ML_clasificacion_con_SKLearn.ipynb

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    {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ahcamachod/1918-machine-learning-clasificacion-con-sklearn/blob/aula-5/ML_clasificacion_con_SKLearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# **Machine Learning: Clasificación con SKLearn**\n",
        "\n",
        "En este notebook estaremos desarrollando varios ejercicios para entender cómo funcionan algunos algoritmos de clasificación.\n"
      ],
      "metadata": {
        "id": "c2kK7nYF0sub"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aula 1: Introducción a la clasificación."
      ],
      "metadata": {
        "id": "OoZr1gK21J3_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Mj0Ithc40lF5"
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      "source": [
        "# features 1 = sí y 0= no\n",
        "# tiene el pelo largo?\n",
        "# tiene las uñas afiladas?\n",
        "# hace miau?\n",
        "\n",
        "perro1= [0,1,1]\n",
        "perro2= [1,0,1]\n",
        "perro3= [1,1,1]\n",
        "\n",
        "gato1= [0,1,0]\n",
        "gato2= [0,1,1]\n",
        "gato3= [1,1,0]\n",
        "\n",
        "x_train= [perro1, perro2, perro3, gato1, gato2, gato3]\n",
        "y_train = [1,1,1,0,0,0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "model = LinearSVC()\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taHXCq4r1L83",
        "outputId": "13fc18d0-7b36-4107-f28c-d42733d57687"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC()"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "animal_misterioso= [1,1,1]\n",
        "model.predict([animal_misterioso])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLgqisZM1L_Q",
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      "outputs": [
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              "array([1])"
            ]
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          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
      "source": [
        "misterio1 = [1,1,1]\n",
        "misterio2 = [1,1,0]\n",
        "misterio3 = [0,1,1]\n",
        "\n",
        "x_test = [misterio1, misterio2, misterio3]\n",
        "y_test = [0,1,1]"
      ],
      "metadata": {
        "id": "6jjSXkruE-oW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "previsiones= model.predict(x_test)"
      ],
      "metadata": {
        "id": "-BtJLBmIE-q8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correctos = (previsiones==y_test).sum()\n",
        "total = len(x_test)\n",
        "tasa_de_acierto = correctos/total\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUNaS3qcFpBf",
        "outputId": "685f16e8-1abd-4478-aa97-ed0f4107d55c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La tasa de acierto fue de: 33.33%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "tasa_de_acierto = accuracy_score(y_test, previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ROh3k3WFpD3",
        "outputId": "b97d4765-ddf9-4855-874c-bf016c94faf0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La tasa de acierto fue de: 33.33%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aula 2: Pruebas replicables, estratificación, lectura de datos de internet."
      ],
      "metadata": {
        "id": "4C3Lzaqc1M0c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "uri = 'https://gist.githubusercontent.com/ahcamachod/38673f75b54ec62ffc290eff8e7c716e/raw/6eaa07e199d9f668bf94a034cb84dac58c82fa4f/tracking.csv'\n",
        "datos = pd.read_csv(uri)\n",
        "datos.sample(5)"
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      "execution_count": 8,
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              "    home  how_it_works  contact  bought\n",
              "50     1             1        0       0\n",
              "75     0             1        0       0\n",
              "23     0             1        0       0\n",
              "47     1             0        1       1\n",
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      "cell_type": "code",
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        "mapa = {\n",
        "          \"home\":\"principal\",\n",
        "          \"how_it_works\":\"como_funciona\",\n",
        "          \"contact\":\"contacto\",\n",
        "          \"bought\":\"compro\"\n",
        "        }\n",
        "\n",
        "datos = datos.rename(columns=mapa)\n",
        "datos.sample(3)"
      ],
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            "text/plain": [
              "    principal  como_funciona  contacto  compro\n",
              "6           1              1         0       0\n",
              "39          1              0         1       1\n",
              "60          1              0         1       1"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "x = datos[['principal','como_funciona','contacto']]\n",
        "x.head()"
      ],
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      "execution_count": 10,
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              "   principal  como_funciona  contacto\n",
              "0          1              1         0\n",
              "1          1              1         0\n",
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              "    .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>principal</th>\n",
              "      <th>como_funciona</th>\n",
              "      <th>contacto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c3078f6-f9e5-4b98-8604-11fa80703445')\"\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-3c3078f6-f9e5-4b98-8604-11fa80703445 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-3c3078f6-f9e5-4b98-8604-11fa80703445');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = datos.compro\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gp0OPjzzK6o4",
        "outputId": "b6539a91-b0fc-4bbf-bac4-9cb674dfcfdb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: compro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11HwtyFDK6rd",
        "outputId": "463009b7-bfe9-49c4-9de1-ab7e0ea0dab9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x[:75]\n",
        "y_train = y[:75]\n",
        "x_test = x[75:]\n",
        "y_test = y[75:]"
      ],
      "metadata": {
        "id": "_XtpztMLK6tx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bv75YZRnQmAp",
        "outputId": "bbbf9189-cec5-46a5-f554-31dc6d8cf33c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    48\n",
              "1    27\n",
              "Name: compro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "27/75"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bl8Yr5JsQx4g",
        "outputId": "69ef8022-a169-49cf-b56f-8447cfef8f01"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.36"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSGPAQvuQmHc",
        "outputId": "e487ed04-77dc-4f8b-e760-e3e5a056a447"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    18\n",
              "1     6\n",
              "Name: compro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "6/24"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhMtiPQvQ2AF",
        "outputId": "89aa4ae0-4262-4750-8828-3883476935d8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Entrenaremos con {len(x_train)} elementos y probaremos con {len(x_test)} elementos.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhrG4njSLW-R",
        "outputId": "2400c901-7d70-4ea8-9098-79ea3037db14"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entrenaremos con 75 elementos y probaremos con 24 elementos.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "model = LinearSVC()\n",
        "model.fit(x_train,y_train)\n",
        "previsiones= model.predict(x_test)\n",
        "\n",
        "tasa_de_acierto = accuracy_score(y_test, previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWG99xGdLXBG",
        "outputId": "0465327f-5ebf-4443-d619-83013414ccda"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La tasa de acierto fue de: 95.83%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "SEED=42\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25,random_state=SEED)\n",
        "\n",
        "model = LinearSVC()\n",
        "model.fit(x_train,y_train)\n",
        "previsiones= model.predict(x_test)\n",
        "\n",
        "tasa_de_acierto = accuracy_score(y_test, previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzYLtx0pLXC-",
        "outputId": "02a3a0b2-d67c-47f8-f590-2fa6e01f1fe8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La tasa de acierto fue de: 96.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "SEED=42\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25,random_state=SEED, stratify=y)\n",
        "\n",
        "model = LinearSVC()\n",
        "model.fit(x_train,y_train)\n",
        "previsiones= model.predict(x_test)\n",
        "\n",
        "tasa_de_acierto = accuracy_score(y_test, previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eajRmeKRQUdm",
        "outputId": "1037ca44-14f7-4989-9bbf-d4bcbfc2370d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La tasa de acierto fue de: 96.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJrg_1u9QUfx",
        "outputId": "341f732d-afb4-47c1-cd27-24c05b73f654"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    49\n",
              "1    25\n",
              "Name: compro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "25/74"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVNcg2PCRdUE",
        "outputId": "dd0eba6c-9bd5-4fb4-a1a5-f8cd2471dbf1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.33783783783783783"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kWbimnCRUx1",
        "outputId": "f249e36e-4df6-43c7-da6c-c3ad7c64fc6d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    17\n",
              "1     8\n",
              "Name: compro, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "8/25"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dijb31dVRiYr",
        "outputId": "4bfd22c0-590e-406f-91dc-b30f3a06baf3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.32"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aula 3: Proyecto de baja dimensionalidad y Baseline"
      ],
      "metadata": {
        "id": "IV4SX1KN1PVH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uri = 'https://gist.githubusercontent.com/ahcamachod/7c55640f0d65bcbd31bb986bb599180c/raw/1b616e97a8719b3ff245fcdd68eaebdb8da38082/projects.csv'\n",
        "\n",
        "datos = pd.read_csv(uri)\n",
        "datos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "piqXs5To1RxA",
        "outputId": "d2348604-eed3-4e69-94c4-eee6efb9ad30"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   unfinished  expected_hours  price\n",
              "0           1              26    192\n",
              "1           1              88   9015\n",
              "2           1              89   2577\n",
              "3           1              42    275\n",
              "4           1              39    170"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc809e2e-8548-45f3-ae60-ae44259dccb3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>unfinished</th>\n",
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              "      <td>170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc809e2e-8548-45f3-ae60-ae44259dccb3')\"\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",
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              "  </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",
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              "          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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapa = {\n",
        "        'unfinished':'no_finalizado',\n",
        "        'expected_hours':'horas_esperadas',\n",
        "        'price':'precio'\n",
        "        }\n",
        "\n",
        "datos = datos.rename(columns=mapa)\n",
        "datos.sample(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "MS5v9O9z1SQn",
        "outputId": "d032ba23-afba-4e32-b312-11afe93aa06f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      no_finalizado  horas_esperadas  precio\n",
              "604               1               20     103\n",
              "65                1               15     174\n",
              "2082              0               68    4586"
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cambio = {1:0, 0:1}\n",
        "\n",
        "datos['finalizado'] = datos.no_finalizado.map(cambio)"
      ],
      "metadata": {
        "id": "p27AEgFG1STd"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datos.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ls00Q2bVUz-B",
        "outputId": "bae4a5c7-3c6e-49b6-b83d-095e87d1243e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      no_finalizado  horas_esperadas  precio  finalizado\n",
              "1455              0               37     448           1\n",
              "1798              0               14     289           1\n",
              "2109              0                8     242           1\n",
              "1059              0               46     915           1\n",
              "1926              0               24     217           1"
            ],
            "text/html": [
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              "      <th>horas_esperadas</th>\n",
              "      <th>precio</th>\n",
              "      <th>finalizado</th>\n",
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              "      <td>1</td>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\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-85b90160-e113-48bf-a77b-57daebda6179 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85b90160-e113-48bf-a77b-57daebda6179');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.scatterplot(x='horas_esperadas', y='precio', data=datos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "e95lHZOuU0Cq",
        "outputId": "ad38da68-10ff-4591-d886-559fe266cc35"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f57fec7d150>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='horas_esperadas', y='precio', hue='finalizado', data=datos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "qwpfSSyfU0FY",
        "outputId": "bbe2d75d-3e03-4468-bf31-d1b3c5fce356"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f57fe3e4590>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.relplot(x='horas_esperadas', y='precio', hue='finalizado', data=datos, col='finalizado')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "x7WGvfyiVhru",
        "outputId": "f1073d14-196f-42c8-950b-999026e0a57f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f57fdf01a90>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 776.375x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "x= datos[['horas_esperadas','precio']]\n",
        "y= datos.finalizado\n",
        "\n",
        "SEED = 42\n",
        "np.random.seed(SEED)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25,stratify=y)\n",
        "print(f\"Entrenaremos con {len(x_train)} elementos y probaremos con {len(x_test)} elementos.\")\n",
        "\n",
        "model = LinearSVC()\n",
        "model.fit(x_train,y_train)\n",
        "previsiones= model.predict(x_test)\n",
        "\n",
        "tasa_de_acierto = accuracy_score(y_test, previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sB_EZ8WRVhuN",
        "outputId": "bfe07353-caaf-461a-9336-2eb009c713d7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entrenaremos con 1617 elementos y probaremos con 540 elementos.\n",
            "La tasa de acierto fue de: 57.59%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_previsiones = np.ones(540)\n",
        "tasa_de_acierto = accuracy_score(y_test, base_previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRLjBb1FXCdP",
        "outputId": "9ee738f4-80d7-485e-842b-6b6a1741e205"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La tasa de acierto fue de: 52.59%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='horas_esperadas', y='precio', hue=y_test, data=x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "G6fFPWvbVhwx",
        "outputId": "88668f39-318b-4e2a-a45a-8654e0a57d3f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f57fec47510>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_min = x_test.horas_esperadas.min()\n",
        "x_max = x_test.horas_esperadas.max()\n",
        "y_min = x_test.precio.min()\n",
        "y_max = x_test.precio.max()"
      ],
      "metadata": {
        "id": "a7AqHsjRa4U4"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pixels = 100\n",
        "eje_x = np.arange(x_min, x_max, (x_max-x_min)/pixels)\n",
        "eje_y = np.arange(y_min, y_max, (y_max-y_min)/pixels)"
      ],
      "metadata": {
        "id": "djinbfvea4Xd"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xx, yy = np.meshgrid(eje_x, eje_y)\n",
        "puntos = np.c_[xx.ravel(), yy.ravel()]\n",
        "puntos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvMcnsGma4bO",
        "outputId": "8bcc2c5e-0038-4214-bd21-5fbc7f952c72"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.000000e+00, 1.000000e+02],\n",
              "       [1.990000e+00, 1.000000e+02],\n",
              "       [2.980000e+00, 1.000000e+02],\n",
              "       ...,\n",
              "       [9.703000e+01, 2.811502e+04],\n",
              "       [9.802000e+01, 2.811502e+04],\n",
              "       [9.901000e+01, 2.811502e+04]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z = model.predict(puntos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcYdW_Lia4eP",
        "outputId": "81fb4366-13e1-4728-aaff-46efd8c13388"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearSVC was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z = Z.reshape(xx.shape)\n",
        "Z"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1BeGkJbdE5U",
        "outputId": "dac81eaa-f216-4381-e828-9d4ca1b67495"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [1, 1, 1, ..., 1, 1, 1],\n",
              "       [1, 1, 1, ..., 1, 1, 1],\n",
              "       [1, 1, 1, ..., 1, 1, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.contourf(xx, yy, Z, alpha=0.3)\n",
        "plt.scatter(x_test.horas_esperadas, x_test.precio, c=y_test, s=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "xaYgZ3ordE-r",
        "outputId": "7c1c6a04-1382-465c-c764-087c060054a6"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f57f9d81510>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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3TWOmikDoSRzJpjLrgaZpswE0GSilVAMYYxBpgnkPJEjYdyGOK7fxbTWCdhMppdQ+Wvj5Ri5o8xSfjP+uSdqL+UZiewY0SVsNpclAKaX2kcsteP0WHq+V7lCajHYTKaXUbvhCr+Ny1hIK/grEs7O837GdeWnrgTVdux4ZKKXUblixhVj2SjDRRre1dV0FL987k7Jt4SaIrOlpMlBKHTiccjLKfou/clyTNCfiIIDgNLqtT55bwpt/ncvXbzd+qsxU0G4ipdQBw+VsxUUY7MImaS8UvAZxSjCurEa3dfr1h5HbJYPjLurbBJE1PU0GSqkDhuPuRVXgehxXHgALPttAPOYweHTBnlc0EcALu1wq6ljdwOrWJLG1ae/n5Kv7NUlbqaDdREqpA4rtGYCxOgDw5ws+4oFzPsRxdj+jo8teT1b5zXgjbzZXiC2SHhkopQ5YN/57FLGIjcu1+5vDjPhxpC1GcpoxspZHk4FS6oB1zLm961HLwkgAI/6Ux9OSaTeRUqpVE1OG5WzCstekO5S00iMDpdQBxWVvxHG1BcnYfSVjELMNI+1xrO5UZP4JI42/Ymh/pkcGSqkDhjjbyKh8gGDyPgMrvhQrvrhWPU90CpkV9+COzWTDshIu7fIWrz84t7nDbVE0GSil9muu+Eoyyu/AHf0aI22JuQcS8xwFQKBqHMGqx8DUvGnMsQqwXZ1xrE44tiEaihOPNP7Gsv2ZdhMppfZrnthMXKYMT/QL4t6jCQev27ks7L8ciIPU/N1rWz2I+M/DceVTcKjFqyU/2+tw1O8/Np/XHpjD/ZPOpFv/dqnYlbTSZKCU2q9FfOfhuNoR8wyvtSzuPaLOdbyRyfii7xH2/5iY97h6zUtQVRajqixKLGo3OuaWSJOBUmr/5nIT843ep1XinsNwOeuIuw+p9zoX3nEE5/2/wVjuA7N3fa97JSLPiMgWEVlYraydiEwSkeXJf3OS5SIij4hIoYjMF5Eh1da5Mll/uYhcWa38CBFZkFznEWmSqYOUUmr3HCufcPAajKvDPq13oCYCqN8J5OeAU3cpux2YbIzpC0xOvgYYA/RNPq4FxkEieQB3A0cBw4C7v08gyTrXVFtv120ppVRa/OPyT/hF/5eJVMXTHUrK7TUZGGOmAtt3KT4LGJ98Ph44u1r58yZhOpAtIp2BU4BJxpjtxpgdwCTg1OSyNsaY6cYYAzxfrS2llEqrih1hyorDOHbNK40WfbGRC7Of4tMXlqYpsqbX0HMGecaYouTzTUBe8nk+sK5avfXJsj2Vr6+jvE4ici2JIw4KCjo2MHSl1P5o2uuFrJ5fzCV/HLbHsYaa0h/eOx3HMVhW83UP/d9PJ7N6fjF/+/I8PL7mm1az0XuY/EW/+yEBm5Ax5kljzFBjzNDc9m2bY5NKqRbi1Qdm8fY/51G6NYQVm0+w4m+IU5zSbYpInYmg/3FdeK3kGk647OA9rv/Ow/O47fi3qNgRqfc2t6wpZ8uacuKx5r3voaFHBptFpLMxpijZ1bMlWb4BqD5weNdk2QZg1C7lnyXLu9ZRXymlarjnvxWUbNxETkcX7vB3WM5qXM4WbFduyrb58r0zWfntNm579RTcnn3/7bx4WhEr5myjfHuYzBxfvda57+MzceIGj89CnGLc8SWYwCik2hzMqdDQI4N3ge+vCLoSeKda+RXJq4qGA6XJ7qSJwMkikpM8cXwyMDG5rExEhievIrqiWltKqdbMGAIVD+Greg6Arr0iHH50CWCI+M+jMuMubPehKQ1h5gdrmPvxOsIVsQatf8sLo3mq8DI6965/T4ZluXZ2D/nC7+APvwzRLxu0/X2x1yMDEXmZxK/69iKynsRVQX8BXhORq4E1wIXJ6hOA04BCoAq4CsAYs11E7gNmJuvda4z5/qT0L0hcsRQAPkw+lFKtnanE7azAOCuJ8BNCwV8AZufdxI7Vqck3GYvY3DDgZfIPzubu98dy38QzCFfG6v2rflcen0VOp2CD44n6TsVxdcLvParBbdTXXpOBMebi3Sw6sY66BrhhN+08AzxTR/ksYMDe4lBKtTKuTEK+i+D70UQlMTV9qjm2wbETp0Ezsn1kZDcsETRJLFYXolYXAhJI+bb0DmSlVIsV9x3frNvz+Cx+/tjxtMltfRPdaDJQSqmkUHmUv1wwkey8AP9ZcXm6w2lWmgyUUiopkOXl6n8cQ26XPUyMU4e1i7bTrktGg88ttAQH7kAbSqn9jjhbyCy7GW/4g7TFMOa6AQw7o2e96xetKOXmo97gwYsmpjCq1NNkoJRqOYwBYsnHrsviYMIAuOIb8Ybe2Pk6ndp1zuDIsd0ZeUnfdIfSKNpNpJRqMYyVR0XWI8krh2oKVD2MZa+mIutB/FXjsNgOuIgGzm3Ahn6YAxnAii/CsbpjXDXnQfZXjcOyV1OZeS9I3V1AvqCb2145Zd9jqCYec3BZ0mzDbNRFjwyUUi3Lbkaxd1z5OK4ugBvjykmWtW/QJqrPgWzZywiGxuELv7qb2vs22s4Hjy/gvUfm17t+pCrOVd3Gc9fJ7+7TdpqaHhkopdJKnGK8kc+I+hK3LgWqHiPqHUncO6JGPdvqjREf4CaU8Qssex221adB26wxB7KrA1HP0cQ9tW/sCgev3+e2X7jrG+yYw9hfHlavGdRclpDTKUh2XuJegtf/PJs1i7Zz0/gTm3WAPE0GSqm08sRm4Y19iuNqh+3ujeVsxLJXEadmMvBFJuAyW4h5T8S42mC7G95Hb7v7UpX5+52vI4HLGtzWrh6YfBaOY+qVCCBxb8Mjcy/a+fqL1wvZuKyUcHmsWW9402SglEqrqPd4HGlD3DM48do9lLh7WK16Ed9oLHsdRrJqLWtJeg1qWNfV9+7/+CxCFdFmv/NZzxkopZqdJ/IZwYoHwakA/NhWb8CHOzYfb3wWvvDLtdbxRSbijU1FTFmzx9uc2rT3k9ejTbNvV48MlFLNzrKXYzlrcZlyPJEJeGOfE/GeTMxzFAaLuLt/rXVCwatxOdsxrvqPAHr/2RMIV8W5b+IZ9e62aa00GSilml048BMipgLjykmeFAYjPozViYo2j9S5jmN1w7G67dN2NiwrIVwZw5jdXqSkkjQZKKWan3gwkrg8NOYZCaaSuPsYxCkjUDWOqGckcd/wRm/mkW8vAkOTX79fXhzm3Ufmc9JPDiGvZ/N36aSCnjNQSqWVL/IKvtiXeCNvYsUWYjlr8UXfa5K2PV4rJfMIf/PBav73j2/5+JklTd52uuiRgVKqWbjsNYiJYbtr3hsQ84zAstcQ8x6Dy6kAwDEtewjpEef3wbENw8b2SHcoTUaTgVKqWQQrH0EIU571CMgPv9YdqxNx90CMqyNxqy8hLvvhHgITAiwQb3qC3g1f0M3oq1I75WZz02SglGoWYf+5iInUSAQAntgcvLFpOK5OxHw/Iu49OrHAxMgsvx3HlUtV5h/SEHHroslAKdUs4t5j6yyPeo7D4BDzHL3LEgvb6oHjyk19cEpPICulmt+Xb67grxd/TKg8iif2Nf7Ie3jis2pWEhehjJuIBK5o8HY+fnox7z+2oJHRtg6aDJRSze7zF+bzzXur2bSyFNvdh7jVB9uq/4Qy9TX+juk8d/vXOE79Rx796s0VLJy6cY91ls7YzJX5zzHtjcLGhthiaDeRUqrZ3froWrauXEd+/wocqyehjJtSsp0/fjgWO27qfZ9BqDzKQ1dO3uscyOHKGJWlUSpLok0VatppMlBKpYaJEqh6krj7EGK+k2os8nY6j24dColZ3RF7C97IB0T9Z2Ka+PxAnyM67lP9QJaXK/50FLn5mXusd/gJXXl5+9V4vLXvYbjtuLeIRR3+Mf28WkNg2HEHy90yO2RaZlRKqf2GFVuGv/JRcMprlIupxG0vwRP7ttY6jtWVmHcUiAtf5F288Vl4IxOaJJ65k9axaWXDBrMLVcR4/s4ZvHj3N3utW1ciAAhXxglX1p62c8oLS7ko+z/M+Xhtg2JLNT0yUEo1ii/8MpbZghP9mKj/vJ3lxpVDReYfMZKxx/VjniOx4kuIu4c2OpaiwlIeOOdDeg5qz9+m7ft0mL6gm2PP703n3g0fYuL/Zl8AUOuowJfhxp/pwetvmV+7Ysy+TenWUgwecpD5bNpj6Q5DqVbPii3HE/2EcOAykAwsewW21T0tN4rZcYcX7/6GfiM6M3RM92bffqq0DY5usrZEZLYxplbmbZkpSim137A9fbE9iTuG3bE5BEJPE/GeSNRf85e5O/o1YiLEfKNSFovldnHFA40f4K410nMGSqkmY1s9ibkHEHcPqrXMH34Nf+R1MHaNcmMMW9dVsL/2UhwoGpUMRGS1iCwQkW9FZFayrJ2ITBKR5cl/c5LlIiKPiEihiMwXkSHV2rkyWX+5iFzZuF1SSqWLceUQDl6P4+5V19I615n0zBKuP/Qlpr6yPLXBqT1qiiODHxljBlXrg7odmGyM6QtMTr4GGAP0TT6uBcZBInkAdwNHAcOAu79PIEqpA0fYfwlh38W1xibqenAOXfq2pXOf7DRFljrT31nJv66dQiQUT3coe5WKbqKzgPHJ5+OBs6uVP28SpgPZItIZOAWYZIzZbozZAUwCTk1BXEqpJuINvUmg4u+1unz2JO4dRsw3olZ5vxGdeWTuRRx05L7dE1Bd2bZwi+xmmjBuEZ+/tJzNDbzUtTk1NhkY4GMRmS0i1ybL8owxRcnnm4C85PN8YF21ddcny3ZXXouIXCsis0RkVvG20kaGrpTaF+7YPKz4MgC8sc9xO6tw2Ruw4ssIVD6KOCV7XP/VB2bx7G1fNXlccyau5ac9nuftf85r8rYb6+bxJ3Lfx2fSrX+7dIeyV41NBiOMMUNIdAHdICLHV19oEqm6ydK1MeZJY8xQY8zQ3Pb1nxRbKdVIJkYg9CSBqqcACAWuJOI5EccqwB2bi9tegstet8cmPnxiERPGLcKOO00aWk7nIB0KMsnv2/K+E7Lzghx6TKd0h1Evjbq01BizIfnvFhH5H4k+/80i0tkYU5TsBtqSrL4BKKi2etdk2QZg1C7lnzUmLqVU421dW87tI//HqdcN4ILbhxDyX77zBjLbcwS25wiAxNDTJoxt9U6saOIgtb9aHvziHOLRph+OoefA9oxbckmTttkaNfivIiIZIpL1/XPgZGAh8C7w/RVBVwLvJJ+/C1yRvKpoOFCa7E6aCJwsIjnJE8cnJ8uUUmkUDdmUbg1TurUKgLh3OLbnsFr1PLEv8Ma/wW1/hxVfRlb5r/FEPq1V77MXl/Hx04tTHrdqmMYcGeQB/0vecu0GXjLGfCQiM4HXRORqYA1wYbL+BOA0oBCoAq4CMMZsF5H7gJnJevcaY7Y3Ii6lVEMZg5hSjLQl/+BsXi6+Grd3z78Zo76x2FYv4u6BuJwNGIJ1DkHx4ROLqCyNcsUDw9MyWJs7+gWe2CxCwetAginbzsKpGzGO4bBRdZ76BCAajvPhE4s44tRudD2kZVw82eBkYIxZCRxeR3kxcGId5Qa4YTdtPQM809BYlFJNwxP7Gn/4RUL+y4l7h+Px1T0Ymy/0OpZdSFXGbzESwLg6AhaO1Z2KNn+rc536dhPFojZuj6vW2D6N5Y4vxG0X4nLKcKzUJYMHzplAPOrwWtk1u92HRV8U8d/fz6Bw9lZ++9+T6qzT3HQ4CqXUTo6rA460x7g67LGey1mHy9kAJoov8gHe2FSqAj9n44buPP6Lz7nwjiPof1yXGuvk9ag5+JsxptaX5bolO7jpyNc5/YYBXPXgMU2zU0nhwNVETOle962xTv/FYdh27X2rbsDILlz9j2MZdFLXlMayL3Q4CqXUTra7L5VZf8R2995jvVDwV1Rk/Q1cmcQ9Q4i5++NYBaz6dhuLphYx5+M9X1n09C1fckmHZ9i6tuaw116/RZv2ftp2CDR6X2oRb70SwbeT17Ni7tYGb+b9xxbw/r/mY4yhYkeEVfO31arj8VqMua4/nXu3nCug9MhAKbV3xiaj4h4cV7vErGTi5vuvD9vdF9udGKjuqLPa8pep59B9L9fVW5Yr0V20y6/nvJ5teGZ1w+c8bqxQeZT7z5pAdqcg/ym8rEFt3PjkKJzkkcHfL5vEws838n+zLmj0ueOauY0AABsVSURBVIEta7fy5K0vcMmd59JrYNOPyKrJQKnWwlQB3sQXuUle6y81OwdWztvGs7d+xdV/P5Yeh+0665iN7OWOYxGhz5C9//r+yYNH85MHj96H4GuKVMVZs7CYvkd2bNJzC4EsL8PP6kGnXg3/xT7i/D47n//osoMIZHloX7DnmdPqY84nC/j8ta8oOLiLJgOlVMOIXUxG5R9wJJeqrHsJVj6ImFIqMx+oMVbQd19tYsmXm1g6fXPNZCBWom4zsm2HB87+kI49svj5v2rcz8r4O77m46eXcOebpzLklG5Nts1QeZTp76wmOy/AZfcdVWOZO/ot3ugkQsGfYVz1+5U/8uKDGHnxQU0S2+grRtKucw4DR/ZrkvZ2pclAqQOJE8KKL8L2HIGYcrzht4j5foQhgABiEtMxGgkAcUAwxhCujBPI9HDKtf046Kg8eg1qX7vtJr66Z2/smMPiaUVsXVdRa9nws3uxZW05PQY27ZzJgSwvt758Mm06+Gsts+zvsJzVuJyt2PVMBk3JclsMGzM4Ze3rTGdKHUACFX/B7awj4j0Vx8onEHqaqGckkcCFiLMdI0GQml90j1//OZ/+dykPz7mQ/IOyE4PPSd2XlO4TEyVQ9RRx9yHEfDWvNv/v72cw+fnv+MfX51FUWMbjN3zOr/7zIw4ZXnPohvLiMG6fRSDT0/h4GsvEcTnbcKzmH16iOWY606uJlDqAxN1DMPiJuw8h7h5EVeDnRPxngBPDX/VvvOEPaq3TuaCIjvlxAsEQ7thsssp/hbuOSez3lZhK3PZiPLG5tZaFyqOEyqLEYw5FK0vZsrqcLWvKa9XLyvW3jEQAIO60JILmokcGSu3nKksilG+P0KnX7idxd0UXkRF+HINQ0ebRGssyyn+Hy5QQ8l+DETeB0DOEgj/Ddje+b1qcrRjJBKl5qagxBscxWJYLYwwlW0Lk5KXuRrD9nR4ZKKVqMwZf6DW84QkA3HvmBG4c+ArbiyprVQ1XJs4R7Gly+oh3LHGrN3FPX2zPACraPNQkiQBIXNcvte8ZEBEsy7XzeUtNBDs2VbFucesYHUeTgVL7HRtv7HM8sc8BOOa8XhwxphtZ7WqeC1j0xUYuy3uWt/4+F+PqhEGwXT1qtRb3HU0o42aoYzyh1u7u097jpmFvUF4cTncoKadXEym1vxE3lRl3YSTRl372jfmc+4sAtqfmSd+MbB9tOwRol5+BsbKoyHqIff0vL/bmxGWUeziyOJCN/umhrJ5XTDD7wN9/TQZK7Q+Mgyf2JbbVF8fqVONEZkblnxAiVLjuxB1fiS/yFlUZv6HHYd15etXlP7RR7Qv9w38vomJHmAtuP2K3m3TZa8mofJCYewjh4NUp2a2W7owbB6Y7hGaj3URK7QcsexX+8Cv4wq8D8PJ9M/ng8QUA2K4CDD6MZAMxhOhe5yZ+5b5ZvHr/bGLRmvWKVpTy6HWfsXlVGUbaJc4luAfUO87Kkgiv3D+TohWlVOyIcO1BLzDuhs/3bWcbaeYHq9mwbM9TcKra9MhAqTQLlt+LmCoqM+/nhXtmM3n8d/x12rl06lyEP/Qc4cCV2FYfwr5zsN0HE4vavPngXDLb+Tj9F4cRyrxpZ1sx3wmUx47F7/btcZv3TTyDaDiOx1uza+mb91fz2YvL6DEwl7E3HJY4l7APZn+0ljf+MpfKkijn3zaE8uLElU4NVbyhAq/fTVZu7ZvA6rJxeQkPXvQxPQe152/Tzm3wdlsjTQZKpZnYmxEBOxbmi1cLKS+OsOiLIjpfUILLlCYmmndb2FZvHFculrg4aFjHOkf2fPjqT/ni1UJ++dSoPQ6DsLsJ2kdfdSixsM2JVx7coH056qyeXFsZ48jTe9C2Q4AXNl+Fy0rcufzEr6YSKoty03P1G78/Eorz80NfpkNBJo8vurhe6+T1asP5tw3mkKMT3Wi3j/wf4coY/5x5QZPPj3Cg0W4ipZqDU0ag8hGs2NJai0p3eHFswOXhtmfbce71lRx7dnti7iMp3HoXMc8wXPYaMqr+jj80Hsc2rJ5fzOqFtS95rCyLAhAqj7G9qJIHfzyR76ZvqneYn/53Ka/cN4vPXlgGwJY15cQiNrbtcOeJ7/DQFZ/scf2AZx3nXvAkHdrNAcDLKlwm0WXzyTPf8eUbK4mG4/WKxeOzOOqMHgw/u2e947csFz++60gGnZSYbj0SihOpqt/2Wjs9MlCqGXijk3DbS5FwCVXuu3DHZ+G4OuJY3fHnX06USiyPj84dvmXgsDJ87iI+Hr+FJ26cyrUPj+Dkn/Yk5h5EzDMMt8fFU4WX4UrOGLbkq02sW7Kd0T89lDHX9qN0UxWDTy5g+dcLmfn+Gjrll3HI8AvqFefAUfkMPrmAASPzWTlvG7ce+xYjLujNDf8exZoFxVSW1O7y+eS573jxDzO4e8JYeh9iA3HEOIizjWDVQ9iurlRl3sG1D48gVBHD66/f147LJdzyYuNutnpoxvkAelRQD5oMlEolpxxcWUS9J+OKryPqG4OYEgKh53CkI5VZd+MPv4xQRblnOI/cEmDm5Fz+PKGI7gMG0n1Au8TooRIgHLxmZ7MZ2T+cE3jil1PZsLSEQScVMH/KRlbM3ca6xTs4amwHHnxjBT2GnVLvcHM6Bxk8uoCcTkHsmEPvIzpw2I/y8Xgtnl51eZ1TVpZuDVG+PUK4Iobt7kNF1r8Sg9oZm6hnBLaVmOvg5KtTM9rmnmgSqD9NBko1ISteiMvZQMxzPP6qcXjsRYR95+BY+Xic5Th2d6JWNxyC2K5Ev70jAVwmDrg486ZRdOixiG5HHIcvw8c/pp+/123e8MQoNizdQYdumVxyz5GMvKRvYnIZEXqf8pc6Rxt99LrPKNsW4o43Tq3xhZn4lf8N8ZjDadcPYMT5vekzpCMA/oy6xwg67/8N5oxfHvbDL/7v2xOLSKB+ff0q/TQZKNVIVnwR4MV298VX9TwWxcStQzGSg0Ew0hbHlYvt6oLj6omYMlxUIfYaAIwrD8f4AOHQUYM4dNSgvW7z+zHFRIQPHp3PvE83MGxsD9Yu3sHbD33LtY8cR26XjJqJwMQAC8TFwqkbKd0awo4b3J4f6oy69CDiUZvjL+rDsukbGH/HdIaMbs+d/9vzlTn17frZ3b6UbgmR3UKHpGgt9ASyUrvhiq/HG37vh2v2zQ9DEoi9FpwwGJtg1eMEqv6VWIfSxHKngkjwYiraPErceyTG1ZGqzN8R9w7CuHKIu3oS9YwAIJRxA1WZvwNxESqPsnJe7Tlzq7PjDtf0eZHbjv9f4rWdGPTNGJj6ynJmf7SWZd9srrFOPFxKYMdNlC35KwDPzNjGWys2497lOzwnL8gFtx9Bdl6QfkdWceOf1nPNH+p/ArohJo9fys96v8AXrxXusZ4xhv11YM39gSYD1So5zg9fKi57E+Ikhk8OVD5GsOJeMDaB0OP4oh/hiX6JO/IFWeW/xRP+BCs2n8zKB8movA+AREtW8rkfA5hdxvkxxrBq/jZiERvESyjzFqKBswF45tav+PtlkzDG8Oh1n3HrsW9ROHvL7oMXCGS58QcT3Tah8hiRqjiObSjbFgKodaK3fEecrRs8rFyQSGxuVxke2bEz+jr5+nLSz8fQ8fD6nXxuqE4929CxexYduu15asg/n/8RV+Y/R6g8mtJ4WivtJlKtTuHsLdw+6m0u/eMwzv1Nb1ZOfojsTm3JHvg7bj8bitbk8uhCG1sKEFOO7SrAF30HAHd8DmH35UTCbmKuzpBlUZVxF0YS/5U2bz0Mv3s5VmZbqvfUz5m4jj+f/xGn3zCAqx48pkY8n/53KeGKGLGIzYgL+xAN23ucg9eyXJx8dT8ycxInkfN6ZLF5dSYen2vnFUZQ8zxBTudcNlbcTe/RiZu3qjLuBEytSWyKN1bw2p/m8OO7hpKTFyTubfg8xfU1YGSXet1H4A248QU9+zzj2tqqPR9x7A8Oa8IhrHdHk4HarzmOw7O3fs2Rp3Vn4Alday40VYhTjrHyahRbHsOJ55eR1TbKjk2V3HJuH/J7xXh4PsSlK7YpB3ETCgVp43Nw8BDynMmmBS+RN/gsvvu6iLvG9OeQITHun0qNcYLef2oH+d0d+p4SolOvH+6a7da/HYce26nO+Xr9QTeRihgigi/oJiPbi+Wu+YUXDcd55b5ZHHl6D/oM7cCmeR8Qt3386LKDufbh43bW6z4gh6/fgi59aieTLn2zdz5/+pavKSsO85tnT6hxAvnfv/yCORPXUbY1xG2v7v4qpFB5lKqyKLn5jZ/ovb5ueWHfvhDXVhUyff1qSspDFJm+KYqqeTx0/N7rNJYmA1Uv6f511Tn2EcHIctYHLyDDrKeT+YKtDGXCW3348IklTHnpO/5UOBKXiSZP2nroGX+FIEUss67GZ7bSyZnKOusMsnuVcOvDq9ghQdabTE65vIK8gzJYW1XINW/3B2BjbCXjLvdRtKA/l/5nIzvWhXnt5o6c/cBGfP5E18r61e5a78uFN22lbWA7S10rWVtV7Qs5F675Xz8gXGud33wyhHjEochexTv/ns+iidsYcmU2PY78Yf3VM0t59+H5LJ2/np+/chg3P7SemO1lWVUh43+2gKWf7eD3s47Gya7A38aixL2JtVW15zf43pfvLKdiW4yzHy7A8vzQWzziVx0oWr+dY25sv8e/+T/HzGTdt+X8cdEIsjq0vBE911YWs3TLViaevhVTBUfeNRJx7ceXmWoyUOm2tqqQSau+Y32oz84ywSHTqqLcrv2rsI27girbT9y4OT57No5xMa10MP0zVtAnuJ53tx5Pv4yVnNnhC8ZvPJ3N0XYMbbuE7yq7UxrPIuAKEzNu4qbmRzPjoSjT3+3PaeM3ctaIZUgATGQ583ofSXBgG7JP6MBzi2M80HscNhZ3rfg513QS8vDy0lbDsLZb6N6+hM/X7mBVqAun5A5kZlk/Vq2OsOy/mbgyLeYeV/NO1Q2lUFXqZuLKGO7cIIF+WSxol4GxE8kgGgzw7OKa67Rxn0m2u4K14QwSE87Xh5V4LI6zrVsmnrxyJoYsvNXaNsEg3e45FA7N4pklhkOCZxAxHlaF4qwthxjw0lIba0gefd/NYxLA4t1vP/+JIzC2w/PLHcCp9kZn0v6xI/gUiE6poPDnc2l3Zic6XVXzLuDI4TlkWBavbQDX1pZ3h29ZmZ/40iPJy5uDz+Vi7OC+u73noKKkktf+9CZHnXEk/Y87tJkjbTn222QQdRK/sNo4y4gToMpVgGWqsAgTlXZ89+FKPntsNZc8dRRtOvnJMquolAJicQ9LXv6SjG6d6TmqT402s+zltDHL2GiditspI89Mo8h1AhjoHnuF7e7DKXEPJcNZiU2QsKsTneMf42crq6xL8JpicpyFbLFGYJkqujifsMV1DBHak7vlRULeHlS2G0nQWYfgUOnqzupZOyictp0Tf90LAbzsIEoOHnsHvXmJ7RzGZut41n/wOa7MtuSPGkzH8Md4nWLW+y8iy6ygvZnFOusMti9exQu/XsP5d7eh44jj2DJtOm16dcGf373Gflp2GQG2UGH1wW0qyAgvotw/CMe4kHXfYNofjMsf4BDnSfo6wl2zb+LOQ6bQvu0Wnv3kBm4+98+0zSjnw5mn8fV3P/xkaesu4rcXP8u8eV15c/71nHHZVwDM+Xgsl/74CbyeGKvnDmFI/kKWL4ajOq1g6UqIrVrE4T23sjB0HrdfdC+bd+Qx7oPfsHn6cqIlVXQ9ZSCLZrYHwiz/sA+vzbAJhiwqXfkEe+QTkPUEt3UmOL0j3oMcomFDcEY+d/29HaVb2zPsVj+fy4lMXt0F36GDcAe8TKIXABkVYWAmXn+Q4Ix8cjKL8bqjbC7pzEmXbcZVtJ1iqxd5FUt45KMvef7tdnz+diIJRteHCc7IJ7KjErEEb5sgcWAbEATWTZzPximLOOgnI8k5pEu9Ptf27LXENkfwTssl2Dm7xrKMQFdYDayGteRDcjuHnJoPpwKLa7ZVuX47S576lG6nD6LjsJqf9Z32cMBXOXsV9o4YpW9vo1e/ETWW9eyXD/2AXaY3jlWEWffRPDodexDBzjk1lpWv3ooTt2nbJ/XzCAeBsYP6wkOH77Xu9g3bWTVvNW1yszQZ7I+2rSjjhQUVuO5fRDDTYcdvO9P2X5+w+ls3nZ84hpW3L6O4yMu7v5/KCdfH+VH/QhYtyebNjSMJTNtC195r+axjD64J/wef3/Av81P+2GUCmVkOX21ow5DMZbTNLKO4pJQdpT4CBWV0jH3BU4V9OeqbL8juEOfN7lfyYK9FWG6YtngJ52RPoX1elHkLKsjKd3Nw2zUUV4T5ZnZXVv6nDX0PW8+8M+MsOjExPk3/yV04f9ubjLm+nFufGsNg/3ecf85qXvigHz86bRtWMEauM4eHvj2cXx6yiHDIxeOLD+PWzGW07RLj4QXbuLTdVwQ7b+fblUvxf72Ywvn5rPhkPV+s/JYv/1+I7gcvpM2/89nywloQ6HhpN/7S+xncluH5laMZGF3AwX028cn8YuYu6ci6Z7cz4ITPKT9rGM//vic9+4U5c2Afugb/RafOMU7sGSdWYfP1tDYMHOQgO9rz3qMTGP3TE8nIDPDdnAAr5uZy2tDefPF+GxxbGDuoL4u+HUyGbxkH5/6Iv/50JZtW5dD/+AIGDd/EJb/ZwuQ3bcYcno9LDO3blDF2UF/++IfXiUfiXPXLsZx8/yZioVJcOcfw9O98bFndgTbtvZzxSxez5q0ltGorl196Cw/d3BXbdjH2Z33xHlXGikUBBuZGmfrWUpbNWMGJVxYw6pKax9wnvdgdX9CHL+hj0CEP4vXsYNbCl1g3600+nxbmsnvdLJ9RhcHg31bBuRecyuOfLabrwfmMOaw3957xJ/yZfu547ZYa7T794ldgoEvUcOKg+vVZ24f1JlRWRWZOJstmLufVB97kojvP46Bh+97nvcazloXhGAe3y+bYQX1Z/OV35Oa3I69Hxxr1unR8lY7tPmbxigeJxtrvLI8e0p0PSsMMPOEweg+q3/hA8z5dwJyZK+mT34HTxgyrsey+e94kGopyz4Tf75zysiXo1r+AG564jtwudQ/e11rst8kgGnYhU3KZNaUtIoZhI/JZvbQdm9ZG6fxVN44YNZlQWSXx7ALmvbaD0X+COR8FKd7aicK3c8hoYzOgYz6B4Q6BTJvgR5156OvuWOLgHnQwbftvotPxZRR+3Q6XzwUFYMcg8nFn/u/+AnwBh0H35PPGJ7nkdoxTtO1QJoXmcN512/j475l0OO1kco6J8+nc0Sz9rJhFk+KsW+6jIC8fx04crnqm5dHntBAuCwriXiLb/PgChsj6LKZPyKXX+ZtZtyKAZ3ouV917KB26ROn163wemnE0VJbgPr4f//pkBVUbdtD+pF4c2rWMP7+ynMIVHZn3ZQdGnL4SEYjOyGfLs9MA6NHnaNa7ghT0qKRoUk98uWEGdN/Eyvn9WPqRh3VzyygqakvfrIHMmbqcovVd+MU5edx6fm9cluGc23P427jRfDtlNT/583HM+mgyhbNWEAvH+Nk/fsL64nF0GhRAxMXKxQFcluGQ3pAVWEafASVMmbKa9gVd2bSqlC59elBSlssnr89j6oQCxh6UzZbi0VSGewBgJ8faj0ZibN5wDhWbptBnxJHkdS9ly+pttC/ogOVOXA3j9rhxHJuJr7bDH3AY+TO47P85dClYzpzFN5E3Zz7LZiynQ7cOtT5Lbdr/MJF80dYz8XqKcYyX//7NTeWOTKa9U0lW+6M5vdtGzvntsQSNwTiGYJsALpcwePTh+DNqD7F88V0XsHz2CvqPqP8wDJblIjMnceQRi8SJhqJEI7F6r19d9/7ddn7x7thUwsv3vkbH7h345ZPX16jncZfgcZcgUvOSTa/fyzk3n7lP2xxwfD/cHje9BtdOHmf+6nSioWiLSgTf69Qzb++VDnCyv97EISJbgTXpjqOZtSfRC9Fatfb9B30PWvv+Q+Pfg+7GmFq/ivbbZNAaicgsY8zQdMeRLq19/0Hfg9a+/5C696DlHa8ppZRqdpoMlFJKaTLYzzyZ7gDSrLXvP+h70Nr3H1L0Hug5A6WUUnpkoJRSSpOBUkopNBm0SCJSICJTRGSxiCwSkV8ny9uJyCQRWZ78N2dvbe3PRMQSkbki8n7ydU8RmSEihSLyqoi0vBHSmpCIZIvIGyLynYgsEZGjW+Fn4Kbk/4GFIvKyiPgP5M+BiDwjIltEZGG1sjr/5pLwSPJ9mC8iQxqzbU0GLVMc+K0xph8wHLhBRPoBtwOTjTF9gcnJ1weyXwNLqr1+EPinMaYPsAO4Oi1RNZ+HgY+MMYcAh5N4L1rNZ0BE8oFfAUONMQNIjOj3Yw7sz8FzJEaaqm53f/MxQN/k41pgXGM2rMmgBTLGFBlj5iSfl5P4EsgHzgLGJ6uNB85OT4SpJyJdgdOB/yRfC3AC8EayyoG+/21JDFz8NIAxJmqMKaEVfQaS3EBARNwkxp8r4gD+HBhjpgLbdyne3d/8LOB5kzAdyBaRzg3dtiaDFk5EegCDgRlAnjGmKLloE3AgD6jyf8Ct/DC+ci5QYoz5frzk9ZAcuvPA1BPYCjyb7Cr7j4hk0Io+A8aYDcDfgbUkkkApMJvW9TmA3f/N84F11eo16r3QZNCCiUgm8CbwG2NMWfVlJnFN8AF5XbCIjAW2GGNmpzuWNHIDQ4BxxpjBQCW7dAkdyJ8BgGTf+FkkEmMXIIPaXSitSir/5poMWigR8ZBIBC8aY95KFm/+/jAw+e8eZk3frx0LnCkiq4FXSHQLPEziMPj7kXa7AhvSE16zWA+sN8bMSL5+g0RyaC2fAYCTgFXGmK3GmBjwFonPRmv6HMDu/+YbgIJq9Rr1XmgyaIGS/eNPA0uMMQ9VW/QucGXy+ZXAO80dW3MwxtxhjOlqjOlB4oThp8aYS4EpwPnJagfs/gMYYzYB60Tk4GTRiSSmr2kVn4GktcBwEQkm/098/x60ms9B0u7+5u8CVySvKhoOlFbrTtpnegdyCyQiI4AvgAX80Gd+J4nzBq8B3UgM332hMWbXk00HFBEZBdxijBkrIr1IHCm0IzHH1mXGmEg640slERlE4gS6F1gJXEXiB1yr+QyIyB+Bi0hcYTcX+BmJfvED8nMgIi8Do0gMU70ZuBt4mzr+5skE+SiJrrMq4CpjzKwGb1uTgVJKKe0mUkoppclAKaWUJgOllFJoMlBKKYUmA6WUUmgyUEophSYDpZRSwP8HJ/3YaNaVc6EAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aula 4: Support Vector Machines y sistemas no lineales."
      ],
      "metadata": {
        "id": "K5DBulW61Sua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "x= datos[['horas_esperadas','precio']]\n",
        "y= datos.finalizado\n",
        "\n",
        "SEED = 42\n",
        "np.random.seed(SEED)\n",
        "\n",
        "raw_x_train, raw_x_test, y_train, y_test = train_test_split(x,y,test_size=0.25,stratify=y)\n",
        "print(f\"Entrenaremos con {len(x_train)} elementos y probaremos con {len(x_test)} elementos.\")\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(raw_x_train)\n",
        "x_train = scaler.transform(raw_x_train)\n",
        "x_test = scaler.transform(raw_x_test)\n",
        "\n",
        "model = SVC()\n",
        "model.fit(x_train,y_train)\n",
        "previsiones= model.predict(x_test)\n",
        "\n",
        "data_x = x_test[:,0]\n",
        "data_y = x_test[:,1]\n",
        "\n",
        "x_min = data_x.min()\n",
        "x_max = data_x.max()\n",
        "y_min = data_y.min()\n",
        "y_max = data_y.max()\n",
        "\n",
        "pixels = 100\n",
        "eje_x = np.arange(x_min, x_max, (x_max-x_min)/pixels)\n",
        "eje_y = np.arange(y_min, y_max, (y_max-y_min)/pixels)\n",
        "\n",
        "xx, yy = np.meshgrid(eje_x, eje_y)\n",
        "puntos = np.c_[xx.ravel(), yy.ravel()]\n",
        "Z = model.predict(puntos)\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "plt.contourf(xx, yy, Z, alpha=0.3)\n",
        "plt.scatter(data_x, data_y, c=y_test, s=1)\n",
        "\n",
        "tasa_de_acierto = accuracy_score(y_test, previsiones)\n",
        "print(f'La tasa de acierto fue de: {round(tasa_de_acierto*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "r1DGS6sMx16j",
        "outputId": "3d63771c-6e07-4c79-f0f4-424500635d5d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entrenaremos con 1617 elementos y probaremos con 540 elementos.\n",
            "La tasa de acierto fue de: 67.78%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aula 5: Dummy classifiers y árboles de decisión."
      ],
      "metadata": {
        "id": "4X3UC5eP1YLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "PH9EglOaKUxi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zFHbRcUTKU39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "v1Vucf-FKVAA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}