Dashboard_Interativo_de_Previsão_Bikes.ipynb

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  "metadata": {
    "colab": {
      "name": "Dashboard Interativo de Previsão Bikes.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "dqQZktXo-roR",
        "0T3c32Jp_-UX",
        "5XVVWcNCAAxa",
        "CJKhKrfYA3Bz"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lkyuc6Ut-i0w"
      },
      "source": [
        "#Dashboard Interativo de Previsão Bikes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svkqhPqH-ogA"
      },
      "source": [
        "##Importação e Instalação de Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3n-WCbR9BTb",
        "outputId": "d5f3c764-8bdc-4f95-e832-f69893c157ae"
      },
      "source": [
        "!pip install evidently\n",
        "!pip install pyforest"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting evidently\n",
            "  Downloading evidently-0.1.22.dev0-py3-none-any.whl (15.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 15.2 MB 125 kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from evidently) (2.23.0)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from evidently) (1.19.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from evidently) (1.1.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22.1 in /usr/local/lib/python3.7/dist-packages (from evidently) (0.22.2.post1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from evidently) (4.4.1)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (from evidently) (0.10.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from evidently) (1.4.1)\n",
            "Collecting dataclasses\n",
            "  Downloading dataclasses-0.6-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.1->evidently) (1.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->evidently) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->evidently) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->evidently) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->evidently) (1.3.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->evidently) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->evidently) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->evidently) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->evidently) (2.10)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->evidently) (0.5.1)\n",
            "Installing collected packages: dataclasses, evidently\n",
            "Successfully installed dataclasses-0.6 evidently-0.1.22.dev0\n",
            "Collecting pyforest\n",
            "  Downloading pyforest-1.1.0.tar.gz (15 kB)\n",
            "Building wheels for collected packages: pyforest\n",
            "  Building wheel for pyforest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyforest: filename=pyforest-1.1.0-py2.py3-none-any.whl size=14606 sha256=7f8c4dd61179c43b39c5c639e57717d6aec6d802715736a6a0af812ae50bef47\n",
            "  Stored in directory: /root/.cache/pip/wheels/61/1c/da/48e6c884142d485475d852d69d20a096aba5beceb338822893\n",
            "Successfully built pyforest\n",
            "Installing collected packages: pyforest\n",
            "Successfully installed pyforest-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRxbQzXG9CnM"
      },
      "source": [
        "from pyforest import *\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from evidently.dashboard import Dashboard\n",
        "from evidently.tabs import RegressionPerformanceTab\n",
        "from evidently.model_profile import Profile\n",
        "from evidently.profile_sections import RegressionPerformanceProfileSection"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dqQZktXo-roR"
      },
      "source": [
        "##Importar Base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "DsAJidRu9mtu",
        "outputId": "2ac4f401-3464-4137-d7e7-728a1d3724b6"
      },
      "source": [
        "data= pd.read_csv(\"/content/drive/MyDrive/Dataset_Excel_CSV/bike-sharing-daily.csv\",header = 0, sep = ',', parse_dates=['dteday'])\n",
        "data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant     dteday  season  yr  ...  windspeed  casual  registered   cnt\n",
              "0        1 2011-01-01       1   0  ...   0.160446     331         654   985\n",
              "1        2 2011-01-02       1   0  ...   0.248539     131         670   801\n",
              "2        3 2011-01-03       1   0  ...   0.248309     120        1229  1349\n",
              "3        4 2011-01-04       1   0  ...   0.160296     108        1454  1562\n",
              "4        5 2011-01-05       1   0  ...   0.186900      82        1518  1600\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0T3c32Jp_-UX"
      },
      "source": [
        "##Separar Variáveis Categóricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKzy1q58A-Vb"
      },
      "source": [
        "categorical_features = ['season', 'holiday', 'weekday', 'workingday', 'weathersit']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frnnufrKFFJ8"
      },
      "source": [
        "data_features=['dteday']"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XVVWcNCAAxa"
      },
      "source": [
        "##Separar Variáveis Numéricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RI0Ar7q5ADn6"
      },
      "source": [
        "numerical_features = ['mnth', 'temp', 'hum', 'windspeed','cnt']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eodn0tvkAtMg"
      },
      "source": [
        "Verificar Correlações"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5aYVGGPDAHI0",
        "outputId": "6e11647c-fdd8-455e-c28e-a7c3a03772c9"
      },
      "source": [
        "sns.heatmap(data[numerical_features].corr(),annot=True);"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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4HwmcOHwUx0UH2+ZtoEm3sExlTsYeJ2HvIUyWexw5LjpwXEgDoFRpP0SkyOLOTbeedzD7e2eLKmrTdgICKlC9RtUrykVt2s7RxONXrB/4YH9mfv49yckpACQdt//D1ufaBqQfj8ecSARHGmlb1lCq6S2Zyvi1CefiugWQ+icA5kyyc4MjzTkBlPID8aKPvnTj/uSFvKolAkwE6onIVmApcBS4F7gG+J8x5nURqQ0sBn4BbgU2Al8AfweqA/cbYyJFZBxQD6gPVAUmGWM+K+wKlAuqxJm4y//gzsSfoMbN9XIs3+i+2zm0ctsV66s3r4uvXymS/zhaKHEqp8AalUiOS8pYTo5Polbz+u6/Prgyj0wfRZXaNVgw4RuvaIUABAXXIO5IQsZyfFwiQcE1sk0Y2alT7zoA/rfoK3x9fZj89ses/HldocTqLgmsgjl1OX5z6jg+192QqYxPtRAA/J95G3x8uBDxHY69Uc7XV6xKmUfH4lM1mAvzvvCKVgjgtcnBXV6UjgF4GfjdGNMcZxJpALQGmgMtReQ2q1x94F2goTUNBNoDLwGjXfbXDOgEtAXGikhIUVTCXQ3ubke1ZnXZOnVBpvVlq1ek0/tPsuLFacX+Dp8lXXL8Cd7rMYpJt79Ay363Ub5qoN0hFYhSpUpRp+513NP7YZ5+dCST3v87AcWhpenji0+1YFKnjObcV+9wzT1PQ5lygDPppL7zLGcnPE6pVp2Q8hVtDtbicLg/eSFvSyKuulnTFiAKZ7JoYG07aIzZYYxJB3YBPxtnX8MOoLbLPuYYY1KNMceBFTgT0hVEZKiIbBKRTWvO7M9X0H8mnKR8yOX+4/LBlfkz4cpvp6Htm9DimT4semQy6VaXCIBfeX96zHiJyEmzOLrl93zFonKXnHiSwJAqGcuBwVVIScx7ayLl6EkS98VSp9UNuRcuJIOH3EfEqtlErJrN0cRjhIQGZWwLDqlBQnyi2/uKj0tkyeIVpKWlcfjQEQ5Ex2S0TuxikpOQipe75KRiVUxyUpYyx0nbGQnpDsyJRNKPxeFTLThzmZQTpMcfwqdu4yKJOzcm3bg9eSNvTiICvGWMaW5N9Y0xn1vbzruUS3dZTidzF13Wdz3bs2CMmWaMCTPGhHUo3yC7Im47uu0AgbWDqFCrGj5+vtTr04aYpVGZylRpch23TXyExY9M5lxSSsZ6Hz9fwj97nn3/WcOBhRvzFYdyT+y236laO4hKNavh6+fLTb3bsnvpZrdeGxhUmVLX+AHgH1CO2mE3cOxAfGGGe1UzP/+e8Nv7E357fxYvWE7/+/oA0CKsGadTzrjdlQUQsfBn2rZrBUClyhWpW782f8QczuVVhSv98H58qoUglWuAbylK3dwBx87MVy+m7fwV3/o3OhfKVcCnWgjpSYlIYBXwK+1c718O3zqNMMeO4BUKcExERLqLyG8iEi0iL+dQ5l4R2S0iu0Tk2/yG721jIqeBS23mCOAfIvKNMeaMiIQCF/O4v7tE5C2gHNARZ3dZoTKOdNa+NpM7vx6J+Prw2w+rOLnvCGEv9uPY9oP8sTSKtmMG4Fe2DF2nPgvAmbgkFj8ymXq92hB8yw2UqVSeG+5x9tytGP4pSbsPFXbY+Tbi9Yls3LKdU6dS6Nz3AZ4aMoh+vcPtDitX6Y505oydwaNfvoKPrw8bZ60kcX8s3V7oT+yOg+xetpmazery4KfDKRtYjkadW9D1hXuY3G0E1euH0mvMAxgMgrD6s/kk/GbvB+0ly5euplPXDqzdvIhzqakMH/ZaxraIVbMJv70/AGPGDadv/574ly3Dxp3L+O6r/2aMf9x2x60s3zCHdIeDN15/l1Mnk+2qjlN6Ouf/+yn+Q8eBjw8XI5eRnniY0t0H4jgcjWNXJI69Ufhe35yyIz/CmHQuzJsBZ0/jc31zSvd5BOf3SOHCyp9Ij//D3vpcUkBXZ4mILzAF6ArEAhtFZK4xZrdLmQbAK0A7Y8xJEame7+NmveLEblZmbAYswvlGPGptOgM8ADiA+caYplb5GdbybGvQfb4xpqk1sF4XZxeY2wPrU2s94F1vSAEYsmW83SEUijFhY+wOocB9m7LD7hAKxd6H6todQqEoP3luvi/JO/1UD7c/cyp8vCjH44lIW2CcMSbcWn4FwBjzlkuZScA+Y8y/PY84M29riWCMyXox+7+yKdbUpfxDLvMxrtuA7caYBwsyPqWUKlB5GOsQkaHAUJdV04wx06z5UMC1KRwLZL4GGq639rMO8MWZdPL1q0uvSyJKKfVXYhzud2dZCWNargVzVgpn70xHoCawWkRuNMacys8OSyRjzDi7Y1BKqVwV3FVXR4BaLss1rXWuYoFfjTEXgYMisg9nUvH4Sh5vvjpLKaVKvAK8xHcj0EBE6ohIaeA+YG6WMj/hbIUgIlVxdm8dyE/8JbYlopRSxUIBtUSMMWkiMgznla2+wHRjzC4RGQ9sMsbMtbZ1E5HdOC9SGmGMScp5r7nTJKKUUnYqwPsvGmMWAguzrBvrMm+A4dZUIDSJKKWUjUxa8b6LryYRpZSyU/HOIZpElFLKTt56Tyx3aRJRSik7aUtEKaWUp7QlopRSynPaElFKKeUpk5Z7GW+mSUQppWxktCWilFLKY5pElFJKeUpbIkoppTymSUR5vZL4BECANze9aXcIBW5ySAe7QygUt357zO4QCsX2yfnfh3Hk++GIttIkopRSNtKWiFJKKY+ZdG2JKKWU8pC2RJRSSnnMGG2JKKWU8pC2RJRSSnksXa/OUkop5SkdWFdKKeUxTSJKKaU8Zor340Q0iSillJ20JaKUUspjeomvUkopjzn06iyllFKe0paIUkopjxX3MREfuwNQSqm/MmPcn3IjIt1F5DcRiRaRl69Srp+IGBEJy2/82hJRSikbFVRLRER8gSlAVyAW2Cgic40xu7OUqwA8B/xaEMfVlohSStnIke7j9pSL1kC0MeaAMeYC8D1wVzbl/gG8DZwriPi1JVIIanVsRrtxgxBfH/Z8t5KtH8/LtL3ZYz1oeF9HjMNBatJpVr40jTNHkqjS+Fo6THiY0uX9MenpRH04h9/nFciXhQJx/e03cdfYBxFfHyJ/WMHKT+Zm2l6ndUP6jH2QoIbX8u0zH7BjUSQAFUOrMvjT4YiP4FOqFOtnRvDLN8vsqEKevTphMqvXRVK5UkV++nqq3eHkyXuTx9OjeyfOpqYyZMgLbNm6M9N2f/8y/PDdNOrWuw6Hw8GCBUsZPeYtAB4cdC9vT3yVI3EJAHz88RdM/+K7Iq9Dbka98QIdOt/KudRzvPbcP9izY98VZZ55+XF639ODgIoVaFOvsw1RXl1efmwoIkOBoS6rphljplnzocBhl22xwC1ZXt8CqGWMWSAiIzwKOAuvTyIiUhuYb4xpanMobhEfof0bg5k/cCJ/xp/gb/PH88fSzZzcH5dR5vjOGP5752uknbtA40GdaTNmAMue+oi01AuseH4qyTGJlK1RkX4L3uDwqh1cSDlrY42cxEe4e/zDfPbABJITknhm7pvsXrqZo9FHMsqcijvODy9N5fbH7sz02tNHT/LR38biuJBG6bLXMHzJP9m9dDMpR08WdTXyrG/Prgzs14fR/3jH7lDypEf3TjSoX4eGjdtzS+sWTPnoLW5t3/uKcpPfm8rKVevx8/NjacQPdA+/g8URKwCY9eNcnnv+1aIO3W3tO7flurq16NX2Hpq1aMKrb4/k/p6PXlFu1ZK1fDd9NvM3zLIhytyl5+HqLCthTMu1YDZExAeYDDzkyetzot1ZBax683qkxCRy+tAx0i86+H3uL9Tu1jJTmbgNe0g7dwGAxKhoygdVBiD5YALJMYkAnE08RWpSMv6VKxRtBXJQq3l9jv+RwInDR3FcdLBt3gaadMs8Jncy9jgJew9hsny1clx04LiQBkCp0n6IFJ+rUcKa30hggHecg7zo3Tucr76ZDcCvkVEEVgwkKKh6pjKpqedYuWo9ABcvXiRqyw5CQ4OLPFZP3RF+G/NmLQJge9QuKgSUp2r1KleU2x61i+NHk4o6PLcZI25PuTgC1HJZrmmtu6QC0BRYKSIxQBtgbn4H14tLEvEVkc9EZJeILBERfxFZeanyIlLVelMQkYdE5CcRWSoiMSIyTESGi8gWEflFRCoXZqDlgipxJu5ExvKZ+BOUC6qUY/lG993OoZXbrlhfvXldfP1KkfzH0UKJM68Ca1QiOe7yP8Tk+CQCauRcryteH1yZFxa9zegNH7Fy6txi0QopzkJDgog9fLn1eyQ2ntCQoBzLBwYG0OvOrixfsTZj3d/u7knU5qX88P00atYMKdR4PVE9uBoJcYkZy4nxx6geXM3GiDxTgFdnbQQaiEgdESkN3Adk9DkbY5KNMVWNMbWNMbWBX4A+xphN+Ym/uCSRBsAUY0wT4BTQL5fyTYG/Aa2AN4GzxpibgQ3Ag1kLi8hQEdkkIpvWnNlfsJFfRYO721GtWV22Tl2QaX3Z6hXp9P6TrHhxWvG/O5slOf4E7/UYxaTbX6Blv9soXzXQ7pCUxdfXl2++msJHU6Zz8OAhAOYvWEq9Bm1o0bIry5at5ovP37c5ypIr3Yjb09UYY9KAYUAEsAeYZYzZJSLjRaRPYcXv9WMiloPGmK3W/Gagdi7lVxhjTgOnRSQZuDSyvQNolrWwaz/j1FoP5OtT+8+Ek5QPudzYKR9cmT8TrvzWHdq+CS2e6cOce94k3erqAfAr70+PGS8ROWkWR7f8np9QClRy4kkCQy53FQQGVyElMe+tiZSjJ0ncF0udVjdkDLyrgvHkE4MZMuR+ADZt2krNWpdbD6E1gzMGybOa+skk9kcf5IMP/52x7sSJy+f28+nfMvGtMYUUdd7838P96He/8/Nw19Y9BIXUyNhWI7gaR+OP2RWax9y46sptxpiFwMIs68bmULZjQRyzuLREzrvMO3AmvzQux1/mKuXTXZbTKeTEeXTbAQJrB1GhVjV8/Hyp16cNMUujMpWp0uQ6bpv4CIsfmcy5pJSM9T5+voR/9jz7/rOGAws3FmaYeRa77Xeq1g6iUs1q+Pr5clPvtuxeutmt1wYGVabUNX4A+AeUo3bYDRw7EF+Y4f4lfTJ1JmGtuhHWqhtz50Yw6P7+ANzSugUpySkkJFzZNTr+7yMJDKzA8Bdfz7Tedfykd+9u7N0bXbjBu+mHL/7DvV0Gc2+XwSxfvJre9/YAoFmLJpw+/adXj33kxORh8kbFpSWSnRigJRAJ9Lc3lMuMI521r83kzq9HIr4+/PbDKk7uO0LYi/04tv0gfyyNou2YAfiVLUPXqc8CcCYuicWPTKZerzYE33IDZSqV54Z7bgNgxfBPSdp9yM4qAZDuSGfO2Bk8+uUr+Pj6sHHWShL3x9Lthf7E7jjI7mWbqdmsLg9+OpyygeVo1LkFXV+4h8ndRlC9fii9xjyAwSAIqz+bT8Jvh3M/qBcY8fpENm7ZzqlTKXTu+wBPDRlEv97hdoeVq4WLfqZ79078tmcdZ1NTefTR4RnbNm1cQlirboSGBjP6lefYs3c/GyMjgMuX8j4z7BF69epGWpqDkydO8cijz9tVlRytWbaeDp1vZcEvP3Iu9TyvPf9GxrZZy2Zyb5fBALzw2tP0vLsbZfzLsDRqDv/9di6fvPO5XWFfIS9XZ3kjyXoljbfJeomviLwElMf5Q5pZOFsmC4AHjDG1ReQhIMwYM8wqH2MtH8+6LTv57c7yRgd8HXaHUCje3PSm3SEUOP+QDnaHUCgaV77W7hAKxfaEDfnOAOuC+rv9mdMuYbbXZRyvb4kYY2JwDpRfWna9YN91fONVa/sMYIZL+dou85m2KaWU3dLtDiCfvD6JKKVUSWbwusZFnmgSUUopG6UV8zERTSJKKWUjbYkopZTymI6JKKWU8pi2RJRSSnlMWyJKKaU85tCWiFJKKU8V0NNxbaNJRCmlbJSuLRGllFKeKu73WdIkopRSNtKBdaWUUh5LL0aPi86OJhGllLJRcb/HtiYRpZSykV6dpZRSymN6dVYJM/LEertDKHAB15S1O4RCMbkEPsApNW6N3SEUinKht9kdgtfSq7OUUkp5TLuzlFJKeUwv8VVKKeUxh7ZElFJKeUpbIkoppf4b5YwAABwMSURBVDymSUQppZTHivkj1vGxOwCllPorS8/DlBsR6S4iv4lItIi8nM324SKyW0S2i8jPInJdfuPXJKKUUjZy5GG6GhHxBaYAPYDGwAARaZyl2BYgzBjTDJgNTMpv/JpElFLKRuni/pSL1kC0MeaAMeYC8D1wl2sBY8wKY8xZa/EXoGZ+49ckopRSNspLd5aIDBWRTS7TUJddhQKHXZZjrXU5GQIsym/8OrCulFI2ysvVWcaYacC0/B5TRB4AwoDb87svTSJKKWWjArx31hGglstyTWtdJiLSBRgD3G6MOZ/fg2oSUUopGxXgvbM2Ag1EpA7O5HEfMNC1gIjcDHwKdDfGHC2Ig2oSUUopGxXUQ6mMMWkiMgyIAHyB6caYXSIyHthkjJkL/BMoD/wozicqHjLG9MnPcTWJKKWUjdILsEPLGLMQWJhl3ViX+S4FdjCLJhGllLKR3vZEKaWUx4r7Q6n0dyKFZNI/x7J1+3LW/7qQm5o3uWK7v38ZfvzP52yKWsqvGxczbvzIjG23tmvF6nVzOZG8j7v69ijKsHM1/q1XWLtpIUvX/JemzRplW2bkmGeJ3LGM3w5FXrGtV99wlm+Yw8/rf+KjaW8Xdrhue2/yePbuXkvU5qXc3LzpFdv9/csw96cv2bljFdu2LmfCm69kbHtw0L3EH9nOpo1L2LRxCY88PKAoQ/fIqxMmc9ud99H3gSfsDiXPJk8ez+7da9m8aSnNczhXP/00kx3bV7J1y8+8+cblc/XYYw8QtXkZGyMjWLH8vzRq2KAoQ89WQd72xA75TiIislBEKuahfG0R2Znf43pCRM4UxXG6hXekXv3aNG/WieeGjea99/+RbbkP/vUZYS260v7W3rRp05Ku3ZyXbMcejuPJx0fy46y5RRGu2zp16UCdetfSPqwno14Yx1vvvpZtuWURK+nV5b4r1tepey3Dnn+Uu7sPovOtfXl9tHckkR7dO9Ggfh0aNm7Pk0+OYspHb2VbbvJ7U2l64+2EtQrn1rat6B5+R8a2WT/OJaxVN8JadWP6F98VVege69uzK1Mnv2F3GHnWvXsn6tevQ+PG7XnyqVF89GH25+q99z7lxmYdadW6O23bhhFunavvv/+JFi270Kp1OO9O/oRJ/3y9KMPPVpoYtydvlO8kYozpaYw5VRDBlBQ97+zCd9/+D4CNG7cSGBhAjaBqmcqkpp5jzepfALh48SLbtu0kJCQIgEOHjrBr517S073ru0e3nncw+3tnYovatJ2AgApUr1H1inJRm7ZzNPH4FesHPtifmZ9/T3JyCgBJx08UbsBu6t07nK++mQ3Ar5FRBFYMJCioeqYyqannWLlqPeA8X1FbdhAaGlzksRaUsOY3EhhQwe4w8qx3725887XzXEVGRlGxYkC252qVy7nasnVnxrk6ffry98hyZctijP0fzCYPkzfKNYmIyAgRedaaf09EllvznUTkGxGJEZGqVgtjj4h8JiK7RGSJiPhbZVuKyDYR2QY87bLvJiISKSJbrbtKNrD2s9fa9x4RmS0iZV32s0pENotIhIgEW+vrichia/0aEWlora8jIhtEZIeIFNnXrpCQIGJj4zOWj8QlEBIclGP5wMAKdO/RmVUr1xdFeB4LCq5B3JGEjOX4uESCgmu4/fo69a6jbr3r+N+ir5i75Bs6dm5XGGHmWWhIELGH4zKWj8TGExpytfMVQK87u7J8xdqMdX+7uydRm5fyw/fTqFkzpFDj/SsLCQnicOzlcxV7JD7jy1d2AgMDuPPOLqxwOVdPPDGYPXvWMmHCGIYPH5vja4vKX6E7aw3QwZoPA8qLiJ+1bnWWsg2AKcaYJsApoJ+1/gvgGWPMTVnKPwH8yxjT3Np3rLX+BuBjY0wjIAV4yjrmh0B/Y0xLYDrwplV+mrX/lsBLwMfW+n8BnxhjbgQuf6p7EV9fX6bP+BeffjKTmJjDub+gGCtVqhR16l7HPb0f5ulHRzLp/b8TUMy+Dfv6+vLNV1P4aMp0Dh48BMD8BUup16ANLVp2Zdmy1Xzx+fs2R6nAea6++moKU1zOFcDUqTNp1Kg9Y8ZM4JWXn7UxQqd0jNuTN3IniWwGWopIAHAe2IDzA78DzgTj6qAxZqvL62pb4yUVjTGXEs5XLuU3AKNFZBRwnTEm1Vp/2Bizzpr/GmiPM7E0BZaKyFbgVaCmiJQHbsX545mtOH+NeamfoR1wqYPa9biZuN7U7EJaihtvyZUeGzqItRvms3bDfBISjlKz5uWujtCQIOLiE7J93QcfTeD36Bg+nvKFR8ctbIOH3EfEqtlErJrN0cRjhIRe/tYXHFKDhPhEt/cVH5fIksUrSEtL4/ChIxyIjqFOvXw/zsAjTz4xOGMgPD4hkZq1LrceQmsGcyQu+/M19ZNJ7I8+yAcf/jtj3YkTJ7lw4QIAn0//lhYtbizc4P9innhiMBsjI9gYGUFC/FFqubT0aoYGE5fDufrk47eJjj7Ihx9+nu32H2bNoU+f8EKJOS9KfHeWMeYicBB4CFiPM3HcAdQH9mQp7nofFge5XEJsjPkW6AOkAgtFpNOlTVmLAgLsMsY0t6YbjTHdrDqcclnf3GrBkMO+sotjmjEmzBgTVrpUQG7Fs/XZtK9o37YX7dv2YsG8pQwYeDcArVo1JyXlNIkJx654zWtjhxMQUIFRI7MfePcGMz//nvDb+xN+e38WL1hO//ucP25tEdaM0ylnsh37yEnEwp9p264VAJUqV6Ru/dr8YVPr65OpMzMGwufOjWDQ/f0BuKV1C1KSU0hIuPKOEOP/PpLAwAoMfzHzYKxrn3zv3t3Yuze6cIP/i5k6dSatWofTqnU4c+ct5v4HnOeqdesWJCefzvZc/X3cCAIDA3gxy7mqX79OxnzPnp2Jjj5YuMG74a/QnQXOxPESzu6rNTi7obYYN0alrEH3UyLS3lp1/6VtIlIXOGCM+QCYAzSzNl0rIm2t+YHAWuA3oNql9SLiJyJNjDEpwEERucdaLyJyqdtsHc77x2Q6bmGLiFhBzMHDbNuxgg+mvMXw5y/3u67dMB9w9u2OGDWMhg3rs2b9PNZumM+Dg+8FoEWLZuzZt46+d/fkXx+8wa8bFxdV6Fe1fOlq/oiJZe3mRUx6fxyjR1weZopYNTtjfsy44WzcuQz/smXYuHMZw0c9BcDKn9dx8uQplm+Yw49zp/PG6+9y6mRykdcjq4WLfubAwUP8tmcdU6dOYtgzozO2bdq4BIDQ0GBGv/IcjRpdz8bIiEyX8j4z7BG2bV3O5k1LeebpITzy6PO21CMvRrw+kfsff4GYQ7F07vsA/5kXYXdIblm0aDkHD/7Bnj1rmfrJJJ559vK52hjprENoaDCvvPIcjRo1IPLXxWyMjOBh61w9+eRDbN3yMxsjI3ju2aEMGfKCLfVw5cC4PXkjcefqBBHpDCzG2S31p4jsA6YaYyaLSAzWWAkw3xjT1HrNS0B5Y8w4Ebk0hmGAJUBPY0xT6/GNg4CLQALOhBFgHWsT0BLYDQwyxpwVkebAB0AgzlbO+8aYz6wbjn2CsxvLD/jeGDPeWv+tFdsc4HljTPmr1TWgXF3vPFP5EHBNWbtDKBQJZ07aHUKBS43L2kNcMpQLvc3uEArFhfOx+b594nO173P7M+dfMd973RPZ3UoiRUlEauOSjIqaJpHiQ5NI8aFJJGfP1v4/tz9zPoj5weuSiN72RCmlbOStYx3u8rokYoyJwXkVllJKlXjeeumuu7wuiSil1F9J8U4hmkSUUspWacU8jWgSUUopGxlNIkoppTylA+tKKaU8pi0RpZRSHtOWiFJKKY85vOwH33mlSUQppWykvxNRSinlMR0TUUop5TEdE1FKKeWx4t6d5e7zRJRSShUCk4f/ciMi3UXkNxGJth61kXX7NSLyg7X9V+uu6fmiSUQppWzkMMbt6WpExBeYAvQAGgMDRKRxlmJDgJPGmPrAe8Db+Y1fk4hSStkoHeP2lIvWQLQx5oAx5gLwPXBXljJ3ATOt+dlAZxHJ1zNKdEwki+kBbXMvVMx0H3Da7hAKxa3fXvnc+uKupD686c8jq+0OwWsV4MB6KHDYZTkWuCWnMsaYNBFJBqoAxz09qLZElFLKRnkZExGRoSKyyWUaanf82hJRSikb5eXqLGPMNGBaDpuPALVclmta67IrEysipYBAIMntALKhLRGllLKRMcbtKRcbgQYiUkdESgP3AXOzlJkLDLbm+wPLjRs7vhptiSillI0cBfQ7EWuMYxgQAfgC040xu0RkPLDJGDMX+Bz4SkSigRM4E02+aBJRSikbFeSPDY0xC4GFWdaNdZk/B9xTYAdEk4hSStkqn71JttMkopRSNirutz3RJKKUUjbSu/gqpZTymD6USimllMe0O0sppZTHNIkopZTymF6dpZRSymPaElFKKeUxvTpLKaWUxxymeD9lXZOIUkrZSMdElFJKeUzHRNQVatzRjObjByG+Phz8diW/fTQv0/YGj/egzsA7SE9zcCEphU3DP+NsrPPBYje+OoCgLs0RHyFx1U62vfalDTXInm/DFlzT91Hw8eXiL0u4uPw/V5QpdVM7SocPwADpcQc5//W7SKVqlHl4NIiAbykurplP2obFRV8BN4164wU6dL6Vc6nneO25f7Bnx74ryjzz8uP0vqcHARUr0KZeZxuizN3kyePp3r0TqWdTGfLoC2zdujPTdn//Mnz33afUq3sdDoeDBQuWMebVtwB47LEHePKJh3A4HJw58ydPPTWKPXv321ENt706YTKr10VSuVJFfvp6qt3huK24j4n8JZ4nIiK1RWRgkRzMR7h5wkOsvX8SEbePpFbftlS4PjRTkVM7/uDn7q+yrPMrxM6P5MZXBwBQJawBVVpdz9JOL7Ok4ygqN69LtbaNiiTsXIkP1/ztcVKn/Z2zbz9NqRa3ITVqZS5SNRi/zvdw9sNRpE4axoWf/g2ASTlJ6r9GkPru86S+/xKlO/dDAirbUYtcte/cluvq1qJX23sY/9JEXn17ZLblVi1Zy8AeQ4o4Ovd1796J+vXr0Lhxe558ahQfffhWtuXee+9TbmzWkVatu9O2bRjh4XcA8P33P9GiZRdatQ7n3cmfMOmfrxdl+B7p27MrUye/YXcYeZZujNuTN/pLJBGgNlAkSaTyzfU4E5PIn4eOYS46ODznF0LCW2Yqc2z9bhypFwA4ERWNf7DzA9UYg28ZP3xKl8L3Gj/Ez5dzx5OLIuxc+VzbgPTj8ZgTieBII23LGko1zfz4Zr824VxctwBS/wTAnLFid6Q5J4BSfiDe+2d3R/htzJu1CIDtUbuoEFCeqtWrXFFue9Qujh/N1wPhClXv3t345uvZAERGRlGxYgBBQdUzlUlNPceqVesBuHjxIlu27iQ0NBiA06fPZJQrV7Zssei3D2t+I4EBFewOI8/y8nhcb1Ssu7NE5EHgJcAA2wEHkAKEAUHASGPMbGAi0EhEtgIzjTHvFVZM/kGVST1y+cMlNf4ElW+ul2P52gM6krBiGwAnNkdzbN1uem2dgogQ/cUSTu+PK6xQ80QCq2BOHc9YNqeO43PdDZnK+FQLAcD/mbfBx4cLEd/h2BvlfH3FqpR5dCw+VYO5MO8LTMqJogs+D6oHVyMhLjFjOTH+GNWDq3l1wshOSEgQh2Mv/+3EHoknJCSIhISj2ZYPDAzgzju78NFHn2ese+KJwTz33GOU9itNePf/K/SY/6qK+9VZ3vuVMBci0gR4FehkjLkJeM7aFAy0B3rhTB4ALwNrjDHNs0sgIjL00oPvl56NLoLona7t145KN9Vl38fzAShXuwYVGoSwoMUzzL95GNXbNaHqLTfkshcv4uOLT7VgUqeM5txX73DNPU9DmXKAM+mkvvMsZyc8TqlWnZDyFW0OVl3i6+vLV19NYcqU6Rw8eChj/dSpM2nUqD1jxkzglZeftTHCkk27s+zTCfjRGHMcwBhz6avtT8aYdGPMbqCGOzsyxkwzxoQZY8K6lq2fr6BSE07gH3q5+8M/uDKpCSevKFe9QxMaPncX6we/S/oFZ1dPaI8wTkRF4zh7HsfZ8yQs30bllg3yFU9BMclJSMWqGctSsSomOSlLmeOk7YyEdAfmRCLpx+LwqRacuUzKCdLjD+FTt3GRxO2O/3u4H7OWzWTWspkcTzxOUMjlP5sawdU4Gn/Mxujc98QTg9kYGcHGyAgS4o9Sq2ZIxraaocHExSVk+7pPPn6b6OiDfPjh59lu/2HWHPr0CS+UmFXx784qzkkkJ+dd5qWoD35y6wHK1wmibK1qiJ8vte5qQ3zE5kxlKja9jhaThrB+8LucT0rJWH/2SBJV2zRCfH2QUr5Ua9uQ0/uPFHUVspV+eD8+1UKQyjXAtxSlbu6AY+evmcqk7fwV3/o3OhfKVcCnWgjpSYlIYBXwK+1c718O3zqNMMe8o14AP3zxH+7tMph7uwxm+eLV9L63BwDNWjTh9Ok/i01X1tSpM2nVOpxWrcOZO28x9z/QH4DWrVuQnHw6266sv48bQWBgAC++mHngvH79OhnzPXt2Jjr6YOEG/xdW3FsixXlMZDnwPxGZbIxJEpGrXe5zGiiSETfjSGfr6Bl0+G4U4utDzPerSNl3hMYj+nFy20Hil0Rx42sDKVWuDG2mOXvgzh45zvqHJhM7/1eqt29M1xUTwUDCim3EL91SFGHnLj2d8//9FP+h48DHh4uRy0hPPEzp7gNxHI7GsSsSx94ofK9vTtmRH2FMOhfmzYCzp/G5vjml+zyCc+hKuLDyJ9Lj/7C3PjlYs2w9HTrfyoJffuRc6nlee/7y1T6zls3k3i6DAXjhtafpeXc3yviXYWnUHP777Vw+eSf7b/J2WLRoOd27d2LPnrWknj3Ho48Nz9i2MTKCVq3DCQ0N5pVXnmPv3v1E/uq85PrjT2bwxRff8eSTD9G5U3suXkzj5Mlkhgx5wa6quG3E6xPZuGU7p06l0LnvAzw1ZBD9ent/C8pbWxjukuJw1UVORGQwMALngPqlT9v51mA6InLGGFNeRPyACKAKMONqA+uzg+8vvm9IDroPOG13CIXi1m+LRzdTXuw9edjuEArFn0dW2x1CofCrWjffvR3XVWnm9mfOH0nbi7x3JTfFuSWCMWYmMPMq28tb/7+IcwxFKaW8SnH+Ig/FPIkopVRxp7c9UUop5TFtiSillPKYt1515S5NIkopZaPifnVWSfydiFJKFRsOk+72lB8iUllElorIfuv/lbIp01xENojILhHZLiK53u9Gk4hSStnIGOP2lE8vAz8bYxoAP1vLWZ0FHjTGNAG6A++LyFXvUaRJRCmlbFSEv1i/i8s/iZgJ9M1awBizzxiz35qPA44C1a62U00iSillo7y0RFxvFmtNQ/NwqBrGmHhrPoFc7i0oIq2B0sDvVyunA+tKKWWjvPxOxBgzDZiW03YRWYbzMRhZjcmyHyMiOR5YRIKBr4DBxlx9MEaTiFJK2aggfydijOmS0zYRSRSRYGNMvJUksn24jIgEAAuAMcaYX3I7pnZnKaWUjYrq6ixgLjDYmh8MzMlaQERKA/8Dvrx0D8LcaBJRSikbFeHA+kSgq4jsB7pYy4hImIj82ypzL3Ab8JCIbLWm5lfbqXZnKaWUjYrqtifGmCSgczbrNwGPWvNfA1/nZb+aRJRSykbF/RfrmkSUUspGegNGpZRSHivuN2As1k82LO5EZKh13XeJURLrBFqv4qQk1smb6dVZ9srLr02Li5JYJ9B6FSclsU5eS5OIUkopj2kSUUop5TFNIvYqif22JbFOoPUqTkpinbyWDqwrpZTymLZElFJKeUyTiFJKKY9pEvESIlJRRJ5yWe4oIvPtjMmKI1NcJY2I1BaRnXbHkRcisjC3R5ZmKW9bHUXkjB3HzRJDbREZaHccJZUmEe9REfDGD2tvjesvyxjT0xhzyu44ipHagCaRQqJJpABZ33j2isgMEdknIt+ISBcRWSci+0WktYiME5HpIrJSRA6IyLPWyycC9axbL//TWldeRGZb+/xGRMSGamWKS0RGiMhGEdkuIn93t95WuXEi8pWIbLDWP2ZDfbLjKyKficguEVkiIv7W+QkDEJGqIhJjzT8kIj+JyFIRiRGRYSIyXES2iMgvIlI5v8FY7/Gz1vx7IrLcmu9kvbcxVky1RWRP1titsi1FZJuIbAOedtl3ExGJtM7ndhFp4HL+vrH2N1tEyrrsZ5WIbBaRCOthRohIPRFZbK1fIyINrfV1rPO7Q0TeyO97kcv79KBVh23W39UMEflARNZb/7b6W0UnAh2sOr9QmDH9JeXl+b46XX3C+Y0nDbgRZ4LeDEwHBLgL+AkYB6wHrgGqAkmAn/XanS776ggkAzWtfW0A2ttUp53WfDecl0+KFdN8nM8eyLXe1uvHAdsAf6vuh4EQLzlnza3lWcADwEogzFpXFYix5h8CooEKQDXrHD1hbXsPeL4AYmoD/GjNrwEirb+R14HHgRgrpmxjt+a3A7dZ8/90OYcfAvdb86Wtc1EbMEA7a/104CXrmOuBatb6/wOmW/M/Aw2s+VuA5db8XOBBa/5p4EwhnbcmwD6gqrVcGZgB/Gj9DTYGol3+Lc238++sJE/aEil4B40xO4zzucS7gJ+N8y95B85/rAALjDHnjTHHcT6iskYO+4o0xsRa+9rq8nq7dLOmLUAU0BBoYG1zp94Ac4wxqVbdVwCtiyr4qzhojNlqzW8m9/d5hTHmtDHmGM4kMs9an7WuntoMtBTnY0rP4/wCEQZ0wJlUXF0RuzVeUtEYs9pa/5VL+Q3AaBEZBVxnjEm11h82xqyz5r8G2gM3AE2BpSKyFXgVqCki5YFbgR+t9Z8CwdZr2wHfZXPcgtYJZ6I9DmCMOWGt/8kYk26M2U3O/65UAdK7+Ba88y7z6S7L6Vx+v13LOMj5PLhbrqgI8JYx5tNMK0Vq4169gSsenuANP1TK+j774/yGf+lLVpmrlL9aXT1ijLkoIgdxtnrW42xV3AHUB/a4EfvV9v2tiPwK3AksFJHHgQNkf14E2GWMaeu6wUpup4wxOT3xzs5z6vp+2NH9+5ejLRHvcRpnF4m3cY0rAnjE+iaKiISKSPU87u8uESkjIlVwdjNsLLBIC1YM0NKa73+VcoVlDc4updXW/BPAFqt1d1XGOeh+SkTaW6vuv7RNROoCB4wxH+B8xnYza9O1InIpWQwE1gK/AdUurRcRPxFpYoxJAQ6KyD3WehGRm6zXrgPuy3rcQrAcuMf6OyKXsShv/bdVImgS8RLG+ejKdSKyUy4PrNvONS6gK/AtsEFEdgCzyfs/zu04u7F+Af5hjIkryHgL0DvAkyKyBef4Q1Fbg7OLaIMxJhE4x5VdWVfzMDDF6m5y/UZ+L7DTWt8U+NJa/xvwtIjsASoBnxhjLuBMoG9bA/RbcXZjgTNBDLHW78I59gXwnLWfHUBoXiqcF8aYXcCbwCorhslXKb4dcFgD8DqwXsD0tieqyIjIOJwDre/YHYu6zOqOnG+MaWpzKKoY0paIUkopj2lLRCmllMe0JaKUUspjmkSUUkp5TJOIUkopj2kSUUop5TFNIkoppTz2/4fk2xYUBNpuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJKhKrfYA3Bz"
      },
      "source": [
        "##Separar em variáveis preditoras e classe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHAllkKKA6b0"
      },
      "source": [
        "data_filtrado = data[categorical_features + numerical_features]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JskIESdNE6J_"
      },
      "source": [
        "data_filtrado_com_data=data[categorical_features + data_features +numerical_features]"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slonSYSiBrQY"
      },
      "source": [
        "X=data_filtrado.iloc[:,0:-1].values\n",
        "y=data_filtrado.iloc[:,-1:].values"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoYottxpIcZN"
      },
      "source": [
        "df_X = data_filtrado_com_data.iloc[:,0:-1]\n",
        "df_y = data_filtrado_com_data.iloc[:,-1:]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yeXexLyc_0xK"
      },
      "source": [
        "##Separar em bases de treinamento e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "DlLazJFO_7_M",
        "outputId": "cd7307e5-31b4-41cb-bb17-d525be184062"
      },
      "source": [
        "X_train, X_test, y_train,y_test = train_test_split(X,y,test_size=0.3)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from statsmodels.tsa.arima_model import ARIMA\\nimport sklearn\\nfrom sklearn.model_selection import train_test_split\\nimport pandas as pd\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.model_selection import cross_val_score\\nimport seaborn as sns\\nimport bokeh\\nimport cv2\\nimport altair as alt\\nfrom sklearn.feature_extraction.text import CountVectorizer'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "w1F3RjRBIhas",
        "outputId": "7e907c63-36d6-417b-be72-080ae52c6cbe"
      },
      "source": [
        "df_X_train, df_X_test, df_y_train,df_y_test = train_test_split(df_X,df_y,test_size=0.3)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from statsmodels.tsa.arima_model import ARIMA\\nimport sklearn\\nfrom sklearn.model_selection import train_test_split\\nimport pandas as pd\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.model_selection import cross_val_score\\nimport seaborn as sns\\nimport bokeh\\nimport cv2\\nimport altair as alt\\nfrom sklearn.feature_extraction.text import CountVectorizer'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "meoeoT0sB2YR",
        "outputId": "00d35a8a-1c86-45ac-9464-b94abfa6b784"
      },
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((511, 9), (220, 9), (511, 1), (220, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69FRLhU-ClZF"
      },
      "source": [
        "##Criar e Treinar Modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "jH06SsFUB7rY",
        "outputId": "95f66cd8-6e51-47b3-cadf-70d3b9984ef7"
      },
      "source": [
        "model = RandomForestRegressor(random_state = 0)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from statsmodels.tsa.arima_model import ARIMA\\nimport sklearn\\nfrom sklearn.model_selection import train_test_split\\nimport pandas as pd\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.model_selection import cross_val_score\\nimport seaborn as sns\\nimport bokeh\\nimport cv2\\nimport altair as alt\\nfrom sklearn.feature_extraction.text import CountVectorizer'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_I3-LzFfB810",
        "outputId": "b7e5d31b-0d43-4850-b5bd-de1c43642398"
      },
      "source": [
        "model.fit(X_train, y_train);"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyforest/__init__.py:1: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "JLuSEcbvCFjv",
        "outputId": "50c28bcf-6675-4fd7-c907-cffb3f40207e"
      },
      "source": [
        "df_treinamento=pd.concat([df_X_train,df_y_train],axis=1)\n",
        "df_treinamento['prediction'] = model.predict(X_train)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from statsmodels.tsa.arima_model import ARIMA\\nimport sklearn\\nfrom sklearn.model_selection import train_test_split\\nimport pandas as pd\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.model_selection import cross_val_score\\nimport seaborn as sns\\nimport bokeh\\nimport cv2\\nimport altair as alt\\nfrom sklearn.feature_extraction.text import CountVectorizer'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "zW3rA_KHJLT4",
        "outputId": "cb1fa6bb-2ea8-4f2e-9486-452856508b50"
      },
      "source": [
        "df_test = pd.concat([df_X_test,df_y_test],axis=1)\n",
        "df_test['prediction'] = model.predict(X_test)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from statsmodels.tsa.arima_model import ARIMA\\nimport sklearn\\nfrom sklearn.model_selection import train_test_split\\nimport pandas as pd\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.model_selection import cross_val_score\\nimport seaborn as sns\\nimport bokeh\\nimport cv2\\nimport altair as alt\\nfrom sklearn.feature_extraction.text import CountVectorizer'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxUZYvIzCdJg"
      },
      "source": [
        "mapeamento_colunas = {}\n",
        "mapeamento_colunas['target'] = 'cnt'\n",
        "mapeamento_colunas['prediction'] = 'prediction'\n",
        "mapeamento_colunas['datetime'] = 'dteday'\n",
        "mapeamento_colunas['numerical_features'] = numerical_features\n",
        "mapeamento_colunas['categorical_features'] = categorical_features\n",
        "dashboard = Dashboard(tabs=[RegressionPerformanceTab])\n",
        "dashboard.calculate(df_treinamento,df_test, column_mapping=mapeamento_colunas)\n",
        "dashboard.save('Demanda Bikes.html')"
      ],
      "execution_count": 46,
      "outputs": []
    }
  ]
}