secuencial_red_neuronal.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "### Librerias para autenticarnos con Google"
      ],
      "metadata": {
        "id": "67f9YPcZQoKH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1kgFQLYkOZs5"
      },
      "outputs": [],
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "metadata": {
        "id": "LxlZbat1OmHS"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Librerias para trabajar"
      ],
      "metadata": {
        "id": "fCFDEFD6Qnbw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sO0UfbaHOm6p",
        "outputId": "afdfa2f5-f545-4a86-aa55-7de42edd8022"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "colab": {
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        },
        "id": "ItG97X4H4rbY",
        "outputId": "6f540f08-0c24-40aa-c93c-7c5274e6e365"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.1.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Libreria para plotteo"
      ],
      "metadata": {
        "id": "QKiETgZSQ549"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "mhhAoOdMO6sX"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shippings = pd.read_csv('/content/silkroad_shippings.csv')\n",
        "shippings.head()\n"
      ],
      "metadata": {
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              "      <td>jj-luna-privacy-ebooks</td>\n",
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              "      <td>1 day</td>\n",
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              "      <td>2014-02-23T05:39:22</td>\n",
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              "      <td>the-morality-of-capitalism</td>\n",
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              "      <td>2014-02-23T05:39:33</td>\n",
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              "      <th>3</th>\n",
              "      <td>a-non-religious-new-testament</td>\n",
              "      <td>media mail parcel (domestic)</td>\n",
              "      <td>4 days</td>\n",
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              "      <td>2014-02-23T05:39:37</td>\n",
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              "                                             item_id  ...            timestamp\n",
              "0                             jj-luna-privacy-ebooks  ...  2014-02-23T05:39:22\n",
              "1                         the-morality-of-capitalism  ...  2014-02-23T05:39:29\n",
              "2                                            survive  ...  2014-02-23T05:39:33\n",
              "3                      a-non-religious-new-testament  ...  2014-02-23T05:39:37\n",
              "4  mindless-slogans-101-cheap-substitutes-for-act...  ...  2014-02-23T05:39:41\n",
              "\n",
              "[5 rows x 5 columns]"
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      "source": [
        "bitcoin = pd.read_csv('/content/bitcoins.csv')\n",
        "bitcoin.head()"
      ],
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              "      <th>Date</th>\n",
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              "      <th>Open</th>\n",
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              "      <th>0</th>\n",
              "      <td>Feb 23, 2014</td>\n",
              "      <td>272.6</td>\n",
              "      <td>210.9</td>\n",
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              "      <td>7.08K</td>\n",
              "      <td>29.29%</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Feb 24, 2014</td>\n",
              "      <td>153.6</td>\n",
              "      <td>272.6</td>\n",
              "      <td>252.1</td>\n",
              "      <td>129.0</td>\n",
              "      <td>12.05K</td>\n",
              "      <td>-43.65%</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Feb 25, 2014</td>\n",
              "      <td>198.8</td>\n",
              "      <td>153.6</td>\n",
              "      <td>226.6</td>\n",
              "      <td>163.9</td>\n",
              "      <td>7.70K</td>\n",
              "      <td>29.36%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Feb 26, 2014</td>\n",
              "      <td>430.6</td>\n",
              "      <td>198.8</td>\n",
              "      <td>442.1</td>\n",
              "      <td>372.3</td>\n",
              "      <td>2.33K</td>\n",
              "      <td>116.67%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Feb 27, 2014</td>\n",
              "      <td>432.3</td>\n",
              "      <td>430.6</td>\n",
              "      <td>438.3</td>\n",
              "      <td>424.2</td>\n",
              "      <td>1.05K</td>\n",
              "      <td>0.39%</td>\n",
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            "text/plain": [
              "           Date  Price   Open   High    Low    Vol. Change %\n",
              "0  Feb 23, 2014  272.6  210.9  297.8  217.7   7.08K   29.29%\n",
              "1  Feb 24, 2014  153.6  272.6  252.1  129.0  12.05K  -43.65%\n",
              "2  Feb 25, 2014  198.8  153.6  226.6  163.9   7.70K   29.36%\n",
              "3  Feb 26, 2014  430.6  198.8  442.1  372.3   2.33K  116.67%\n",
              "4  Feb 27, 2014  432.3  430.6  438.3  424.2   1.05K    0.39%"
            ]
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          "metadata": {},
          "execution_count": 9
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como el bitcoin es inestable, se debe mapear al precio en euros, que es el que tenemos en el `bitcoins.csv`"
      ],
      "metadata": {
        "id": "zc-PzkQqz5WU"
      }
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    {
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      "source": [
        "bitcoin['Date'] = pd.to_datetime(bitcoin['Date'])\n",
        "bitcoin.head()"
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      "execution_count": 11,
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Price</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Vol.</th>\n",
              "      <th>Change %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-02-23</td>\n",
              "      <td>272.6</td>\n",
              "      <td>210.9</td>\n",
              "      <td>297.8</td>\n",
              "      <td>217.7</td>\n",
              "      <td>7.08K</td>\n",
              "      <td>29.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-02-24</td>\n",
              "      <td>153.6</td>\n",
              "      <td>272.6</td>\n",
              "      <td>252.1</td>\n",
              "      <td>129.0</td>\n",
              "      <td>12.05K</td>\n",
              "      <td>-43.65%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-02-25</td>\n",
              "      <td>198.8</td>\n",
              "      <td>153.6</td>\n",
              "      <td>226.6</td>\n",
              "      <td>163.9</td>\n",
              "      <td>7.70K</td>\n",
              "      <td>29.36%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-02-26</td>\n",
              "      <td>430.6</td>\n",
              "      <td>198.8</td>\n",
              "      <td>442.1</td>\n",
              "      <td>372.3</td>\n",
              "      <td>2.33K</td>\n",
              "      <td>116.67%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-02-27</td>\n",
              "      <td>432.3</td>\n",
              "      <td>430.6</td>\n",
              "      <td>438.3</td>\n",
              "      <td>424.2</td>\n",
              "      <td>1.05K</td>\n",
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              "                                                     [key], {});\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Date  Price   Open   High    Low    Vol. Change %\n",
              "0 2014-02-23  272.6  210.9  297.8  217.7   7.08K   29.29%\n",
              "1 2014-02-24  153.6  272.6  252.1  129.0  12.05K  -43.65%\n",
              "2 2014-02-25  198.8  153.6  226.6  163.9   7.70K   29.36%\n",
              "3 2014-02-26  430.6  198.8  442.1  372.3   2.33K  116.67%\n",
              "4 2014-02-27  432.3  430.6  438.3  424.2   1.05K    0.39%"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shippings['timestamp'] = pd.to_datetime(shippings['timestamp'])\n",
        "shippings.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "jd0UkwA71eej",
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      "execution_count": 16,
      "outputs": [
        {
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          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>item_id</th>\n",
              "      <th>description</th>\n",
              "      <th>est_delivery</th>\n",
              "      <th>price</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>jj-luna-privacy-ebooks</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>2014-02-23 05:39:22</td>\n",
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              "      <th>1</th>\n",
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              "      <td>4 days</td>\n",
              "      <td>0.001722</td>\n",
              "      <td>2014-02-23 05:39:29</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>survive</td>\n",
              "      <td>small book (domestic)</td>\n",
              "      <td>4 days</td>\n",
              "      <td>0.017217</td>\n",
              "      <td>2014-02-23 05:39:33</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a-non-religious-new-testament</td>\n",
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              "      <td>2014-02-23 05:39:37</td>\n",
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              "      <td>mindless-slogans-101-cheap-substitutes-for-act...</td>\n",
              "      <td>ebook link</td>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "                                             item_id  ...           timestamp\n",
              "0                             jj-luna-privacy-ebooks  ... 2014-02-23 05:39:22\n",
              "1                         the-morality-of-capitalism  ... 2014-02-23 05:39:29\n",
              "2                                            survive  ... 2014-02-23 05:39:33\n",
              "3                      a-non-religious-new-testament  ... 2014-02-23 05:39:37\n",
              "4  mindless-slogans-101-cheap-substitutes-for-act...  ... 2014-02-23 05:39:41\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Paso desde est_delivery a los numeros de delivery"
      ],
      "metadata": {
        "id": "w4uR0tYK7DUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_numeric(x):\n",
        "    num = \"\"\n",
        "    for letter in x:\n",
        "        if letter.isnumeric():\n",
        "            num +=letter\n",
        "    return num"
      ],
      "metadata": {
        "id": "IyWvnpo_7Il-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def a(x):\n",
        "    if x == '':\n",
        "        return 0\n",
        "    else:\n",
        "        return x"
      ],
      "metadata": {
        "id": "7Sxhfx-d7gjp"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shippings['est_delivery'].map(make_numeric)\n",
        "shippings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "S3-1b2QA7KQv",
        "outputId": "66f35a42-466b-4411-ed6d-2717c5967962"
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      "execution_count": 28,
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "          const element = document.querySelector('#df-cb0a9861-49d6-4e8a-9b58-4382d2bb28d5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    description est_delivery     price       date\n",
              "0                    ebook link            1  0.000000 2014-02-23\n",
              "1                        letter            4  0.001722 2014-02-23\n",
              "2         small book (domestic)            4  0.017217 2014-02-23\n",
              "3  media mail parcel (domestic)            4  0.006870 2014-02-23\n",
              "4                    ebook link            1  0.000000 2014-02-23"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shippings['est_delivery'] = shippings['est_delivery'].map(a)\n",
        "shippings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
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      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
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              "                    description est_delivery     price       date\n",
              "0                    ebook link            1  0.000000 2014-02-23\n",
              "1                        letter            4  0.001722 2014-02-23\n",
              "2         small book (domestic)            4  0.017217 2014-02-23\n",
              "3  media mail parcel (domestic)            4  0.006870 2014-02-23\n",
              "4                    ebook link            1  0.000000 2014-02-23"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shippings['est_delivery'] = shippings['est_delivery'].astype(int)\n",
        "shippings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "rxn06XKC7kz-",
        "outputId": "a0c03c63-8649-4fea-8c92-6f8ee7f4bb86"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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          "data": {
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    }\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-cc53c8dd-82c7-4c5c-9e3c-e94bbee90b38 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-cc53c8dd-82c7-4c5c-9e3c-e94bbee90b38');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    description  est_delivery     price       date\n",
              "0                    ebook link             1  0.000000 2014-02-23\n",
              "1                        letter             4  0.001722 2014-02-23\n",
              "2         small book (domestic)             4  0.017217 2014-02-23\n",
              "3  media mail parcel (domestic)             4  0.006870 2014-02-23\n",
              "4                    ebook link             1  0.000000 2014-02-23"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shippings['date'] = shippings['timestamp'].dt.date\n",
        "shippings = shippings[['description','est_delivery','price','date']]\n",
        "shippings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fjaScrsZ3Gq5",
        "outputId": "225f0f7a-20a2-462b-d694-ea72c34cbe7d"
      },
      "execution_count": 18,
      "outputs": [
        {
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              "\n",
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              "      <td>small book (domestic)</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ebook link</td>\n",
              "      <td>1 day</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2014-02-23</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-9992ff8c-2d7a-41aa-904c-ccfa3e0c87ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    description est_delivery     price        date\n",
              "0                    ebook link        1 day  0.000000  2014-02-23\n",
              "1                        letter       4 days  0.001722  2014-02-23\n",
              "2         small book (domestic)       4 days  0.017217  2014-02-23\n",
              "3  media mail parcel (domestic)       4 days  0.006870  2014-02-23\n",
              "4                    ebook link        1 day  0.000000  2014-02-23"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bitcoin.rename(columns={'Date':'date'},inplace=True)\n",
        "bitcoin.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U2dXTeuQ6UBo",
        "outputId": "660458a4-f1d4-41f1-e1f8-8e369d82e45b"
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      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Price</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
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              "      <th>Change %</th>\n",
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              "      <th>0</th>\n",
              "      <td>2014-02-23</td>\n",
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              "      <td>210.9</td>\n",
              "      <td>297.8</td>\n",
              "      <td>217.7</td>\n",
              "      <td>7.08K</td>\n",
              "      <td>29.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-02-24</td>\n",
              "      <td>153.6</td>\n",
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              "      <td>252.1</td>\n",
              "      <td>129.0</td>\n",
              "      <td>12.05K</td>\n",
              "      <td>-43.65%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-02-25</td>\n",
              "      <td>198.8</td>\n",
              "      <td>153.6</td>\n",
              "      <td>226.6</td>\n",
              "      <td>163.9</td>\n",
              "      <td>7.70K</td>\n",
              "      <td>29.36%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-02-26</td>\n",
              "      <td>430.6</td>\n",
              "      <td>198.8</td>\n",
              "      <td>442.1</td>\n",
              "      <td>372.3</td>\n",
              "      <td>2.33K</td>\n",
              "      <td>116.67%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-02-27</td>\n",
              "      <td>432.3</td>\n",
              "      <td>430.6</td>\n",
              "      <td>438.3</td>\n",
              "      <td>424.2</td>\n",
              "      <td>1.05K</td>\n",
              "      <td>0.39%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "          const element = document.querySelector('#df-801813a1-3646-46b5-bfb8-407f2b691634');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        date  Price   Open   High    Low    Vol. Change %\n",
              "0 2014-02-23  272.6  210.9  297.8  217.7   7.08K   29.29%\n",
              "1 2014-02-24  153.6  272.6  252.1  129.0  12.05K  -43.65%\n",
              "2 2014-02-25  198.8  153.6  226.6  163.9   7.70K   29.36%\n",
              "3 2014-02-26  430.6  198.8  442.1  372.3   2.33K  116.67%\n",
              "4 2014-02-27  432.3  430.6  438.3  424.2   1.05K    0.39%"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bitcoin['date'] = bitcoin['date'].astype('datetime64[ns]')\n",
        "shippings['date'] = shippings['date'].astype('datetime64[ns]')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2_jusvd6ohZ",
        "outputId": "ef69a2a4-5f50-4274-a1bd-b64365b6e66c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged = shippings.merge(bitcoin,on='date',how='left')\n",
        "complete_shipping = merged[['description','est_delivery','price','Price','date']]\n",
        "complete_shipping.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TwvY6y536Fqk",
        "outputId": "df009afb-6359-4736-b160-d937127d9844"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <td>2014-02-23</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                    description  est_delivery     price  Price       date\n",
              "0                    ebook link             1  0.000000  272.6 2014-02-23\n",
              "1                        letter             4  0.001722  272.6 2014-02-23\n",
              "2         small book (domestic)             4  0.017217  272.6 2014-02-23\n",
              "3  media mail parcel (domestic)             4  0.006870  272.6 2014-02-23\n",
              "4                    ebook link             1  0.000000  272.6 2014-02-23"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "complete_shipping['total'] = complete_shipping['price'] * complete_shipping['Price']\n",
        "complete_shipping"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Qx_L7yDY6cSp",
        "outputId": "b83ec834-f9f6-44d5-94b6-de17c3e4ca5b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
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        {
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              "\n",
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              "      <th>3</th>\n",
              "      <td>media mail parcel (domestic)</td>\n",
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              "      <td>2014-02-23</td>\n",
              "      <td>1.872762</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2014-02-23</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>560453</th>\n",
              "      <td>Priority Mail</td>\n",
              "      <td>3</td>\n",
              "      <td>0.018512</td>\n",
              "      <td>220.4</td>\n",
              "      <td>2015-01-24</td>\n",
              "      <td>4.080045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560454</th>\n",
              "      <td>Priority Mail</td>\n",
              "      <td>3</td>\n",
              "      <td>0.018363</td>\n",
              "      <td>220.4</td>\n",
              "      <td>2015-01-24</td>\n",
              "      <td>4.047205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560455</th>\n",
              "      <td>Priority Mail Express</td>\n",
              "      <td>1</td>\n",
              "      <td>0.064269</td>\n",
              "      <td>220.4</td>\n",
              "      <td>2015-01-24</td>\n",
              "      <td>14.164888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560456</th>\n",
              "      <td>Priority Mail</td>\n",
              "      <td>3</td>\n",
              "      <td>0.018363</td>\n",
              "      <td>220.4</td>\n",
              "      <td>2015-01-24</td>\n",
              "      <td>4.047205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560457</th>\n",
              "      <td>EMS Express Shipping</td>\n",
              "      <td>7</td>\n",
              "      <td>0.035321</td>\n",
              "      <td>220.4</td>\n",
              "      <td>2015-01-24</td>\n",
              "      <td>7.784748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>560458 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94551ab4-6318-4d6c-b5d6-f7a654a0ce18')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-94551ab4-6318-4d6c-b5d6-f7a654a0ce18 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-94551ab4-6318-4d6c-b5d6-f7a654a0ce18');\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",
              "  "
            ],
            "text/plain": [
              "                         description  est_delivery  ...       date      total\n",
              "0                         ebook link             1  ... 2014-02-23   0.000000\n",
              "1                             letter             4  ... 2014-02-23   0.469417\n",
              "2              small book (domestic)             4  ... 2014-02-23   4.693354\n",
              "3       media mail parcel (domestic)             4  ... 2014-02-23   1.872762\n",
              "4                         ebook link             1  ... 2014-02-23   0.000000\n",
              "...                              ...           ...  ...        ...        ...\n",
              "560453                 Priority Mail             3  ... 2015-01-24   4.080045\n",
              "560454                 Priority Mail             3  ... 2015-01-24   4.047205\n",
              "560455         Priority Mail Express             1  ... 2015-01-24  14.164888\n",
              "560456                 Priority Mail             3  ... 2015-01-24   4.047205\n",
              "560457          EMS Express Shipping             7  ... 2015-01-24   7.784748\n",
              "\n",
              "[560458 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del merged"
      ],
      "metadata": {
        "id": "26IyLB558bUd"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "complete_shipping['description'].fillna('unknown',inplace=True)"
      ],
      "metadata": {
        "id": "jVaRnvu28eeV"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "complete_shipping = complete_shipping[(complete_shipping['total'] >= 0) & (complete_shipping['total'] <= 5000)]\n",
        "complete_shipping"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1KtJCEbDTSt9",
        "outputId": "b336225d-c4cb-402d-dc8c-7affb97d00ac"
      },
      "execution_count": 80,
      "outputs": [
        {
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          "data": {
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              "\n",
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              "    <tr>\n",
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              "      <td>EMS Express Shipping</td>\n",
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              "      <td>7.784748</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>560260 rows × 4 columns</p>\n",
              "</div>\n",
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              "\n",
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              "          const element = document.querySelector('#df-e0f45aa2-96f2-45c3-be18-e73291b2d4e1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                         description  est_delivery       date      total\n",
              "0                         ebook link             1 2014-02-23   0.000000\n",
              "1                             letter             4 2014-02-23   0.469417\n",
              "2              small book (domestic)             4 2014-02-23   4.693354\n",
              "3       media mail parcel (domestic)             4 2014-02-23   1.872762\n",
              "4                         ebook link             1 2014-02-23   0.000000\n",
              "...                              ...           ...        ...        ...\n",
              "560453                 Priority Mail             3 2015-01-24   4.080045\n",
              "560454                 Priority Mail             3 2015-01-24   4.047205\n",
              "560455         Priority Mail Express             1 2015-01-24  14.164888\n",
              "560456                 Priority Mail             3 2015-01-24   4.047205\n",
              "560457          EMS Express Shipping             7 2015-01-24   7.784748\n",
              "\n",
              "[560260 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = complete_shipping['total']\n",
        "X = complete_shipping.drop(['total'],axis=1)\n",
        "print(labels)\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGTOKpg88p9Y",
        "outputId": "004f2a52-72e7-4355-f344-052faf4eba06"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0          0.000000\n",
            "1          0.469417\n",
            "2          4.693354\n",
            "3          1.872762\n",
            "4          0.000000\n",
            "            ...    \n",
            "560453     4.080045\n",
            "560454     4.047205\n",
            "560455    14.164888\n",
            "560456     4.047205\n",
            "560457     7.784748\n",
            "Name: total, Length: 560260, dtype: float64\n",
            "                         description  est_delivery       date\n",
            "0                         ebook link             1 2014-02-23\n",
            "1                             letter             4 2014-02-23\n",
            "2              small book (domestic)             4 2014-02-23\n",
            "3       media mail parcel (domestic)             4 2014-02-23\n",
            "4                         ebook link             1 2014-02-23\n",
            "...                              ...           ...        ...\n",
            "560453                 Priority Mail             3 2015-01-24\n",
            "560454                 Priority Mail             3 2015-01-24\n",
            "560455         Priority Mail Express             1 2015-01-24\n",
            "560456                 Priority Mail             3 2015-01-24\n",
            "560457          EMS Express Shipping             7 2015-01-24\n",
            "\n",
            "[560260 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para lograr un mejor encoding, separare las fechas en columnas distintas\n",
        "X['year'] = X['date'].dt.year\n",
        "X['month']= X['date'].dt.month\n",
        "X['day']= X['date'].dt.day\n",
        "del X['date']\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Xxxp6rfl8_x1",
        "outputId": "e7623c89-757e-41ab-89a5-32524add53b8"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-08cb0be5-c0dd-4eaf-a99a-8a47a5e36558\">\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>description</th>\n",
              "      <th>est_delivery</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ebook link</td>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>letter</td>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>small book (domestic)</td>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>media mail parcel (domestic)</td>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ebook link</td>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560453</th>\n",
              "      <td>Priority Mail</td>\n",
              "      <td>3</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560454</th>\n",
              "      <td>Priority Mail</td>\n",
              "      <td>3</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560455</th>\n",
              "      <td>Priority Mail Express</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560456</th>\n",
              "      <td>Priority Mail</td>\n",
              "      <td>3</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560457</th>\n",
              "      <td>EMS Express Shipping</td>\n",
              "      <td>7</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>560260 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08cb0be5-c0dd-4eaf-a99a-8a47a5e36558')\"\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-08cb0be5-c0dd-4eaf-a99a-8a47a5e36558 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-08cb0be5-c0dd-4eaf-a99a-8a47a5e36558');\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",
              "  "
            ],
            "text/plain": [
              "                         description  est_delivery  year  month  day\n",
              "0                         ebook link             1  2014      2   23\n",
              "1                             letter             4  2014      2   23\n",
              "2              small book (domestic)             4  2014      2   23\n",
              "3       media mail parcel (domestic)             4  2014      2   23\n",
              "4                         ebook link             1  2014      2   23\n",
              "...                              ...           ...   ...    ...  ...\n",
              "560453                 Priority Mail             3  2015      1   24\n",
              "560454                 Priority Mail             3  2015      1   24\n",
              "560455         Priority Mail Express             1  2015      1   24\n",
              "560456                 Priority Mail             3  2015      1   24\n",
              "560457          EMS Express Shipping             7  2015      1   24\n",
              "\n",
              "[560260 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del complete_shipping['price']\n",
        "del complete_shipping['Price']"
      ],
      "metadata": {
        "id": "IYkq1T599Iwi"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nojk-VnM9YVx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engeniering\n",
        "Dado que tenemos varias descripciones lo que se hára es un feature engineering aplicandole el OrdinalEncoder de sklearn"
      ],
      "metadata": {
        "id": "dDVNLI4H9Zom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OrdinalEncoder"
      ],
      "metadata": {
        "id": "5ZEWnPr29lqh"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_validation, labels_train, labels_validation = train_test_split(X, labels, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "HGl23lUg9sf1"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hago un label enconding para description\n",
        "ordinal_encoder = OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1)\n",
        "X_train['description'] = ordinal_encoder.fit_transform(X_train['description'].astype(str).values.reshape(-1,1))\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "sPjBpLl49wLV",
        "outputId": "279bf98d-c89b-47d6-dfb7-8ebda783b225"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-afe5cd5d-0010-4a13-85fc-7e7acebfeb3b\">\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>description</th>\n",
              "      <th>est_delivery</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>428450</th>\n",
              "      <td>2970.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378537</th>\n",
              "      <td>794.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2014</td>\n",
              "      <td>9</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6593</th>\n",
              "      <td>995.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429028</th>\n",
              "      <td>2377.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134691</th>\n",
              "      <td>2970.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110334</th>\n",
              "      <td>900.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259337</th>\n",
              "      <td>1364.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2014</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366014</th>\n",
              "      <td>1369.0</td>\n",
              "      <td>11</td>\n",
              "      <td>2014</td>\n",
              "      <td>9</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131998</th>\n",
              "      <td>2812.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122024</th>\n",
              "      <td>462.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>448208 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afe5cd5d-0010-4a13-85fc-7e7acebfeb3b')\"\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",
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              "    <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-afe5cd5d-0010-4a13-85fc-7e7acebfeb3b 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-afe5cd5d-0010-4a13-85fc-7e7acebfeb3b');\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",
              "  "
            ],
            "text/plain": [
              "        description  est_delivery  year  month  day\n",
              "428450       2970.0             2  2014     10   10\n",
              "378537        794.0             5  2014      9   26\n",
              "6593          995.0             1  2014      6   10\n",
              "429028       2377.0             5  2014     10   10\n",
              "134691       2970.0             4  2014      6   10\n",
              "...             ...           ...   ...    ...  ...\n",
              "110334        900.0             1  2014      6   10\n",
              "259337       1364.0             2  2014      8   15\n",
              "366014       1369.0            11  2014      9   19\n",
              "131998       2812.0             2  2014      6   10\n",
              "122024        462.0             3  2014      6   10\n",
              "\n",
              "[448208 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hago los encodings del validation\n",
        "X_validation['description'] = ordinal_encoder.transform(X_validation['description'].astype(str).values.reshape(-1,1))\n",
        "X_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HuYJA-4--D5S",
        "outputId": "3a41674b-69b8-43c4-c1b2-d579f3aea813"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-18460844-8be0-42e2-94ed-0dcaf1f157c0\">\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>description</th>\n",
              "      <th>est_delivery</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>237213</th>\n",
              "      <td>878.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466449</th>\n",
              "      <td>2812.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412003</th>\n",
              "      <td>3051.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11583</th>\n",
              "      <td>3170.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145560</th>\n",
              "      <td>1889.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365907</th>\n",
              "      <td>3027.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2014</td>\n",
              "      <td>9</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35617</th>\n",
              "      <td>1361.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181028</th>\n",
              "      <td>3433.0</td>\n",
              "      <td>14</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112997</th>\n",
              "      <td>1963.0</td>\n",
              "      <td>8</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61380</th>\n",
              "      <td>3515.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2014</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>112052 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18460844-8be0-42e2-94ed-0dcaf1f157c0')\"\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-18460844-8be0-42e2-94ed-0dcaf1f157c0 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-18460844-8be0-42e2-94ed-0dcaf1f157c0');\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",
              "  "
            ],
            "text/plain": [
              "        description  est_delivery  year  month  day\n",
              "237213        878.0             4  2014      8    6\n",
              "466449       2812.0             4  2014     10   19\n",
              "412003       3051.0             5  2014     10    6\n",
              "11583        3170.0             3  2014      6   10\n",
              "145560       1889.0             5  2014      6   10\n",
              "...             ...           ...   ...    ...  ...\n",
              "365907       3027.0             7  2014      9   19\n",
              "35617        1361.0             3  2014      6   10\n",
              "181028       3433.0            14  2014      6   18\n",
              "112997       1963.0             8  2014      6   10\n",
              "61380        3515.0            10  2014      6   10\n",
              "\n",
              "[112052 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Armado de la red"
      ],
      "metadata": {
        "id": "tMWRdzGV-MXL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Dense?"
      ],
      "metadata": {
        "id": "r66TCCYcClp3"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.engine import input_layer\n",
        "model = Sequential()\n",
        "\n",
        "# Agregamos una capa, esta debe tener una entrada de la cantidad de columnas que tenemos\n",
        "\n",
        "model.add(Dense(5,activation='relu',input_shape=(5,),name='capa_1'))\n",
        "model.add(Dense(3,activation='relu',name='capa_2'))\n",
        "\n",
        "# Al agregar Dropouts a nuetra red, bajamos la probabilidad de que nos de overfitting\n",
        "model.add((Dense(1,activation='relu',name='capa_3')))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEThkzov-PXh",
        "outputId": "2a59502f-f1d2-48f2-863a-f635880f1a1a"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " capa_1 (Dense)              (None, 5)                 30        \n",
            "                                                                 \n",
            " capa_2 (Dense)              (None, 3)                 18        \n",
            "                                                                 \n",
            " capa_3 (Dense)              (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 52\n",
            "Trainable params: 52\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compilado"
      ],
      "metadata": {
        "id": "BbBamuPkD1w1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Como estamos ante una regression, para el optimizer se usará RMSprop()\n",
        "# Para el loss, usare mse\n",
        "model.compile(optimizer='rmsprop',loss='mse')"
      ],
      "metadata": {
        "id": "dvvDjnlfDVHR"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GcSaQe2bM2pG",
        "outputId": "fd94c21b-a906-457b-cef3-ddad02608fea"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "description     0\n",
              "est_delivery    0\n",
              "year            0\n",
              "month           0\n",
              "day             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_validation.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vN4eYXOiM8e_",
        "outputId": "1b5e1a33-7ab1-4e3e-c086-f1acd7c091f3"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "description     0\n",
              "est_delivery    0\n",
              "year            0\n",
              "month           0\n",
              "day             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del X_train['price']\n",
        "del X_train['Price']\n",
        "\n",
        "del X_validation['price']\n",
        "del X_validation['Price']"
      ],
      "metadata": {
        "id": "n57jKqRHN-w_"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entrenamiento"
      ],
      "metadata": {
        "id": "OoZqTqyrEhVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "custom_early_stopping = EarlyStopping(monitor='val_loss',\n",
        "                                      restore_best_weights=True,\n",
        "                              min_delta=0,\n",
        "                              patience=5,\n",
        "                              verbose=0, mode='auto')"
      ],
      "metadata": {
        "id": "k78tTejnbMTL"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,labels_train,batch_size=2048, epochs=100, verbose=1, validation_data=(X_validation, labels_validation),callbacks=[custom_early_stopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WJDyGsKEg9W",
        "outputId": "2757c943-553c-4f5f-f552-2e85be75cbe8"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2716.7954 - val_loss: 2702.4929\n",
            "Epoch 2/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2716.8655 - val_loss: 2698.1313\n",
            "Epoch 3/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2717.1892 - val_loss: 2697.7441\n",
            "Epoch 4/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2715.9556 - val_loss: 2700.5212\n",
            "Epoch 5/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2715.6025 - val_loss: 2696.9873\n",
            "Epoch 6/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2714.6853 - val_loss: 2709.6733\n",
            "Epoch 7/100\n",
            "219/219 [==============================] - 1s 4ms/step - loss: 2715.0598 - val_loss: 2699.4551\n",
            "Epoch 8/100\n",
            "219/219 [==============================] - 1s 4ms/step - loss: 2715.1143 - val_loss: 2695.0161\n",
            "Epoch 9/100\n",
            "219/219 [==============================] - 1s 4ms/step - loss: 2712.8931 - val_loss: 2707.3804\n",
            "Epoch 10/100\n",
            "219/219 [==============================] - 1s 4ms/step - loss: 2714.6343 - val_loss: 2700.3154\n",
            "Epoch 11/100\n",
            "219/219 [==============================] - 1s 4ms/step - loss: 2715.2095 - val_loss: 2694.5610\n",
            "Epoch 12/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2713.9006 - val_loss: 2701.7937\n",
            "Epoch 13/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2712.7312 - val_loss: 2696.7668\n",
            "Epoch 14/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2714.3003 - val_loss: 2700.5723\n",
            "Epoch 15/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2714.0762 - val_loss: 2699.6982\n",
            "Epoch 16/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 2713.9695 - val_loss: 2744.0349\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "fsi9NRCmUyeO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTAGH5NvKL20",
        "outputId": "90b6d657-1b25-484d-ea57-079b547b607e"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'loss': [2723.250244140625,\n",
              "  2723.2490234375,\n",
              "  2723.25,\n",
              "  2723.250244140625,\n",
              "  2723.24853515625,\n",
              "  2723.25],\n",
              " 'val_loss': [2711.161865234375,\n",
              "  2711.161865234375,\n",
              "  2711.161865234375,\n",
              "  2711.161865234375,\n",
              "  2711.161865234375,\n",
              "  2711.161865234375]}"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X_validation,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcW1e3gs1xI6",
        "outputId": "7562c7be-bb99-4f9c-a53d-27842bbf8954"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3502/3502 [==============================] - 8s 2ms/step\n"
          ]
        }
      ]
    }
  ]
}