app_case.ipynb

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    "# Case Técnico - Rank My App\n",
    "\n",
    "## Situação\n",
    "\n",
    "O cliente B4 Bank é um aplicativo do setor financeiro que tem como objetivo conseguir uma **maior visibilidade e retenção de usuários de forma orgânica**. Como esse mercado tem crescido muito e é muito competitivo, ele resolveu investir em ASO com o RankMyApp, focando seus esforços não só em **visualizações mas também em instalações**, ou seja, não apenas os usuários visualizarão o app na loja como também irão instalá-lo e utilizá-lo por um longo período de tempo. (Retenção de 15-30 dias).\n",
    "Através do nosso sistema, recebemos informações relacionadas às instalações, concorrentes, performance das palavras-chave (keywords), notas e comentários deixados na loja.\n",
    "\n",
    "## Problema\n",
    "\n",
    "O B4 Bank é cliente há alguns meses e **eles gostariam de saber como tem sido a performance do app**. Como trabalhamos com ASO para este cliente, o que mais nos interessa são as **métricas de Canal Orgânico**. É a partir deste KPI que nos baseamos para verificar o impacto do nosso trabalho. O cliente também deseja ter **insights sobre o que os usuários estão comentando na loja e qual a sua situação diante dos seus concorrentes**. Realize um estudo utilizando Python para fazer a **limpeza e manipulação de dados e crie um modelo de machine learning com uma das bases**. Depois **crie um relatório ou dashboard com a ferramenta que desejar para apresentar os dados**.\n",
    "\n",
    "## Processo para o Case\n",
    "\n",
    "Tendo em vista o problema, a base de dados fornecida e o que foi pedido sobre ele, dividirei minha análise nas seguintes etapas:\n",
    "\n",
    "1. Exploração\n",
    "2. Limpeza\n",
    "3. Manipulação\n",
    "4. Criação de insights sobre o que os usuários estão comentando na loja - ratings + sentiment; most common words\n",
    "5. Criação de modelo de machine learning\n",
    "6. Relatório "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importação de Bibliotecas e Leitura dos Dados\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "[nltk_data] Downloading package stopwords to\n[nltk_data]     /home/cafecoteca/nltk_data...\n[nltk_data]   Package stopwords is already up-to-date!\n[nltk_data] Downloading package punkt to /home/cafecoteca/nltk_data...\n[nltk_data]   Package punkt is already up-to-date!\n"
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from datetime import datetime as dt\n",
    "from natsort import natsorted\n",
    "\n",
    "#Para Natural Language Processing\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Para construção de modelos de Machine Learning\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from  sklearn.metrics  import accuracy_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews_bank = pd.read_excel('data_scientist_-_case.xlsx', sheet_name=1, parse_dates=['Review Last Update Date and Time'])\n",
    "retained_bank = pd.read_excel('data_scientist_-_case.xlsx', sheet_name=2, parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploração \n",
    "\n",
    "## Reviews Bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Package Name App Version Name Reviewer Language       Device  \\\n0         B4 Bank           6.10.6                pt     greatlte   \n1         B4 Bank           6.10.6                pt         mido   \n2         B4 Bank           6.10.6                pt     on5xelte   \n3         B4 Bank           6.10.6                pt     dreamlte   \n4         B4 Bank           6.10.6                pt  starqltechn   \n...           ...              ...               ...          ...   \n5674      B4 Bank           6.10.5                pt         hlte   \n5675      B4 Bank           6.10.5                pt    potter_nt   \n5676      B4 Bank            4.1.7                pt   j4primelte   \n5677      B4 Bank           6.10.5                pt       K10a40   \n5678      B4 Bank           6.10.5                pt        mcv5a   \n\n     Review Last Update Date and Time  Star Rating Review Title  \\\n0           2019-10-01 00:09:07+00:00            5          NaN   \n1           2019-10-01 00:18:49+00:00            5          NaN   \n2           2019-10-01 00:34:35+00:00            5          NaN   \n3           2019-10-01 00:44:18+00:00            2          NaN   \n4           2019-10-01 01:09:56+00:00            4          NaN   \n...                               ...          ...          ...   \n5674        2019-08-31 21:51:26+00:00            1          NaN   \n5675        2019-08-31 22:36:38+00:00            4          NaN   \n5676        2019-08-31 22:44:22+00:00            5          NaN   \n5677        2019-08-31 23:39:14+00:00            5          NaN   \n5678        2019-08-31 23:49:22+00:00            1          NaN   \n\n                                            Review Text Sentiment  \n0                                                   NaN  Positive  \n1                                                   NaN  Positive  \n2                                                   Boa  Positive  \n3     Não consigo gerar a fatura em PDF, no mesmo fo...  Negative  \n4                                                   NaN  Positive  \n...                                                 ...       ...  \n5674  Baixei a última versão recentemente, ainda não...       NaN  \n5675                                                NaN       NaN  \n5676                                                NaN       NaN  \n5677                Gostei muito prático, descomplicado       NaN  \n5678                          Vive parando de funcionar       NaN  \n\n[5679 rows x 9 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>Package Name</th>\n      <th>App Version Name</th>\n      <th>Reviewer Language</th>\n      <th>Device</th>\n      <th>Review Last Update Date and Time</th>\n      <th>Star Rating</th>\n      <th>Review Title</th>\n      <th>Review Text</th>\n      <th>Sentiment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B4 Bank</td>\n      <td>6.10.6</td>\n      <td>pt</td>\n      <td>greatlte</td>\n      <td>2019-10-01 00:09:07+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>B4 Bank</td>\n      <td>6.10.6</td>\n      <td>pt</td>\n      <td>mido</td>\n      <td>2019-10-01 00:18:49+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>B4 Bank</td>\n      <td>6.10.6</td>\n      <td>pt</td>\n      <td>on5xelte</td>\n      <td>2019-10-01 00:34:35+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Boa</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>B4 Bank</td>\n      <td>6.10.6</td>\n      <td>pt</td>\n      <td>dreamlte</td>\n      <td>2019-10-01 00:44:18+00:00</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>Não consigo gerar a fatura em PDF, no mesmo fo...</td>\n      <td>Negative</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B4 Bank</td>\n      <td>6.10.6</td>\n      <td>pt</td>\n      <td>starqltechn</td>\n      <td>2019-10-01 01:09:56+00:00</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Positive</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    </tr>\n    <tr>\n      <th>5674</th>\n      <td>B4 Bank</td>\n      <td>6.10.5</td>\n      <td>pt</td>\n      <td>hlte</td>\n      <td>2019-08-31 21:51:26+00:00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>Baixei a última versão recentemente, ainda não...</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5675</th>\n      <td>B4 Bank</td>\n      <td>6.10.5</td>\n      <td>pt</td>\n      <td>potter_nt</td>\n      <td>2019-08-31 22:36:38+00:00</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5676</th>\n      <td>B4 Bank</td>\n      <td>4.1.7</td>\n      <td>pt</td>\n      <td>j4primelte</td>\n      <td>2019-08-31 22:44:22+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5677</th>\n      <td>B4 Bank</td>\n      <td>6.10.5</td>\n      <td>pt</td>\n      <td>K10a40</td>\n      <td>2019-08-31 23:39:14+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Gostei muito prático, descomplicado</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5678</th>\n      <td>B4 Bank</td>\n      <td>6.10.5</td>\n      <td>pt</td>\n      <td>mcv5a</td>\n      <td>2019-08-31 23:49:22+00:00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>Vive parando de funcionar</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5679 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "reviews_bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função auxiliar para exploração geral dos dados\n",
    "def explore_df(df):\n",
    "    explore = pd.DataFrame({\n",
    "        \"columns\" : df.columns,\n",
    "        \"type\": df.dtypes,\n",
    "        \"unique values\": df.nunique(),\n",
    "        \"missing values\": df.isna().sum(),\n",
    "        \"% of missing values\": df.isna().sum()/df.shape[0]*100\n",
    "    })\n",
    "    return explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "------- DADOS DO BANCO DE REVIEW --------\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                                           columns  \\\nPackage Name                                          Package Name   \nApp Version Name                                  App Version Name   \nReviewer Language                                Reviewer Language   \nDevice                                                      Device   \nReview Last Update Date and Time  Review Last Update Date and Time   \nStar Rating                                            Star Rating   \nReview Title                                          Review Title   \nReview Text                                            Review Text   \nSentiment                                                Sentiment   \n\n                                                 type  unique values  \\\nPackage Name                                   object              1   \nApp Version Name                               object             50   \nReviewer Language                              object              9   \nDevice                                         object            367   \nReview Last Update Date and Time  datetime64[ns, UTC]           5678   \nStar Rating                                     int64              5   \nReview Title                                   object              1   \nReview Text                                    object           2133   \nSentiment                                      object              3   \n\n                                  missing values  % of missing values  \nPackage Name                                   0             0.000000  \nApp Version Name                             256             4.507836  \nReviewer Language                              0             0.000000  \nDevice                                         6             0.105652  \nReview Last Update Date and Time               0             0.000000  \nStar Rating                                    0             0.000000  \nReview Title                                5678            99.982391  \nReview Text                                 2923            51.470329  \nSentiment                                   4251            74.854728  ",
      "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>columns</th>\n      <th>type</th>\n      <th>unique values</th>\n      <th>missing values</th>\n      <th>% of missing values</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Package Name</th>\n      <td>Package Name</td>\n      <td>object</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>App Version Name</th>\n      <td>App Version Name</td>\n      <td>object</td>\n      <td>50</td>\n      <td>256</td>\n      <td>4.507836</td>\n    </tr>\n    <tr>\n      <th>Reviewer Language</th>\n      <td>Reviewer Language</td>\n      <td>object</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Device</th>\n      <td>Device</td>\n      <td>object</td>\n      <td>367</td>\n      <td>6</td>\n      <td>0.105652</td>\n    </tr>\n    <tr>\n      <th>Review Last Update Date and Time</th>\n      <td>Review Last Update Date and Time</td>\n      <td>datetime64[ns, UTC]</td>\n      <td>5678</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Star Rating</th>\n      <td>Star Rating</td>\n      <td>int64</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>Review Title</th>\n      <td>Review Title</td>\n      <td>object</td>\n      <td>1</td>\n      <td>5678</td>\n      <td>99.982391</td>\n    </tr>\n    <tr>\n      <th>Review Text</th>\n      <td>Review Text</td>\n      <td>object</td>\n      <td>2133</td>\n      <td>2923</td>\n      <td>51.470329</td>\n    </tr>\n    <tr>\n      <th>Sentiment</th>\n      <td>Sentiment</td>\n      <td>object</td>\n      <td>3</td>\n      <td>4251</td>\n      <td>74.854728</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "print(\"------- DADOS DO BANCO DE REVIEW --------\")\n",
    "\n",
    "explore_df(reviews_bank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considerações:\n",
    "\n",
    "- Vemos que quase toda a coluna de `Review Title` está faltante, por isso não a utilizarei na análise.\n",
    "- A coluna de `sentiment` é baseada na coluna de `Star Rating`, que está completa! Assim, imputarei valores de acordo com o Star Rating\n",
    "- Para análise, utilizarei as colunas: `App Version Name` (removerei as linhas com informações faltantes já que são tão poucas),`Review Last Update Date and Time`, `Star Rating`, `Review Text` (sem imputação) e `Sentiment`\n",
    "- \n",
    "\n",
    "### Limpeza e Manipulação\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     App Version Name Review Last Update Date and Time  Star Rating  \\\n0              6.10.6        2019-10-01 00:09:07+00:00            5   \n1              6.10.6        2019-10-01 00:18:49+00:00            5   \n2              6.10.6        2019-10-01 00:34:35+00:00            5   \n3              6.10.6        2019-10-01 00:44:18+00:00            2   \n4              6.10.6        2019-10-01 01:09:56+00:00            4   \n...               ...                              ...          ...   \n5674           6.10.5        2019-08-31 21:51:26+00:00            1   \n5675           6.10.5        2019-08-31 22:36:38+00:00            4   \n5676            4.1.7        2019-08-31 22:44:22+00:00            5   \n5677           6.10.5        2019-08-31 23:39:14+00:00            5   \n5678           6.10.5        2019-08-31 23:49:22+00:00            1   \n\n                                            Review Text Sentiment  \n0                                                   NaN  Positive  \n1                                                   NaN  Positive  \n2                                                   Boa  Positive  \n3     Não consigo gerar a fatura em PDF, no mesmo fo...  Negative  \n4                                                   NaN  Positive  \n...                                                 ...       ...  \n5674  Baixei a última versão recentemente, ainda não...       NaN  \n5675                                                NaN       NaN  \n5676                                                NaN       NaN  \n5677                Gostei muito prático, descomplicado       NaN  \n5678                          Vive parando de funcionar       NaN  \n\n[5679 rows x 5 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>App Version Name</th>\n      <th>Review Last Update Date and Time</th>\n      <th>Star Rating</th>\n      <th>Review Text</th>\n      <th>Sentiment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6.10.6</td>\n      <td>2019-10-01 00:09:07+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6.10.6</td>\n      <td>2019-10-01 00:18:49+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6.10.6</td>\n      <td>2019-10-01 00:34:35+00:00</td>\n      <td>5</td>\n      <td>Boa</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6.10.6</td>\n      <td>2019-10-01 00:44:18+00:00</td>\n      <td>2</td>\n      <td>Não consigo gerar a fatura em PDF, no mesmo fo...</td>\n      <td>Negative</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.10.6</td>\n      <td>2019-10-01 01:09:56+00:00</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>Positive</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5674</th>\n      <td>6.10.5</td>\n      <td>2019-08-31 21:51:26+00:00</td>\n      <td>1</td>\n      <td>Baixei a última versão recentemente, ainda não...</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5675</th>\n      <td>6.10.5</td>\n      <td>2019-08-31 22:36:38+00:00</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5676</th>\n      <td>4.1.7</td>\n      <td>2019-08-31 22:44:22+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5677</th>\n      <td>6.10.5</td>\n      <td>2019-08-31 23:39:14+00:00</td>\n      <td>5</td>\n      <td>Gostei muito prático, descomplicado</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5678</th>\n      <td>6.10.5</td>\n      <td>2019-08-31 23:49:22+00:00</td>\n      <td>1</td>\n      <td>Vive parando de funcionar</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5679 rows × 5 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "# Datafram com colunas relevantes\n",
    "relevant_columns = ['App Version Name', 'Review Last Update Date and Time', 'Star Rating', 'Review Text', 'Sentiment']\n",
    "reviews = reviews_bank.loc[:,relevant_columns]\n",
    "reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "App Version Name                       0\nReview Last Update Date and Time       0\nStar Rating                            0\nReview Text                         2784\nSentiment                           4078\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# Removendo fileiras com valores faltantes em App Version Name\n",
    "reviews.dropna(subset=['App Version Name'], inplace=True)\n",
    "reviews.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputando valores para a coluna de \"sentiment\" de acordo com o \"Star Rating\"\n",
    "sentiment_values = {\n",
    "    5: \"Positive\",\n",
    "    4: \"Positive\",\n",
    "    3: \"Neutral\",\n",
    "    2: \"Negative\",\n",
    "    1: \"Negative\"\n",
    "}\n",
    "\n",
    "reviews['Sentiment'] = reviews['Star Rating'].map(sentiment_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adicionando colunas para semana e mês referente para acompanharmos a evolução de variáveis/métricas ao longo do tempo\n",
    "\n",
    "reviews['week_month'] = reviews['Review Last Update Date and Time'].dt.strftime(\"%U/%b\")\n",
    "\n",
    "# Ordenar ascendentemente nosso dataset\n",
    "\n",
    "reviews.sort_values(by='Review Last Update Date and Time', inplace=True,  ignore_index=True)\n",
    "\n",
    "# Organizar data para formato DD/MM/AAAA\n",
    "\n",
    "reviews['Date'] = reviews['Review Last Update Date and Time'].dt.strftime(\"%d/%m/%Y\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      index App Version Name Review Last Update Date and Time  Star Rating  \\\n0         0           6.10.0        2019-08-01 00:00:50+00:00            3   \n1         1           6.10.0        2019-08-01 00:13:01+00:00            5   \n2         2           6.10.0        2019-08-01 00:14:36+00:00            5   \n3         3           6.10.0        2019-08-01 00:29:12+00:00            4   \n4         4           6.10.0        2019-08-01 00:31:05+00:00            5   \n...     ...              ...                              ...          ...   \n5418   5418           6.11.1        2019-10-31 22:29:42+00:00            4   \n5419   5419           6.11.1        2019-10-31 22:56:45+00:00            5   \n5420   5420           6.11.1        2019-10-31 23:14:06+00:00            5   \n5421   5421           6.11.1        2019-10-31 23:15:09+00:00            5   \n5422   5422           6.11.1        2019-10-31 23:58:15+00:00            5   \n\n                                            Review Text Sentiment week_month  \\\n0     Bom, falta disponibilizar cancelamento de agen...   Neutral     30/Aug   \n1     Este aplicativo supre todas as minhas necessid...  Positive     30/Aug   \n2                                                 Top 😎  Positive     30/Aug   \n3     O app é muito bom. Poderia ser mais intuitivo ...  Positive     30/Aug   \n4                                             funcional  Positive     30/Aug   \n...                                                 ...       ...        ...   \n5418  App funciona perfeitamente nas operações que e...  Positive     43/Oct   \n5419          Por enquanto tudo as mil maravilhas!!!!!!  Positive     43/Oct   \n5420                                  Sempre muito boa!  Positive     43/Oct   \n5421                                                NaN  Positive     43/Oct   \n5422                                                NaN  Positive     43/Oct   \n\n            Date  \n0     01/08/2019  \n1     01/08/2019  \n2     01/08/2019  \n3     01/08/2019  \n4     01/08/2019  \n...          ...  \n5418  31/10/2019  \n5419  31/10/2019  \n5420  31/10/2019  \n5421  31/10/2019  \n5422  31/10/2019  \n\n[5423 rows x 8 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>index</th>\n      <th>App Version Name</th>\n      <th>Review Last Update Date and Time</th>\n      <th>Star Rating</th>\n      <th>Review Text</th>\n      <th>Sentiment</th>\n      <th>week_month</th>\n      <th>Date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>6.10.0</td>\n      <td>2019-08-01 00:00:50+00:00</td>\n      <td>3</td>\n      <td>Bom, falta disponibilizar cancelamento de agen...</td>\n      <td>Neutral</td>\n      <td>30/Aug</td>\n      <td>01/08/2019</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>6.10.0</td>\n      <td>2019-08-01 00:13:01+00:00</td>\n      <td>5</td>\n      <td>Este aplicativo supre todas as minhas necessid...</td>\n      <td>Positive</td>\n      <td>30/Aug</td>\n      <td>01/08/2019</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>6.10.0</td>\n      <td>2019-08-01 00:14:36+00:00</td>\n      <td>5</td>\n      <td>Top 😎</td>\n      <td>Positive</td>\n      <td>30/Aug</td>\n      <td>01/08/2019</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>6.10.0</td>\n      <td>2019-08-01 00:29:12+00:00</td>\n      <td>4</td>\n      <td>O app é muito bom. Poderia ser mais intuitivo ...</td>\n      <td>Positive</td>\n      <td>30/Aug</td>\n      <td>01/08/2019</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>6.10.0</td>\n      <td>2019-08-01 00:31:05+00:00</td>\n      <td>5</td>\n      <td>funcional</td>\n      <td>Positive</td>\n      <td>30/Aug</td>\n      <td>01/08/2019</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    </tr>\n    <tr>\n      <th>5418</th>\n      <td>5418</td>\n      <td>6.11.1</td>\n      <td>2019-10-31 22:29:42+00:00</td>\n      <td>4</td>\n      <td>App funciona perfeitamente nas operações que e...</td>\n      <td>Positive</td>\n      <td>43/Oct</td>\n      <td>31/10/2019</td>\n    </tr>\n    <tr>\n      <th>5419</th>\n      <td>5419</td>\n      <td>6.11.1</td>\n      <td>2019-10-31 22:56:45+00:00</td>\n      <td>5</td>\n      <td>Por enquanto tudo as mil maravilhas!!!!!!</td>\n      <td>Positive</td>\n      <td>43/Oct</td>\n      <td>31/10/2019</td>\n    </tr>\n    <tr>\n      <th>5420</th>\n      <td>5420</td>\n      <td>6.11.1</td>\n      <td>2019-10-31 23:14:06+00:00</td>\n      <td>5</td>\n      <td>Sempre muito boa!</td>\n      <td>Positive</td>\n      <td>43/Oct</td>\n      <td>31/10/2019</td>\n    </tr>\n    <tr>\n      <th>5421</th>\n      <td>5421</td>\n      <td>6.11.1</td>\n      <td>2019-10-31 23:15:09+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Positive</td>\n      <td>43/Oct</td>\n      <td>31/10/2019</td>\n    </tr>\n    <tr>\n      <th>5422</th>\n      <td>5422</td>\n      <td>6.11.1</td>\n      <td>2019-10-31 23:58:15+00:00</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Positive</td>\n      <td>43/Oct</td>\n      <td>31/10/2019</td>\n    </tr>\n  </tbody>\n</table>\n<p>5423 rows × 8 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "reviews.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com nossos dados da primeira planilha `reviews_bank` organizados e utilizáveis para análise, vamos ajustar o do `retained_bank`\n",
    "\n",
    "## Retained Bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "------- DADOS DO BANCO DE RETENÇÃO --------\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                                                   columns  \\\nDate                                                                  Date   \nPackage Name                                                  Package Name   \nAcquisition Channel                                    Acquisition Channel   \nStore Listing Visitors                              Store Listing Visitors   \nInstallers                                                      Installers   \nVisitor-to-Installer conversion rate  Visitor-to-Installer conversion rate   \nInstallers retained for 1 day                Installers retained for 1 day   \nInstaller-to-1 day retention rate        Installer-to-1 day retention rate   \nInstallers retained for 7 days              Installers retained for 7 days   \nInstaller-to-7 days retention rate      Installer-to-7 days retention rate   \nInstallers retained for 15 days            Installers retained for 15 days   \nInstaller-to-15 days retention rate    Installer-to-15 days retention rate   \nInstallers retained for 30 days            Installers retained for 30 days   \nInstaller-to-30 days retention rate    Installer-to-30 days retention rate   \n\n                                         type  unique values  missing values  \\\nDate                                   object             92               0   \nPackage Name                           object              1               0   \nAcquisition Channel                    object              4               0   \nStore Listing Visitors                  int64            268               0   \nInstallers                              int64            165               0   \nVisitor-to-Installer conversion rate  float64            184               0   \nInstallers retained for 1 day           int64            151               0   \nInstaller-to-1 day retention rate     float64            138               0   \nInstallers retained for 7 days          int64            144               0   \nInstaller-to-7 days retention rate    float64            160               0   \nInstallers retained for 15 days         int64            142               0   \nInstaller-to-15 days retention rate   float64            172               0   \nInstallers retained for 30 days         int64            143               0   \nInstaller-to-30 days retention rate   float64            167               0   \n\n                                      % of missing values  \nDate                                                  0.0  \nPackage Name                                          0.0  \nAcquisition Channel                                   0.0  \nStore Listing Visitors                                0.0  \nInstallers                                            0.0  \nVisitor-to-Installer conversion rate                  0.0  \nInstallers retained for 1 day                         0.0  \nInstaller-to-1 day retention rate                     0.0  \nInstallers retained for 7 days                        0.0  \nInstaller-to-7 days retention rate                    0.0  \nInstallers retained for 15 days                       0.0  \nInstaller-to-15 days retention rate                   0.0  \nInstallers retained for 30 days                       0.0  \nInstaller-to-30 days retention rate                   0.0  ",
      "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>columns</th>\n      <th>type</th>\n      <th>unique values</th>\n      <th>missing values</th>\n      <th>% of missing values</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Date</th>\n      <td>Date</td>\n      <td>object</td>\n      <td>92</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Package Name</th>\n      <td>Package Name</td>\n      <td>object</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Acquisition Channel</th>\n      <td>Acquisition Channel</td>\n      <td>object</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Store Listing Visitors</th>\n      <td>Store Listing Visitors</td>\n      <td>int64</td>\n      <td>268</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installers</th>\n      <td>Installers</td>\n      <td>int64</td>\n      <td>165</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Visitor-to-Installer conversion rate</th>\n      <td>Visitor-to-Installer conversion rate</td>\n      <td>float64</td>\n      <td>184</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installers retained for 1 day</th>\n      <td>Installers retained for 1 day</td>\n      <td>int64</td>\n      <td>151</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installer-to-1 day retention rate</th>\n      <td>Installer-to-1 day retention rate</td>\n      <td>float64</td>\n      <td>138</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installers retained for 7 days</th>\n      <td>Installers retained for 7 days</td>\n      <td>int64</td>\n      <td>144</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installer-to-7 days retention rate</th>\n      <td>Installer-to-7 days retention rate</td>\n      <td>float64</td>\n      <td>160</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installers retained for 15 days</th>\n      <td>Installers retained for 15 days</td>\n      <td>int64</td>\n      <td>142</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installer-to-15 days retention rate</th>\n      <td>Installer-to-15 days retention rate</td>\n      <td>float64</td>\n      <td>172</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installers retained for 30 days</th>\n      <td>Installers retained for 30 days</td>\n      <td>int64</td>\n      <td>143</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>Installer-to-30 days retention rate</th>\n      <td>Installer-to-30 days retention rate</td>\n      <td>float64</td>\n      <td>167</td>\n      <td>0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "print(\"------- DADOS DO BANCO DE RETENÇÃO --------\")\n",
    "explore_df(retained_bank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Felizmente, não temos nenhum valor faltante. Como estamos interessados apenas na retenção de 15 a 30 dias, vindo do canal orgânico, vamos filtrar nossos dados com o que nos interessa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                    Date     Acquisition Channel  Installers  \\\n0    2019-08-01 00:00:00                 Organic         414   \n1    2019-08-01 00:00:00   Third-party referrers          46   \n2    2019-08-01 00:00:00                   Other          23   \n3    2019-08-02 00:00:00                 Organic         407   \n4    2019-08-02 00:00:00  Tracked channels (UTM)          10   \n..                   ...                     ...         ...   \n312  2019-10-29 00:00:00                   Other          12   \n313  2019-10-30 00:00:00                 Organic         545   \n314  2019-10-30 00:00:00  Tracked channels (UTM)          15   \n315  2019-10-30 00:00:00   Third-party referrers         207   \n316  2019-10-30 00:00:00                   Other          18   \n\n     Visitor-to-Installer conversion rate  Installers retained for 15 days  \\\n0                                   0.254                              335   \n1                                   0.089                               32   \n2                                   0.204                               14   \n3                                   0.253                              330   \n4                                   0.182                                9   \n..                                    ...                              ...   \n312                                 0.218                                6   \n313                                 0.222                              386   \n314                                 0.072                                6   \n315                                 0.079                               96   \n316                                 0.188                               12   \n\n     Installer-to-15 days retention rate  Installers retained for 30 days  \\\n0                                  0.809                              330   \n1                                  0.696                               30   \n2                                  0.609                               13   \n3                                  0.811                              322   \n4                                  0.900                                8   \n..                                   ...                              ...   \n312                                0.500                                6   \n313                                0.708                              374   \n314                                0.400                                6   \n315                                0.464                               83   \n316                                0.667                               12   \n\n     Installer-to-30 days retention rate  \n0                                  0.797  \n1                                  0.652  \n2                                  0.565  \n3                                  0.791  \n4                                  0.800  \n..                                   ...  \n312                                0.500  \n313                                0.686  \n314                                0.400  \n315                                0.401  \n316                                0.667  \n\n[317 rows x 8 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>Date</th>\n      <th>Acquisition Channel</th>\n      <th>Installers</th>\n      <th>Visitor-to-Installer conversion rate</th>\n      <th>Installers retained for 15 days</th>\n      <th>Installer-to-15 days retention rate</th>\n      <th>Installers retained for 30 days</th>\n      <th>Installer-to-30 days retention rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2019-08-01 00:00:00</td>\n      <td>Organic</td>\n      <td>414</td>\n      <td>0.254</td>\n      <td>335</td>\n      <td>0.809</td>\n      <td>330</td>\n      <td>0.797</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2019-08-01 00:00:00</td>\n      <td>Third-party referrers</td>\n      <td>46</td>\n      <td>0.089</td>\n      <td>32</td>\n      <td>0.696</td>\n      <td>30</td>\n      <td>0.652</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2019-08-01 00:00:00</td>\n      <td>Other</td>\n      <td>23</td>\n      <td>0.204</td>\n      <td>14</td>\n      <td>0.609</td>\n      <td>13</td>\n      <td>0.565</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2019-08-02 00:00:00</td>\n      <td>Organic</td>\n      <td>407</td>\n      <td>0.253</td>\n      <td>330</td>\n      <td>0.811</td>\n      <td>322</td>\n      <td>0.791</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2019-08-02 00:00:00</td>\n      <td>Tracked channels (UTM)</td>\n      <td>10</td>\n      <td>0.182</td>\n      <td>9</td>\n      <td>0.900</td>\n      <td>8</td>\n      <td>0.800</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    </tr>\n    <tr>\n      <th>312</th>\n      <td>2019-10-29 00:00:00</td>\n      <td>Other</td>\n      <td>12</td>\n      <td>0.218</td>\n      <td>6</td>\n      <td>0.500</td>\n      <td>6</td>\n      <td>0.500</td>\n    </tr>\n    <tr>\n      <th>313</th>\n      <td>2019-10-30 00:00:00</td>\n      <td>Organic</td>\n      <td>545</td>\n      <td>0.222</td>\n      <td>386</td>\n      <td>0.708</td>\n      <td>374</td>\n      <td>0.686</td>\n    </tr>\n    <tr>\n      <th>314</th>\n      <td>2019-10-30 00:00:00</td>\n      <td>Tracked channels (UTM)</td>\n      <td>15</td>\n      <td>0.072</td>\n      <td>6</td>\n      <td>0.400</td>\n      <td>6</td>\n      <td>0.400</td>\n    </tr>\n    <tr>\n      <th>315</th>\n      <td>2019-10-30 00:00:00</td>\n      <td>Third-party referrers</td>\n      <td>207</td>\n      <td>0.079</td>\n      <td>96</td>\n      <td>0.464</td>\n      <td>83</td>\n      <td>0.401</td>\n    </tr>\n    <tr>\n      <th>316</th>\n      <td>2019-10-30 00:00:00</td>\n      <td>Other</td>\n      <td>18</td>\n      <td>0.188</td>\n      <td>12</td>\n      <td>0.667</td>\n      <td>12</td>\n      <td>0.667</td>\n    </tr>\n  </tbody>\n</table>\n<p>317 rows × 8 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# Colunas relevantes para nossa análise\n",
    "\n",
    "relevant_columns_retained = ['Date','Acquisition Channel', 'Installers', 'Visitor-to-Installer conversion rate', 'Installers retained for 15 days','Installer-to-15 days retention rate','Installers retained for 30 days', 'Installer-to-30 days retention rate']\n",
    "\n",
    "retained = retained_bank.loc[:, relevant_columns_retained]\n",
    "retained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtrando por canal orgânico\n",
    "\n",
    "retained_organic = retained.loc[retained['Acquisition Channel'] == \"Organic\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ajustando a coluna de datas para o tipo datetime para que ficasse no mesmo formato dos dados em `reviews_bank`, foi detectado que há um dia incoerente com o mês que ele pertence. Uma análise rápida, levou à entrada com o dia `31/09/2019` que não existe. Essa entrada será, então, removida para que a análise continue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Removendo entrada errônea\n",
    "\n",
    "retained_organic[retained_organic['Date'] == \"31/09/2019\"] #index 196\n",
    "retained_organic.drop(index=196, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          Date Acquisition Channel  Installers  \\\n0   01/08/2019             Organic         414   \n1   02/08/2019             Organic         407   \n2   03/08/2019             Organic         401   \n3   04/08/2019             Organic         410   \n4   05/08/2019             Organic         374   \n..         ...                 ...         ...   \n86  26/10/2019             Organic         385   \n87  27/10/2019             Organic         413   \n88  28/10/2019             Organic         270   \n89  29/10/2019             Organic         258   \n90  30/10/2019             Organic         545   \n\n    Visitor-to-Installer conversion rate  Installers retained for 15 days  \\\n0                                  0.254                              335   \n1                                  0.253                              330   \n2                                  0.232                              348   \n3                                  0.260                              341   \n4                                  0.251                              317   \n..                                   ...                              ...   \n86                                 0.181                              305   \n87                                 0.200                              357   \n88                                 0.176                              208   \n89                                 0.195                              184   \n90                                 0.222                              386   \n\n    Installer-to-15 days retention rate  Installers retained for 30 days  \\\n0                                 0.809                              330   \n1                                 0.811                              322   \n2                                 0.868                              340   \n3                                 0.832                              330   \n4                                 0.848                              303   \n..                                  ...                              ...   \n86                                0.792                              296   \n87                                0.864                              348   \n88                                0.770                              195   \n89                                0.713                              175   \n90                                0.708                              374   \n\n    Installer-to-30 days retention rate week_month  \n0                                 0.797     30/Aug  \n1                                 0.791     30/Aug  \n2                                 0.848     30/Aug  \n3                                 0.805     31/Aug  \n4                                 0.810     31/Aug  \n..                                  ...        ...  \n86                                0.769     42/Oct  \n87                                0.843     43/Oct  \n88                                0.722     43/Oct  \n89                                0.678     43/Oct  \n90                                0.686     43/Oct  \n\n[91 rows x 9 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>Date</th>\n      <th>Acquisition Channel</th>\n      <th>Installers</th>\n      <th>Visitor-to-Installer conversion rate</th>\n      <th>Installers retained for 15 days</th>\n      <th>Installer-to-15 days retention rate</th>\n      <th>Installers retained for 30 days</th>\n      <th>Installer-to-30 days retention rate</th>\n      <th>week_month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>01/08/2019</td>\n      <td>Organic</td>\n      <td>414</td>\n      <td>0.254</td>\n      <td>335</td>\n      <td>0.809</td>\n      <td>330</td>\n      <td>0.797</td>\n      <td>30/Aug</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>02/08/2019</td>\n      <td>Organic</td>\n      <td>407</td>\n      <td>0.253</td>\n      <td>330</td>\n      <td>0.811</td>\n      <td>322</td>\n      <td>0.791</td>\n      <td>30/Aug</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>03/08/2019</td>\n      <td>Organic</td>\n      <td>401</td>\n      <td>0.232</td>\n      <td>348</td>\n      <td>0.868</td>\n      <td>340</td>\n      <td>0.848</td>\n      <td>30/Aug</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>04/08/2019</td>\n      <td>Organic</td>\n      <td>410</td>\n      <td>0.260</td>\n      <td>341</td>\n      <td>0.832</td>\n      <td>330</td>\n      <td>0.805</td>\n      <td>31/Aug</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>05/08/2019</td>\n      <td>Organic</td>\n      <td>374</td>\n      <td>0.251</td>\n      <td>317</td>\n      <td>0.848</td>\n      <td>303</td>\n      <td>0.810</td>\n      <td>31/Aug</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    </tr>\n    <tr>\n      <th>86</th>\n      <td>26/10/2019</td>\n      <td>Organic</td>\n      <td>385</td>\n      <td>0.181</td>\n      <td>305</td>\n      <td>0.792</td>\n      <td>296</td>\n      <td>0.769</td>\n      <td>42/Oct</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>27/10/2019</td>\n      <td>Organic</td>\n      <td>413</td>\n      <td>0.200</td>\n      <td>357</td>\n      <td>0.864</td>\n      <td>348</td>\n      <td>0.843</td>\n      <td>43/Oct</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>28/10/2019</td>\n      <td>Organic</td>\n      <td>270</td>\n      <td>0.176</td>\n      <td>208</td>\n      <td>0.770</td>\n      <td>195</td>\n      <td>0.722</td>\n      <td>43/Oct</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>29/10/2019</td>\n      <td>Organic</td>\n      <td>258</td>\n      <td>0.195</td>\n      <td>184</td>\n      <td>0.713</td>\n      <td>175</td>\n      <td>0.678</td>\n      <td>43/Oct</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>30/10/2019</td>\n      <td>Organic</td>\n      <td>545</td>\n      <td>0.222</td>\n      <td>386</td>\n      <td>0.708</td>\n      <td>374</td>\n      <td>0.686</td>\n      <td>43/Oct</td>\n    </tr>\n  </tbody>\n</table>\n<p>91 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# Ajustando a data para mesmo formato que o dos dados de review\n",
    "retained_organic['Date'] = pd.to_datetime(retained_organic['Date'])\n",
    "\n",
    "# # Adicionando colunas para semana e mês referente para acompanharmos a evolução de variáveis/métricas ao longo do tempo\n",
    "\n",
    "retained_organic['week_month'] = retained_organic['Date'].dt.strftime(\"%U/%b\")\n",
    "\n",
    "# # Ordenar ascendentemente nosso dataset\n",
    "\n",
    "retained_organic.sort_values(by='Date', inplace=True,  ignore_index=True)\n",
    "\n",
    "# # Organizar data para formato DD/MM/AAAA\n",
    "\n",
    "retained_organic['Date'] = retained_organic['Date'].dt.strftime(\"%d/%m/%Y\") \n",
    "\n",
    "retained_organic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insights\n",
    "\n",
    "## Ratings e Sentimento\n",
    "\n",
    "### Quantidade de Ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-a018cc0df550>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_traces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhovertemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Star Rating: %{x} <br>Quantidade: %{y}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "fig = px.histogram(reviews, x = 'Star Rating', color='Star Rating', title = 'Star Rating')\n",
    "fig.update_layout(\n",
    "    xaxis_title_text='Star Rating',\n",
    "    yaxis_title_text='Quantidade',\n",
    "    bargap=0.2\n",
    ")\n",
    "fig.update_traces(hovertemplate='Star Rating: %{x} <br>Quantidade: %{y}') \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise de Sentimento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-e0c14de90cf4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      7\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_traces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhovertemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Sentiment: %{x} <br>Quantidade: %{y}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "fig = px.histogram(reviews, x = 'Sentiment', color='Sentiment', title = 'Sentiment', color_discrete_map={\"Neutral\": \"orange\", 'Positive': 'green', 'Negative': 'red' })\n",
    "fig.update_layout(\n",
    "    xaxis_title_text='Sentiment', # xaxis label\n",
    "    yaxis_title_text='Quantidade',\n",
    "    bargap=0.2\n",
    ")\n",
    "fig.update_traces(hovertemplate='Sentiment: %{x} <br>Quantidade: %{y}') \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De um modo geral, tanto a os *ratings* como o sentimento dos usuários em relação ao app está muito bom!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolução de Ratings ao longo do tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-eb380c6f152f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mbargap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m )\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "# Agrupando os ratings por dias\n",
    "\n",
    "reviews_ratings = reviews.loc[:,['week_month', 'Star Rating', 'Date']]\n",
    "grouped = reviews_ratings.groupby('Date')\n",
    "ratings_time = grouped.agg(np.mean).reset_index()\n",
    "ratings_time['Star Rating'] = ratings_time['Star Rating'].round(2)\n",
    "fig = go.Figure(data=go.Scatter(x = ratings_time['Date'], y= ratings_time['Star Rating'], mode='lines+markers'), layout = go.Layout(height=600, width=1000))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = 'Evolução Média Star Rating - Dias',\n",
    "    xaxis_title_text='Sentiment',\n",
    "    yaxis_title_text='Quantidade',\n",
    "    bargap=0.2\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-86d94ebb9f8b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mbargap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m )\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "# Agrupando os ratings por semanas\n",
    "\n",
    "reviews_ratings = reviews.loc[:,['week_month', 'Star Rating', 'Date']]\n",
    "grouped = reviews_ratings.groupby('week_month')\n",
    "ratings_time = grouped.agg(np.mean).reset_index()\n",
    "ratings_time['Star Rating'] = ratings_time['Star Rating'].round(2)\n",
    "fig = go.Figure(data=go.Scatter(x = ratings_time['week_month'], y= ratings_time['Star Rating']), layout = go.Layout(height=600, width=1000))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = 'Evolução Média Star Rating - Semanas',\n",
    "    xaxis_title_text='Sentiment',\n",
    "    yaxis_title_text='Quantidade',\n",
    "    bargap=0.2\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A evolução dos *ratings* com o tempo se apresenta instável, mas mesmo assim se mantém acima do 4.2, o que é bom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratings por versão do app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agrupando os ratings por versão do app\n",
    "\n",
    "reviews_ratings = reviews.loc[:,['App Version Name', 'Star Rating']]\n",
    "grouped = reviews_ratings.groupby('App Version Name')\n",
    "ratings_unsorted = grouped.agg(np.mean).reset_index()\n",
    "sorted_version = pd.DataFrame({'app_version':natsorted(ratings_unsorted['App Version Name'])})\n",
    "ratings_app_version = pd.merge(sorted_version, ratings_unsorted, how='left', left_on='app_version', right_on='App Version Name')\n",
    "ratings_app_version['Star Rating'] = ratings_app_version['Star Rating'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-67afee916cdd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0myaxis_title_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Quantidade'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "fig = go.Figure(data=go.Scatter(x = ratings_app_version['App Version Name'], y = ratings_app_version['Star Rating'], mode='lines+markers'), layout = go.Layout(height=600, width=1000))\n",
    "fig.update_layout(\n",
    "    title_text = 'Evolução Média Star Rating - Versão App',\n",
    "    xaxis_title_text='Sentiment',\n",
    "    yaxis_title_text='Quantidade'\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que nas últimas versões, o app vem se tornando mais estável, com menos flutuações de *ratings* e mantendo uma boa média acima de 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Taxa de Retenção para 15 e 30 dias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-13e92dbddab6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0myaxis_title_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Taxa retenção %'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "fig = go.Figure(layout = go.Layout(height=600, width=1000))\n",
    "fig.add_trace(go.Scatter(x = retained_organic['Date'], y = retained_organic['Installer-to-15 days retention rate'], name='15 dias',mode='lines+markers'))\n",
    "fig.add_trace(go.Scatter(x = retained_organic['Date'], y = retained_organic['Installer-to-30 days retention rate'], name='30 dias',mode='lines+markers'))\n",
    "fig.update_layout(\n",
    "    title_text = 'Taxa de retenção 15 e 30 dias',\n",
    "    xaxis_title_text='Data',\n",
    "    yaxis_title_text='Taxa retenção %'\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Por semana\n",
    "\n",
    "retained_rate = retained_organic.loc[:,['week_month', 'Installer-to-15 days retention rate', 'Installer-to-30 days retention rate']]\n",
    "grouped = retained_rate.groupby('week_month')\n",
    "retained_rate_week = grouped.agg(np.mean).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-63522ab56005>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0myaxis_title_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Taxa retenção %'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2869\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2871\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2873\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/app_case_env/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnbformat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"4.2.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             raise ValueError(\n\u001b[0;32m--> 386\u001b[0;31m                 \u001b[0;34m\"Mime type rendering requires nbformat>=4.2.0 but it is not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             )\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "fig = go.Figure(layout = go.Layout(height=600, width=1000))\n",
    "fig.add_trace(go.Scatter(x = retained_rate_week['week_month'], y = retained_organic['Installer-to-15 days retention rate'], name='15 dias'))\n",
    "fig.add_trace(go.Scatter(x = retained_rate_week['week_month'], y = retained_organic['Installer-to-30 days retention rate'], name='30 dias'))\n",
    "fig.update_layout(\n",
    "    title_text = 'Taxa de retenção 15 e 30 dias',\n",
    "    xaxis_title_text='Data',\n",
    "    yaxis_title_text='Taxa retenção %'\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A taxa de rentenção teve uma queda brusca nos últimos dias do mês de outubro e na segunda e terceira semana do mesmo mês. A média da última semana, no entanto, subiu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo de Machine Learning\n",
    "\n",
    "Para esse case, será desenvolvido um modelo que classifica o `sentiment` do usuário de acordo com seu *review*.\n",
    "\n",
    "Primeiro, vamos analisar os reviews que já temos para só depois construir nosso classificador.\n",
    "\n",
    "## Analisando os Reviews\n",
    "\n",
    "A análise das reviews se dará através de mapeamento dos termos, tanto isolados quanto em pares. Assim, por exemplo, serão contabilizados tanto termos \"ótimo\" (isolados) como \"muito bom\" (em pares)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Isolando dataset para o modelo de ML\n",
    "\n",
    "reviews_text = reviews.loc[:,['Review Text', 'Sentiment']]\n",
    "reviews_text.dropna(inplace=True)\n",
    "reviews_text.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Transformando todos os reviews em um corpo só\n",
    "reviews_concat = reviews_text['Review Text'].str.cat(sep=' ')\n",
    "\n",
    "#Stopwords\n",
    "stopwords_list = ['de', 'a', 'o', 'O', 'que','q', 'e', 'do', 'da', 'em', 'um', 'que', 'para','fica', 'com','nao', 'sim', 'uma', 'os', 'no', 'se', 'na', 'por', 'mais', 'as', 'dos', 'como', 'mas', 'foi', 'ao', 'ele', 'das', 'tem', 'à', 'seu', 'sua', 'ou', 'ser', 'quando', 'muito', 'sempre','há', 'nos', 'para','pra', 'ja', 'está', 'eu', 'também', 'so', 'pelo', 'pela', 'ate', 'isso', 'ela', 'voce', 'entre', 'era', 'depois', 'sem', 'mesmo', 'aos', 'ter', 'seus', 'quem', 'nas', 'me', 'esse', 'eles', 'estão', 'você', 'tinha', 'foram', 'essa', 'num', 'nem', 'suas', 'meu', 'às', 'minha', 'têm', 'numa', 'pelos', 'elas', 'havia', 'seja', 'qual', 'será', 'nós', 'tenho', 'lhe', 'deles', 'essas', 'esses', 'pelas', 'este', 'fosse', 'dele', 'tu', 'te', 'vocês', 'vos', 'lhes', 'meus', 'minhas', 'teu', 'tua', 'teus', 'tuas', 'nosso', 'nossa', 'nossos', 'nossas', 'dela', 'delas', 'esta', 'estes', 'estas', 'aquele', 'aquela', 'aqueles', 'aquelas', 'isto', 'aquilo', 'estou', 'estamos', 'estao', 'estive', 'esteve', 'estivemos', 'estiveram', 'estava', 'estávamos', 'estavam', 'estivera', 'estivéramos', 'esteja', 'estejamos', 'estejam', 'estivesse', 'estivéssemos', 'estivessem', 'estiver', 'estivermos', 'estiverem', 'hei', 'ha', 'havemos', 'hao', 'houve', 'houvemos', 'houveram', 'houvera', 'houvéramos', 'haja', 'hajamos', 'hajam', 'houvesse', 'houvéssemos', 'houvessem', 'houver', 'houvermos', 'houverem', 'houverei', 'houverá', 'houveremos', 'houverão', 'houveria', 'houveríamos', 'houveriam', 'sou', 'somos', 'são', 'era', 'éramos', 'eram', 'fui', 'fica', 'ficou','ir', 'foi', 'fomos', 'foram', 'fora', 'fôramos', 'seja', 'sejamos', 'sejam', 'fosse', 'fôssemos', 'fossem', 'for', 'formos', 'forem', 'serei', 'sera', 'seremos', 'serao', 'seria', 'seríamos', 'seriam', 'tenho', 'tem', 'temos', 'tinha', 'tínhamos', 'tinham', 'tive', 'teve', 'tivemos', 'tiveram', 'tivera', 'tivéramos', 'tenha', 'tenhamos', 'tenham', 'tivesse', 'tivéssemos', 'tivessem', 'tiver', 'tivermos', 'tiverem', 'terei', 'terá', 'teremos', 'terão', 'teria', 'teríamos', 'teriam', 'app', 'App','APP', 'aplicativo', 'fazer', 'faz', 'só', 'mas','uso', 'todos', 'todas','todo', 'toda', 'tudo', 'vezes', 'dia', 'agora', 'pois', 'ainda', 'Ainda', 'cada', 'algum', 'alguns', 'algumas', 'vez', 'nada', 'nem', 'após','Após', 'dá', 'dar', 'deve', 'deveria', 'acho', 'banco','consigo', 'usar', 'b4', 'bank', '!', ',', '/', '.', '...', '``', '..', '....', '(', ')', '?', ':', \"''\"]\n",
    "\n",
    "stopwords_portuguese = set(stopwords_list)\n",
    "\n",
    "# Instanciando módulo para vetorização e tokens\n",
    "count_vect = CountVectorizer(stop_words=stopwords_list, lowercase=True, strip_accents='ascii', ngram_range = (1,2))\n",
    "analyzer = count_vect.build_analyzer()\n",
    "\n",
    "# Listando todas as palavras que aparecem\n",
    "tokens = analyzer(reviews_concat)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokens = [word for word in tokens if not word in stopwords_portuguese]\n",
    "vocabulary = set(tokens)\n",
    "frequency_dist = nltk.FreqDist(tokens)\n",
    "words_df = pd.DataFrame({\n",
    "    'words' : list(frequency_dist.keys()),\n",
    "    'frequency': list(frequency_dist.values())\n",
    "    })\n",
    "words_df.sort_values(by='frequency', ascending=False, inplace=True)\n",
    "# words_df.iloc[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "words_df_50 = words_df.iloc[0:50]\n",
    "ax = sns.barplot(x='words', y='frequency', data=words_df_50)\n",
    "ax.set_title('50 termos mais frequentes')\n",
    "ax.set_ylabel('Frequência')\n",
    "ax.set_xlabel('Palavra')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que os termos que mais aparecem são positivos para o aplicativo!\n",
    "\n",
    "## Construindo um Classificador para *Sentiment Analysis*\n",
    "\n",
    "Para a construção do classificador, irei utilizar como parâmetro para relavância dos termos nas *reviews* o método de **TD-IDF**. Após, irei testar 3 algoritmos de *Machine Learning* para, a partir da métrica de acurácia de cada um, escolher o melhor. Esses métodos serão: *Suport Vector Machine*, *Naives Bayes* e *Logistic Regression*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "size_train is 1847 and size_test is 792\n"
    }
   ],
   "source": [
    "# Dividindo nosso dataset em treino e teste\n",
    "\n",
    "size = reviews_text.shape[0]\n",
    "size_train = round(0.70* size)\n",
    "size_test = size - size_train\n",
    "print(f'size_train is {size_train} and size_test is {size_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = reviews_text.loc[:size_train, 'Review Text'].values\n",
    "y_train = reviews_text.loc[:size_train, 'Sentiment'].values\n",
    "X_test = reviews_text.loc[(size_train+1):, 'Review Text'].values\n",
    "y_test = reviews_text.loc[(size_train+1):, 'Sentiment'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train_counts = count_vect.fit_transform(X_train)\n",
    "X_test_counts = count_vect.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(1848, 8585) (791, 8585)\n"
    }
   ],
   "source": [
    "tfidf_transformer = TfidfTransformer()\n",
    "train_vectors = tfidf_transformer.fit_transform(X_train_counts)\n",
    "test_vectors = tfidf_transformer.transform(X_test_counts)\n",
    "print(train_vectors.shape, test_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "---------- LinearSVC ----------\n\nAccuracy:  0.8495575221238938 \n\n---------- MultinomialNB ----------\n\nAccuracy:  0.7850821744627055 \n\n---------- LogisticRegression ----------\n\nAccuracy:  0.8445006321112516 \n\n"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "ml_algorithms = {'LinearSVC' : LinearSVC(), 'MultinomialNB': MultinomialNB(), 'LogisticRegression': LogisticRegression()}\n",
    "\n",
    "for model in ml_algorithms:\n",
    "    clf = ml_algorithms[model].fit(train_vectors, y_train)\n",
    "    predicted = clf.predict(test_vectors)\n",
    "    print(f'---------- {model} ----------\\n')\n",
    "    print('Accuracy: ',accuracy_score(y_test,predicted), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, o modelo escolhido para nosso problema será o de `LinearSVC`\n",
    "\n",
    "Agora, para melhor sumarizar e deixar nosso processo de construção deste modelo mais escalável, irei construir uma simples *pipeline*.\n",
    "\n",
    "## Pipeline do modelo de *Machine Learning* escolhido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "---------- LogisticRegression ----------\n\nAccuracy:  0.8495575221238938 \n\n"
    }
   ],
   "source": [
    "reviews_classifier = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('clf', LinearSVC())\n",
    "])\n",
    "\n",
    "# Testando nossa pipeline\n",
    "\n",
    "reviews_classifier.fit(X_train, y_train)\n",
    "predicted = reviews_classifier.predict(X_test)\n",
    "print(f'---------- {model} ----------\\n')\n",
    "print('Accuracy: ',accuracy_score(y_test,predicted), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Considerações Finais\n",
    "\n",
    "Com os dados disponibilizados e as análises feitas neste relatório, vemos que o aplicativo do B4 Bank tem um desempenho no geral muito bom. As métricas utilizadas de acompanhamento de *star ratings* e *sentiment* não só no tempo, mas também na versão do aplicativo, são importantes para a avaliação do desempenho do aplicativo e seus *releases*. Essas métricas, tendo o manuseio adequado dos dados aqui feito, são facilmente construídas dentro de ferramentas de visualização/*dashboards*.\n",
    "\n",
    "Quanto aos modelos de *machine learning* utilizados, teve-se uma boa acurácia(85%) mesmo utilizando hiperparâmetros *default* dentro das opções testadas.\n"
   ]
  }
 ]
}