Caso_1.ipynb

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    "colab": {
      "name": "Caso 1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP8s9QfT1NLCW1RhmcrtyO0",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/naokityokoyama/Projetos_Diversos/blob/master/Caso_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y750OVOBgdgr"
      },
      "source": [
        "O termo Produtividade mede a eficiência com que uma econômia usa seus recursos para produir bens e serviços. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81dj-ad0Khil"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "import plotly.offline as py\r\n",
        "import plotly.graph_objs as go\r\n",
        "from fbprophet import Prophet"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBUxZyyAKjdl"
      },
      "source": [
        "df= pd.read_csv('https://raw.githubusercontent.com/naokityokoyama/Projeto_extra/main/TFP.csv')"
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wm0m8tEaLS-R"
      },
      "source": [
        "USA = df[df['isocode']=='USA']\r\n",
        "MEX = df[df['isocode']=='MEX']\r\n",
        "CAN = df[df['isocode']=='CAN']"
      ],
      "execution_count": 144,
      "outputs": []
    },
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        "id": "PvCPEL-0K_et",
        "outputId": "5a9027c8-4011-423a-8333-316fcc34b748"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>isocode</th>\n",
              "      <th>year</th>\n",
              "      <th>rtfpna</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>1950</td>\n",
              "      <td>0.617148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USA</td>\n",
              "      <td>1951</td>\n",
              "      <td>0.629588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>USA</td>\n",
              "      <td>1952</td>\n",
              "      <td>0.638451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USA</td>\n",
              "      <td>1953</td>\n",
              "      <td>0.651858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>USA</td>\n",
              "      <td>1954</td>\n",
              "      <td>0.646179</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "  isocode  year    rtfpna\n",
              "0     USA  1950  0.617148\n",
              "1     USA  1951  0.629588\n",
              "2     USA  1952  0.638451\n",
              "3     USA  1953  0.651858\n",
              "4     USA  1954  0.646179"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfOFijhDH_hW"
      },
      "source": [
        "dataset com 183 instancias e com todas as features completas (183)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnAlmk_CLJwa",
        "outputId": "483400eb-d0ec-4267-ac2d-53119edac0b0"
      },
      "source": [
        "#confirmado nenhum dado nulo\r\n",
        "df.isnull().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "isocode    0\n",
              "year       0\n",
              "rtfpna     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
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        "id": "kcdnUXUHtIXy",
        "outputId": "a23cf299-fd86-4db2-b2b5-17ba2776a89a"
      },
      "source": [
        "# rtfpna em relação ao tempo\r\n",
        "trace1 = go.Scatter(x = USA['year'],\r\n",
        "                    y = USA['rtfpna'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'USA')\r\n",
        "# Gráfico de apenas linhas\r\n",
        "trace2 = go.Scatter(x = CAN['year'],\r\n",
        "                    y = CAN['rtfpna'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'CANADA')\r\n",
        "# Criando gráfico com marcadores e linhas\r\n",
        "trace3 = go.Scatter(x = MEX['year'],\r\n",
        "                    y = MEX['rtfpna'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'MEXICO')\r\n",
        "\r\n",
        "layout = go.Layout(title = 'Evolução Produtiva')\r\n",
        "data = [trace1, trace2, trace3]\r\n",
        "\r\n",
        "fig = go.Figure(data=data, layout=layout)\r\n",
        "py.iplot(fig)"
      ],
      "execution_count": 137,
      "outputs": [
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              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcpKOTk2ZNs9"
      },
      "source": [
        "O Mexico teve um aumento expressivo na evolução produtiva entre a década de 60 e final da decada de 70 e em seguinda um declínio bem considerado até inicio do ano 2010 que parece ter uma tendência a subir.\r\n",
        "\r\n",
        "O Canada teve um pico de crescimento no meio da década de 50 e em seguinda uma leve queda, depois seguiu em crescimento até o ano 2000 onde se iniciou uma queda até os dias de hoje, parece está se estabilizando a essa queda no fim da serie. \r\n",
        "\r\n",
        "O EUA teve um crescimento a partir dos anos 50 em constante evolução (sem nenhum pico expressivo em toda a serie) tanto para queda quanto para subida."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebmQCRpIYWWu",
        "outputId": "f5fc3432-2125-4cdd-9df9-e473b483ccbb"
      },
      "source": [
        "df.groupby(['isocode']).size()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "isocode\n",
              "CAN    62\n",
              "MEX    62\n",
              "USA    62\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYxNfwVKIKNg"
      },
      "source": [
        "dataset com anos completos para os 3 paises"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "224ws8d8KvOv",
        "outputId": "abbf4705-6e15-4e2a-cb6f-4a4a5e105268"
      },
      "source": [
        "#pequeno resumo de estatistica descritiva dos dados\r\n",
        "df.groupby(['isocode']).describe().T"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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>isocode</th>\n",
              "      <th>CAN</th>\n",
              "      <th>MEX</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">year</th>\n",
              "      <th>count</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1980.500000</td>\n",
              "      <td>1980.500000</td>\n",
              "      <td>1980.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.041619</td>\n",
              "      <td>18.041619</td>\n",
              "      <td>18.041619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1950.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1965.250000</td>\n",
              "      <td>1965.250000</td>\n",
              "      <td>1965.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1980.500000</td>\n",
              "      <td>1980.500000</td>\n",
              "      <td>1980.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1995.750000</td>\n",
              "      <td>1995.750000</td>\n",
              "      <td>1995.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2011.000000</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>2011.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">rtfpna</th>\n",
              "      <th>count</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.992886</td>\n",
              "      <td>1.130132</td>\n",
              "      <td>0.803824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.050126</td>\n",
              "      <td>0.158209</td>\n",
              "      <td>0.116121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.843335</td>\n",
              "      <td>0.798159</td>\n",
              "      <td>0.617148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.973294</td>\n",
              "      <td>1.012231</td>\n",
              "      <td>0.739949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.004211</td>\n",
              "      <td>1.093859</td>\n",
              "      <td>0.766047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.029513</td>\n",
              "      <td>1.299591</td>\n",
              "      <td>0.880544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.066104</td>\n",
              "      <td>1.383718</td>\n",
              "      <td>1.020392</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "isocode               CAN          MEX          USA\n",
              "year   count    62.000000    62.000000    62.000000\n",
              "       mean   1980.500000  1980.500000  1980.500000\n",
              "       std      18.041619    18.041619    18.041619\n",
              "       min    1950.000000  1950.000000  1950.000000\n",
              "       25%    1965.250000  1965.250000  1965.250000\n",
              "       50%    1980.500000  1980.500000  1980.500000\n",
              "       75%    1995.750000  1995.750000  1995.750000\n",
              "       max    2011.000000  2011.000000  2011.000000\n",
              "rtfpna count    62.000000    62.000000    62.000000\n",
              "       mean      0.992886     1.130132     0.803824\n",
              "       std       0.050126     0.158209     0.116121\n",
              "       min       0.843335     0.798159     0.617148\n",
              "       25%       0.973294     1.012231     0.739949\n",
              "       50%       1.004211     1.093859     0.766047\n",
              "       75%       1.029513     1.299591     0.880544\n",
              "       max       1.066104     1.383718     1.020392"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUZYAd-3IWZV"
      },
      "source": [
        "Os dataset foram separados por paises\r\n",
        "\r\n",
        "Modelando com Prophet "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHqRyIpFu1Sz"
      },
      "source": [
        "dataset_usa = USA.drop('isocode', axis=1)\r\n",
        "dataset_can = CAN.drop('isocode', axis=1)\r\n",
        "dataset_mex = MEX.drop('isocode', axis=1)"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a04ctEuIvDh9"
      },
      "source": [
        "dataset_usa.rename(columns={'year':'ds', 'rtfpna':'y'}, inplace=True)\r\n",
        "dataset_can.rename(columns={'year':'ds', 'rtfpna':'y'}, inplace=True)\r\n",
        "dataset_mex.rename(columns={'year':'ds', 'rtfpna':'y'}, inplace=True)"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UENujwMwbXv"
      },
      "source": [
        "#dataset_usa['DS'] = pd.to_datetime(dataset_usa['DS'],  format='%Y')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9sPSTjazxXUO"
      },
      "source": [
        "df.rename(columns={'DS':'ds', 'Y':'y'}, inplace=True)"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "de_jXUVbxqFn"
      },
      "source": [
        "dataset_usa['ds'] = pd.to_datetime(dataset_usa['ds'], format='%Y')\r\n",
        "dataset_can['ds'] = pd.to_datetime(dataset_can['ds'], format='%Y')\r\n",
        "dataset_mex['ds'] = pd.to_datetime(dataset_mex['ds'], format='%Y')"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sQpM3K4u1cK"
      },
      "source": [
        "#criada uma função pois o Prophet espera sempre um df \r\n",
        "def prophet(dataset):\r\n",
        "  m = Prophet()\r\n",
        "  df = dataset\r\n",
        "  return m.fit (df)"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7-yLwX-_Esa",
        "outputId": "553ced6a-38ba-4896-98c8-fbcdfb22f8f0"
      },
      "source": [
        "usa = prophet(dataset_usa)\r\n",
        "can = prophet(dataset_can)\r\n",
        "mex = prophet(dataset_mex)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6xbTgKau1j3"
      },
      "source": [
        "future_usa = usa.make_future_dataframe(periods=10, freq='Y')\r\n",
        "future_can = can.make_future_dataframe(periods=10, freq='Y')\r\n",
        "future_mex = mex.make_future_dataframe(periods=10, freq='Y')"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "Web3QeNcyBrV",
        "outputId": "e26e306c-8830-4909-deb6-8a79958df3d4"
      },
      "source": [
        "forecast_usa = usa.predict(future_usa)\r\n",
        "forecast_usa[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\r\n",
        "\r\n",
        "forecast_can = can.predict(future_can)\r\n",
        "forecast_can[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\r\n",
        "\r\n",
        "forecast_mex = mex.predict(future_mex)\r\n",
        "forecast_mex[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\r\n",
        "\r\n",
        "forecast_can.head(2)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1950-01-01</td>\n",
              "      <td>0.386272</td>\n",
              "      <td>0.847824</td>\n",
              "      <td>0.912538</td>\n",
              "      <td>0.386272</td>\n",
              "      <td>0.386272</td>\n",
              "      <td>0.493607</td>\n",
              "      <td>0.493607</td>\n",
              "      <td>0.493607</td>\n",
              "      <td>0.493607</td>\n",
              "      <td>0.493607</td>\n",
              "      <td>0.493607</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.87988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1951-01-01</td>\n",
              "      <td>0.393592</td>\n",
              "      <td>0.854552</td>\n",
              "      <td>0.920384</td>\n",
              "      <td>0.393592</td>\n",
              "      <td>0.393592</td>\n",
              "      <td>0.493768</td>\n",
              "      <td>0.493768</td>\n",
              "      <td>0.493768</td>\n",
              "      <td>0.493768</td>\n",
              "      <td>0.493768</td>\n",
              "      <td>0.493768</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.88736</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ds     trend  ...  multiplicative_terms_upper     yhat\n",
              "0 1950-01-01  0.386272  ...                         0.0  0.87988\n",
              "1 1951-01-01  0.393592  ...                         0.0  0.88736\n",
              "\n",
              "[2 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "irAgwruCu1q0",
        "outputId": "b1b3481e-1d63-4cc5-ab70-f30398895f45"
      },
      "source": [
        "# Forecast \r\n",
        "trace1 = go.Scatter(x = forecast_usa['ds'],\r\n",
        "                    y = forecast_usa['yhat'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'USA')\r\n",
        "# Gráfico de apenas linhas\r\n",
        "trace2 = go.Scatter(x = forecast_can['ds'],\r\n",
        "                    y = forecast_can['yhat'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'CANADA')\r\n",
        "# Criando gráfico com marcadores e linhas\r\n",
        "trace3 = go.Scatter(x = forecast_mex['ds'],\r\n",
        "                    y = forecast_mex['yhat'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'MEXICO')\r\n",
        "\r\n",
        "layout = go.Layout(title = 'Forecast')\r\n",
        "data = [trace1, trace2, trace3]\r\n",
        "\r\n",
        "fig = go.Figure(data=data, layout=layout)\r\n",
        "py.iplot(fig)\r\n"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlCoxm2XImvg"
      },
      "source": [
        "Essa é a previsão para os proximos 10 anos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "BPFhAfgRy-Ij",
        "outputId": "239c1729-b77a-4f06-d254-c73eaaa8e530"
      },
      "source": [
        "# Trend\r\n",
        "trace1 = go.Scatter(x = forecast_usa['ds'],\r\n",
        "                    y = forecast_usa['trend'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'USA')\r\n",
        "# Gráfico de apenas linhas\r\n",
        "trace2 = go.Scatter(x = forecast_can['ds'],\r\n",
        "                    y = forecast_can['trend'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'CANADA')\r\n",
        "# Criando gráfico com marcadores e linhas\r\n",
        "trace3 = go.Scatter(x = forecast_mex['ds'],\r\n",
        "                    y = forecast_mex['trend'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'MEXICO')\r\n",
        "\r\n",
        "layout = go.Layout(title = 'Trend')\r\n",
        "data = [trace1, trace2, trace3]\r\n",
        "\r\n",
        "fig = go.Figure(data=data, layout=layout)\r\n",
        "py.iplot(fig)\r\n"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "display_data",
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      "cell_type": "markdown",
      "metadata": {
        "id": "0ZEtUHimIvht"
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      "source": [
        "Tendencia para os proximos anos temos uma estimativa da seguinte:\r\n",
        "USA continuar a subir \r\n",
        "MEX tendendo a cair\r\n",
        "CAN tendendo a equilibrio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "3iI3xqPVHMdq",
        "outputId": "d970165c-7e23-432d-d562-601ca17be3d8"
      },
      "source": [
        "# seasonality\r\n",
        "trace1 = go.Scatter(x = forecast_usa['ds'],\r\n",
        "                    y = forecast_usa['yearly'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'USA')\r\n",
        "# Gráfico de apenas linhas\r\n",
        "trace2 = go.Scatter(x = forecast_can['ds'],\r\n",
        "                    y = forecast_can['yearly'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'CANADA')\r\n",
        "# Criando gráfico com marcadores e linhas\r\n",
        "trace3 = go.Scatter(x = forecast_mex['ds'],\r\n",
        "                    y = forecast_mex['yearly'],\r\n",
        "                    mode = 'markers+lines',\r\n",
        "                    name = 'MEXICO')\r\n",
        "\r\n",
        "layout = go.Layout(title = 'seasonality')\r\n",
        "data = [trace1, trace2, trace3]\r\n",
        "\r\n",
        "fig = go.Figure(data=data, layout=layout)\r\n",
        "py.iplot(fig)"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "Depois de analisar o dataset as features que existem também para incrementar o modelo podemos destacar algumas\r\n",
        "\r\n",
        "*    População (total da população) variavel **pop**\r\n",
        "*    Emprego (Porcentagem de pessoas que trabalham) variavel **emp**\r\n",
        "*    PIB (Produto interno Bruto) que podemos entender pela variavel **labsh ** pois seria o PIB por participação na força de trabalho\r\n",
        "\r\n",
        "Com o numero da polulação vs Empregabilidade podemos ver a capacidade total de individuos para contribuir na produtividade\r\n",
        "Com o PIB por produção podemos ver o desempenho da força de trabalho para elevar o PIB \r\n"
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