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corrects reference link
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guide/02-api-overview/release_notes_210.ipynb

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"#### [`arcgis.raster`](/python/api-reference/arcgis.raster.toc.html#)\n",
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"* [`ImageryLayer`](/python/api-reference/arcgis.raster.toc.html#imagerylayer)\n",
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" * Adds methods:\n",
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" * [`spectral_profile()`](/api-reference/arcgis.raster.toc.html#arcgis.raster.ImageryLayer.spectral_profile)\n",
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" * [`spectral_profile()`](/python/api-reference/arcgis.raster.toc.html#arcgis.raster.ImageryLayer.spectral_profile)\n",
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" * Adds capability to work with tiles only services to numerous methods:\n",
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" * `calculate_volume`\n",
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" * `compute_class_stats`\n",

samples/04_gis_analysts_data_scientists/house_price_prediction_using_automl.ipynb

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"id": "93be01a8",
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"metadata": {},
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"source": [
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"### Property valuation in King County (USA) using AutoML and Geo-Enriched data"
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"# Property valuation in King County (USA) using AutoML and Geo-Enriched data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b48ce718",
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"metadata": {},
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"source": [
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"### Table of Content"
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"## Table of Contents"
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]
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{
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"id": "13f88e88",
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"metadata": {},
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"source": [
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"### Introduction <a id='Introduction'></a>"
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"## Introduction <a id='Introduction'></a>"
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},
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"id": "78eb87ee",
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"metadata": {},
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"source": [
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"### Necessary imports <a id='Necessary-Imports'></a>"
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"## Necessary imports <a id='Necessary-Imports'></a>"
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]
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},
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{
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"id": "b1d1b71c",
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"metadata": {},
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"source": [
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"### Accessing the King County dataset for housing prediction <a id='Accessing-the-King-County-dataset-for-housing-prediction'></a>\n"
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"## Accessing the King County dataset for housing prediction <a id='Accessing-the-King-County-dataset-for-housing-prediction'></a>\n"
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]
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},
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{
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"id": "e7432736",
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"metadata": {},
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"source": [
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"### Column definitions <a id='DataFrame-&-Definition'></a>"
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"## Column definitions <a id='DataFrame-&-Definition'></a>"
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]
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},
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{
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"id": "14d3aa07",
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"metadata": {},
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"source": [
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"### Exploring and visualizing dataset <a id='Visualizing-dataset'></a>"
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"## Exploring and visualizing dataset <a id='Visualizing-dataset'></a>"
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]
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},
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"id": "a9cd8f15",
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"metadata": {},
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"source": [
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"### Model building using AutoML <a id='Model-building'></a>"
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"## Model building using AutoML <a id='Model-building'></a>"
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]
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},
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{
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"id": "ba2c9aae",
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"metadata": {},
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"source": [
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"### Data geo-enrichment <a id='geoenrich'></a>"
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"## Data geo-enrichment <a id='geoenrich'></a>"
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]
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},
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{
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"id": "4b41e361",
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"metadata": {},
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"source": [
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"### Model building using AutoML on enriched data <a id='Model-Building-using-AutoML-on-Enrich-data'></a>"
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"## Model building using AutoML on enriched data <a id='Model-Building-using-AutoML-on-Enrich-data'></a>"
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]
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},
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"id": "fcebe1eb",
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"metadata": {},
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"source": [
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"### Result visualization <a id='Result-Visualization'></a>"
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"## Result visualization <a id='Result-Visualization'></a>"
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]
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},
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{
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"id": "6bb74700",
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"metadata": {},
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"source": [
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"### Conclusion <a id='Conclusion'></a>"
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"## Conclusion <a id='Conclusion'></a>"
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"id": "c59f7c1b",
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"metadata": {},
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"source": [
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"### References"
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"## References"
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]
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda env:dl-env] *",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "conda-env-dl-env-py"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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"version": "3.7.11"
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}
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},
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"nbformat": 4,

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