diff --git a/NotebookExamples/fsharp/Samples/HousingML.ipynb b/NotebookExamples/fsharp/Samples/HousingML.ipynb
new file mode 100644
index 000000000..887bf9f96
--- /dev/null
+++ b/NotebookExamples/fsharp/Samples/HousingML.ipynb
@@ -0,0 +1,499 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#r \"nuget:bl=true\"\n",
+    "#r \"nuget:RestoreSources=https://dotnet.myget.org/F/dotnet-corefxlab/api/v3/index.json\"\n",
+    "#r \"nuget:Microsoft.ML,version=1.4.0-preview\"\n",
+    "#r \"nuget:Microsoft.ML.AutoML\"\n",
+    "#r \"nuget:Microsoft.Data.DataFrame,version=0.1.1-e191008-1\"\n",
+    "    \n",
+    "open Microsoft.Data\n",
+    "open XPlot.Plotly\n",
+    "open Microsoft.AspNetCore.Html\n",
+    "open System.IO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<null>"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "let register (df:DataFrame) (writer:TextWriter) =\n",
+    "    let headers = new ResizeArray<IHtmlContent> ()\n",
+    "    headers.Add(th.innerHTML(i.innerHTML(\"index\")))\n",
+    "    headers.AddRange(df.Columns.Select(fun c -> (th.innerHTML(c) :> IHtmlContent)))\n",
+    "    let rows = ResizeArray<ResizeArray<IHtmlContent>>()\n",
+    "    let take = 20\n",
+    "    for i in 0 .. (Math.Min(take, int(df.RowCount)) - 1) do\n",
+    "        let cells = ResizeArray<IHtmlContent>()\n",
+    "        cells.Add(td.innerHTML(i));\n",
+    "        for o in df.[int64(i)] do\n",
+    "            cells.Add(td.innerHTML(o))\n",
+    "        rows.Add(cells)\n",
+    "    \n",
+    "    let t =\n",
+    "        table.innerHTML([|\n",
+    "            thead.innerHTML(headers)\n",
+    "            tbody.innerHTML(rows.Select(fun r -> tr.innerHTML(r)))\n",
+    "        |])\n",
+    "\n",
+    "    writer.Write(t)\n",
+    "\n",
+    "Formatter<DataFrame>.Register( (fun df writer -> register df writer), mimeType = \"text/html\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<null>"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "open System.Net.Http\n",
+    "let housingPath = \"housing.csv\"\n",
+    "if not(File.Exists(housingPath)) then\n",
+    "    let contents = HttpClient().GetStringAsync(\"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\").Result\n",
+    "    File.WriteAllText(\"housing.csv\", contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table><thead><th><i>index</i></th><th>longitude</th><th>latitude</th><th>housing_median_age</th><th>total_rooms</th><th>total_bedrooms</th><th>population</th><th>households</th><th>median_income</th><th>median_house_value</th><th>ocean_proximity</th></thead><tbody><tr><td>0</td><td>-122.23</td><td>37.88</td><td>41</td><td>880</td><td>129</td><td>322</td><td>126</td><td>8.3252</td><td>452600</td><td>NEAR BAY</td></tr><tr><td>1</td><td>-122.22</td><td>37.86</td><td>21</td><td>7099</td><td>1106</td><td>2401</td><td>1138</td><td>8.3014</td><td>358500</td><td>NEAR BAY</td></tr><tr><td>2</td><td>-122.24</td><td>37.85</td><td>52</td><td>1467</td><td>190</td><td>496</td><td>177</td><td>7.2574</td><td>352100</td><td>NEAR BAY</td></tr><tr><td>3</td><td>-122.25</td><td>37.85</td><td>52</td><td>1274</td><td>235</td><td>558</td><td>219</td><td>5.6431</td><td>341300</td><td>NEAR BAY</td></tr><tr><td>4</td><td>-122.25</td><td>37.85</td><td>52</td><td>1627</td><td>280</td><td>565</td><td>259</td><td>3.8462</td><td>342200</td><td>NEAR BAY</td></tr><tr><td>5</td><td>-122.25</td><td>37.85</td><td>52</td><td>919</td><td>213</td><td>413</td><td>193</td><td>4.0368</td><td>269700</td><td>NEAR BAY</td></tr><tr><td>6</td><td>-122.25</td><td>37.84</td><td>52</td><td>2535</td><td>489</td><td>1094</td><td>514</td><td>3.6591</td><td>299200</td><td>NEAR BAY</td></tr><tr><td>7</td><td>-122.25</td><td>37.84</td><td>52</td><td>3104</td><td>687</td><td>1157</td><td>647</td><td>3.12</td><td>241400</td><td>NEAR BAY</td></tr><tr><td>8</td><td>-122.26</td><td>37.84</td><td>42</td><td>2555</td><td>665</td><td>1206</td><td>595</td><td>2.0804</td><td>226700</td><td>NEAR BAY</td></tr><tr><td>9</td><td>-122.25</td><td>37.84</td><td>52</td><td>3549</td><td>707</td><td>1551</td><td>714</td><td>3.6912</td><td>261100</td><td>NEAR BAY</td></tr><tr><td>10</td><td>-122.26</td><td>37.85</td><td>52</td><td>2202</td><td>434</td><td>910</td><td>402</td><td>3.2031</td><td>281500</td><td>NEAR BAY</td></tr><tr><td>11</td><td>-122.26</td><td>37.85</td><td>52</td><td>3503</td><td>752</td><td>1504</td><td>734</td><td>3.2705</td><td>241800</td><td>NEAR BAY</td></tr><tr><td>12</td><td>-122.26</td><td>37.85</td><td>52</td><td>2491</td><td>474</td><td>1098</td><td>468</td><td>3.075</td><td>213500</td><td>NEAR BAY</td></tr><tr><td>13</td><td>-122.26</td><td>37.84</td><td>52</td><td>696</td><td>191</td><td>345</td><td>174</td><td>2.6736</td><td>191300</td><td>NEAR BAY</td></tr><tr><td>14</td><td>-122.26</td><td>37.85</td><td>52</td><td>2643</td><td>626</td><td>1212</td><td>620</td><td>1.9167</td><td>159200</td><td>NEAR BAY</td></tr><tr><td>15</td><td>-122.26</td><td>37.85</td><td>50</td><td>1120</td><td>283</td><td>697</td><td>264</td><td>2.125</td><td>140000</td><td>NEAR BAY</td></tr><tr><td>16</td><td>-122.27</td><td>37.85</td><td>52</td><td>1966</td><td>347</td><td>793</td><td>331</td><td>2.775</td><td>152500</td><td>NEAR BAY</td></tr><tr><td>17</td><td>-122.27</td><td>37.85</td><td>52</td><td>1228</td><td>293</td><td>648</td><td>303</td><td>2.1202</td><td>155500</td><td>NEAR BAY</td></tr><tr><td>18</td><td>-122.26</td><td>37.84</td><td>50</td><td>2239</td><td>455</td><td>990</td><td>419</td><td>1.9911</td><td>158700</td><td>NEAR BAY</td></tr><tr><td>19</td><td>-122.27</td><td>37.84</td><td>52</td><td>1503</td><td>298</td><td>690</td><td>275</td><td>2.6033</td><td>162900</td><td>NEAR BAY</td></tr></tbody></table>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "let housingData = DataFrame.ReadCsv(housingPath)\n",
+    "housingData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table><thead><th><i>index</i></th><th>Description</th><th>longitude</th><th>latitude</th><th>housing_median_age</th><th>total_rooms</th><th>total_bedrooms</th><th>population</th><th>households</th><th>median_income</th><th>median_house_value</th></thead><tbody><tr><td>0</td><td>Length</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td></tr><tr><td>1</td><td>Max</td><td>-114.31</td><td>41.95</td><td>52</td><td>39320</td><td>6445</td><td>35682</td><td>6082</td><td>15.0001</td><td>500001</td></tr><tr><td>2</td><td>Min</td><td>-124.35</td><td>32.54</td><td>1</td><td>2</td><td>0</td><td>3</td><td>1</td><td>0.4999</td><td>14999</td></tr><tr><td>3</td><td>Mean</td><td>-119.569115</td><td>35.631866</td><td>28.639486</td><td>2635.7588</td><td>532.4762</td><td>1425.4779</td><td>499.53967</td><td>3.8706622</td><td>206854.97</td></tr></tbody></table>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housingData.Description()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div id=\"79d5cb2c-c0f1-47db-af97-b5f28e09bf65\" style=\"width: 900px; height: 500px;\"></div><script type=\"text/javascript\">\r\n",
+       "\r\n",
+       "var renderPlotly = function() {\r\n",
+       "    var xplotRequire = requirejs.config({context:'xplot-2.0.0',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}});\r\n",
+       "    xplotRequire(['plotly'], function(Plotly) {\r\n",
+       "\n",
+       "            var data = 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+       "            var layout = \"\";\n",
+       "            Plotly.newPlot('79d5cb2c-c0f1-47db-af97-b5f28e09bf65', data, layout);\n",
+       "        });\r\n",
+       "};\r\n",
+       "if ((typeof(requirejs) !==  typeof(Function)) || (typeof(requirejs.config) !== typeof(Function))) { \r\n",
+       "    var script = document.createElement(\"script\"); \r\n",
+       "    script.setAttribute(\"src\", \"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"); \r\n",
+       "    script.onload = function(){\r\n",
+       "        renderPlotly();\r\n",
+       "    };\r\n",
+       "    document.getElementsByTagName(\"head\")[0].appendChild(script); \r\n",
+       "}\r\n",
+       "else {\r\n",
+       "    renderPlotly();\r\n",
+       "}\r\n",
+       "</script>"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "let graph =\n",
+    "    Histogram(x = housingData.[\"median_house_value\"],\n",
+    "              nbinsx = 20)\n",
+    "graph |> Chart.Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div id=\"2e9c5661-99ea-4108-849a-0975bbe3808d\" style=\"width: 600px; height: 600px;\"></div><script type=\"text/javascript\">\r\n",
+       "\r\n",
+       "var renderPlotly = function() {\r\n",
+       "    var xplotRequire = requirejs.config({context:'xplot-2.0.0',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}});\r\n",
+       "    xplotRequire(['plotly'], function(Plotly) {\r\n",
+       "\n",
+       "            var data = 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+       "            var layout = \"\";\n",
+       "            Plotly.newPlot('2e9c5661-99ea-4108-849a-0975bbe3808d', data, layout);\n",
+       "        });\r\n",
+       "};\r\n",
+       "if ((typeof(requirejs) !==  typeof(Function)) || (typeof(requirejs.config) !== typeof(Function))) { \r\n",
+       "    var script = document.createElement(\"script\"); \r\n",
+       "    script.setAttribute(\"src\", \"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"); \r\n",
+       "    script.onload = function(){\r\n",
+       "        renderPlotly();\r\n",
+       "    };\r\n",
+       "    document.getElementsByTagName(\"head\")[0].appendChild(script); \r\n",
+       "}\r\n",
+       "else {\r\n",
+       "    renderPlotly();\r\n",
+       "}\r\n",
+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Microsoft.DotNet.Interactive.DisplayedValue"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "let graph =\n",
+    "    Graph.Scattergl(\n",
+    "        x = housingData.[\"longitude\"],\n",
+    "        y = housingData.[\"latitude\"],\n",
+    "        mode = \"markers\",\n",
+    "        marker =\n",
+    "            Graph.Marker(\n",
+    "                color = housingData.[\"median_house_value\"],\n",
+    "                colorscale = \"Jet\"))\n",
+    "\n",
+    "let plot = Chart.Plot(graph)\n",
+    "plot.Width <- 600\n",
+    "plot.Height <- 600\n",
+    "display(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "18575"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "2065"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Microsoft.DotNet.Interactive.DisplayedValue"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "let Shuffle (arr:int[]) =\n",
+    "    let rnd = Random()\n",
+    "    for i in 0 .. arr.Length - 1 do\n",
+    "        let r = i + rnd.Next(arr.Length - i)\n",
+    "        let temp = arr.[r]\n",
+    "        arr.[r] <- arr.[i]\n",
+    "        arr.[i] <- temp\n",
+    "    arr\n",
+    "\n",
+    "let randomIndices = (Shuffle(Enumerable.Range(0, (int (housingData.RowCount) - 1)).ToArray()))\n",
+    "\n",
+    "let testSize = int (float (housingData.RowCount) * 0.1)\n",
+    "let trainRows = randomIndices.[testSize..]\n",
+    "let testRows = randomIndices.[..testSize]\n",
+    "\n",
+    "let housing_train = housingData.[trainRows]\n",
+    "let housing_test = housingData.[testRows]\n",
+    "\n",
+    "display(housing_train.RowCount)\n",
+    "display(housing_test.RowCount)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Wall time: 15676.887700000001ms"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "open Microsoft.ML\n",
+    "open Microsoft.ML.Data\n",
+    "open Microsoft.ML.AutoML\n",
+    "\n",
+    "let mlContext = MLContext()\n",
+    "\n",
+    "let experiment = mlContext.Auto().CreateRegressionExperiment(maxExperimentTimeInSeconds = 15u)\n",
+    "let result = experiment.Execute(housing_train, labelColumnName = \"median_house_value\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div id=\"f7dda5a0-c165-447a-b2f0-21defcf41c0b\" style=\"width: 900px; height: 500px;\"></div><script type=\"text/javascript\">\r\n",
+       "\r\n",
+       "var renderPlotly = function() {\r\n",
+       "    var xplotRequire = requirejs.config({context:'xplot-2.0.0',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}});\r\n",
+       "    xplotRequire(['plotly'], function(Plotly) {\r\n",
+       "\n",
+       "            var data = [{\"type\":\"scattergl\",\"x\":[0.3460159,0.16411199999999998,0.21396759999999998,0.14423039999999998,0.17663109999999999,0.2261793,0.2595554,0.2044803,0.2162014,0.1322266,0.28877959999999997,0.2812581,0.8576764,0.2784116,0.5305709,0.5789424,0.21099269999999998,0.1372778,0.2837134],\"y\":[49222.846646815124,52722.59006728217,52684.08577162092,54201.980411096905,54106.2317497613,51078.898344445886,53701.43261263594,52291.430001051915,49709.9454945624,51361.2068228897,49686.444098750646,48864.40757703262,50036.722828197824,49758.74175055023,49847.874352667015,50554.81693423097,49400.39119141636,55645.902812054956,53407.894873122736],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"SdcaRegression\"},{\"type\":\"scattergl\",\"x\":[0.3727333,0.20009849999999998,0.3195861,0.3531995,0.5319689,0.25135779999999996,0.28252879999999997,0.3169886,0.2703869,0.23450079999999998,0.3632207,0.242449,0.49163789999999996,0.6740938,0.22270979999999999,0.32270869999999996,0.33367399999999997,0.255919,0.3176732],\"y\":[49074.05140885956,50402.960273983685,50207.044209808715,49061.92837968507,49299.19247536089,49880.10935729949,50161.84488536137,49751.82118816958,50264.535241212456,49070.57151967488,49069.4849788403,50126.67209145197,49092.249814903225,49128.46250981114,49973.79325296964,49069.128900686985,49069.64901301949,49069.68029285749,49068.30427684814],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"LbfgsPoissonRegression\"},{\"type\":\"scattergl\",\"x\":[0.1250113,0.129775,0.1371955,0.1781514,0.1363956,0.1307643,0.121152,0.1363353],\"y\":[61129.125647332985,144145.9966156622,153699.53317885002,174171.1215610435,161920.29666259387,163470.19648781396,147002.24182742104,162750.4272600013],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"OnlineGradientDescentRegression\"}];\n",
+       "            var layout = {\"xaxis\":{\"title\":\"Training Time\",\"_isSubplotObj\":true},\"yaxis\":{\"title\":\"Error\",\"_isSubplotObj\":true}};\n",
+       "            Plotly.newPlot('f7dda5a0-c165-447a-b2f0-21defcf41c0b', data, layout);\n",
+       "        });\r\n",
+       "};\r\n",
+       "if ((typeof(requirejs) !==  typeof(Function)) || (typeof(requirejs.config) !== typeof(Function))) { \r\n",
+       "    var script = document.createElement(\"script\"); \r\n",
+       "    script.setAttribute(\"src\", \"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"); \r\n",
+       "    script.onload = function(){\r\n",
+       "        renderPlotly();\r\n",
+       "    };\r\n",
+       "    document.getElementsByTagName(\"head\")[0].appendChild(script); \r\n",
+       "}\r\n",
+       "else {\r\n",
+       "    renderPlotly();\r\n",
+       "}\r\n",
+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Trainer:SdcaRegression\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<null>"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// I don't know why it's only 3 trainers !!!!!!\n",
+    "type RunDetails = System.Collections.Generic.IEnumerable<RunDetail<RegressionMetrics>>\n",
+    "let scatters =\n",
+    "    result.RunDetails\n",
+    "        .Where(fun d -> not (d.ValidationMetrics = null))\n",
+    "        .GroupBy(\n",
+    "            (fun r -> r.TrainerName),\n",
+    "            (fun (name:string) (details:RunDetails) -> \n",
+    "                Graph.Scattergl(\n",
+    "                    name = name,\n",
+    "                    x = details.Select(fun r -> r.RuntimeInSeconds),\n",
+    "                    y = details.Select(fun r -> r.ValidationMetrics.MeanAbsoluteError),\n",
+    "                    mode = \"markers\",\n",
+    "                    marker = Graph.Marker(size = 12))))\n",
+    "\n",
+    "let chart = Chart.Plot(scatters)\n",
+    "chart.WithXTitle(\"Training Time\")\n",
+    "chart.WithYTitle(\"Error\")\n",
+    "display(chart)\n",
+    "\n",
+    "Console.WriteLine(\"Best Trainer:{0}\", result.BestRun.TrainerName);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div id=\"7633579d-abc5-4e16-bc62-ad5df51641a5\" style=\"width: 900px; height: 500px;\"></div><script type=\"text/javascript\">\r\n",
+       "\r\n",
+       "var renderPlotly = function() {\r\n",
+       "    var xplotRequire = requirejs.config({context:'xplot-2.0.0',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}});\r\n",
+       "    xplotRequire(['plotly'], function(Plotly) {\r\n",
+       "\n",
+       "            var data = 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+   ],
+   "source": [
+    "let testResults = result.BestRun.Model.Transform(housing_test)\n",
+    "\n",
+    "let trueValues = testResults.GetColumn<float32>(\"median_house_value\")\n",
+    "let predictedValues = testResults.GetColumn<float32>(\"Score\")\n",
+    "\n",
+    "let predictedVsTrue =\n",
+    "    Graph.Scattergl(\n",
+    "        x = trueValues,\n",
+    "        y = predictedValues,\n",
+    "        mode = \"markers\")\n",
+    "\n",
+    "let maximumValue = Math.Max(trueValues.Max(), predictedValues.Max())\n",
+    "\n",
+    "let perfectLine =\n",
+    "    Graph.Scattergl(\n",
+    "        x = [| 0.0f; maximumValue |],\n",
+    "        y = [| 0.0f; maximumValue |],\n",
+    "        mode = \"lines\")\n",
+    "\n",
+    "let chart = Chart.Plot([| predictedVsTrue; perfectLine |])\n",
+    "chart.WithXTitle(\"True Values\")\n",
+    "chart.WithYTitle(\"Predicted Values\")\n",
+    "chart.WithLegend(false)\n",
+    "chart.Width = 600\n",
+    "chart.Height = 600\n",
+    "display(chart)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".NET (F#)",
+   "language": "F#",
+   "name": ".net-fsharp"
+  },
+  "language_info": {
+   "file_extension": ".fs",
+   "mimetype": "text/x-fsharp",
+   "name": "C#",
+   "pygments_lexer": "fsharp",
+   "version": "4.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}