diff --git "a/quicklab.ipynb" "b/quicklab.ipynb"
new file mode 100644--- /dev/null
+++ "b/quicklab.ipynb"
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+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.cloud import bigquery\n",
+        "from google.cloud import aiplatform\n",
+        "import bigframes.pandas as bpd\n",
+        "import pandas as pd\n",
+        "from vertexai.language_models._language_models import TextGenerationModel\n",
+        "from vertexai.generative_models import GenerativeModel\n",
+        "from bigframes.ml.cluster import KMeans\n",
+        "from bigframes.ml.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "4riwiDAljBre"
+      },
+      "execution_count": 1,
+      "outputs": []
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+      "cell_type": "code",
+      "source": [
+        "project_id = 'qwiklabs-gcp-00-98416c45f92c'\n",
+        "dataset_name = \"ecommerce\"\n",
+        "model_name = \"customer_segmentation_model\"\n",
+        "table_name = \"customer_stats\"\n",
+        "location = \"us-central1\"\n",
+        "client = bigquery.Client(project=project_id)\n",
+        "aiplatform.init(project=project_id, location=location)"
+      ],
+      "metadata": {
+        "id": "2EdFJzBojE60"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%bigquery\n",
+        "CREATE OR REPLACE TABLE ecommerce.customer_stats AS\n",
+        "SELECT\n",
+        "  user_id,\n",
+        "  DATE_DIFF(CURRENT_DATE(), CAST(MAX(order_created_date) AS DATE), day) AS days_since_last_order, ---RECENCY\n",
+        "  COUNT(order_id) AS count_orders, --FREQUENCY\n",
+        "  AVG(sale_price) AS average_spend --MONETARY\n",
+        "  FROM (\n",
+        "      SELECT\n",
+        "        user_id,\n",
+        "        order_id,\n",
+        "        sale_price,\n",
+        "        created_at AS order_created_date\n",
+        "        FROM `bigquery-public-data.thelook_ecommerce.order_items`\n",
+        "        WHERE\n",
+        "        created_at\n",
+        "            BETWEEN '2022-01-01' AND '2023-01-01'\n",
+        "  )\n",
+        "GROUP BY user_id;"
+      ],
+      "metadata": {
+        "id": "WxRYDHOxjJqo",
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+            "text/plain": [
+              "Query is running:   0%|          |"
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+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: []\n",
+              "Index: []"
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+              "\n",
+              "  <div id=\"df-25dd50a8-f7c4-4116-90a0-6a00d4fa3f0f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "      buttonEl.style.display =\n",
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+              "        const element = document.querySelector('#df-25dd50a8-f7c4-4116-90a0-6a00d4fa3f0f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "\n",
+              "\n",
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+              "  </div>\n"
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+          "metadata": {},
+          "execution_count": 3
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+    {
+      "cell_type": "markdown",
+      "source": [
+        "Convert the table ecommerce.customer_stats to a bigframes dataframe and show the top 10 records"
+      ],
+      "metadata": {
+        "id": "cJb1YMV3mUqQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: Convert the table ecommerce.customer_stats to a bigframes dataframe and show the top 10 records\n",
+        "\n",
+        "import pandas as pd\n",
+        "df_customer_stats = bpd.read_gbq(f'{project_id}.{dataset_name}.{table_name}')\n",
+        "df_customer_stats.head(10)"
+      ],
+      "metadata": {
+        "id": "wOdxjx4hmxvN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 427
+        },
+        "outputId": "cebac645-84a4-4f27-d543-bec2422a3748"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 81fb7c6c-6c74-4b2a-a556-69c99ae25d73 is DONE. 491.6 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:81fb7c6c-6c74-4b2a-a556-69c99ae25d73&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   user_id  days_since_last_order  count_orders  average_spend\n",
+              "0    46814                    935             1           12.0\n",
+              "1     8956                    888             1           31.5\n",
+              "2     7763                    850             2      70.169998\n",
+              "3    62570                   1005             1           55.0\n",
+              "4    20181                   1066             1           9.56\n",
+              "5    42866                   1000             1          128.0\n",
+              "6    44876                    899             1      89.989998\n",
+              "7    88957                    846             2      28.940001\n",
+              "8     3996                   1016             1          265.0\n",
+              "9     9940                    813             3      25.876666\n",
+              "\n",
+              "[10 rows x 4 columns]"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>user_id</th>\n",
+              "      <th>days_since_last_order</th>\n",
+              "      <th>count_orders</th>\n",
+              "      <th>average_spend</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>46814</td>\n",
+              "      <td>935</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>8956</td>\n",
+              "      <td>888</td>\n",
+              "      <td>1</td>\n",
+              "      <td>31.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7763</td>\n",
+              "      <td>850</td>\n",
+              "      <td>2</td>\n",
+              "      <td>70.169998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>62570</td>\n",
+              "      <td>1005</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>20181</td>\n",
+              "      <td>1066</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9.56</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>42866</td>\n",
+              "      <td>1000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>128.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>44876</td>\n",
+              "      <td>899</td>\n",
+              "      <td>1</td>\n",
+              "      <td>89.989998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>88957</td>\n",
+              "      <td>846</td>\n",
+              "      <td>2</td>\n",
+              "      <td>28.940001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>3996</td>\n",
+              "      <td>1016</td>\n",
+              "      <td>1</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>9940</td>\n",
+              "      <td>813</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.876666</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10 rows × 4 columns</p>\n",
+              "</div>[10 rows x 4 columns in total]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: bqdf = client.read_gbq(f\"{project_id}.{dataset_name}.{table_name}\")\n",
+        "# df.head(10)\n",
+        "\n",
+        "df = client.query(f\"SELECT * FROM `{project_id}.{dataset_name}.{table_name}`\").to_dataframe()\n",
+        "df.head(10)"
+      ],
+      "metadata": {
+        "id": "scCCqQFYnBFj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "outputId": "eb96bc85-14cf-4c64-a8d7-0c3359aab2da"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   user_id  days_since_last_order  count_orders  average_spend\n",
+              "0    51080                    768             1      39.990002\n",
+              "1    48685                    768             1      23.969999\n",
+              "2    82490                    768             1      61.790001\n",
+              "3     1335                    768             1      68.000000\n",
+              "4    47601                    768             1      59.990002\n",
+              "5    49291                    768             1       8.950000\n",
+              "6    12158                    768             1      65.000000\n",
+              "7    45522                    768             1      13.900000\n",
+              "8    25489                    768             1      20.000000\n",
+              "9    84077                    768             1      58.840000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-518a2b8b-52a1-448c-b6dc-f0a2ce6280c3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>user_id</th>\n",
+              "      <th>days_since_last_order</th>\n",
+              "      <th>count_orders</th>\n",
+              "      <th>average_spend</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>51080</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>39.990002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>48685</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>23.969999</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>82490</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>61.790001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1335</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>68.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>47601</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>59.990002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>49291</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8.950000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>12158</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>65.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>45522</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>13.900000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>25489</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>84077</td>\n",
+              "      <td>768</td>\n",
+              "      <td>1</td>\n",
+              "      <td>58.840000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-518a2b8b-52a1-448c-b6dc-f0a2ce6280c3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-518a2b8b-52a1-448c-b6dc-f0a2ce6280c3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-518a2b8b-52a1-448c-b6dc-f0a2ce6280c3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ced7a562-1a58-4bf1-83c8-09be5d35b83c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ced7a562-1a58-4bf1-83c8-09be5d35b83c')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ced7a562-1a58-4bf1-83c8-09be5d35b83c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bqdf = bpd.read_gbq(f\"{project_id}.{dataset_name}.{table_name}\")\n",
+        "bqdf.head(10)"
+      ],
+      "metadata": {
+        "id": "o_eDCzH2nFs8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 483
+        },
+        "outputId": "9a5ba2af-d18d-4ec6-efcf-0598d5aefac5"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py:3553: UserWarning: Reading cached table from 2025-02-02 21:19:05.561566+00:00 to avoid incompatibilies with previous reads of this table. To read the latest version, set `use_cache=False` or close the current session with Session.close() or bigframes.pandas.close_session().\n",
+            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 382b4244-8552-44ee-b281-baeb219208ca is DONE. 491.6 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:382b4244-8552-44ee-b281-baeb219208ca&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   user_id  days_since_last_order  count_orders  average_spend\n",
+              "0    46814                    935             1           12.0\n",
+              "1     8956                    888             1           31.5\n",
+              "2     7763                    850             2      70.169998\n",
+              "3    62570                   1005             1           55.0\n",
+              "4    20181                   1066             1           9.56\n",
+              "5    42866                   1000             1          128.0\n",
+              "6    44876                    899             1      89.989998\n",
+              "7    88957                    846             2      28.940001\n",
+              "8     3996                   1016             1          265.0\n",
+              "9     9940                    813             3      25.876666\n",
+              "\n",
+              "[10 rows x 4 columns]"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>user_id</th>\n",
+              "      <th>days_since_last_order</th>\n",
+              "      <th>count_orders</th>\n",
+              "      <th>average_spend</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>46814</td>\n",
+              "      <td>935</td>\n",
+              "      <td>1</td>\n",
+              "      <td>12.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>8956</td>\n",
+              "      <td>888</td>\n",
+              "      <td>1</td>\n",
+              "      <td>31.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7763</td>\n",
+              "      <td>850</td>\n",
+              "      <td>2</td>\n",
+              "      <td>70.169998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>62570</td>\n",
+              "      <td>1005</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>20181</td>\n",
+              "      <td>1066</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9.56</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>42866</td>\n",
+              "      <td>1000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>128.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>44876</td>\n",
+              "      <td>899</td>\n",
+              "      <td>1</td>\n",
+              "      <td>89.989998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>88957</td>\n",
+              "      <td>846</td>\n",
+              "      <td>2</td>\n",
+              "      <td>28.940001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>3996</td>\n",
+              "      <td>1016</td>\n",
+              "      <td>1</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>9940</td>\n",
+              "      <td>813</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.876666</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10 rows × 4 columns</p>\n",
+              "</div>[10 rows x 4 columns in total]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: #prompt: 1. Split df (using random state and test size 0.2) into test and training data for a K-means clustering algorithm store these as df_test and df_train. 2. Create a K-means cluster model using bigframes.ml.cluster KMeans with 5 clusters. 3. Save the model using the to_gbq method where the model name is project_id.dataset_name.model_name.\n",
+        "# df_train, df_test = train_test_split(bq_df, test_siz\n",
+        "\n",
+        "df_train, df_test = train_test_split(bqdf, test_size=0.2, random_state=42)\n",
+        "\n",
+        "kmeans = KMeans(n_clusters=5)\n",
+        "kmeans.fit(df_train)\n",
+        "\n",
+        "kmeans.to_gbq(f\"{project_id}.{dataset_name}.{model_name}\")"
+      ],
+      "metadata": {
+        "id": "EvlR9VN3nHtv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 87
+        },
+        "outputId": "fa873f05-dc70-44ae-e6b8-1af6324ae47b"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 6e1ae22e-916a-4cd9-b0b2-a7e860a790d0 is DONE. 491.6 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:6e1ae22e-916a-4cd9-b0b2-a7e860a790d0&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job a0949707-b90b-423c-9f3e-77c6a1f0eb8d is DONE. 393.3 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:a0949707-b90b-423c-9f3e-77c6a1f0eb8d&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Copy job a64579ae-6be6-4c9d-8bf9-48563fc356de is DONE. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:a64579ae-6be6-4c9d-8bf9-48563fc356de&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KMeans(distance_type='EUCLIDEAN', init='KMEANS_PLUS_PLUS', n_clusters=5)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: # prompt: 1. Call the K-means prediction model on the df dataframe, and store the results as predictions_df and show the first 10 records.\n",
+        "# predictions_df = kmeans.predict(df_test)\n",
+        "# predictions_df.head(10)\n",
+        "\n",
+        "predictions_df = kmeans.predict(df_test)\n",
+        "predictions_df.head(10)"
+      ],
+      "metadata": {
+        "id": "8RZVP9ANnL7x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 638
+        },
+        "outputId": "55490359-178e-4e42-882b-64aeab846234"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 2640c1ea-ebd8-42ec-8aa4-16ae2b277adf is DONE. 491.9 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:2640c1ea-ebd8-42ec-8aa4-16ae2b277adf&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 9c474258-bf50-4be8-9f6d-70d553a3bc04 is DONE. 393.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:9c474258-bf50-4be8-9f6d-70d553a3bc04&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    CENTROID_ID                         NEAREST_CENTROIDS_DISTANCE  user_id  \\\n",
+              "3             2  [{'CENTROID_ID': 2, 'DISTANCE': 0.502886469936...    62570   \n",
+              "10            2  [{'CENTROID_ID': 2, 'DISTANCE': 0.975565658564...    62138   \n",
+              "23            2  [{'CENTROID_ID': 2, 'DISTANCE': 0.809171980251...    86508   \n",
+              "24            4  [{'CENTROID_ID': 4, 'DISTANCE': 1.454758143394...    69005   \n",
+              "27            2  [{'CENTROID_ID': 2, 'DISTANCE': 0.449974263383...    77262   \n",
+              "30            3  [{'CENTROID_ID': 3, 'DISTANCE': 1.780868234976...     3304   \n",
+              "31            3  [{'CENTROID_ID': 3, 'DISTANCE': 0.830723873815...    21263   \n",
+              "50            4  [{'CENTROID_ID': 4, 'DISTANCE': 1.111220229685...    90269   \n",
+              "56            4  [{'CENTROID_ID': 4, 'DISTANCE': 1.156224784307...    92107   \n",
+              "57            2  [{'CENTROID_ID': 2, 'DISTANCE': 1.674490032565...    75358   \n",
+              "\n",
+              "    days_since_last_order  count_orders  average_spend  \n",
+              "3                    1005             1           55.0  \n",
+              "10                   1104             1          15.99  \n",
+              "23                   1005             1           28.0  \n",
+              "24                    834             1          134.0  \n",
+              "27                   1015             1      47.990002  \n",
+              "30                    842             1          135.0  \n",
+              "31                   1000             1          24.99  \n",
+              "50                    889             2      47.974998  \n",
+              "56                    772             1           25.0  \n",
+              "57                    990             1     144.399994  \n",
+              "\n",
+              "[10 rows x 6 columns]"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CENTROID_ID</th>\n",
+              "      <th>NEAREST_CENTROIDS_DISTANCE</th>\n",
+              "      <th>user_id</th>\n",
+              "      <th>days_since_last_order</th>\n",
+              "      <th>count_orders</th>\n",
+              "      <th>average_spend</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2</td>\n",
+              "      <td>[{'CENTROID_ID': 2, 'DISTANCE': 0.502886469936...</td>\n",
+              "      <td>62570</td>\n",
+              "      <td>1005</td>\n",
+              "      <td>1</td>\n",
+              "      <td>55.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>2</td>\n",
+              "      <td>[{'CENTROID_ID': 2, 'DISTANCE': 0.975565658564...</td>\n",
+              "      <td>62138</td>\n",
+              "      <td>1104</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>2</td>\n",
+              "      <td>[{'CENTROID_ID': 2, 'DISTANCE': 0.809171980251...</td>\n",
+              "      <td>86508</td>\n",
+              "      <td>1005</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>4</td>\n",
+              "      <td>[{'CENTROID_ID': 4, 'DISTANCE': 1.454758143394...</td>\n",
+              "      <td>69005</td>\n",
+              "      <td>834</td>\n",
+              "      <td>1</td>\n",
+              "      <td>134.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>2</td>\n",
+              "      <td>[{'CENTROID_ID': 2, 'DISTANCE': 0.449974263383...</td>\n",
+              "      <td>77262</td>\n",
+              "      <td>1015</td>\n",
+              "      <td>1</td>\n",
+              "      <td>47.990002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>3</td>\n",
+              "      <td>[{'CENTROID_ID': 3, 'DISTANCE': 1.780868234976...</td>\n",
+              "      <td>3304</td>\n",
+              "      <td>842</td>\n",
+              "      <td>1</td>\n",
+              "      <td>135.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>3</td>\n",
+              "      <td>[{'CENTROID_ID': 3, 'DISTANCE': 0.830723873815...</td>\n",
+              "      <td>21263</td>\n",
+              "      <td>1000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>24.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>4</td>\n",
+              "      <td>[{'CENTROID_ID': 4, 'DISTANCE': 1.111220229685...</td>\n",
+              "      <td>90269</td>\n",
+              "      <td>889</td>\n",
+              "      <td>2</td>\n",
+              "      <td>47.974998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>56</th>\n",
+              "      <td>4</td>\n",
+              "      <td>[{'CENTROID_ID': 4, 'DISTANCE': 1.156224784307...</td>\n",
+              "      <td>92107</td>\n",
+              "      <td>772</td>\n",
+              "      <td>1</td>\n",
+              "      <td>25.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>57</th>\n",
+              "      <td>2</td>\n",
+              "      <td>[{'CENTROID_ID': 2, 'DISTANCE': 1.674490032565...</td>\n",
+              "      <td>75358</td>\n",
+              "      <td>990</td>\n",
+              "      <td>1</td>\n",
+              "      <td>144.399994</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10 rows × 6 columns</p>\n",
+              "</div>[10 rows x 6 columns in total]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: #prompt: 1. Using predictions_df, and matplotlib, generate a scatterplot. 2. On the x-axis of the scatterplot, display days_since_last_order and on the y-axis, display average_spend from predictions_df. 3. Color by cluster. 4. The chart should be titled \"Attribute grouped by K-means cluster.\"\n",
+        "# import matplotlib.pyplot as plt\n",
+        "# # Create the scatter plot\n",
+        "# plt.figure(figsize=(10, 6))  # Adjust figure s\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Create the scatter plot\n",
+        "plt.figure(figsize=(10, 6))  # Adjust figure size as needed\n",
+        "plt.scatter(predictions_df['days_since_last_order'], predictions_df['average_spend'], c=predictions_df['CENTROID_ID'], cmap='viridis')\n",
+        "plt.xlabel('Days Since Last Order')\n",
+        "plt.ylabel('Average Spend')\n",
+        "plt.title('Attribute grouped by K-means cluster')\n",
+        "plt.colorbar(label='CENTROID_ID')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "DmUF-tDKnr5n",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 665
+        },
+        "outputId": "585047cb-37eb-4c00-bb1f-e46076d7e147"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 16cb6035-632b-4aef-8d4c-df1b4db3b964 is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:16cb6035-632b-4aef-8d4c-df1b4db3b964&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 58b4ae4b-8568-46ff-a19a-acf6918f2ce0 is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:58b4ae4b-8568-46ff-a19a-acf6918f2ce0&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 7aece2a9-f92b-4b7e-9b16-b5ceec2fc268 is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:7aece2a9-f92b-4b7e-9b16-b5ceec2fc268&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job d027f4e0-b62a-450b-92c3-9c204eca6aba is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:d027f4e0-b62a-450b-92c3-9c204eca6aba&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 5540b8af-1b2e-4bdf-ab20-08aa16388058 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:5540b8af-1b2e-4bdf-ab20-08aa16388058&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 2e72a52b-8be7-4320-93b4-60da780fefa8 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:2e72a52b-8be7-4320-93b4-60da780fefa8&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 0d49b6d6-3f12-4b61-b95a-247834fb52e9 is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:0d49b6d6-3f12-4b61-b95a-247834fb52e9&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 1d00d71a-edca-4caf-bbc3-d89068de9624 is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:1d00d71a-edca-4caf-bbc3-d89068de9624&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: 1. Using predictions_df, and matplotlib, generate a scatterplot. 2. On the x-axis of the scatterplot, display days_since_last_order and on the y-axis, display average_spend from predictions_df. 3. Color by cluster. 4. The chart should be titled \"Attribute grouped by K-means cluster.\"\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "# Create the scatter plot\n",
+        "plt.figure(figsize=(10, 6))  # Adjust figure size as needed\n",
+        "plt.scatter(predictions_df['days_since_last_order'], predictions_df['average_spend'], c=predictions_df['CENTROID_ID'], cmap='viridis')\n",
+        "plt.xlabel('Days Since Last Order')\n",
+        "plt.ylabel('Average Spend')\n",
+        "plt.title('Attribute grouped by K-means cluster')\n",
+        "plt.colorbar(label='CENTROID_ID')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "-eFp9yzWoaTS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 665
+        },
+        "outputId": "213f3a98-878d-4bbe-9aab-74e12935cb2d"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 7d69b526-b2ba-4873-8aaa-c8da5968b292 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:7d69b526-b2ba-4873-8aaa-c8da5968b292&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 5df5e01e-b5c7-4078-ba9b-18dbde472fb6 is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:5df5e01e-b5c7-4078-ba9b-18dbde472fb6&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job ecfc5c30-25d0-4f1d-afc5-b7a4db425785 is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:ecfc5c30-25d0-4f1d-afc5-b7a4db425785&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job d57893ce-95f9-4305-8ff6-a217a566e00f is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:d57893ce-95f9-4305-8ff6-a217a566e00f&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job d503f0e8-25c3-4800-8a4e-1ec5f45dfc5f is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:d503f0e8-25c3-4800-8a4e-1ec5f45dfc5f&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job f5f5f510-08d9-4ca4-a77c-96726c0d355c is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:f5f5f510-08d9-4ca4-a77c-96726c0d355c&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job 7f30d8cb-b5eb-48c8-a4f8-2c0a724580dc is DONE. 49.2 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:7f30d8cb-b5eb-48c8-a4f8-2c0a724580dc&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Query job d7d54553-4b25-48dc-934d-104d0d2eabad is DONE. 0 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=qwiklabs-gcp-00-98416c45f92c&j=bq:US:d7d54553-4b25-48dc-934d-104d0d2eabad&page=queryresults\">Open Job</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "query = \"\"\"\n",
+        "SELECT\n",
+        " CONCAT('cluster ', CAST(centroid_id as STRING)) as centroid,\n",
+        " average_spend,\n",
+        " count_orders,\n",
+        " days_since_last_order\n",
+        "FROM (\n",
+        " SELECT centroid_id, feature, ROUND(numerical_value, 2) as value\n",
+        " FROM ML.CENTROIDS(MODEL `{0}.{1}`)\n",
+        ")\n",
+        "PIVOT (\n",
+        " SUM(value)\n",
+        " FOR feature IN ('average_spend',  'count_orders', 'days_since_last_order')\n",
+        ")\n",
+        "ORDER BY centroid_id\n",
+        "\"\"\".format(dataset_name, model_name)\n",
+        "\n",
+        "df_centroid = client.query(query).to_dataframe()\n",
+        "df_centroid.head()"
+      ],
+      "metadata": {
+        "id": "Q75P5-srplah",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "32eb9856-b77d-4a5d-e491-b707af95a362"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    centroid  average_spend  count_orders  days_since_last_order\n",
+              "0  cluster 1          57.13          3.72                 896.48\n",
+              "1  cluster 2          50.88          1.31                1035.24\n",
+              "2  cluster 3          50.18          1.28                 931.69\n",
+              "3  cluster 4          49.96          1.33                 838.38\n",
+              "4  cluster 5         264.59          1.23                 927.18"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ec3f4f2f-17fa-417d-9119-c9d8209bbc68\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>centroid</th>\n",
+              "      <th>average_spend</th>\n",
+              "      <th>count_orders</th>\n",
+              "      <th>days_since_last_order</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>cluster 1</td>\n",
+              "      <td>57.13</td>\n",
+              "      <td>3.72</td>\n",
+              "      <td>896.48</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>cluster 2</td>\n",
+              "      <td>50.88</td>\n",
+              "      <td>1.31</td>\n",
+              "      <td>1035.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>cluster 3</td>\n",
+              "      <td>50.18</td>\n",
+              "      <td>1.28</td>\n",
+              "      <td>931.69</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>cluster 4</td>\n",
+              "      <td>49.96</td>\n",
+              "      <td>1.33</td>\n",
+              "      <td>838.38</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>cluster 5</td>\n",
+              "      <td>264.59</td>\n",
+              "      <td>1.23</td>\n",
+              "      <td>927.18</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec3f4f2f-17fa-417d-9119-c9d8209bbc68')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ec3f4f2f-17fa-417d-9119-c9d8209bbc68 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ec3f4f2f-17fa-417d-9119-c9d8209bbc68');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-22ea62c3-4c94-4394-b259-f60134857a5a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-22ea62c3-4c94-4394-b259-f60134857a5a')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-22ea62c3-4c94-4394-b259-f60134857a5a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_query = client.query(query).to_dataframe()\n",
+        "df_query.to_string(header=False, index=False)\n",
+        "\n",
+        "cluster_info = []\n",
+        "for i, row in df_query.iterrows():\n",
+        " cluster_info.append(\"{0}, average spend ${2}, count of orders per person {1}, days since last order {3}\"\n",
+        "  .format(row[\"centroid\"], row[\"count_orders\"], row[\"average_spend\"], row[\"days_since_last_order\"]) )\n",
+        "\n",
+        "cluster_info = (str.join(\"\\n\", cluster_info))\n",
+        "print(cluster_info)"
+      ],
+      "metadata": {
+        "id": "dv0TAsAeqOq9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c62b8f23-6812-4be0-d4a2-8a4069641e3a"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cluster 1, average spend $57.13, count of orders per person 3.72, days since last order 896.48\n",
+            "cluster 2, average spend $50.88, count of orders per person 1.31, days since last order 1035.24\n",
+            "cluster 3, average spend $50.18, count of orders per person 1.28, days since last order 931.69\n",
+            "cluster 4, average spend $49.96, count of orders per person 1.33, days since last order 838.38\n",
+            "cluster 5, average spend $264.59, count of orders per person 1.23, days since last order 927.18\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prompt = f\"\"\"\n",
+        "You're a creative brand strategist, given the following clusters, come up with \\\n",
+        "creative brand persona, a catchy title, and next marketing action, \\\n",
+        "explained step by step.\n",
+        "\n",
+        "Clusters:\n",
+        "{cluster_info}\n",
+        "\n",
+        "For each Cluster:\n",
+        "* Title:\n",
+        "* Persona:\n",
+        "* Next marketing step:\n",
+        "\"\"\""
+      ],
+      "metadata": {
+        "id": "TMb2WwrmqQ7b"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = GenerativeModel(\"gemini-1.0-pro\")\n",
+        "\n",
+        "prompt = f\"\"\"\n",
+        "You're a creative brand strategist, given the following clusters, come up with \\\n",
+        "creative brand persona, a catchy title, and next marketing action, \\\n",
+        "explained step by step. Identify the cluster number, the title of the person, a persona for them and the next marketing step.\n",
+        "\n",
+        "Clusters:\n",
+        "{cluster_info}\n",
+        "\n",
+        "For each Cluster:\n",
+        "* Title:\n",
+        "* Persona:\n",
+        "* Next marketing step:\n",
+        "\"\"\"\n",
+        "\n",
+        "responses = model.generate_content(\n",
+        "   prompt,\n",
+        "   generation_config={\n",
+        "      \"temperature\": 0.1,\n",
+        "      \"max_output_tokens\": 800,\n",
+        "      \"top_p\": 1.0,\n",
+        "      \"top_k\": 40,\n",
+        "   }\n",
+        ")\n",
+        "\n",
+        "print(responses.text)"
+      ],
+      "metadata": {
+        "id": "JaYoCYv3rD4x",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bcf9ed97-5b11-458e-b09a-e334064be4f8"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "## Creative Brand Personas and Marketing Actions for Each Cluster:\n",
+            "\n",
+            "**Cluster 1:**\n",
+            "\n",
+            "* **Title:** The Loyal Lapsed Customer\n",
+            "* **Persona:** This customer has a history of making multiple purchases but hasn't engaged with the brand in nearly two and a half years. They likely had positive experiences in the past and may be open to re-engagement with the right incentive.\n",
+            "* **Next Marketing Step:** \n",
+            "    1. **Targeted Email Campaign:** Send a personalized email acknowledging their past purchases and highlighting new products, promotions, or loyalty program benefits. \n",
+            "    2. **Exclusive Offer:** Provide a special discount or offer tailored to their previous purchase history to entice them back.\n",
+            "    3. **Win-back Campaign:** Utilize retargeting ads on social media or search engines to remind them of the brand and encourage a return purchase.\n",
+            "\n",
+            "**Cluster 2:**\n",
+            "\n",
+            "* **Title:** The One-Time Wonder\n",
+            "* **Persona:** This customer made a single purchase over three years ago and hasn't returned. They may have had a satisfactory experience but haven't found a compelling reason to purchase again.\n",
+            "* **Next Marketing Step:**\n",
+            "    1. **Product Recommendations:** Leverage purchase data to recommend similar or complementary products based on their previous purchase.\n",
+            "    2. **Content Marketing:** Share educational or entertaining content related to their interests and the product they purchased to build brand awareness and engagement.\n",
+            "    3. **Social Proof:** Showcase positive customer testimonials and reviews to build trust and encourage repeat purchases.\n",
+            "\n",
+            "**Cluster 3 & 4:**\n",
+            "\n",
+            "* **Title:** The Curious Newcomer (Combined due to similar characteristics)\n",
+            "* **Persona:** These customers made a single purchase over two and a half years ago and haven't returned. They may have been intrigued by the brand but haven't found a strong enough reason to become repeat customers.\n",
+            "* **Next Marketing Step:**\n",
+            "    1. **Welcome Series:** Implement an automated email series introducing the brand, its values, and unique offerings.\n",
+            "    2. **Personalized Offers:** Utilize purchase data and browsing behavior to personalize future offers and promotions.\n",
+            "    3. **Community Building:** Encourage engagement through social media communities, loyalty programs, or interactive content to foster a sense of belonging and brand loyalty.\n",
+            "\n",
+            "**Cluster 5:**\n",
+            "\n",
+            "* **Title:** The High-Value Buyer\n",
+            "* **Persona:** This customer made a single purchase with a significantly higher average spend than other clusters. They may be a high-income individual or someone with a specific need for the product.\n",
+            "* **Next Marketing Step:**\n",
+            "    1. **VIP Treatment:** Offer exclusive benefits and personalized experiences to show appreciation and encourage continued high-value purchases.\n",
+            "    2. **Product Exclusives:** Provide early access to new products, limited-edition items, or special collaborations to maintain their interest and loyalty.\n",
+            "    3. **Personalized Communication:** Engage in direct communication through dedicated account managers or personalized email campaigns to build stronger relationships and cater to their specific needs. \n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "B2rIUH0Mj62Q"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file