diff --git a/Technocollab_Mini_Project.ipynb b/Technocollab_Mini_Project.ipynb
new file mode 100644
index 0000000..12f6fa6
--- /dev/null
+++ b/Technocollab_Mini_Project.ipynb
@@ -0,0 +1,1438 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Technocollab Mini Project.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 474
+        },
+        "id": "7DvkOHqVAwuT",
+        "outputId": "cf04fefd-41a9-48b9-ac21-59222705ba81"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "There are 51 rows and 5 columns.\n",
+            "\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 51 entries, 0 to 50\n",
+            "Data columns (total 5 columns):\n",
+            " #   Column                Non-Null Count  Dtype  \n",
+            "---  ------                --------------  -----  \n",
+            " 0   state                 51 non-null     object \n",
+            " 1   drvr_fatl_col_bmiles  51 non-null     float64\n",
+            " 2   perc_fatl_speed       51 non-null     int64  \n",
+            " 3   perc_fatl_alcohol     51 non-null     int64  \n",
+            " 4   perc_fatl_1st_time    51 non-null     int64  \n",
+            "dtypes: float64(1), int64(3), object(1)\n",
+            "memory usage: 2.1+ KB\n",
+            "None\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            state  drvr_fatl_col_bmiles  perc_fatl_speed  perc_fatl_alcohol  \\\n",
+              "46       Virginia                  12.7               19                 27   \n",
+              "47     Washington                  10.6               42                 33   \n",
+              "48  West Virginia                  23.8               34                 28   \n",
+              "49      Wisconsin                  13.8               36                 33   \n",
+              "50        Wyoming                  17.4               42                 32   \n",
+              "\n",
+              "    perc_fatl_1st_time  \n",
+              "46                  88  \n",
+              "47                  86  \n",
+              "48                  87  \n",
+              "49                  84  \n",
+              "50                  90  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-45dc1110-ec93-4f07-8085-36e0e5108f7c\">\n",
+              "    <div 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>state</th>\n",
+              "      <th>drvr_fatl_col_bmiles</th>\n",
+              "      <th>perc_fatl_speed</th>\n",
+              "      <th>perc_fatl_alcohol</th>\n",
+              "      <th>perc_fatl_1st_time</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>12.7</td>\n",
+              "      <td>19</td>\n",
+              "      <td>27</td>\n",
+              "      <td>88</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>Washington</td>\n",
+              "      <td>10.6</td>\n",
+              "      <td>42</td>\n",
+              "      <td>33</td>\n",
+              "      <td>86</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>West Virginia</td>\n",
+              "      <td>23.8</td>\n",
+              "      <td>34</td>\n",
+              "      <td>28</td>\n",
+              "      <td>87</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>Wisconsin</td>\n",
+              "      <td>13.8</td>\n",
+              "      <td>36</td>\n",
+              "      <td>33</td>\n",
+              "      <td>84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>Wyoming</td>\n",
+              "      <td>17.4</td>\n",
+              "      <td>42</td>\n",
+              "      <td>32</td>\n",
+              "      <td>90</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45dc1110-ec93-4f07-8085-36e0e5108f7c')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-45dc1110-ec93-4f07-8085-36e0e5108f7c 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-45dc1110-ec93-4f07-8085-36e0e5108f7c');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "# Read in `road-accidents.csv`\n",
+        "car_acc = pd.read_csv('road-accidents.csv', comment = '#', sep = '|')\n",
+        "\n",
+        "# Save the number of rows columns as a tuple\n",
+        "rows_and_cols = car_acc.shape\n",
+        "print('There are {} rows and {} columns.\\n'.format(\n",
+        "    rows_and_cols[0], rows_and_cols[1]))\n",
+        "\n",
+        "# Generate an overview of the DataFrame\n",
+        "car_acc_information = car_acc.info()\n",
+        "print(car_acc_information)\n",
+        "\n",
+        "# Display the last five rows of the DataFrame\n",
+        "car_acc.tail()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Grapical Summary"
+      ],
+      "metadata": {
+        "id": "ZYUXbJ3pBXv2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# Compute the summary statistics of all columns in the `car_acc` DataFrame\n",
+        "sum_stat_car = car_acc.describe()\n",
+        "print(\"Summary: \",sum_stat_car)\n",
+        "\n",
+        "# Create a pairwise scatter plot to explore the data\n",
+        "sns.pairplot(sum_stat_car)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "TFRWu0tKBHrD",
+        "outputId": "f7a8eaf6-7386-4e03-dfa2-b32056c0db4d"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Summary:         drvr_fatl_col_bmiles  perc_fatl_speed  perc_fatl_alcohol  \\\n",
+            "count             51.000000        51.000000          51.000000   \n",
+            "mean              15.790196        31.725490          30.686275   \n",
+            "std                4.122002         9.633438           5.132213   \n",
+            "min                5.900000        13.000000          16.000000   \n",
+            "25%               12.750000        23.000000          28.000000   \n",
+            "50%               15.600000        34.000000          30.000000   \n",
+            "75%               18.500000        38.000000          33.000000   \n",
+            "max               23.900000        54.000000          44.000000   \n",
+            "\n",
+            "       perc_fatl_1st_time  \n",
+            "count            51.00000  \n",
+            "mean             88.72549  \n",
+            "std               6.96011  \n",
+            "min              76.00000  \n",
+            "25%              83.50000  \n",
+            "50%              88.00000  \n",
+            "75%              95.00000  \n",
+            "max             100.00000  \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.PairGrid at 0x7f934077c710>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x720 with 20 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Finding Correlation***"
+      ],
+      "metadata": {
+        "id": "LypR_rGKDGeJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "corr_columns = car_acc.corr()\n",
+        "corr_columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "br-qkkcVB6tj",
+        "outputId": "aa1d5023-cf59-4563-afee-c9e9248c5d7b"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                      drvr_fatl_col_bmiles  perc_fatl_speed  \\\n",
+              "drvr_fatl_col_bmiles              1.000000        -0.029080   \n",
+              "perc_fatl_speed                  -0.029080         1.000000   \n",
+              "perc_fatl_alcohol                 0.199426         0.286244   \n",
+              "perc_fatl_1st_time               -0.017942         0.014066   \n",
+              "\n",
+              "                      perc_fatl_alcohol  perc_fatl_1st_time  \n",
+              "drvr_fatl_col_bmiles           0.199426           -0.017942  \n",
+              "perc_fatl_speed                0.286244            0.014066  \n",
+              "perc_fatl_alcohol              1.000000           -0.245455  \n",
+              "perc_fatl_1st_time            -0.245455            1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-48b336f9-d022-4374-bd33-5e8efeeb329c\">\n",
+              "    <div 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>drvr_fatl_col_bmiles</th>\n",
+              "      <th>perc_fatl_speed</th>\n",
+              "      <th>perc_fatl_alcohol</th>\n",
+              "      <th>perc_fatl_1st_time</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>drvr_fatl_col_bmiles</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.029080</td>\n",
+              "      <td>0.199426</td>\n",
+              "      <td>-0.017942</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>perc_fatl_speed</th>\n",
+              "      <td>-0.029080</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.286244</td>\n",
+              "      <td>0.014066</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>perc_fatl_alcohol</th>\n",
+              "      <td>0.199426</td>\n",
+              "      <td>0.286244</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.245455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>perc_fatl_1st_time</th>\n",
+              "      <td>-0.017942</td>\n",
+              "      <td>0.014066</td>\n",
+              "      <td>-0.245455</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48b336f9-d022-4374-bd33-5e8efeeb329c')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-48b336f9-d022-4374-bd33-5e8efeeb329c 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-48b336f9-d022-4374-bd33-5e8efeeb329c');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Applying Linear Regression Model***"
+      ],
+      "metadata": {
+        "id": "YM0fqRHnC-uW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn import linear_model\n",
+        "\n",
+        "# Create the features and target DataFrames\n",
+        "features = car_acc[['perc_fatl_speed', 'perc_fatl_alcohol', 'perc_fatl_1st_time']]\n",
+        "target = car_acc['drvr_fatl_col_bmiles']\n",
+        "\n",
+        "# Create a linear regression object\n",
+        "reg = linear_model.LinearRegression()\n",
+        "\n",
+        "# Fit a multivariate linear regression model\n",
+        "reg.fit(features, target)\n",
+        "\n",
+        "# Retrieve the regression coefficients\n",
+        "fit_coef = reg.coef_\n",
+        "fit_coef"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_w0XqMgiCvhI",
+        "outputId": "3b3ea174-2b23-4701-f139-1e62272aba5c"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-0.04180041,  0.19086404,  0.02473301])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Perform PCA on standardized data***"
+      ],
+      "metadata": {
+        "id": "z3Z24yNsDOFB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Standardize and center the feature columns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "scaler = StandardScaler()\n",
+        "features_scaled = scaler.fit_transform(features)\n",
+        "\n",
+        "# Import the PCA class function from sklearn\n",
+        "from sklearn.decomposition import PCA\n",
+        "pca = PCA()\n",
+        "\n",
+        "# Fit the standardized data to the pca\n",
+        "pca.fit(features_scaled)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ew8dM3XDC7Ps",
+        "outputId": "62c5af1e-3dca-4510-faab-13781a22f858"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PCA()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "plt.bar(range(1, pca.n_components_ + 1),  pca.explained_variance_ratio_)\n",
+        "plt.xlabel('Principal component #')\n",
+        "plt.ylabel('Proportion of variance explained')\n",
+        "plt.xticks([1, 2, 3])\n",
+        "\n",
+        "# Compute the cumulative proportion of variance explained by the first two principal components\n",
+        "two_first_comp_var_exp = pca.explained_variance_ratio_[0].cumsum()[0] + pca.explained_variance_ratio_[1].cumsum()[0]\n",
+        "print(\"The cumulative variance of the first two principal components is {}\".format(\n",
+        "    round(two_first_comp_var_exp, 5)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "id": "6MpklQTIDclL",
+        "outputId": "d2823b6e-1c28-449c-d4a5-38195fef1603"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The cumulative variance of the first two principal components is 0.7947\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Visualize first two principal components***"
+      ],
+      "metadata": {
+        "id": "aTSsZShDDqHL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Transform the scaled features using two principal components\n",
+        "pca = PCA(n_components = 2)\n",
+        "p_comps = pca.fit_transform(features_scaled)\n",
+        "\n",
+        "# Extract the first and second component to use for the scatter plot\n",
+        "p_comp1 = p_comps[:, 0]\n",
+        "p_comp2 = p_comps[:, 1]\n",
+        "\n",
+        "# Plot the first two principal components in a scatter plot\n",
+        "plt.scatter(p_comp1, p_comp2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "id": "ygaFfiodDgdC",
+        "outputId": "a7498101-3434-4420-c910-1a9bfc206f40"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f933edb6d10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Finding Clusters***"
+      ],
+      "metadata": {
+        "id": "H-bJAJB0D1fi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import KMeans from sklearn\n",
+        "from sklearn.cluster import KMeans\n",
+        "\n",
+        "# A loop will be used to plot the explanatory power for up to 10 KMeans clusters\n",
+        "ks = range(1, 10)\n",
+        "inertias = []\n",
+        "for k in ks:\n",
+        "    # Initialize the KMeans object using the current number of clusters (k)\n",
+        "    km = KMeans(n_clusters=k, random_state=8)\n",
+        "    # Fit the scaled features to the KMeans object\n",
+        "    km.fit(features_scaled)\n",
+        "    # Append the inertia for `km` to the list of inertias\n",
+        "    inertias.append(km.inertia_)\n",
+        "    \n",
+        "# Plot the results in a line plot\n",
+        "plt.plot(ks, inertias, marker='o')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "id": "6sYe3bjEDxzT",
+        "outputId": "d772bd39-34de-4374-9b74-5c527e569699"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f933df62310>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***KMeans to visualize clusters in the PCA scatter plot***"
+      ],
+      "metadata": {
+        "id": "0bBVtbTgEA_6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a KMeans object with 3 clusters, use random_state=8 \n",
+        "km = KMeans(n_clusters = 3, random_state = 8)\n",
+        "\n",
+        "# Fit the data to the `km` object\n",
+        "km.fit(features_scaled)\n",
+        "\n",
+        "# Create a scatter plot of the first two principal components\n",
+        "# and color it according to the KMeans cluster assignment \n",
+        "plt.scatter(p_comps[:, 0], p_comps[:, 1], c = km.labels_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "id": "USmAfs1ED2ZP",
+        "outputId": "72c32c34-342c-4848-88ff-76aa1f06b60b"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f933df624d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Visualize the feature differences between the clusters***"
+      ],
+      "metadata": {
+        "id": "z1IGFmGIEK9F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a new column with the labels from the KMeans clustering\n",
+        "car_acc['cluster'] = km.labels_\n",
+        "\n",
+        "# Reshape the DataFrame to the long format\n",
+        "melt_car = pd.melt(car_acc, id_vars = ['cluster'], var_name ='measurement', value_name = 'percent', \n",
+        "                                                   value_vars =['perc_fatl_speed', 'perc_fatl_alcohol', 'perc_fatl_1st_time'])\n",
+        "\n",
+        "# Create a violin plot splitting and coloring the results according to the km-clusters\n",
+        "sns.violinplot(melt_car['percent'], melt_car['measurement'], hue = melt_car['cluster'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 353
+        },
+        "id": "l6MMNIaXEFOQ",
+        "outputId": "68981a2b-0ee9-4036-d821-0bfb35f9ec7a"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+            "  FutureWarning\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f933de56c90>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Compute the number of accidents within each cluster***"
+      ],
+      "metadata": {
+        "id": "RRunv6r1EWBq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Read in the new dataset\n",
+        "miles_driven = pd.read_csv('miles-driven.csv', sep='|')\n",
+        "\n",
+        "display(miles_driven.head())\n",
+        "\n",
+        "# Merge the `car_acc` DataFrame with the `miles_driven` DataFrame\n",
+        "car_acc_miles = car_acc.merge(miles_driven, on='state')\n",
+        "\n",
+        "# Create a new column for the number of drivers involved in fatal accidents\n",
+        "car_acc_miles['num_drvr_fatl_col'] = (car_acc_miles['drvr_fatl_col_bmiles'] * car_acc_miles['million_miles_annually']) / 1000\n",
+        "\n",
+        "display(car_acc_miles.head())\n",
+        "\n",
+        "# Create a barplot of the total number of accidents per cluster\n",
+        "sns.barplot(x='cluster', y='num_drvr_fatl_col', data=car_acc_miles, estimator=sum, ci=None)\n",
+        "\n",
+        "# Calculate the number of states in each cluster and their 'num_drvr_fatl_col' mean and sum.\n",
+        "count_mean_sum = car_acc_miles.groupby('cluster')['num_drvr_fatl_col'].agg(['count', 'mean', 'sum'])\n",
+        "count_mean_sum"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 835
+        },
+        "id": "rYEcmGeJESJj",
+        "outputId": "8c91f2cf-3b87-443e-97e4-25073a794b0c"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "        state  million_miles_annually\n",
+              "0     Alabama                   64914\n",
+              "1      Alaska                    4593\n",
+              "2     Arizona                   59575\n",
+              "3    Arkansas                   32953\n",
+              "4  California                  320784"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-42837a43-37fc-4ac2-892c-81a96fc09d54\">\n",
+              "    <div 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>state</th>\n",
+              "      <th>million_miles_annually</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>64914</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alaska</td>\n",
+              "      <td>4593</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>59575</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Arkansas</td>\n",
+              "      <td>32953</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>California</td>\n",
+              "      <td>320784</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42837a43-37fc-4ac2-892c-81a96fc09d54')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-42837a43-37fc-4ac2-892c-81a96fc09d54 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-42837a43-37fc-4ac2-892c-81a96fc09d54');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "        state  drvr_fatl_col_bmiles  perc_fatl_speed  perc_fatl_alcohol  \\\n",
+              "0     Alabama                  18.8               39                 30   \n",
+              "1      Alaska                  18.1               41                 25   \n",
+              "2     Arizona                  18.6               35                 28   \n",
+              "3    Arkansas                  22.4               18                 26   \n",
+              "4  California                  12.0               35                 28   \n",
+              "\n",
+              "   perc_fatl_1st_time  cluster  million_miles_annually  num_drvr_fatl_col  \n",
+              "0                  80        0                   64914          1220.3832  \n",
+              "1                  94        2                    4593            83.1333  \n",
+              "2                  96        2                   59575          1108.0950  \n",
+              "3                  95        2                   32953           738.1472  \n",
+              "4                  89        2                  320784          3849.4080  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5c138347-b979-4acc-bc75-f8aebf6b68da\">\n",
+              "    <div 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>state</th>\n",
+              "      <th>drvr_fatl_col_bmiles</th>\n",
+              "      <th>perc_fatl_speed</th>\n",
+              "      <th>perc_fatl_alcohol</th>\n",
+              "      <th>perc_fatl_1st_time</th>\n",
+              "      <th>cluster</th>\n",
+              "      <th>million_miles_annually</th>\n",
+              "      <th>num_drvr_fatl_col</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>18.8</td>\n",
+              "      <td>39</td>\n",
+              "      <td>30</td>\n",
+              "      <td>80</td>\n",
+              "      <td>0</td>\n",
+              "      <td>64914</td>\n",
+              "      <td>1220.3832</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alaska</td>\n",
+              "      <td>18.1</td>\n",
+              "      <td>41</td>\n",
+              "      <td>25</td>\n",
+              "      <td>94</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4593</td>\n",
+              "      <td>83.1333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>18.6</td>\n",
+              "      <td>35</td>\n",
+              "      <td>28</td>\n",
+              "      <td>96</td>\n",
+              "      <td>2</td>\n",
+              "      <td>59575</td>\n",
+              "      <td>1108.0950</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Arkansas</td>\n",
+              "      <td>22.4</td>\n",
+              "      <td>18</td>\n",
+              "      <td>26</td>\n",
+              "      <td>95</td>\n",
+              "      <td>2</td>\n",
+              "      <td>32953</td>\n",
+              "      <td>738.1472</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>California</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>28</td>\n",
+              "      <td>89</td>\n",
+              "      <td>2</td>\n",
+              "      <td>320784</td>\n",
+              "      <td>3849.4080</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c138347-b979-4acc-bc75-f8aebf6b68da')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-5c138347-b979-4acc-bc75-f8aebf6b68da 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-5c138347-b979-4acc-bc75-f8aebf6b68da');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         count        mean         sum\n",
+              "cluster                               \n",
+              "0           18  911.406439  16405.3159\n",
+              "1           11  860.505945   9465.5654\n",
+              "2           22  898.378595  19764.3291"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0d485bb7-c45d-4b3e-9f67-d34883ca50b9\">\n",
+              "    <div 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>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>sum</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cluster</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18</td>\n",
+              "      <td>911.406439</td>\n",
+              "      <td>16405.3159</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>11</td>\n",
+              "      <td>860.505945</td>\n",
+              "      <td>9465.5654</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>22</td>\n",
+              "      <td>898.378595</td>\n",
+              "      <td>19764.3291</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d485bb7-c45d-4b3e-9f67-d34883ca50b9')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-0d485bb7-c45d-4b3e-9f67-d34883ca50b9 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-0d485bb7-c45d-4b3e-9f67-d34883ca50b9');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## *I would choose cluster 2...*"
+      ],
+      "metadata": {
+        "id": "a90qB4aVEqqa"
+      }
+    }
+  ]
+}
\ No newline at end of file