TrabajoFinal_Miguel.ipynb

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    "colab": {
      "name": "TrabajoFinalMod_1.ipynb",
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmzTpzRQwz2b"
      },
      "source": [
        "**Trabajo Final Fundamentos de aprendizaje de máquina**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "RYedb6CzxDhv",
        "outputId": "18708c11-4db1-4113-ac88-0a4b26628701"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\r\n",
        "from sklearn import preprocessing\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.svm import SVR\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from sklearn.cluster import KMeans\r\n",
        "# Online Shoppers Purchasing Intention\r\n",
        "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00468/online_shoppers_intention.csv\")\r\n",
        "df.describe()\r\n",
        "#df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.315166</td>\n",
              "      <td>80.818611</td>\n",
              "      <td>0.503569</td>\n",
              "      <td>34.472398</td>\n",
              "      <td>31.731468</td>\n",
              "      <td>1194.746220</td>\n",
              "      <td>0.022191</td>\n",
              "      <td>0.043073</td>\n",
              "      <td>5.889258</td>\n",
              "      <td>0.061427</td>\n",
              "      <td>2.124006</td>\n",
              "      <td>2.357097</td>\n",
              "      <td>3.147364</td>\n",
              "      <td>4.069586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.321784</td>\n",
              "      <td>176.779107</td>\n",
              "      <td>1.270156</td>\n",
              "      <td>140.749294</td>\n",
              "      <td>44.475503</td>\n",
              "      <td>1913.669288</td>\n",
              "      <td>0.048488</td>\n",
              "      <td>0.048597</td>\n",
              "      <td>18.568437</td>\n",
              "      <td>0.198917</td>\n",
              "      <td>0.911325</td>\n",
              "      <td>1.717277</td>\n",
              "      <td>2.401591</td>\n",
              "      <td>4.025169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>184.137500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>598.936905</td>\n",
              "      <td>0.003112</td>\n",
              "      <td>0.025156</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>93.256250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1464.157213</td>\n",
              "      <td>0.016813</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>3398.750000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2549.375000</td>\n",
              "      <td>705.000000</td>\n",
              "      <td>63973.522230</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>361.763742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Administrative  Administrative_Duration  ...        Region   TrafficType\n",
              "count    12330.000000             12330.000000  ...  12330.000000  12330.000000\n",
              "mean         2.315166                80.818611  ...      3.147364      4.069586\n",
              "std          3.321784               176.779107  ...      2.401591      4.025169\n",
              "min          0.000000                 0.000000  ...      1.000000      1.000000\n",
              "25%          0.000000                 0.000000  ...      1.000000      2.000000\n",
              "50%          1.000000                 7.500000  ...      3.000000      2.000000\n",
              "75%          4.000000                93.256250  ...      4.000000      4.000000\n",
              "max         27.000000              3398.750000  ...      9.000000     20.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 2
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      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXxk8fQkpzdZ"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrTDr1-m0ws1",
        "outputId": "676e956e-af44-460d-b40f-726d7eb858c8"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12330 entries, 0 to 12329\n",
            "Data columns (total 18 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Administrative           12330 non-null  int64  \n",
            " 1   Administrative_Duration  12330 non-null  float64\n",
            " 2   Informational            12330 non-null  int64  \n",
            " 3   Informational_Duration   12330 non-null  float64\n",
            " 4   ProductRelated           12330 non-null  int64  \n",
            " 5   ProductRelated_Duration  12330 non-null  float64\n",
            " 6   BounceRates              12330 non-null  float64\n",
            " 7   ExitRates                12330 non-null  float64\n",
            " 8   PageValues               12330 non-null  float64\n",
            " 9   SpecialDay               12330 non-null  float64\n",
            " 10  Month                    12330 non-null  object \n",
            " 11  OperatingSystems         12330 non-null  int64  \n",
            " 12  Browser                  12330 non-null  int64  \n",
            " 13  Region                   12330 non-null  int64  \n",
            " 14  TrafficType              12330 non-null  int64  \n",
            " 15  VisitorType              12330 non-null  object \n",
            " 16  Weekend                  12330 non-null  bool   \n",
            " 17  Revenue                  12330 non-null  bool   \n",
            "dtypes: bool(2), float64(7), int64(7), object(2)\n",
            "memory usage: 1.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "bYPndnXQ3ysf",
        "outputId": "878adca6-a32d-45fc-8651-1f594b5548fd"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>Month</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "      <th>VisitorType</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>627.500000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Administrative  Administrative_Duration  ...  Weekend  Revenue\n",
              "0               0                      0.0  ...    False    False\n",
              "1               0                      0.0  ...    False    False\n",
              "2               0                      0.0  ...    False    False\n",
              "3               0                      0.0  ...    False    False\n",
              "4               0                      0.0  ...     True    False\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnglQ9qA4SpM"
      },
      "source": [
        "df_v1=df.drop(['Month','VisitorType','Weekend','Revenue'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "xnRrOxUm5Lr9",
        "outputId": "d5e8103d-86f1-4464-9bbc-ea527f3f38b6"
      },
      "source": [
        "df_v1.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.315166</td>\n",
              "      <td>80.818611</td>\n",
              "      <td>0.503569</td>\n",
              "      <td>34.472398</td>\n",
              "      <td>31.731468</td>\n",
              "      <td>1194.746220</td>\n",
              "      <td>0.022191</td>\n",
              "      <td>0.043073</td>\n",
              "      <td>5.889258</td>\n",
              "      <td>0.061427</td>\n",
              "      <td>2.124006</td>\n",
              "      <td>2.357097</td>\n",
              "      <td>3.147364</td>\n",
              "      <td>4.069586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.321784</td>\n",
              "      <td>176.779107</td>\n",
              "      <td>1.270156</td>\n",
              "      <td>140.749294</td>\n",
              "      <td>44.475503</td>\n",
              "      <td>1913.669288</td>\n",
              "      <td>0.048488</td>\n",
              "      <td>0.048597</td>\n",
              "      <td>18.568437</td>\n",
              "      <td>0.198917</td>\n",
              "      <td>0.911325</td>\n",
              "      <td>1.717277</td>\n",
              "      <td>2.401591</td>\n",
              "      <td>4.025169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>184.137500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>598.936905</td>\n",
              "      <td>0.003112</td>\n",
              "      <td>0.025156</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>93.256250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1464.157213</td>\n",
              "      <td>0.016813</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>3398.750000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2549.375000</td>\n",
              "      <td>705.000000</td>\n",
              "      <td>63973.522230</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>361.763742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Administrative  Administrative_Duration  ...        Region   TrafficType\n",
              "count    12330.000000             12330.000000  ...  12330.000000  12330.000000\n",
              "mean         2.315166                80.818611  ...      3.147364      4.069586\n",
              "std          3.321784               176.779107  ...      2.401591      4.025169\n",
              "min          0.000000                 0.000000  ...      1.000000      1.000000\n",
              "25%          0.000000                 0.000000  ...      1.000000      2.000000\n",
              "50%          1.000000                 7.500000  ...      3.000000      2.000000\n",
              "75%          4.000000                93.256250  ...      4.000000      4.000000\n",
              "max         27.000000              3398.750000  ...      9.000000     20.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "W5WZbzxo56Ra",
        "outputId": "6deb778b-fe70-458d-c129-64a60afaac75"
      },
      "source": [
        "#normalizar los datos\r\n",
        "df_norm1=(df_v1-df_v1.min())/(df_v1.max()-df_v1.min())\r\n",
        "df_norm1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001418</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002837</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.052632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001418</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.105263</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002837</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.157895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.014184</td>\n",
              "      <td>0.009809</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.157895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12325</th>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.042663</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.075177</td>\n",
              "      <td>0.027883</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.145153</td>\n",
              "      <td>0.033839</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12326</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.007092</td>\n",
              "      <td>0.007280</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.368421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12327</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008511</td>\n",
              "      <td>0.002880</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12328</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.022067</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.021277</td>\n",
              "      <td>0.005408</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.105263</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.526316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12329</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004255</td>\n",
              "      <td>0.000332</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.052632</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12330 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Administrative  Administrative_Duration  ...  Region  TrafficType\n",
              "0            0.000000                 0.000000  ...   0.000     0.000000\n",
              "1            0.000000                 0.000000  ...   0.000     0.052632\n",
              "2            0.000000                 0.000000  ...   1.000     0.105263\n",
              "3            0.000000                 0.000000  ...   0.125     0.157895\n",
              "4            0.000000                 0.000000  ...   0.000     0.157895\n",
              "...               ...                      ...  ...     ...          ...\n",
              "12325        0.111111                 0.042663  ...   0.000     0.000000\n",
              "12326        0.000000                 0.000000  ...   0.000     0.368421\n",
              "12327        0.000000                 0.000000  ...   0.000     0.631579\n",
              "12328        0.148148                 0.022067  ...   0.250     0.526316\n",
              "12329        0.000000                 0.000000  ...   0.000     0.052632\n",
              "\n",
              "[12330 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "hx-zTMl56zjA",
        "outputId": "3d0f4e1e-0518-4d2b-e88a-03cd70bbb3aa"
      },
      "source": [
        "df_norm1.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.085747</td>\n",
              "      <td>0.023779</td>\n",
              "      <td>0.020982</td>\n",
              "      <td>0.013522</td>\n",
              "      <td>0.045009</td>\n",
              "      <td>0.018676</td>\n",
              "      <td>0.110957</td>\n",
              "      <td>0.215364</td>\n",
              "      <td>0.016279</td>\n",
              "      <td>0.061427</td>\n",
              "      <td>0.160572</td>\n",
              "      <td>0.113091</td>\n",
              "      <td>0.268421</td>\n",
              "      <td>0.161557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.123029</td>\n",
              "      <td>0.052013</td>\n",
              "      <td>0.052923</td>\n",
              "      <td>0.055209</td>\n",
              "      <td>0.063086</td>\n",
              "      <td>0.029913</td>\n",
              "      <td>0.242442</td>\n",
              "      <td>0.242983</td>\n",
              "      <td>0.051328</td>\n",
              "      <td>0.198917</td>\n",
              "      <td>0.130189</td>\n",
              "      <td>0.143106</td>\n",
              "      <td>0.300199</td>\n",
              "      <td>0.211851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009929</td>\n",
              "      <td>0.002878</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.052632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.002207</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.025532</td>\n",
              "      <td>0.009362</td>\n",
              "      <td>0.015562</td>\n",
              "      <td>0.125782</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.052632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.027438</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.053901</td>\n",
              "      <td>0.022887</td>\n",
              "      <td>0.084063</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.157895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Administrative  Administrative_Duration  ...        Region   TrafficType\n",
              "count    12330.000000             12330.000000  ...  12330.000000  12330.000000\n",
              "mean         0.085747                 0.023779  ...      0.268421      0.161557\n",
              "std          0.123029                 0.052013  ...      0.300199      0.211851\n",
              "min          0.000000                 0.000000  ...      0.000000      0.000000\n",
              "25%          0.000000                 0.000000  ...      0.000000      0.052632\n",
              "50%          0.037037                 0.002207  ...      0.250000      0.052632\n",
              "75%          0.148148                 0.027438  ...      0.375000      0.157895\n",
              "max          1.000000                 1.000000  ...      1.000000      1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUHUS57XYlL-"
      },
      "source": [
        "wcss= []\r\n",
        "\r\n",
        "for i in range(1,11):\r\n",
        "    kmeans= KMeans(n_clusters = i, max_iter = 300)\r\n",
        "    kmeans.fit(df_norm1)\r\n",
        "    wcss.append(kmeans.inertia_)\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3pFoIeRLbldS",
        "outputId": "49f048ac-fe25-48ce-c356-26337c1a0952"
      },
      "source": [
        "plt.plot(range(1,11), wcss)\r\n",
        "plt.title(\"codo de Jambu\")\r\n",
        "plt.xlabel('Numero de Clusters')\r\n",
        "plt.ylabel('WCSS')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8v4IfNwRcezN",
        "outputId": "b8708b50-1685-407f-be82-b0b761329335"
      },
      "source": [
        "clustering =KMeans(n_clusters = 5, max_iter = 300)\r\n",
        "clustering.fit(df_norm1)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "dkyPvYR_dCwN",
        "outputId": "1d26dd8f-bfac-4a60-9c53-2172798f2492"
      },
      "source": [
        "df['KMeans_clusters']= clustering.labels_\r\n",
        "df.head()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>Month</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "      <th>VisitorType</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>KMeans_clusters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>627.500000</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Administrative  Administrative_Duration  ...  Revenue  KMeans_clusters\n",
              "0               0                      0.0  ...    False                1\n",
              "1               0                      0.0  ...    False                4\n",
              "2               0                      0.0  ...    False                1\n",
              "3               0                      0.0  ...    False                4\n",
              "4               0                      0.0  ...    False                4\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "VVpwA5HydzQ8",
        "outputId": "be52de4b-b299-471b-c223-db679672f6c8"
      },
      "source": [
        "from sklearn.decomposition import PCA\r\n",
        "\r\n",
        "pca = PCA(n_components=2)\r\n",
        "pca_df = pca.fit_transform(df_norm1)\r\n",
        "pca_df2 = pd.DataFrame(data = pca_df, columns = ['Componente_1','Componente_2'])\r\n",
        "pca_nombres_df = pd.concat([pca_df2, df[['KMeans_Clusters']]], axis =1)\r\n",
        "\r\n",
        "pca_nombres_df\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-f74c0d109efa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpca_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_norm1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpca_df2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpca_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Componente_1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Componente_2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpca_nombres_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpca_df2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'KMeans_Clusters'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mpca_nombres_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2910\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2911\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2912\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2914\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1252\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1296\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1297\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1298\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1300\u001b[0m             \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['KMeans_Clusters'], dtype='object')] are in the [columns]\""
          ]
        }
      ]
    }
  ]
}