCANARSIE\t8148\t2000\t1449\t426000\n2005\tBEDFORD 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"text": "%python\nTRAIN, TEST = build_data.split(ratio = (0.6,0.4))\nX_train = TRAIN.drop('SALE_PRICE')\ny_train = TRAIN['SALE_PRICE']\nX_test = TEST.drop('SALE_PRICE')\ny_test = TEST['SALE_PRICE']",
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"data": "\nModel Name: NN_BROOKLYN_NB\n\nModel Owner: BROOKLYN\n\nAlgorithm Name: Neural Network\n\nMining Function: REGRESSION\n\nTarget: SALE_PRICE\n\nSettings: \n setting name setting value\n0 ALGO_NAME ALGO_NEURAL_NETWORK\n1 LBFGS_GRADIENT_TOLERANCE .000000001\n2 LBFGS_HISTORY_DEPTH 20\n3 LBFGS_SCALE_HESSIAN LBFGS_SCALE_HESSIAN_ENABLE\n4 NNET_ACTIVATIONS 'NNET_ACTIVATIONS_BIPOLAR_SIG'\n5 NNET_HELDASIDE_MAX_FAIL 6\n6 NNET_HELDASIDE_RATIO .25\n7 NNET_HIDDEN_LAYERS 1\n8 NNET_ITERATIONS 100\n9 NNET_NODES_PER_LAYER 50\n10 NNET_REGULARIZER NNET_REGULARIZER_HELDASIDE\n11 NNET_TOLERANCE .000001\n12 ODMS_DETAILS ODMS_ENABLE\n13 ODMS_MISSING_VALUE_TREATMENT ODMS_MISSING_VALUE_AUTO\n14 ODMS_RANDOM_SEED 0\n15 ODMS_SAMPLING ODMS_SAMPLING_DISABLE\n16 PREP_AUTO ON\n\nGlobal Statistics: \n attribute name attribute value\n0 CONVERGED NO\n1 ITERATIONS 100\n2 LOSS_VALUE 0.15801\n3 NUM_ROWS 49713\n\nAttributes: \nBLOCK\nGROSS_SQFT\nLAND_SQFT\nNEIGHBORHOOD\nYEAR\n\nPartition: NO\n\nTopology: \n\n HIDDEN_LAYER_ID NUM_NODE ACTIVATION_FUNCTION\n0 0 50 NNET_ACTIVATIONS_BIPOLAR_SIG\n\nWeights: \n\n LAYER IDX_FROM IDX_TO ... ATTRIBUTE_VALUE TARGET_VALUE WEIGHT\n0 0 0.0 0 ... None None -0.629313\n1 0 0.0 1 ... None None -0.237244\n2 0 0.0 2 ... None None -0.467075\n3 0 0.0 3 ... None None 0.506832\n4 0 0.0 4 ... None None 0.778683\n... ... ... ... ... ... ... ...\n4046 1 46.0 0 ... None None -0.521999\n4047 1 47.0 0 ... None None 1.048846\n4048 1 48.0 0 ... None None 0.064221\n4049 1 49.0 0 ... None None 0.464713\n4050 1 NaN 0 ... None None 1.721465\n\n[4051 rows x 8 columns]\n\n\n"
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"title": "Display model weights",
"text": "%python\nweights = nn_mod.weights.round(4)\nweights = weights.sort_values(by='WEIGHT', ascending=True)\nz.show(weights)",
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HILL\tNone\t0.1952\n0\t8\t36\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t-0.0789\n0\t8\t37\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.1112\n0\t8\t38\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.0481\n0\t8\t39\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.4429\n0\t8\t40\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t-0.2653\n0\t8\t41\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t-0.1727\n0\t8\t42\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.507\n0\t8\t43\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t-0.3166\n0\t8\t44\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.0123\n0\t8\t45\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t-0.0162\n0\t8\t46\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t-0.0508\n0\t8\t47\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.2436\n0\t8\t48\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.1321\n0\t8\t49\tNEIGHBORHOOD\tNone\tBOERUM HILL\tNone\t0.2035\n0\t9\t0\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1656\n0\t9\t1\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0747\n0\t9\t2\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.2905\n0\t9\t3\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0959\n0\t9\t4\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1248\n0\t9\t5\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1023\n0\t9\t6\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.1113\n0\t9\t7\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1943\n0\t9\t8\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.1159\n0\t9\t9\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.1343\n0\t9\t10\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0278\n0\t9\t11\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.4753\n0\t9\t12\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0401\n0\t9\t13\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.059\n0\t9\t14\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0907\n0\t9\t15\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0225\n0\t9\t16\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0958\n0\t9\t17\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.3774\n0\t9\t18\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0664\n0\t9\t19\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0171\n0\t9\t20\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.2099\n0\t9\t21\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1635\n0\t9\t22\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0969\n0\t9\t23\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0138\n0\t9\t24\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0239\n0\t9\t25\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0454\n0\t9\t26\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.3061\n0\t9\t27\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.3344\n0\t9\t28\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1207\n0\t9\t29\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0002\n0\t9\t30\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0806\n0\t9\t31\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1868\n0\t9\t32\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0316\n0\t9\t33\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0293\n0\t9\t34\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0441\n0\t9\t35\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0341\n0\t9\t36\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.1196\n0\t9\t37\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0042\n0\t9\t38\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0862\n0\t9\t39\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.0954\n0\t9\t40\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.2219\n0\t9\t41\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.017\n0\t9\t42\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.5782\n0\t9\t43\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1001\n0\t9\t44\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0752\n0\t9\t45\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.2135\n0\t9\t46\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.1728\n0\t9\t47\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.6098\n0\t9\t48\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t0.0477\n0\t9\t49\tNEIGHBORHOOD\tNone\tBOROUGH PARK\tNone\t-0.197\n0\t10\t0\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0444\n0\t10\t1\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0523\n0\t10\t2\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0459\n0\t10\t3\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0032\n0\t10\t4\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.2188\n0\t10\t5\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0585\n0\t10\t6\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.22\n0\t10\t7\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0782\n0\t10\t8\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0298\n0\t10\t9\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0508\n0\t10\t10\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0083\n0\t10\t11\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0459\n0\t10\t12\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0153\n0\t10\t13\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0219\n0\t10\t14\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1891\n0\t10\t15\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.047\n0\t10\t16\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0241\n0\t10\t17\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0024\n0\t10\t18\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.029\n0\t10\t19\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1477\n0\t10\t20\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.1363\n0\t10\t21\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0389\n0\t10\t22\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.1951\n0\t10\t23\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0523\n0\t10\t24\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1486\n0\t10\t25\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0169\n0\t10\t26\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0307\n0\t10\t27\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.1446\n0\t10\t28\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1952\n0\t10\t29\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0013\n0\t10\t30\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.047\n0\t10\t31\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.3426\n0\t10\t32\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0093\n0\t10\t33\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0501\n0\t10\t34\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.007\n0\t10\t35\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0949\n0\t10\t36\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.2283\n0\t10\t37\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1699\n0\t10\t38\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1177\n0\t10\t39\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0182\n0\t10\t40\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0653\n0\t10\t41\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0558\n0\t10\t42\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.4128\n0\t10\t43\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1236\n0\t10\t44\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0039\n0\t10\t45\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.0144\n0\t10\t46\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.1457\n0\t10\t47\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.1321\n0\t10\t48\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t-0.0271\n0\t10\t49\tNEIGHBORHOOD\tNone\tBRIGHTON BEACH\tNone\t0.17\n0\t11\t0\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-0.5748\n0\t11\t1\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.8534\n0\t11\t2\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.6014\n0\t11\t3\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t1.0434\n0\t11\t4\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.6563\n0\t11\t5\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-1.4347\n0\t11\t6\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-0.3725\n0\t11\t7\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-0.2326\n0\t11\t8\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-0.9412\n0\t11\t9\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-1.0816\n0\t11\t10\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.5496\n0\t11\t11\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-0.5721\n0\t11\t12\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.0857\n0\t11\t13\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.5791\n0\t11\t14\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t-0.1769\n0\t11\t15\tNEIGHBORHOOD\tNone\tBROOKLYN HEIGHTS\tNone\t0.2841\n0\t11\t16\tNEIGHBORHOOD\tNone\tBROOKLYN 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"text": "%md\nUse the OML4Py Explainability module to identify the important features that impact a trained model’s predictions. Machine Learning Explainability (MLX) is the process of explaining and interpreting machine learning models. The OML MLX Python module supports the ability to help better understand a model's behavior and why it makes its predictions. MLX currently provides model-agnostic explanations for classification and regression tasks where explanations treat the ML model as a black-box, instead of using properties from the model to guide the explanation. The global feature importance explainer object is the interface to the MLX permutation importance explainer. The global feature importance explainer identifies the most important features for a given model and data set. The explainer is model-agnostic and currently supports tabular classification and regression data sets with both numerical and categorical features.\n\nThe algorithm estimates feature importance by evaluating the model's sensitivity to changes in a specific feature. Higher sensitivity suggests that the model places higher importance on that feature when making its predictions than on another feature with lower sensitivity. For information on the oml.GlobalFeatureImportance class attributes and methods, call help(oml.mlx.GlobalFeatureImportance) or see Oracle Machine Learning for Python API Reference.",
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"data": "<p>Use the OML4Py Explainability module to identify the important features that impact a trained model’s predictions. Machine Learning Explainability (MLX) is the process of explaining and interpreting machine learning models. The OML MLX Python module supports the ability to help better understand a model's behavior and why it makes its predictions. MLX currently provides model-agnostic explanations for classification and regression tasks where explanations treat the ML model as a black-box, instead of using properties from the model to guide the explanation. The global feature importance explainer object is the interface to the MLX permutation importance explainer. The global feature importance explainer identifies the most important features for a given model and data set. The explainer is model-agnostic and currently supports tabular classification and regression data sets with both numerical and categorical features.</p>\n<p>The algorithm estimates feature importance by evaluating the model's sensitivity to changes in a specific feature. Higher sensitivity suggests that the model places higher importance on that feature when making its predictions than on another feature with lower sensitivity. For information on the oml.GlobalFeatureImportance class attributes and methods, call help(oml.mlx.GlobalFeatureImportance) or see Oracle Machine Learning for Python API Reference.</p>\n"
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"text": "%python\nfrom oml.mlx import GlobalFeatureImportance\nimport pandas as pd\nimport numpy as np\n\ngfi = GlobalFeatureImportance(mining_function='regression', \n score_metric='r2', random_state=0, \n parallel=4)\nexplanation = gfi.explain(nn_mod, build_data, 'SALE_PRICE')\nexplanation",
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"data": "Global Feature Importance: \n\t[0] NEIGHBORHOOD: Value: 0.8458, Error: 0.0190\n\t[1] YEAR: Value: 0.4042, Error: 0.0152\n\t[2] LAND_SQFT: Value: 0.1372, Error: 0.0083\n\t[3] GROSS_SQFT: Value: 0.1172, Error: 0.0097\n\t[4] BLOCK: Value: 0.1137, Error: 0.0083\n\n"
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"text": "%sql\n-- Drop Duplicate Feature Importance Table if Necesary\nDROP TABLE FEATURE_IMPORTANCE_TABLE;",
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"text": "%python\n# Create a Dataframe\ndata = explanation.to_dict()\ndata = data['explanation']\nmy_list = []\nfor i in data:\n del i['feature_importance_per_iter']\n my_list.append(i)\ndf = pd.DataFrame(my_list)\n\n# Create Feature Importance Table\nfeature_importance_table = oml.create(df, table = 'FEATURE_IMPORTANCE_TABLE')\nz.show(feature_importance_table)",
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"data": "name\tvalue\terror\nNEIGHBORHOOD\t0.8458093122688197\t0.019018133634142273\nYEAR\t0.4042242754940218\t0.015184773555021066\nLAND_SQFT\t0.13715830693229863\t0.008258893257281987\nGROSS_SQFT\t0.11715647153867334\t0.00967317296447729\nBLOCK\t0.1136614418126527\t0.008338972515966655\n"
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"text": "%python\nexplanation.to_pyplot_figure()\nexplanation.show_in_notebook()",
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"dateFinished": "2022-07-12T15:46:50+0000",
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{
"title": "Score data",
"text": "%python\nmod_predict = nn_mod.predict(build_data ,supplemental_cols = build_data[:, ['SALE_PRICE']]).pull()\ny_true = mod_predict['SALE_PRICE']\ny_pred = mod_predict['PREDICTION']\nz.show(mod_predict.round(2))",
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"dateUpdated": "2022-07-12T17:10:54+0000",
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},
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"visible": true,
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"filters": [
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"type": "TABLE",
"data": 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"title": "Compute R2 using sklearn",
"text": "%python\nimport sklearn as skl\nmetric_score = skl.metrics.r2_score(y_true, y_pred)\nprint(metric_score.round(4))",
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"title": "Model quality statistics on the predicted values dataframe",
"text": "%python\n# The coefficient of determination Rˆ2 is part of the \"score\" method for OML models\nCoefficientOfDeterminationR2 = nn_mod.score(X_test, y_test)\n \n# Compute other statistics by hand - no need to pull data locally to Python\n# PRED_DF[['PREDICTION', 'SALE_PRICE']\nMeanAbsoluteError = (abs(mod_predict['SALE_PRICE'] - mod_predict['PREDICTION']).sum())/mod_predict['SALE_PRICE'].count()\nMedian = mod_predict['SALE_PRICE'].median()\nMeanSquaredError = ((mod_predict['SALE_PRICE'] - mod_predict['PREDICTION'])**2).sum()/mod_predict['SALE_PRICE'].count()\nRootMeanSquaredError = np.sqrt(MeanSquaredError)\naverage_sale_price = mod_predict[\"SALE_PRICE\"].mean()\n\nprint('Average Actual Sale Price: ', \"{:,}\".format(average_sale_price.round(0)))\nprint('Median Sale Price', \"{:,}\".format(Median.round(0)))\nprint('R^2: ', \"{:,}\".format(CoefficientOfDeterminationR2.round(4)))\nprint('Mean Absolute Error: ', \"{:,}\".format(MeanAbsoluteError.round(0)))\nprint('Mean Squared Error: ', \"{:,}\".format(MeanSquaredError.round(0)))\nprint('Root Mean Squared Error: ', \"{:,}\".format(RootMeanSquaredError.round(0)))",
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"data": "Average Actual Sale Price: 802,787.0\nMedian Sale Price 650,000.0\nR^2: 0.6424\nMean Absolute Error: 167,569.0\nMean Squared Error: 92,960,614,181.0\nRoot Mean Squared Error: 304,894.0\n"
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"dateStarted": "2022-07-12T15:46:55+0000",
"dateFinished": "2022-07-12T15:46:56+0000",
"status": "FINISHED",
"$$hashKey": "object:54"
},
{
"title": "Using matplotlib, plot the predicted and actual sale price, log scale",
"text": "%python\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn')\nplt.figure(figsize=[9,7])\nplt.margins(0.05)\n\nPRED_DF = mod_predict\nx = PRED_DF[['SALE_PRICE']]\ny = PRED_DF[['PREDICTION']]\n\nn = len(x)\n\nplt.plot(x, y, '.', c='black', alpha=0.8, ms=7)\nplt.plot( [np.min(y),np.max(y)],[np.min(y),np.max(y)],\n c='red', alpha=0.8, ms=3,\n label='Perfect prediction reference line')\nplt.yscale('log')\nplt.xscale('log')\nplt.legend(frameon=True, facecolor='lightgray') \nplt.xlabel('SALE_PRICE')\nplt.ylabel('PREDICTION')\nplt.xlim((250000,15000000))\nplt.ylim((150000,15000000))\nplt.title('The prediction vs. actual value for sale price',\n fontsize=16)\nplt.show()",
"user": "BROOKLYN",
"dateUpdated": "2022-07-12T17:10:54+0000",
"progress": 0,
"config": {
"editorSetting": {
"language": "sql",
"editOnDblClick": false
},
"colWidth": 12,
"editorMode": "ace/mode/undefined",
"fontSize": 9,
"title": true,
"results": {},
"enabled": true
},
"settings": {
"params": {},
"forms": {}
},
"results": {
"code": "SUCCESS",
"msg": [
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"title": "Using matplotlib, plot the residuals v. actual, log scale",
"text": "%python\nplt.style.use('seaborn')\nplt.figure(figsize=[9,7])\nplt.margins(0.05)\nPRED_DF['RESIDUAL'] = PRED_DF['SALE_PRICE'] - PRED_DF['PREDICTION']\nx = PRED_DF[['SALE_PRICE']]\ny = PRED_DF[['RESIDUAL']]\nplt.plot(x, y, '.', c='black', alpha=0.8, ms=7)\nplt.plot( [np.min(x),np.max(x)],[0,0],\n c='red', alpha=0.8, ms=3,\n label='Perfect prediction (Zero residuals)')\nplt.legend(frameon=True, facecolor='lightgray',loc='upper left') \nplt.xlabel('SALE_PRICE')\nplt.ylabel('RESIDUAL')\nplt.xlim((350000,15000000))\nplt.title('Sale Price vs. Residuals', fontsize=16)\nplt.grid(True)\nplt.xscale('log')\nplt.show()",
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}
]
},
"apps": [],
"interrupted": false,
"runtimeInfos": {},
"progressUpdateIntervalMs": 500,
"jobName": "paragraph_1656738605197_-1515513065",
"id": "20220702-051005_795466504",
"dateCreated": "2022-07-02T04:19:38+0000",
"dateStarted": "2022-07-12T15:47:00+0000",
"dateFinished": "2022-07-12T15:47:00+0000",
"status": "FINISHED",
"$$hashKey": "object:56"
},
{
"text": "%python\nNbins = 101\ndf = PRED_DF\ndf = df[['SALE_PRICE', 'PREDICTION']].copy()\ndf['log_SALE_PRICE'] = np.log(df.SALE_PRICE)\ndf['COUNT'] = 0\ndf['bin'] = df.log_SALE_PRICE - df.log_SALE_PRICE.min()\ndf.bin /= df.bin.max()\ndf.bin *= Nbins\ndf.bin = df.bin.astype(int)\nagger = {'COUNT':'count', 'SALE_PRICE':['mean', 'std'], 'PREDICTION':['mean', 'std']}\ndf = df.groupby('bin').agg(agger).reset_index()\ndf['N_records'] = df['COUNT']['count']\ndf['SALE_PRICE_mean'] = df['SALE_PRICE']['mean']\ndf['SALE_PRICE_sigma'] = df['SALE_PRICE']['std']/np.sqrt(df['N_records'])\ndf['SALE_PRICE_pred_mean'] = df['PREDICTION']['mean']\ndf['SALE_PRICE_pred_sigma'] = df['PREDICTION']['std']/np.sqrt(df['N_records'])\ndf_test_pred_mean = df\nz.show(df_test_pred_mean)",
"user": "BROOKLYN",
"dateUpdated": "2022-07-12T17:10:54+0000",
"progress": 0,
"config": {
"editorSetting": {
"language": "sql",
"editOnDblClick": false
},
"colWidth": 12,
"editorMode": "ace/mode/undefined",
"fontSize": 9,
"results": {
"0": {
"graph": {
"mode": "table",
"height": 300,
"optionOpen": false,
"setting": {
"table": {
"tableGridState": {},
"tableColumnTypeState": {
"names": {
"('bin', '')": "number",
"('COUNT', 'count')": "number",
"('SALE_PRICE', 'mean')": "number",
"('SALE_PRICE', 'std')": "number",
"('PREDICTION', 'mean')": "number",
"('PREDICTION', 'std')": "number",
"('N_records', '')": "number",
"('SALE_PRICE_mean', '')": "number",
"('SALE_PRICE_sigma', '')": "number",
"('SALE_PRICE_pred_mean', '')": "number",
"('SALE_PRICE_pred_sigma', '')": "number"
}
},
"tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]",
"tableOptionValue": {
"useFilter": false,
"showPagination": false,
"showAggregationFooter": false
},
"updated": false,
"initialized": false
}
},
"commonSetting": {}
}
}
},
"enabled": true
},
"settings": {
"params": {},
"forms": {}
},
"results": {
"code": "SUCCESS",
"msg": [
{
"type": "TABLE",
"data": "('bin', '')\t('COUNT', 'count')\t('SALE_PRICE', 'mean')\t('SALE_PRICE', 'std')\t('PREDICTION', 'mean')\t('PREDICTION', 'std')\t('N_records', '')\t('SALE_PRICE_mean', '')\t('SALE_PRICE_sigma', '')\t('SALE_PRICE_pred_mean', '')\t('SALE_PRICE_pred_sigma', 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