case_01.ipynb

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    "\n",
    "<div style=\"margin-left: 10px\">\n",
    "<h1>Gol Linhas Aéreas Inteligentes - Case 01</h1>\n",
    "Processo seletivo - Desenvolvedor (E)<br>\n",
    "<b>Candidato:</b> Maycon Felipe da Silva Mota <br>\n",
    "<b>Email:</b> mayconfelipemotamw3@gmail.com<br>\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Importando bibliotecas que usarei no CASE 01"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import plotly.graph_objects as go\n",
    "import seaborn as sns\n",
    "from pandas import DataFrame\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "source": [
    "# 1 - Faça um ranking para o número total de PAX por dia da semana."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     Data Venda Canal de Venda Local de Venda   PAX  Monetário Vendido    RPK\n",
       "0    2016-11-01      Telégrafo      Mindscape    30        2188.109487   3476\n",
       "1    2016-11-01      TeleVenda          Arena    52        3302.375951   8197\n",
       "2    2016-11-01      TeleVenda      Mindscape    19        1041.544400   2425\n",
       "3    2016-11-01  Porta a Porta      Mindscape   496       15566.340547  53278\n",
       "4    2016-11-01      TeleVenda           Vast    46        2721.427289   2039\n",
       "...         ...            ...            ...   ...                ...    ...\n",
       "2427 2017-04-01  Porta a Porta           Vast  1079       28486.396741  54551\n",
       "2428 2017-04-01  Porta a Porta       Ellipsis   841       23600.998318  46988\n",
       "2429 2017-04-01      TeleVenda          Arena   175       10270.292519  26599\n",
       "2430 2017-04-01      Telégrafo          Arena   105        5470.410450  15130\n",
       "2431 2017-04-01      Telégrafo           Vast    61        2911.981488   3050\n",
       "\n",
       "[2432 rows x 6 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "# Leitura do Dataset do Case 01 \n",
    "df_case01 = pd.read_excel('./input/case_analytics-ds.xlsx', sheet_name='Dados')\n",
    "df_case01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Gerar coluna dia da semana\n",
    "df_case01['Dia_Semana_Venda']= df_case01['Data Venda'].dt.day_name(locale='pt')\n",
    "\n",
    "# Agregar pela soma, agrupando pelo dia da semana e criar um ranking do maior ao menor\n",
    "df_ranking_pax = df_case01.groupby(by= df_case01['Dia_Semana_Venda']).sum()\n",
    "df_ranking_pax['rank'] = df_ranking_pax['PAX'].rank(ascending=False)\n",
    "df_ranking_pax = df_ranking_pax.sort_values(by= ['rank'])\n",
    "\n",
    "# Exibir o DF com PAX, RPK e posição do ranking\n",
    "df_ranking_pax[['PAX', 'RPK', 'rank']].to_csv('./output/ranking_pax_semanas.csv')"
   ]
  },
  {
   "source": [
    "### Como vimos no dataframe acima, quarta-feira e quinta-feira são os dias onde há maior número de PAX."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 2 - Qual a correlação de sábado e domingo somados com o total de RPK?\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Dia_Semana_Venda     PAX       RPK  pct_total\n",
       "7    SábadoDomingo  495087  45590272  28.088781\n",
       "0     Quarta-feira  293025  26263705  16.181423\n",
       "1     Quinta-feira  258488  23501102  14.479346\n",
       "2      Sexta-feira  255625  23309936  14.361566\n",
       "5    Segunda-feira  236316  23275908  14.340601\n",
       "6      Terça-feira  219802  20366840  12.548285"
      ],
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     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "source": [
    "# Para verificarmos a correlação de sábado e domingo somados com o valor total de RPK, irei criar um gráfico de barras. \n",
    "# Transformatar o dataset anterior e resetar o índice\n",
    "df_correlacao = df_ranking_pax.reset_index()[['Dia_Semana_Venda', 'PAX', 'RPK']]\n",
    "\n",
    "# Fazer uma linha somando sábado e domingo\n",
    "df_correlacao = df_correlacao.append(df_correlacao.loc[(df_correlacao['Dia_Semana_Venda'] == 'Sábado') | (df_correlacao['Dia_Semana_Venda'] == 'Domingo')].sum(), ignore_index=True)\n",
    "df_correlacao = df_correlacao.drop([3, 4])\n",
    "df_correlacao['pct_total'] = (df_correlacao['RPK']/df_correlacao['RPK'].sum())*100\n",
    "df_correlacao.to_csv('./output/relacao_total_rpk.csv')\n",
    "df_correlacao.sort_values(by= 'pct_total', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
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     "data": {
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       "<AxesSubplot:>"
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     "execution_count": 56
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "## A questão não apresenta informações concretas para que seja possível criar uma correlação, uma vez que para que seja possível fazer correlações deve-se, ao mínimo, utilizar duas medidas sendo X e Y em uma escala contínua para que seja possível obter o coeficiente de correlação. \n",
    "\n",
    "df_correlacao[['PAX', 'RPK']].corr()\n",
    "\n",
    "sns.heatmap(df_correlacao[['PAX', 'RPK']].corr(), annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "text": [
          26263705,
          23501102,
          23309936,
          23275908,
          20366840,
          45590272
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Quarta-feira",
          "Quinta-feira",
          "Sexta-feira",
          "Segunda-feira",
          "Terça-feira",
          "SábadoDomingo"
         ],
         "y": [
          26263705,
          23501102,
          23309936,
          23275908,
          20366840,
          45590272
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
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   ],
   "source": [
    "fig = go.Figure(data=[go.Bar(\n",
    "            x=df_correlacao['Dia_Semana_Venda'], y=df_correlacao['RPK'],\n",
    "            text=df_correlacao['RPK'],\n",
    "            textposition='auto',\n",
    "        )])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "source": [
    "# 3 - Qual a média de ‘Monetário’ por mês por Canal? E a mediana?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                               Média       Mediana\n",
       "Canal de Venda Mes_Ano                            \n",
       "Porta a Porta  01/2017  31320.569487  28823.025305\n",
       "               02/2017  31710.148886  29711.580146\n",
       "               03/2017  31055.939762  30488.191027\n",
       "               04/2017  33319.496263  32421.609418\n",
       "               11/2016  34638.071796  30262.977968\n",
       "               12/2016  28615.452500  25527.519295\n",
       "TeleVenda      01/2017  32868.881429  19085.123238\n",
       "               02/2017  32208.445818  18926.690232\n",
       "               03/2017  32132.246712  18666.879969\n",
       "               04/2017  35795.781915  28091.885619\n",
       "               11/2016  30963.459349  18757.707785\n",
       "               12/2016  28045.721461  17567.600938\n",
       "Telégrafo      01/2017   4234.022567   3782.248868\n",
       "               02/2017   4307.626273   4045.893131\n",
       "               03/2017   4568.148531   4345.559552\n",
       "               04/2017   3778.591835   3458.884666\n",
       "               11/2016   4614.310076   4029.132131\n",
       "               12/2016   5212.648055   4227.577948"
      ],
      "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></th>\n      <th>Média</th>\n      <th>Mediana</th>\n    </tr>\n    <tr>\n      <th>Canal de Venda</th>\n      <th>Mes_Ano</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"6\" valign=\"top\">Porta a Porta</th>\n      <th>01/2017</th>\n      <td>31320.569487</td>\n      <td>28823.025305</td>\n    </tr>\n    <tr>\n      <th>02/2017</th>\n      <td>31710.148886</td>\n      <td>29711.580146</td>\n    </tr>\n    <tr>\n      <th>03/2017</th>\n      <td>31055.939762</td>\n      <td>30488.191027</td>\n    </tr>\n    <tr>\n      <th>04/2017</th>\n      <td>33319.496263</td>\n      <td>32421.609418</td>\n    </tr>\n    <tr>\n      <th>11/2016</th>\n      <td>34638.071796</td>\n      <td>30262.977968</td>\n    </tr>\n    <tr>\n      <th>12/2016</th>\n      <td>28615.452500</td>\n      <td>25527.519295</td>\n    </tr>\n    <tr>\n      <th rowspan=\"6\" valign=\"top\">TeleVenda</th>\n      <th>01/2017</th>\n      <td>32868.881429</td>\n      <td>19085.123238</td>\n    </tr>\n    <tr>\n      <th>02/2017</th>\n      <td>32208.445818</td>\n      <td>18926.690232</td>\n    </tr>\n    <tr>\n      <th>03/2017</th>\n      <td>32132.246712</td>\n      <td>18666.879969</td>\n    </tr>\n    <tr>\n      <th>04/2017</th>\n      <td>35795.781915</td>\n      <td>28091.885619</td>\n    </tr>\n    <tr>\n      <th>11/2016</th>\n      <td>30963.459349</td>\n      <td>18757.707785</td>\n    </tr>\n    <tr>\n      <th>12/2016</th>\n      <td>28045.721461</td>\n      <td>17567.600938</td>\n    </tr>\n    <tr>\n      <th rowspan=\"6\" valign=\"top\">Telégrafo</th>\n      <th>01/2017</th>\n      <td>4234.022567</td>\n      <td>3782.248868</td>\n    </tr>\n    <tr>\n      <th>02/2017</th>\n      <td>4307.626273</td>\n      <td>4045.893131</td>\n    </tr>\n    <tr>\n      <th>03/2017</th>\n      <td>4568.148531</td>\n      <td>4345.559552</td>\n    </tr>\n    <tr>\n      <th>04/2017</th>\n      <td>3778.591835</td>\n      <td>3458.884666</td>\n    </tr>\n    <tr>\n      <th>11/2016</th>\n      <td>4614.310076</td>\n      <td>4029.132131</td>\n    </tr>\n    <tr>\n      <th>12/2016</th>\n      <td>5212.648055</td>\n      <td>4227.577948</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "# Gerando uma coluna de Numero de mês\n",
    "df_case01['Mes_Ano'] = df_case01['Data Venda'].dt.strftime('%m/%Y')\n",
    "\n",
    "# Ordenando valores pela data venda \n",
    "df_case01 = df_case01.sort_values(by= 'Mes_Ano')\n",
    "\n",
    "# Agrupando os dados agregando média e mediana por canal de venda e mês\n",
    "df_media = df_case01.groupby(by= ['Canal de Venda', 'Mes_Ano'])['Monetário Vendido'].agg(['mean', 'median'])\n",
    "\n",
    "# renomeando o nome das colunas\n",
    "df_media.columns = ['Média', 'Mediana']\n",
    "df_media.to_csv('./output/media_monetario_por_mes_canal.csv')\n",
    "df_media"
   ]
  },
  {
   "source": [
    "# 4 - Crie um forecast de PAX por ‘Local de Venda’ para os próximos 15 dias a contar da última data de venda. (Aqui a técnica é livre) "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para analisar essa série temporal, irei utilizar a biblioteca Prophet que foi desenvolvida pelo Facebook e é utilizada em grande parte das aplicações. \r\n",
    "### https://facebook.github.io/prophet/\r\n",
    "\r\n",
    "from prophet import Prophet\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x15cb89373d0>"
      ]
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "# Transformação do dataset para o formato ds | y, conforme a documentação do Prophet indica \n",
    "\n",
    "df_forecast = df_case01[['Data Venda', 'PAX']]\n",
    "df_forecast.columns = ['ds', 'y']\n",
    "df_forecast = df_forecast.groupby(by = 'ds').sum()\n",
    "df_forecast = df_forecast.reset_index()\n",
    "df_forecast\n",
    "\n",
    "# Intanciação do modelo, aqui fiz a definição de alguns parametros como o modo da sazonalidade, sazonalidade anual, mensal ou diária. Em seguida, adicionei as sazonalidades mensais e trimestrais, por fim, passei a biblioteca os feriados do Brasil. \n",
    "\n",
    "m = Prophet(seasonality_mode='additive', yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=False, seasonality_prior_scale=10, changepoint_prior_scale=3, holidays_prior_scale=20)\n",
    "m.add_seasonality(name='monthly', period=30.5, fourier_order=12)\n",
    "m.add_seasonality(name='quartely', period=365.25/4, fourier_order=5, prior_scale=15)\n",
    "m.add_country_holidays(country_name='BR')\n",
    "\n",
    "# Ajuste do modelo para treinamento\n",
    "\n",
    "m.fit(df_forecast)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prever os próximos 15 dias e incluir o histórico\n",
    "\n",
    "future = m.make_future_dataframe(periods=15, freq='D', include_history=True)\n",
    "forecast = m.predict(future)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Previsto x Realizado\n",
    "\n",
    "fig1 = m.plot(forecast)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajustes do Modelo com o Forecast e o realizado para comparação\r\n",
    "\r\n",
    "df_final = pd.concat([df_forecast, forecast], axis=1)\r\n",
    "df_final = df_final[['ds', 'y', 'yhat']]\r\n",
    "df_final['diff'] = df_final['yhat'] - df_final['y']\r\n",
    "df_final.to_csv('./output/forecast_general_15days.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Criando forecast para cada um dos canais de vendas\n",
    "\n",
    "### Arena \n",
    "\n",
    "## Tratamento para o Dataset Arena\n",
    "df_forecast_arena = df_case01[df_case01['Local de Venda'] == 'Arena']\n",
    "df_forecast = df_forecast_arena[['Data Venda', 'PAX']]\n",
    "df_forecast.columns = ['ds', 'y']\n",
    "df_forecast = df_forecast.groupby(by = 'ds').sum()\n",
    "df_forecast = df_forecast.reset_index()\n",
    "df_forecast\n",
    "\n",
    "## Instanciação do modelo Prophet para o Canal de Venda Arena\n",
    "m = Prophet(seasonality_mode='additive', yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=False, seasonality_prior_scale=10, changepoint_prior_scale=3, holidays_prior_scale=20)\n",
    "m.add_seasonality(name='monthly', period=30.5, fourier_order=12)\n",
    "m.add_seasonality(name='quartely', period=365.25/4, fourier_order=5, prior_scale=15)\n",
    "m.add_country_holidays(country_name='BR')\n",
    "m.fit(df_forecast)\n",
    "\n",
    "## Predição para os próximos 15 dias \n",
    "future = m.make_future_dataframe(periods=15, freq='D', include_history=True)\n",
    "forecast = m.predict(future)\n",
    "\n",
    "## Ajustes para o dataset final \n",
    "df_final = pd.concat([df_forecast, forecast], axis=1)\n",
    "df_final['Local_Venda'] = 'Arena'\n",
    "df_final = df_final[['ds', 'y', 'yhat', 'Local_Venda']]\n",
    "df_final['diff'] = df_final['yhat'] - df_final['y']\n",
    "df_final.to_csv('./output/forecast_arena_15days.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Arena \n",
    "\n",
    "## Tratamento para o Dataset Arena\n",
    "df_forecast_arena = df_case01[df_case01['Local de Venda'] == 'Mindscape']\n",
    "df_forecast = df_forecast_arena[['Data Venda', 'PAX']]\n",
    "df_forecast.columns = ['ds', 'y']\n",
    "df_forecast = df_forecast.groupby(by = 'ds').sum()\n",
    "df_forecast = df_forecast.reset_index()\n",
    "df_forecast\n",
    "\n",
    "## Instanciação do modelo Prophet para o Canal de Venda Arena\n",
    "m = Prophet(seasonality_mode='additive', yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=False, seasonality_prior_scale=10, changepoint_prior_scale=3, holidays_prior_scale=20)\n",
    "m.add_seasonality(name='monthly', period=30.5, fourier_order=12)\n",
    "m.add_seasonality(name='quartely', period=365.25/4, fourier_order=5, prior_scale=15)\n",
    "m.add_country_holidays(country_name='BR')\n",
    "m.fit(df_forecast)\n",
    "\n",
    "## Predição para os próximos 15 dias \n",
    "future = m.make_future_dataframe(periods=15, freq='D', include_history=True)\n",
    "forecast = m.predict(future)\n",
    "df_final = pd.concat([df_forecast, forecast], axis=1)\n",
    "\n",
    "## Ajustes para o modelo final\n",
    "df_final['Local_Venda'] = 'Mindscape'\n",
    "df_final = df_final[['ds', 'y', 'yhat', 'Local_Venda']]\n",
    "df_final['diff'] = df_final['yhat'] - df_final['y']\n",
    "df_final.to_csv('./output/forecast_mindscape_15days.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Arena \n",
    "\n",
    "## Tratamento para o Dataset Arena\n",
    "df_forecast_arena = df_case01[df_case01['Local de Venda'] == 'Vast']\n",
    "df_forecast = df_forecast_arena[['Data Venda', 'PAX']]\n",
    "df_forecast.columns = ['ds', 'y']\n",
    "df_forecast = df_forecast.groupby(by = 'ds').sum()\n",
    "df_forecast = df_forecast.reset_index()\n",
    "df_forecast\n",
    "\n",
    "## Instanciação do modelo Prophet para o Canal de Venda Arena\n",
    "m = Prophet(seasonality_mode='additive', yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=False, seasonality_prior_scale=10, changepoint_prior_scale=3, holidays_prior_scale=20)\n",
    "m.add_seasonality(name='monthly', period=30.5, fourier_order=12)\n",
    "m.add_seasonality(name='quartely', period=365.25/4, fourier_order=5, prior_scale=15)\n",
    "m.add_country_holidays(country_name='BR')\n",
    "m.fit(df_forecast)\n",
    "\n",
    "## Predição para os próximos 15 dias \n",
    "future = m.make_future_dataframe(periods=15, freq='D', include_history=True)\n",
    "forecast = m.predict(future)\n",
    "df_final = pd.concat([df_forecast, forecast], axis=1)\n",
    "\n",
    "## Ajustes para o modelo final\n",
    "df_final['Local_Venda'] = 'Vast'\n",
    "df_final = df_final[['ds', 'y', 'yhat', 'Local_Venda']]\n",
    "df_final['diff'] = df_final['yhat'] - df_final['y']\n",
    "df_final.to_csv('./output/forecast_vast_15days.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Arena \n",
    "\n",
    "## Tratamento para o Dataset Arena\n",
    "df_forecast_arena = df_case01[df_case01['Local de Venda'] == 'Ellipsis']\n",
    "df_forecast = df_forecast_arena[['Data Venda', 'PAX']]\n",
    "df_forecast.columns = ['ds', 'y']\n",
    "df_forecast = df_forecast.groupby(by = 'ds').sum()\n",
    "df_forecast = df_forecast.reset_index()\n",
    "df_forecast\n",
    "\n",
    "## Instanciação do modelo Prophet para o Canal de Venda Arena\n",
    "m = Prophet(seasonality_mode='additive', yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=False, seasonality_prior_scale=10, changepoint_prior_scale=3, holidays_prior_scale=20)\n",
    "m.add_seasonality(name='monthly', period=30.5, fourier_order=12)\n",
    "m.add_seasonality(name='quartely', period=365.25/4, fourier_order=5, prior_scale=15)\n",
    "m.add_country_holidays(country_name='BR')\n",
    "m.fit(df_forecast)\n",
    "\n",
    "## Predição para os próximos 15 dias \n",
    "future = m.make_future_dataframe(periods=15, freq='D', include_history=True)\n",
    "forecast = m.predict(future)\n",
    "df_final = pd.concat([df_forecast, forecast], axis=1)\n",
    "\n",
    "## Ajustes para o modelo final\n",
    "df_final['Local_Venda'] = 'Ellipsis'\n",
    "df_final = df_final[['ds', 'y', 'yhat', 'Local_Venda']]\n",
    "df_final['diff'] = df_final['yhat'] - df_final['y']\n",
    "df_final.to_csv('./output/forecast_ellipsis_15days.csv')\n"
   ]
  },
  {
   "source": [
    "# 6 - Criar modelo relacionando o comporatamento de venda com variaveis não apresentada nos dados (Ex : PIB, Dolar, e etc)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Data  Último Abertura  Máxima  Mínima  Var%\n",
       "0   2017-04-25  3.1471   3,1283  3.1712  3.1275  0.61\n",
       "1   2017-04-24  3.1280   3,1506  3.1506  3.1182 -0.64\n",
       "2   2017-04-21  3.1482   3,1479  3.1488  3.1479 -0.01\n",
       "3   2017-04-20  3.1486   3,1507  3.1705  3.1324 -0.11\n",
       "4   2017-04-19  3.1522   3,1075  3.1545  3.1075  1.42\n",
       "..         ...     ...      ...     ...     ...   ...\n",
       "121 2016-07-11  3.2020   3,2348  3.2366  3.1885 -0.98\n",
       "122 2016-04-11  3.2337   3,2423  3.2649  3.2169 -0.27\n",
       "123 2016-03-11  3.2425   3,2358  3.2565  3.2210  0.20\n",
       "124 2016-02-11  3.2359   3,2370  3.2425  3.2355 -0.02\n",
       "125 2016-01-11  3.2367   3,1888  3.2553  3.1780  1.49\n",
       "\n",
       "[126 rows x 6 columns]"
      ],
      "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>Data</th>\n      <th>Último</th>\n      <th>Abertura</th>\n      <th>Máxima</th>\n      <th>Mínima</th>\n      <th>Var%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2017-04-25</td>\n      <td>3.1471</td>\n      <td>3,1283</td>\n      <td>3.1712</td>\n      <td>3.1275</td>\n      <td>0.61</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2017-04-24</td>\n      <td>3.1280</td>\n      <td>3,1506</td>\n      <td>3.1506</td>\n      <td>3.1182</td>\n      <td>-0.64</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2017-04-21</td>\n      <td>3.1482</td>\n      <td>3,1479</td>\n      <td>3.1488</td>\n      <td>3.1479</td>\n      <td>-0.01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2017-04-20</td>\n      <td>3.1486</td>\n      <td>3,1507</td>\n      <td>3.1705</td>\n      <td>3.1324</td>\n      <td>-0.11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2017-04-19</td>\n      <td>3.1522</td>\n      <td>3,1075</td>\n      <td>3.1545</td>\n      <td>3.1075</td>\n      <td>1.42</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>2016-07-11</td>\n      <td>3.2020</td>\n      <td>3,2348</td>\n      <td>3.2366</td>\n      <td>3.1885</td>\n      <td>-0.98</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>2016-04-11</td>\n      <td>3.2337</td>\n      <td>3,2423</td>\n      <td>3.2649</td>\n      <td>3.2169</td>\n      <td>-0.27</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>2016-03-11</td>\n      <td>3.2425</td>\n      <td>3,2358</td>\n      <td>3.2565</td>\n      <td>3.2210</td>\n      <td>0.20</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>2016-02-11</td>\n      <td>3.2359</td>\n      <td>3,2370</td>\n      <td>3.2425</td>\n      <td>3.2355</td>\n      <td>-0.02</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>2016-01-11</td>\n      <td>3.2367</td>\n      <td>3,1888</td>\n      <td>3.2553</td>\n      <td>3.1780</td>\n      <td>1.49</td>\n    </tr>\n  </tbody>\n</table>\n<p>126 rows × 6 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "cotacao_dolar = pd.read_csv('./input/USD_BRL Dados Históricos.csv') ## Leitura do Dataset de Dados Históricos sobre o dólar. Fonte: https://br.investing.com/\n",
    "cotacao_dolar['Data'] = cotacao_dolar['Data'].str.replace('.', '/') ## Transformação para parsear o campo de data\n",
    "cotacao_dolar['Data'] = pd.to_datetime(cotacao_dolar['Data']) ## Parsear o campo data \n",
    "cotacao_dolar['Último'] = pd.to_numeric(cotacao_dolar['Último'].str.replace(',','.')) ## Parsear o campo Ultimo e trocar , por .\n",
    "cotacao_dolar['Máxima'] = pd.to_numeric(cotacao_dolar['Máxima'].str.replace(',','.')) ## Parsear o campo Máxima  e trocar , por .\n",
    "cotacao_dolar['Var%'] = cotacao_dolar['Var%'].str.replace('\\%|\\%', '', regex=True) ## Remover a porcentagem da variação\n",
    "cotacao_dolar['Var%'] = pd.to_numeric(cotacao_dolar['Var%'].str.replace(',', '.')) ## Parsear o campo Variação e remover %\n",
    "cotacao_dolar['Mínima'] = pd.to_numeric(cotacao_dolar['Mínima'].str.replace(',','.')) ## Parsear o campo Mínima\n",
    "cotacao_dolar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Data Canal de Venda Local de Venda   PAX     Monetário     RPK  \\\n",
       "0   2016-11-01      Telégrafo      Mindscape    30   2188.109487    3476   \n",
       "1   2016-11-01      TeleVenda          Arena    52   3302.375951    8197   \n",
       "2   2016-11-01      TeleVenda      Mindscape    19   1041.544400    2425   \n",
       "3   2016-11-01  Porta a Porta      Mindscape   496  15566.340547   53278   \n",
       "4   2016-11-01      TeleVenda           Vast    46   2721.427289    2039   \n",
       "..         ...            ...            ...   ...           ...     ...   \n",
       "121 2016-11-08  Porta a Porta      Mindscape  1118  31953.297811  113157   \n",
       "122 2016-11-08      TeleVenda      Mindscape    45   2433.649660    5716   \n",
       "123 2016-11-08      TeleVenda       Ellipsis   935  18316.166113   50592   \n",
       "124 2016-11-08      TeleVenda           Vast   108   6365.224509    4713   \n",
       "125 2016-11-08  Porta a Porta           Vast  1962  30148.736786   82711   \n",
       "\n",
       "          Data  Último Abertura  Máxima  Mínima  Var%  \n",
       "0   2017-04-25  3.1471   3,1283  3.1712  3.1275  0.61  \n",
       "1   2017-04-24  3.1280   3,1506  3.1506  3.1182 -0.64  \n",
       "2   2017-04-21  3.1482   3,1479  3.1488  3.1479 -0.01  \n",
       "3   2017-04-20  3.1486   3,1507  3.1705  3.1324 -0.11  \n",
       "4   2017-04-19  3.1522   3,1075  3.1545  3.1075  1.42  \n",
       "..         ...     ...      ...     ...     ...   ...  \n",
       "121 2016-07-11  3.2020   3,2348  3.2366  3.1885 -0.98  \n",
       "122 2016-04-11  3.2337   3,2423  3.2649  3.2169 -0.27  \n",
       "123 2016-03-11  3.2425   3,2358  3.2565  3.2210  0.20  \n",
       "124 2016-02-11  3.2359   3,2370  3.2425  3.2355 -0.02  \n",
       "125 2016-01-11  3.2367   3,1888  3.2553  3.1780  1.49  \n",
       "\n",
       "[126 rows x 12 columns]"
      ],
      "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>Data</th>\n      <th>Canal de Venda</th>\n      <th>Local de Venda</th>\n      <th>PAX</th>\n      <th>Monetário</th>\n      <th>RPK</th>\n      <th>Data</th>\n      <th>Último</th>\n      <th>Abertura</th>\n      <th>Máxima</th>\n      <th>Mínima</th>\n      <th>Var%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016-11-01</td>\n      <td>Telégrafo</td>\n      <td>Mindscape</td>\n      <td>30</td>\n      <td>2188.109487</td>\n      <td>3476</td>\n      <td>2017-04-25</td>\n      <td>3.1471</td>\n      <td>3,1283</td>\n      <td>3.1712</td>\n      <td>3.1275</td>\n      <td>0.61</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2016-11-01</td>\n      <td>TeleVenda</td>\n      <td>Arena</td>\n      <td>52</td>\n      <td>3302.375951</td>\n      <td>8197</td>\n      <td>2017-04-24</td>\n      <td>3.1280</td>\n      <td>3,1506</td>\n      <td>3.1506</td>\n      <td>3.1182</td>\n      <td>-0.64</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2016-11-01</td>\n      <td>TeleVenda</td>\n      <td>Mindscape</td>\n      <td>19</td>\n      <td>1041.544400</td>\n      <td>2425</td>\n      <td>2017-04-21</td>\n      <td>3.1482</td>\n      <td>3,1479</td>\n      <td>3.1488</td>\n      <td>3.1479</td>\n      <td>-0.01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016-11-01</td>\n      <td>Porta a Porta</td>\n      <td>Mindscape</td>\n      <td>496</td>\n      <td>15566.340547</td>\n      <td>53278</td>\n      <td>2017-04-20</td>\n      <td>3.1486</td>\n      <td>3,1507</td>\n      <td>3.1705</td>\n      <td>3.1324</td>\n      <td>-0.11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2016-11-01</td>\n      <td>TeleVenda</td>\n      <td>Vast</td>\n      <td>46</td>\n      <td>2721.427289</td>\n      <td>2039</td>\n      <td>2017-04-19</td>\n      <td>3.1522</td>\n      <td>3,1075</td>\n      <td>3.1545</td>\n      <td>3.1075</td>\n      <td>1.42</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>2016-11-08</td>\n      <td>Porta a Porta</td>\n      <td>Mindscape</td>\n      <td>1118</td>\n      <td>31953.297811</td>\n      <td>113157</td>\n      <td>2016-07-11</td>\n      <td>3.2020</td>\n      <td>3,2348</td>\n      <td>3.2366</td>\n      <td>3.1885</td>\n      <td>-0.98</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>2016-11-08</td>\n      <td>TeleVenda</td>\n      <td>Mindscape</td>\n      <td>45</td>\n      <td>2433.649660</td>\n      <td>5716</td>\n      <td>2016-04-11</td>\n      <td>3.2337</td>\n      <td>3,2423</td>\n      <td>3.2649</td>\n      <td>3.2169</td>\n      <td>-0.27</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>2016-11-08</td>\n      <td>TeleVenda</td>\n      <td>Ellipsis</td>\n      <td>935</td>\n      <td>18316.166113</td>\n      <td>50592</td>\n      <td>2016-03-11</td>\n      <td>3.2425</td>\n      <td>3,2358</td>\n      <td>3.2565</td>\n      <td>3.2210</td>\n      <td>0.20</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>2016-11-08</td>\n      <td>TeleVenda</td>\n      <td>Vast</td>\n      <td>108</td>\n      <td>6365.224509</td>\n      <td>4713</td>\n      <td>2016-02-11</td>\n      <td>3.2359</td>\n      <td>3,2370</td>\n      <td>3.2425</td>\n      <td>3.2355</td>\n      <td>-0.02</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>2016-11-08</td>\n      <td>Porta a Porta</td>\n      <td>Vast</td>\n      <td>1962</td>\n      <td>30148.736786</td>\n      <td>82711</td>\n      <td>2016-01-11</td>\n      <td>3.2367</td>\n      <td>3,1888</td>\n      <td>3.2553</td>\n      <td>3.1780</td>\n      <td>1.49</td>\n    </tr>\n  </tbody>\n</table>\n<p>126 rows × 12 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "df_case01 = pd.read_excel('./input/case_analytics-ds.xlsx', sheet_name='Dados') ## Realizar novamente a leitura do Dados disponibilizados pelo dataset do Case\n",
    "df_case01.columns = ['Data', 'Canal de Venda', 'Local de Venda', 'PAX', 'Monetário', 'RPK'] ## Imprimir o dataframe \n",
    "df_correlacao_dolar = pd.concat([df_case01, cotacao_dolar], join='inner', axis=1) ## Realizar a união dos datasets do Case e da cotação do dólar através do campo data \n",
    "df_correlacao_dolar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                PAX  Monetário       RPK    Último    Máxima    Mínima  \\\n",
       "PAX        1.000000   0.886475  0.808995  0.042631  0.059908  0.074611   \n",
       "Monetário  0.886475   1.000000  0.953686 -0.025684 -0.016907  0.009230   \n",
       "RPK        0.808995   0.953686  1.000000  0.025457  0.037242  0.058532   \n",
       "Último     0.042631  -0.025684  0.025457  1.000000  0.987637  0.980871   \n",
       "Máxima     0.059908  -0.016907  0.037242  0.987637  1.000000  0.981353   \n",
       "Mínima     0.074611   0.009230  0.058532  0.980871  0.981353  1.000000   \n",
       "Var%      -0.183203  -0.201514 -0.189349  0.140041  0.043536 -0.030480   \n",
       "\n",
       "               Var%  \n",
       "PAX       -0.183203  \n",
       "Monetário -0.201514  \n",
       "RPK       -0.189349  \n",
       "Último     0.140041  \n",
       "Máxima     0.043536  \n",
       "Mínima    -0.030480  \n",
       "Var%       1.000000  "
      ],
      "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>PAX</th>\n      <th>Monetário</th>\n      <th>RPK</th>\n      <th>Último</th>\n      <th>Máxima</th>\n      <th>Mínima</th>\n      <th>Var%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PAX</th>\n      <td>1.000000</td>\n      <td>0.886475</td>\n      <td>0.808995</td>\n      <td>0.042631</td>\n      <td>0.059908</td>\n      <td>0.074611</td>\n      <td>-0.183203</td>\n    </tr>\n    <tr>\n      <th>Monetário</th>\n      <td>0.886475</td>\n      <td>1.000000</td>\n      <td>0.953686</td>\n      <td>-0.025684</td>\n      <td>-0.016907</td>\n      <td>0.009230</td>\n      <td>-0.201514</td>\n    </tr>\n    <tr>\n      <th>RPK</th>\n      <td>0.808995</td>\n      <td>0.953686</td>\n      <td>1.000000</td>\n      <td>0.025457</td>\n      <td>0.037242</td>\n      <td>0.058532</td>\n      <td>-0.189349</td>\n    </tr>\n    <tr>\n      <th>Último</th>\n      <td>0.042631</td>\n      <td>-0.025684</td>\n      <td>0.025457</td>\n      <td>1.000000</td>\n      <td>0.987637</td>\n      <td>0.980871</td>\n      <td>0.140041</td>\n    </tr>\n    <tr>\n      <th>Máxima</th>\n      <td>0.059908</td>\n      <td>-0.016907</td>\n      <td>0.037242</td>\n      <td>0.987637</td>\n      <td>1.000000</td>\n      <td>0.981353</td>\n      <td>0.043536</td>\n    </tr>\n    <tr>\n      <th>Mínima</th>\n      <td>0.074611</td>\n      <td>0.009230</td>\n      <td>0.058532</td>\n      <td>0.980871</td>\n      <td>0.981353</td>\n      <td>1.000000</td>\n      <td>-0.030480</td>\n    </tr>\n    <tr>\n      <th>Var%</th>\n      <td>-0.183203</td>\n      <td>-0.201514</td>\n      <td>-0.189349</td>\n      <td>0.140041</td>\n      <td>0.043536</td>\n      <td>-0.030480</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "df_correlacao_dolar.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 70
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.heatmap(df_correlacao_dolar.corr(), annot=True)"
   ]
  }
 ]
}