restore sample to original

Author: jyaistMap

samples/04_gis_analysts_data_scientists/house_price_prediction_using_automl.ipynb

@@ -5,15 +5,15 @@
    "id": "93be01a8",
    "metadata": {},
    "source": [
-    "# Property valuation in King County (USA) using AutoML and Geo-Enriched data"
+    "### Property valuation in King County (USA) using AutoML and Geo-Enriched data"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "b48ce718",
    "metadata": {},
    "source": [
-    "## Table of Contents"
+    "### Table of Content"
    ]
   },
   {
@@ -38,7 +38,7 @@
    "id": "13f88e88",
    "metadata": {},
    "source": [
-    "## Introduction <a id='Introduction'></a>"
+    "### Introduction <a id='Introduction'></a>"
    ]
   },
   {
@@ -58,7 +58,7 @@
    "id": "78eb87ee",
    "metadata": {},
    "source": [
-    "## Necessary imports <a id='Necessary-Imports'></a>"
+    "### Necessary imports <a id='Necessary-Imports'></a>"
    ]
   },
   {
@@ -102,7 +102,7 @@
    "id": "b1d1b71c",
    "metadata": {},
    "source": [
-    "## Accessing the King County dataset for housing prediction <a id='Accessing-the-King-County-dataset-for-housing-prediction'></a>\n"
+    "### Accessing the King County dataset for housing prediction <a id='Accessing-the-King-County-dataset-for-housing-prediction'></a>\n"
    ]
   },
   {
@@ -170,7 +170,7 @@
    "id": "e7432736",
    "metadata": {},
    "source": [
-    "## Column definitions <a id='DataFrame-&-Definition'></a>"
+    "### Column definitions <a id='DataFrame-&-Definition'></a>"
    ]
   },
   {
@@ -536,7 +536,7 @@
    "id": "14d3aa07",
    "metadata": {},
    "source": [
-    "## Exploring and visualizing dataset  <a id='Visualizing-dataset'></a>"
+    "### Exploring and visualizing dataset  <a id='Visualizing-dataset'></a>"
    ]
   },
   {
@@ -1162,7 +1162,7 @@
    "id": "a9cd8f15",
    "metadata": {},
    "source": [
-    "## Model building using AutoML <a id='Model-building'></a>"
+    "### Model building using AutoML <a id='Model-building'></a>"
    ]
   },
   {
@@ -1371,7 +1371,7 @@
    "id": "ba2c9aae",
    "metadata": {},
    "source": [
-    "## Data geo-enrichment <a id='geoenrich'></a>"
+    "### Data geo-enrichment <a id='geoenrich'></a>"
    ]
   },
   {
@@ -3075,7 +3075,7 @@
    "id": "4b41e361",
    "metadata": {},
    "source": [
-    "## Model building using AutoML on enriched data <a id='Model-Building-using-AutoML-on-Enrich-data'></a>"
+    "### Model building using AutoML on enriched data <a id='Model-Building-using-AutoML-on-Enrich-data'></a>"
    ]
   },
   {
@@ -3442,7 +3442,7 @@
    "id": "fcebe1eb",
    "metadata": {},
    "source": [
-    "## Result visualization <a id='Result-Visualization'></a>"
+    "### Result visualization <a id='Result-Visualization'></a>"
    ]
   },
   {
@@ -7707,7 +7707,7 @@
    "id": "6bb74700",
    "metadata": {},
    "source": [
-    "## Conclusion <a id='Conclusion'></a>"
+    "### Conclusion <a id='Conclusion'></a>"
    ]
   },
   {
@@ -7723,7 +7723,7 @@
    "id": "c59f7c1b",
    "metadata": {},
    "source": [
-    "## References"
+    "### References"
    ]
   },
   {
@@ -7738,9 +7738,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dl-env] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dl-env-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -7752,7 +7752,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.11"
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,